U.S. patent application number 13/024139 was filed with the patent office on 2011-10-06 for methods of predicting and monitoring tyrosine kinase inhibitor therapy.
This patent application is currently assigned to Prometheus Laboratories Inc.. Invention is credited to Jeanne Harvey, Bruce Neri, Sharat Singh.
Application Number | 20110244465 13/024139 |
Document ID | / |
Family ID | 38179692 |
Filed Date | 2011-10-06 |
United States Patent
Application |
20110244465 |
Kind Code |
A1 |
Harvey; Jeanne ; et
al. |
October 6, 2011 |
METHODS OF PREDICTING AND MONITORING TYROSINE KINASE INHIBITOR
THERAPY
Abstract
The present invention provides methods for analyzing a
combination of biomarkers to individualize tyrosine kinase
inhibitor therapy in patients who have been diagnosed with cancer.
In particular, the assay methods of the present invention are
useful for predicting, identifying, or monitoring the response of a
tumor, tumor cell, or patient to treatment with a tyrosine kinase
inhibitor using an algorithm based upon biomarker profiling. The
assay methods of the present invention are also useful for
predicting whether a patient has a risk of developing toxicity or
resistance to treatment with a tyrosine kinase inhibitor. In
addition, the assay methods of the present invention are useful for
monitoring tyrosine kinase inhibitor therapy in a patient receiving
the drug to evaluate whether the patient will develop resistance to
the drug. Furthermore, the assay methods of the present invention
are useful for optimizing the dose of a tyrosine kinase inhibitor
in a patient receiving the drug to achieve therapeutic efficacy
and/or reduce toxic side-effects.
Inventors: |
Harvey; Jeanne; (Livermore,
CA) ; Neri; Bruce; (Carlsbad, CA) ; Singh;
Sharat; (Los Altos Hills, CA) |
Assignee: |
Prometheus Laboratories
Inc.
San Diego
CA
|
Family ID: |
38179692 |
Appl. No.: |
13/024139 |
Filed: |
February 9, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11687254 |
Mar 16, 2007 |
7908091 |
|
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13024139 |
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60829812 |
Oct 17, 2006 |
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60783743 |
Mar 17, 2006 |
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Current U.S.
Class: |
435/6.12 ;
435/29 |
Current CPC
Class: |
C12Q 2600/154 20130101;
G01N 2500/04 20130101; A61P 35/00 20180101; G01N 33/57484 20130101;
C12Q 1/6886 20130101; C12Q 2600/106 20130101; G01N 2333/91215
20130101; G16B 40/00 20190201; G01N 2800/52 20130101; G16B 20/00
20190201 |
Class at
Publication: |
435/6.12 ;
435/29 |
International
Class: |
C12Q 1/02 20060101
C12Q001/02; C12Q 1/68 20060101 C12Q001/68 |
Claims
1-75. (canceled)
76. An assay method for predicting the response of a subject
diagnosed with non-small cell lung cancer to treatment with
gefitinib (Iressa.RTM.), said method comprising: (a) analyzing a
sample obtained from said subject to determine the presence or
absence of an activating mutation in the EGFR gene in said sample;
(b) analyzing said sample to determine the presence or absence of
an activating mutation in the K-Ras gene when said activating
mutation in the EGFR gene is absent; and (c) predicting an
increased likelihood that said subject will respond to treatment
with gefitinib when said activating mutation in the EGFR gene is
present, and predicting a decreased likelihood that said subject
will respond to treatment with gefitinib when said activating
mutation in the K-Ras gene is present.
77. The method of claim 76, wherein said activating mutation in the
EGFR gene comprises a deletion, an insertion, or a single
nucleotide substitution in the tyrosine kinase domain of the EGFR
gene.
78. The method of claim 76, wherein said activating mutation in the
K-Ras gene results in a substitution in the K-Ras amino acid
sequence selected from the group consisting of a cysteine for
glycine at position 12 (G12C), a cysteine for glycine at position
13 (G13C), an aspartic acid for glycine at position 12 (G12D), a
serine for glycine at position 12 (G12S), and a valine for glycine
at position 12 (G12V).
79. The method of claim 76, wherein said sample is selected from
the group consisting of whole blood, serum, plasma, urine, nipple
aspirate, lymph, saliva, fine needle aspirate, tumor tissue, and
combinations thereof.
80. The method of claim 79, wherein said sample comprises
circulating tumor cells, circulating endothelial cells, circulating
endothelial progenitor cells, cancer stem cells, or combinations
thereof.
81. The method of claim 76, wherein said method further comprises
sending the results from said prediction to a clinician.
82. The method of claim 76, further comprising recommending the
administration of gefitinib to said subject when said subject is
predicted to have an increased likelihood of responding to
treatment with gefitinib.
83. The method of claim 76, further comprising recommending the
administration of another tyrosine kinase inhibitor or an
alternative cancer therapy to said subject when said subject is
predicted to have a decreased likelihood of responding to treatment
with gefitinib.
84. The method of claim 76, further comprising analyzing said
sample to determine the EGFR and HER2 copy number, the level of
EGFR, TGF-.alpha., and PTEN protein expression, and the presence or
absence of Erk (MAPK) and Akt activation when said activating
mutation in the K-Ras gene is absent.
85. The method of claim 84, wherein the EGFR and HER2 copy number,
the level of EGFR, TGF-.alpha., and PTEN protein expression, and
the presence or absence of Erk (MAPK) and Akt activation are each
assigned an index value.
86. The method of claim 85, wherein a cumulative index value (CIV)
is calculated by summing said index values assigned to the EGFR and
HER2 copy number, the level of EGFR, TGF-.alpha., and PTEN protein
expression, and the presence or absence of Erk (MAPK) and Akt
activation, and wherein said index values assigned to the EGFR copy
number and the level of EGFR protein expression are multiplied by a
factor of 2.
87. The method of claim 86, further comprising comparing said CIV
to an index cutoff value.
88. The method of claim 87, wherein said subject is predicted to
have an increased likelihood of responding to treatment with
gefitinib when said CIV is greater than or equal to said index
cut-off value.
89. The method of claim 88, further comprising recommending the
administration of gefitinib to said subject.
90. The method of claim 87, wherein said subject is predicted to
have a decreased likelihood of responding to treatment with
gefitinib when said CIV is less than said index cut-off value.
91. The method of claim 90, further comprising recommending the
administration of another tyrosine kinase inhibitor or an
alternative cancer therapy to said subject.
92. The method of claim 76, wherein the presence or absence of said
activating mutation in the EGFR or K-Ras genes is determined by the
polymerase chain reaction (PCR).
93. The method of claim 84, wherein the EGFR and HER2 copy number
is determined by fluorescence in situ hybridization (FISH) or
PCR.
94. The method of claim 84, wherein the level of EGFR, TGF-.alpha.,
and PTEN protein expression is determined by immunohistochemistry
(IHC) or an immunoassay.
95. The method of claim 84, wherein the presence or absence of Erk
(MAPK) and Akt activation is determined by IHC.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application No. 60/783,743, filed Mar. 17, 2006, and U.S.
Provisional Application No. 60/829,812, filed Oct. 17, 2006, the
disclosures of which are hereby incorporated by reference in their
entirety for all purposes.
BACKGROUND OF THE INVENTION
[0002] Tyrosine kinases are a class of enzymes that catalyze the
transfer of the terminal phosphate of adenosine triphosphate (ATP)
to tyrosine residues in protein substrates. Tyrosine kinases are
believed, by way of substrate phosphorylation, to play critical
roles in signal transduction for a number of cell functions. In
fact, tyrosine kinases have been shown to be important contributing
factors in cell proliferation, carcinogenesis, and cell
differentiation. Tyrosine kinases can be categorized as either
receptor tyrosine kinases or non-receptor tyrosine kinases.
[0003] Receptor tyrosine kinases are key regulators of
intercellular communication that controls cell growth,
proliferation, differentiation, survival, and metabolism. About 20
different receptor tyrosine kinase families have been identified
that share a similar structure, namely an extracellular binding
site for ligands, a transmembrane region, and an intracellular
tyrosine kinase domain (see, e.g., Ullrich et al., Cell, 61:203-212
(1990); Pawson, Eur. J. Cancer, 38(Supp 5):S3-S10 (2002)). For
example, the EGFR family of receptor tyrosine kinases comprises
EGFR/HER1/ErbB1, HER2/Neu/ErbB2, HER3/ErbB3, and HER4/ErbB4.
Ligands of this family of receptors include epithelial growth
factor (EGF), TGF-.alpha., amphiregulin, HB-EGF, betacellulin, and
heregulin. Other receptor tyrosine kinase families include the PDGF
family, the FLK family, and the insulin family of receptors.
[0004] Extracellular ligand binding of receptor tyrosine kinases
induces or stabilizes receptor dimerization, leading to increased
kinase activity. The intracellular catalytic domain displays the
highest level of conservation among receptor tyrosine kinases,
includes the ATP-binding site that catalyzes receptor
autophosphorylation of cytoplasmic tyrosine residues, and serves as
the docking site for Src homology 2 (SH2)- and
phosphotyrosine-binding (PTB) domain-containing proteins such as
Grb2, Shc, Src, Cb1, and phospholipase C-.gamma.. These proteins
subsequently recruit additional effectors containing SH2-, SH3-,
PTB-, and pleckstrin-homology (PH) domains to the activated
receptor, which results in the assembly of signaling complexes at
the membrane and the activation of a cascade of intracellular
biochemical signals. The most important downstream signaling
cascades activated by receptor tyrosine kinases include the
Ras/Raf/mitogen activated protein (MAP) kinase pathway, the
phosphoinositide 3-kinase/Akt pathway, and the JAK/STAT pathway.
The complex signaling network triggered by receptor tyrosine
kinases eventually leads either to activation or repression of
various subsets of genes and thus defines the biological response
to a given signal.
[0005] The activity of receptor tyrosine kinases and their
signaling cascades is precisely coordinated and tightly controlled
in normal cells. However, deregulation of the receptor tyrosine
kinase signaling system, either by stimulation through growth
factor and/or through genetic alteration, produces deregulated
tyrosine kinase activity. These aberrations generally result in
receptor tyrosine kinases with constitutive or strongly enhanced
kinase activity and subsequent signaling capacity, which leads to
malignant transformation. Therefore, they are frequently linked to
human cancer and also to other hyperproliferative diseases such as
psoriasis (Robertson et al., Trends Genet., 16:265-271 (2000)). The
most important mechanisms leading to constitutive receptor tyrosine
kinase signaling include overexpression and/or gene amplification,
genetic alterations such as deletions and mutations within the
extracellular domain or catalytic site, and autocrine-paracrine
stimulation through aberrant growth factor loops.
[0006] More particularly, gene amplification and/or overexpression
of receptor tyrosine kinases occurs in many human cancers, which
might increase the response of cancer cells to normal growth factor
levels. Additionally, overexpression of a specific receptor
tyrosine kinase on the cell surface increases the incidence of
receptor dimerization, even in the absence of an activating ligand.
In many cases, this results in constitutive activation of the
receptor tyrosine kinase, leading to aberrant and uncontrolled cell
proliferation and tumor formation. For example, EGFR/HER1/ErbB1 is
frequently overexpressed in non-small cell lung, bladder, cervical,
ovarian, kidney, and pancreatic cancer as well as in squamous cell
carcinomas of the head and neck (Hong et al., Oncol. Biother.,
1:1-29 (2000)). The predominant mechanism leading to EGFR
overexpression is gene amplification, with up to about 60 copies
per cell reported in certain tumors (Libermann et al., Nature,
313:144-147 (1985)). In general, elevated levels of EGFR expression
are associated with high metastatic rate and increased tumor
proliferation (Pavelic et al., Anticancer Res., 13:1133-1138
(1993)). Therefore, receptor tyrosine kinases such as EGFR are
recognized as attractive targets for the design and development of
compounds that can specifically inhibit their tyrosine kinase
activity in cancer cells.
[0007] Small molecule tyrosine kinase inhibitors compete with the
ATP-binding site of the catalytic domain of target tyrosine
kinases. Such inhibitors are generally orally active and have a
favorable safety profile that can easily be combined with other
forms of cancer therapy. Several tyrosine kinase inhibitors have
been identified to possess effective antitumor activity and have
been approved or are in clinical trials. These include gefitinib
(Iress.RTM.), sunitinib (Sutent.RTM.; SU11248), erlotinib
(Tarceva.RTM.; OSI-1774), lapatinib (GW-572016), canertinib (CI
1033), semaxinib (SU5416), vatalanib (PTK787/ZK222584), sorafenib
(BAY 43-9006), imatinib mesylate (Gleevec.RTM.; STI571), and
leflunomide (SU101). Although tyrosine kinase inhibitors represent
a new class of targeted therapy that interferes with specific cell
signaling pathways and allows target-specific therapy for selected
malignancies, there is currently a lack of tumor response to these
inhibitors in the general population. For example, only about 10%
of patients with non-small cell lung cancer in whom standard
therapy failed respond to the EGFR inhibitor gefitinib (Fukuoka et
al., J. Oncol., 21:2237-2246 (2003); Kris et al., JAMA,
290:2149-2158 (2003)). In addition, patients may be at risk of
toxicity to tyrosine kinase inhibitors. Furthermore, tyrosine
kinase inhibitor therapy is typically very expensive in comparison
to conventional chemotherapy. Moreover, resistance to tyrosine
kinase inhibitors can manifest during treatment, and sometimes a
particular inhibitor becomes wholly ineffective in certain
patients.
[0008] As a result, due to the high cost of tyrosine kinase
inhibitor therapy, the small percentage of responders, the risk of
toxic side-effects, and the possibility of developing resistance
during treatment, it is imperative to prescribe tyrosine kinase
inhibitors only to those patients for whom such therapy will have
some benefit. Thus, there is a need in the art for methods that
utilize a combination of biomarkers to predict a patient's response
to tyrosine kinase inhibitors such as EGFR inhibitors. There is
also a need in the art for methods that utilize a combination of
biomarkers to identify patients who are at greater risk of
developing toxicity to tyrosine kinase inhibitors and to reduce the
toxic effects of tyrosine kinase inhibitors in patients already
receiving the drug. There is a further need in the art for methods
that utilize a combination of biomarkers to identify patients with
acquired resistance to tyrosine kinase inhibitor therapy in
recurring tumors. The present invention satisfies these needs and
provides related advantages as well.
BRIEF SUMMARY OF THE INVENTION
[0009] The present invention provides methods for analyzing a
combination of biomarkers to individualize tyrosine kinase
inhibitor therapy in patients who have been diagnosed with cancer.
In particular, the assay methods of the present invention are
useful for predicting, identifying, or monitoring the response of a
tumor, tumor cell, or patient to treatment with a tyrosine kinase
inhibitor using an algorithm based upon biomarker profiling. The
assay methods of the present invention are also useful for
predicting whether a patient has a risk of developing toxicity or
resistance to treatment with a tyrosine kinase inhibitor. In
addition, the assay methods of the present invention are useful for
monitoring tyrosine kinase inhibitor therapy in a patient receiving
the drug to evaluate whether the patient will develop resistance to
the drug. Furthermore, the assay methods of the present invention
are useful for optimizing the dose of a tyrosine kinase inhibitor
in a patient receiving the drug to achieve therapeutic efficacy
and/or reduce toxic side-effects.
[0010] In one aspect, the present invention provides an assay
method for identifying the response of a tumor to treatment with a
tyrosine kinase inhibitor, the method comprising: [0011] (a)
determining at least one profile selected from the group consisting
of a nucleic acid profile, protein profile, and combinations
thereof in a sample from a subject; and [0012] (b) identifying the
tumor as responsive or non-responsive to treatment with the
tyrosine kinase inhibitor using an algorithm based upon the at
least one profile.
[0013] In another aspect, the present invention provides an assay
method for predicting the response of a subject to treatment with a
tyrosine kinase inhibitor, the method comprising: [0014] (a)
determining at least one profile selected from the group consisting
of a nucleic acid profile, protein profile, and combinations
thereof in a sample from the subject; and [0015] (b) predicting the
likelihood that the subject will respond to treatment with the
tyrosine kinase inhibitor using an algorithm based upon the at
least one profile.
[0016] In yet another aspect, the present invention provides an
assay method for monitoring treatment with a tyrosine kinase
inhibitor in a subject, the method comprising: [0017] (a)
determining at least one profile selected from the group consisting
of a nucleic acid profile, protein profile, and combinations
thereof in a sample from the subject; and [0018] (b) monitoring the
likelihood that the subject will develop resistance to treatment
with the tyrosine kinase inhibitor using an algorithm based upon
the at least one profile.
[0019] In a further aspect, the present invention provides an assay
method for optimizing dose efficacy in a subject receiving a
tyrosine kinase inhibitor, the method comprising: [0020] (a)
determining at least one profile selected from the group consisting
of a nucleic acid profile, protein profile, and combinations
thereof in a sample from the subject; and [0021] (b) recommending a
subsequent dose of the tyrosine kinase inhibitor using an algorithm
based upon the at least one profile.
[0022] Other objects, features, and advantages of the present
invention will be apparent to one of skill in the art from the
following detailed description and figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0023] FIG. 1 shows a flowchart of one embodiment of the present
invention describing an algorithm for individualizing gefitinib
(Iressa.RTM.) therapy in patients with cancer.
[0024] FIG. 2 shows a flowchart of another embodiment of the
present invention describing an algorithm for individualizing
sunitinib (Sutent.RTM.) therapy in patients with cancer.
[0025] FIG. 3 shows a flow diagram illustrating the various
analyses that can be performed on each whole blood fraction.
DETAILED DESCRIPTION OF THE INVENTION
I. Introduction
[0026] The present invention is based, in part, on the surprising
discovery that a combination of biomarkers can be used in an
algorithmic approach to individualize tyrosine kinase inhibitor
therapy in patients with cancers comprising solid tumors such as
colorectal cancer, lung cancer, etc. Given the high inter-patient
variability in response to tyrosine kinase inhibitors, the assay
methods of the present invention are particularly advantageous
because they utilize a combinatorial strategy that takes into
account differences in nucleic acid and/or protein profiles of
multiple molecular determinants (i.e., biomarkers) to determine
whether a tumor or tumor cell from a patient has a high likelihood
of responding to treatment with a specific tyrosine kinase
inhibitor or combination of tyrosine kinase inhibitors. If the
patient is classified as a responder, a dosing regimen tailored to
that patient can then be created to achieve therapeutic efficacy
without inducing toxic side-effects. Consequently, patients
classified as responders can receive the full benefits of tyrosine
kinase inhibitor therapy without experiencing the side-effects
associated with such therapy. Similarly, patients already
undergoing treatment with a tyrosine kinase inhibitor can
experience a reduction in toxic side-effects without compromising
therapeutic efficacy by adjusting the subsequent dose of the drug.
Likewise, patients already undergoing treatment with a tyrosine
kinase inhibitor can be monitored to assess whether resistance to
the drug has developed and an alternative cancer therapy should be
administered. As a result, the methods of the present invention
enable tyrosine kinase inhibitors such as gefitinib and sunitinib
to become first-line therapeutic agents, rather than their current
role as second- or third-line cancer therapies. The tyrosine kinase
inhibitors described herein can be administered alone or
co-administered (e.g., concurrently or sequentially) with
conventional chemotherapy, radiation therapy, hormonal therapy,
and/or immunotherapy for the treatment of cancer.
[0027] Currently, tumor tissue is analyzed using various individual
biomarkers to give an indication of the appropriate therapy in
patients with solid tumors. For example, HER2 or EGFR
immunohistochemistry on tumor tissue is performed prior to
prescribing trastuzumab (Herceptin.RTM.) or cetuximab
(Erbitux.RTM.), respectively. However, there are substantial
limitations associated with the use of tumor tissue for biomarker
analysis. In particular, tumor tissue is only available pre-surgery
or in patients without surgical therapy, formalin-fixation
paraffin-embedding of tumor tissue interferes with the analysis of
many biomarkers, variability in fixation processing alters the
level of many biomarkers in tumor tissue, and for small tumors, as
are increasingly detected in breast cancer, very little tumor
tissue sample is left for biomarker analysis after standard
pathology. In addition, tumor tissue is not available during the
course of tyrosine kinase inhibitor therapy, so it cannot be used
for monitoring efficacy or determining when a change in therapy is
needed. The present invention overcomes these limitations by
utilizing fractional components obtained from a single sample. As a
non-limiting example, a whole blood sample which is separated into
its liquid (e.g., plasma, serum, etc.) and cellular (e.g., red
blood cells, white blood cells, platelets, etc.) components can be
analyzed for an entire spectrum of biomarkers, thereby providing an
advantageous means of individualizing tyrosine kinase inhibitor
therapy according to the methods of the present invention.
[0028] As such, the present invention provides more accurate
methods for predicting, identifying, or monitoring the response of
a tumor (e.g., lung carcinoma, colorectal carcinoma,
gastrointestinal stromal tumor, renal cell carcinoma, etc.), a
tumor cell (e.g., a circulating tumor cell or circulating
endothelial cell derived from a tumor), or a patient who has been
diagnosed with cancer to treatment with a specific tyrosine kinase
inhibitor (e.g., gefitinib, sutent, etc.) or cocktail of tyrosine
kinase inhibitors. The present invention is also useful for
monitoring the development of acquired resistance to treatment with
one or more tyrosine kinase inhibitors in a patient who has been
receiving the drug. In addition, the present invention finds
utility in methods of optimizing tyrosine kinase inhibitor dosages
(e.g., optimizing dose amount, optimizing dose efficacy, reducing
drug toxicity, etc.) in patients undergoing tyrosine kinase
inhibitor therapy.
II. Definitions
[0029] As used herein, the following terms have the meanings
ascribed to them unless specified otherwise.
[0030] The term "cancer" is intended to include any member of a
class of diseases characterized by the uncontrolled growth of
aberrant cells. The term includes all known cancers and neoplastic
conditions, whether characterized as malignant, benign, soft
tissue, or solid, and cancers of all stages and grades including
pre- and post-metastatic cancers. Examples of different types of
cancer include, but are not limited to, lung cancer (e.g.,
non-small cell lung cancer); digestive and gastrointestinal cancers
such as colorectal cancer, gastrointestinal stromal tumors,
gastrointestinal carcinoid tumors, colon cancer, rectal cancer,
anal cancer, bile duct cancer, small intestine cancer, and stomach
(gastric) cancer; esophageal cancer; gallbladder cancer; liver
cancer; pancreatic cancer; appendix cancer; breast cancer; ovarian
cancer; renal cancer (e.g., renal cell carcinoma); cancer of the
central nervous system; skin cancer; lymphomas; choriocarcinomas;
head and neck cancers; osteogenic sarcomas; and blood cancers. As
used herein, a "tumor" comprises one or more cancerous cells.
[0031] The term "tyrosine kinase" as used herein includes enzymes
that catalyze the transfer of the terminal phosphate of adenosine
triphosphate (ATP) to tyrosine residues in protein substrates.
Non-limiting examples of tyrosine kinases include receptor tyrosine
kinases such as EGFR (e.g., EGFR/HER1/ErbB1, HER2/Neu/ErbB2,
HER3/ErbB3, HER4/ErbB4), INSR (insulin receptor), IGF-IR, IGF-II1R,
IRR (insulin receptor-related receptor), PDGFR (e.g., PDGFRA,
PDGFRB), c-KIT/SCFR, VEGFR-1/FLT-1, VEGFR-2/FLK-1/KDR,
VEGFR-3/FLT-4, FLT-3/FLK-2, CSF-1R, FGFR 1-4, CCK4, TRK A-C, MET,
RON, EPHA 1-8, EPHB 1-6, AXL, MER, TYRO3, TIE, TEK, RYK, DDR 1-2,
RET, c-ROS, LTK (leukocyte tyrosine kinase), ALK (anaplastic
lymphoma kinase), ROR 1-2, MUSK, AATYK 1-3, and RTK 106; and
non-receptor tyrosine kinases such as BCR-ABL, Src, Frk, Btk, Csk,
Abl, Zap70, Fes/Fps, Fak, Jak, Ack, and LIMK. One of skill in the
art will know of other receptor and/or non-receptor tyrosine
kinases that can be targeted using the inhibitors described
herein.
[0032] The term "tyrosine kinase inhibitor" includes any of a
variety of therapeutic agents or drugs that act as selective or
non-selective inhibitors of receptor and/or non-receptor tyrosine
kinases. Without being bound to any particular theory, tyrosine
kinase inhibitors generally inhibit target tyrosine kinases by
binding to the ATP-binding site of the enzyme. Examples of tyrosine
kinase inhibitors suitable for use in the methods of the present
invention include, but are not limited to, gefitinib (Iressa.RTM.),
sunitinib (Sutent.RTM.; SU11248), erlotinib (Tarceva.RTM.;
OSI-1774), lapatinib (GW572016; GW2016), canertinib (CI 1033),
semaxinib (SU5416), vatalanib (PTK787/ZK222584), sorafenib (BAY
43-9006), imatinib (Gleevec.RTM.; STI571), dasatinib (BMS-354825),
leflunomide (SU101), vandetanib (Zactima.TM.; ZD6474), derivatives
thereof, analogs thereof, and combinations thereof. Additional
tyrosine kinase inhibitors suitable for use in the present
invention are described in, e.g., U.S. Pat. Nos. 5,618,829,
5,639,757, 5,728,868, 5,804,396, 6,100,254, 6,127,374, 6,245,759,
6,306,874, 6,313,138, 6,316,444, 6,329,380, 6,344,459, 6,420,382,
6,479,512, 6,498,165, 6,544,988, 6,562,818, 6,586,423, 6,586,424,
6,740,665, 6,794,393, 6,875,767, 6,927,293, and 6,958,340. One of
skill in the art will know of other tyrosine kinase inhibitors
suitable for use in the present invention. In certain instances,
the tyrosine kinase inhibitor is administered in a pharmaceutically
acceptable form including, without limitation, an alkali or
alkaline earth metal salt such as an aluminum, calcium, lithium,
magnesium, potassium, sodium, or zinc salt; an ammonium salt such
as a tertiary amine or quaternary ammonium salt; and an acid salt
such as a succinate, tartarate, bitartarate, dihydrochloride,
salicylate, hemisuccinate, citrate, isocitrate, malate, maleate,
mesylate, hydrochloride, hydrobromide, phosphate, acetate,
carbamate, sulfate, nitrate, formate, lactate, gluconate,
glucuronate, pyruvate, oxalacetate, fumarate, propionate,
aspartate, glutamate, or benzoate salt.
[0033] As used herein, the term "biomarker" or "marker" includes
any biochemical marker, serological marker, genetic marker, or
other clinical or echographic characteristic that can be used in
predicting, identifying, evaluating, assessing, determining,
monitoring, and/or optimizing tyrosine kinase inhibitor efficacy,
toxicity, and/or resistance according to the methods of the present
invention. Examples of biochemical or serological markers include,
without limitation, tyrosine kinases such as the receptor and
non-receptor tyrosine kinases described above; growth factors
(e.g., TGF-.alpha., EGF, VEGF, PDGF, amphiregulin, HB-EGF
(heparin-binding EGF-like growth factor), betacellulin, heregulin,
etc.); tumor suppressors (e.g., PTEN (phosphatase and tensin
homolog deleted on chromosome 10), DMBT1 (deleted in malignant
brain tumors 1), LGI1 (leucine-rich gene-glioma inactivated 1),
p53, etc.); and tyrosine kinase signaling components (e.g., Akt,
MAPK/ERK, MEK, RAF, PLA2, MEKK, JNKK, JNK, p38, PI3K, Ras, Rho,
PLC, PKC, p70 S6 kinase, p53, cyclin D1, STAT1, STAT3, PIP2, PIP3,
PDK, mTOR, BAD, p21, p27, ROCK, IP3, TSP-1, NOS, etc.). Examples of
genetic markers include, without limitation, tyrosine kinases such
as the receptor and non-receptor tyrosine kinases described above
and small GTPases such as Ras (e.g., K-Ras, N-Ras, H-Ras, etc.),
Rho, Rac 1, and Cdc42. In some embodiments, the genetic markers
described herein are genotyped to detect the presence or absence of
a variant allele, e.g., an activating mutation. Preferably, one or
more biochemical or serological markers are measured in combination
with one or more genetic markers. One skilled in the art will
appreciate that biochemical or serological markers can also be
categorized as genetic markers and vice versa.
[0034] As used herein, the term "profile" includes any set of data
that represents the distinctive features or characteristics
associated with a tumor, tumor cell, and/or cancer. The term
encompasses a "nucleic acid profile" that analyzes one or more
genetic markers, a "protein profile" that analyzes one or more
biochemical or serological markers, and combinations thereof.
Examples of nucleic acid profiles include, but are not limited to,
a genotypic profile, gene copy number profile, gene expression
profile, DNA methylation profile, and combinations thereof.
Non-limiting examples of protein profiles include a protein
expression profile, protein activation profile, and combinations
thereof. For example, a "genotypic profile" includes a set of
genotypic data that represents the genotype of one or more genes
associated with a tumor, tumor cell, and/or cancer. Similarly, a
"gene copy number profile" includes a set of gene copy number data
that represents the amplification of one or more genes associated
with a tumor, tumor cell, and/or cancer. Likewise, a "gene
expression profile" includes a set of gene expression data that
represents the mRNA levels of one or more genes associated with a
tumor, tumor cell, and/or cancer. In addition, a "DNA methylation
profile" includes a set of methylation data that represents the DNA
methylation levels (e.g., methylation status) of one or more genes
associated with a tumor, tumor cell, and/or cancer. Furthermore, a
"protein expression profile" includes a set of protein expression
data that represents the levels of one or more proteins associated
with a tumor, tumor cell, and/or cancer. Moreover, a "protein
activation profile" includes a set of data that represents the
activation (e.g., phosphorylation status) of one or more proteins
associated with a tumor, tumor cell, and/or cancer.
[0035] The term "gene" includes the segment of DNA involved in
producing a polypeptide chain. Specifically, a gene includes,
without limitation, regions preceding and following the coding
region, such as the promoter and 3'-untranslated region,
respectively, as well as intervening sequences (introns) between
individual coding segments (exons).
[0036] The term "nucleic acid" or "polynucleotide" includes
deoxyribonucleotides or ribonucleotides and polymers thereof in
either single- or double-stranded form. Unless specifically
limited, the term encompasses nucleic acids containing known
analogues of natural nucleotides that have similar binding
properties as the reference nucleic acid and are metabolized in a
manner similar to naturally occurring nucleotides. Unless otherwise
indicated, a particular nucleic acid sequence also implicitly
encompasses conservatively modified variants thereof (e.g.,
degenerate codon substitutions), alleles, orthologs, SNPs, and
complementary sequences as well as the sequence explicitly
indicated. Specifically, degenerate codon substitutions may be
achieved by generating sequences in which the third position of one
or more selected (or all) codons is substituted with mixed-base
and/or deoxyinosine residues (Batzer et al., Nucleic Acid Res.,
19:5081 (1991); Ohtsuka et al., J. Biol. Chem., 260:2605-2608
(1985); Rossolini et al., Mol. Cell. Probes, 8:91-98 (1994)). The
term nucleic acid is used interchangeably with gene, cDNA, and mRNA
encoded by a gene.
[0037] The term "polymorphism" include the occurrence of two or
more genetically determined alternative sequences or alleles in a
population. A "polymorphic site" includes the locus at which
divergence occurs. Preferred polymorphic sites have at least two
alleles, each occurring at frequency of greater than 1%, and more
preferably greater than 10% or 20% of a selected population. A
polymorphic locus can be as small as one base pair (single
nucleotide polymorphism, or SNP) or can comprise an insertion or
deletion of multiple nucleotides. Polymorphic markers include, but
are not limited to, restriction fragment length polymorphisms,
variable number of tandem repeats (VNTR's), hypervariable regions,
minisatellites, dinucleotide repeats, trinucleotide repeats,
tetranucleotide repeats, simple sequence repeats, and insertion
elements such as Alu. The first identified allele is arbitrarily
designated as the reference allele and other alleles are designated
as alternative or "variant alleles." The allele occurring most
frequently in a selected population is sometimes referred to as the
"wild-type" allele. Diploid organisms may be homozygous or
heterozygous for the variant alleles. The variant allele may or may
not produce an observable physical or biochemical characteristic
("phenotype") in an individual carrying the variant allele. For
example, a variant allele may alter the enzymatic activity of a
protein encoded by a gene of interest.
[0038] A "single nucleotide polymorphism" or "SNP" occurs at a
polymorphic site occupied by a single nucleotide, which is the site
of variation between allelic sequences. The site is usually
preceded by and followed by highly conserved sequences of the
allele (e.g., sequences that vary in less than 1/100 or 1/1000
members of the populations). A SNP usually arises due to
substitution of one nucleotide for another at the polymorphic site.
A transition is the replacement of one purine by another purine or
one pyrimidine by another pyrimidine. A transversion is the
replacement of a purine by a pyrimidine or vice versa. Single
nucleotide polymorphisms can also arise from a deletion of a
nucleotide or an insertion of a nucleotide relative to a reference
allele.
[0039] The term "genotype" as used herein includes to the genetic
composition of an organism, including, for example, whether a
diploid organism is heterozygous or homozygous for one or more
variant alleles of interest.
[0040] The term "gene amplification" comprises a cellular process
characterized by the production of multiple copies of any
particular piece of DNA. For example, a tumor cell amplifies, or
copies, chromosomal segments naturally as a result of cell signals
and sometimes environmental events. The process of gene
amplification leads to the production of many copies of the genes
that are located on that region of the chromosome. In certain
instances, so many copies of the amplified region are produced that
they can form their own small pseudo-chromosomes called
double-minute chromosomes. The genes on each of the copies can be
transcribed and translated, leading to an overproduction of the
mRNA and protein corresponding to the amplified genes.
[0041] The term "subject" or "patient" typically includes humans,
but can also include other animals such as, e.g., other primates,
rodents, canines, felines, equines, ovines, porcines, and the
like.
[0042] The term "sample" as used herein includes any biological
specimen obtained from a subject. Samples include, without
limitation, whole blood, plasma, serum, red blood cells, white
blood cells (e.g., peripheral blood mononuclear cells), saliva,
urine, stool (i.e., feces), tears, nipple aspirate, lymph, fine
needle aspirate, any other bodily fluid, a tissue sample (e.g.,
tumor tissue) such as a biopsy of a tumor, and cellular extracts
thereof. In some embodiments, the sample is whole blood or a
fractional component thereof such as plasma, serum, or a cell
pellet. In preferred embodiments, the sample is obtained by
isolating circulating cells of a solid tumor from a whole blood
cell pellet using any technique known in the art. As used herein,
the term "circulating cells" comprises cells that have either
metastasized or micrometastasized from a solid tumor and includes
circulating tumor cells, cancer stem cells, and/or cells that are
migrating to the tumor (e.g., circulating endothelial progenitor
cells, circulating endothelial cells, circulating pro-angiogenic
myeloid cells, circulating dendritic cells, etc.). In other
embodiments, the sample is a formalin fixed paraffin embedded
(FFPE) tumor tissue sample, e.g., from a solid tumor of the lung,
colon, or rectum.
[0043] The term "course of therapy" or "therapy" includes any
therapeutic approach taken to relieve and/or prevent one or more
symptoms associated with cancer. The term encompasses administering
any compound, drug, therapeutic agent, procedure, or regimen useful
for improving the health of a subject with cancer. One skilled in
the art will appreciate that either the course of therapy or the
dose of the current course of therapy can be changed based upon the
panel of biomarkers determined using the methods of the present
invention. Examples of therapies suitable for use in the methods of
the present invention include, without limitation, targeted cancer
therapy using tyrosine kinase inhibitors, conventional
chemotherapy, radiation therapy, hormonal therapy, immunotherapy,
and combinations thereof.
[0044] The term "recommending" as used herein includes providing
dosing instructions for a tyrosine kinase inhibitor or alternative
cancer therapy based on the nucleic acid and/or protein profiles
determined for a particular subject. In some embodiments, the
methods of the present invention provide a recommendation of an
initial dose of the drug. In other embodiments, the methods of the
present invention provide a recommendation of a subsequent dose of
the drug or an alternative therapy. Dosing instructions include,
without limitation, lab results with preferred drug doses, data
sheets, look-up tables setting forth preferred drug doses,
instructions or guidelines for using the drug, package inserts to
accompany the drug, and the like. In certain embodiments, the term
"recommending" associates the result obtained from the use of a
particular algorithm (e.g., index value) with side-effects or
efficacy.
[0045] As used herein, the term "administering" includes oral
administration, administration as a suppository, topical contact,
intravenous, intraperitoneal, intramuscular, intralesional,
intrathecal, intranasal or subcutaneous administration, or the
implantation of a slow-release device, e.g., a mini-osmotic pump,
to a subject. Administration is by any route, including parenteral
and transmucosal (e.g., buccal, sublingual, palatal, gingival,
nasal, vaginal, rectal, or transdermal). Parenteral administration
includes, e.g., intravenous, intramuscular, intra-arteriole,
intradermal, subcutaneous, intraperitoneal, intraventricular, and
intracranial. Other modes of delivery include, but are not limited
to, the use of liposomal formulations, intravenous infusion,
transdermal patches, etc. By "co-administer" it is meant that a
tyrosine kinase inhibitor is administered at the same time, just
prior to, or just after the administration of one or more
additional drugs or therapeutic regimens (e.g., other tyrosine
kinase inhibitors; other anti-cancer agents such as
chemotherapeutic agents, monoclonal antibodies, antibiotics,
immunosuppressive agents, and anti-inflammatory agents; other
cancer therapies such as radiation therapy, hormonal therapy, and
immunotherapy; etc.).
[0046] The term "identifying the response of a tumor to treatment
with a tyrosine kinase inhibitor" includes the use of the
algorithms of the present invention to determine whether a tumor or
tumor cell is responsive (i.e., sensitive) or non-responsive (i.e.,
resistant) to the effects of a particular tyrosine kinase inhibitor
or combinations thereof. Generally, a tumor or tumor cell which is
responsive to treatment with a tyrosine kinase inhibitor exhibits
an improvement in one or more desired results (e.g., tumor cell
death, inhibition of tumor growth, reduction in tumor size,
prevention of tumor metastasis, etc.) when compared to the absence
of treatment in control samples. In certain instances, a tumor or
tumor cell is considered to be responsive to treatment with a
tyrosine kinase inhibitor when it responds to initial treatment but
then develops resistance as treatment is continued.
[0047] The term "predicting the response of a subject to treatment
with a tyrosine kinase inhibitor" includes the use of the
algorithms of the present invention to determine whether a subject
would likely respond to a particular tyrosine kinase inhibitor or
combinations thereof. Although cancer is used herein as a
non-limiting example, one skilled in the art will appreciate that
subjects having other diseases or disorders in which tyrosine
kinase inhibitors provide some therapeutic benefit can also be
evaluated according to the methods of the present invention.
Generally, a patient who is responsive to treatment with a tyrosine
kinase inhibitor exhibits an improvement in one or more desired
clinical results (e.g., alleviation of symptoms, diminishment of
the extent of cancer, stabilization of cancer, delaying or slowing
the progression of cancer, amelioration of cancer, remission, etc.)
when compared to the absence of treatment (e.g., placebo) in
control patients. In certain instances, a patient is considered to
be responsive to treatment with a tyrosine kinase inhibitor when
that patient responds to initial treatment but then develops
resistance as treatment is continued.
[0048] The term "monitoring treatment with a tyrosine kinase
inhibitor in a subject" includes the use of the algorithms of the
present invention to determine whether a subject will develop or
has developed resistance to treatment with a tyrosine kinase
inhibitor. In certain instances, the result obtained from the use
of a particular algorithm indicates that the subject has an
increased likelihood of developing or has developed resistance to
tyrosine kinase inhibitor therapy. In certain other instances, the
result obtained from the use of a particular algorithm indicates
that the subject has a decreased likelihood of developing or has
not developed resistance to tyrosine kinase inhibitor therapy.
[0049] The term "optimizing dose efficacy in a subject receiving a
tyrosine kinase inhibitor" includes the use of the algorithms of
the present invention to adjust the subsequent dose of the tyrosine
kinase inhibitor or to change the course of therapy for a subject
after the drug has been administered in order to optimize its
therapeutic efficacy. In certain instances, the result obtained
from the use of a particular algorithm indicates that the
subsequent dose of the tyrosine kinase inhibitor should be
increased, decreased, or maintained. In certain other instances,
the result obtained from the use of a particular algorithm
indicates that an alternative cancer therapy should be administered
to the subject.
III. Description of the Embodiments
[0050] The present invention provides methods for analyzing a
combination of biomarkers in a sample such as whole blood to
individualize tyrosine kinase inhibitor therapy in subjects who
have been diagnosed with cancer. As a result, the present invention
enables tyrosine kinase inhibitors such as gefitinib and sunitinib
to become first-line therapeutic agents for the treatment of solid
tumors, rather than their current role as second- or third-line
cancer therapies.
[0051] Accordingly, in one aspect, the present invention provides
an assay method for identifying the response of a tumor to
treatment with a tyrosine kinase inhibitor, the method comprising:
[0052] (a) determining at least one profile selected from the group
consisting of a nucleic acid profile, protein profile, and
combinations thereof in a sample from a subject; and [0053] (b)
identifying the tumor as responsive or non-responsive to treatment
with the tyrosine kinase inhibitor using an algorithm based upon
the at least one profile.
[0054] In one embodiment, the tumor comprises a solid tumor of a
tissue selected from the group consisting of lung, colon, rectum,
gall bladder, brain, breast, kidney, pancreas, stomach, liver,
bone, skin, spleen, ovary, testis, prostate, and muscle.
Preferably, the tumor is non-small cell lung carcinoma, a
gastrointestinal stromal tumor, colorectal carcinoma, or renal cell
carcinoma. In another embodiment, the subject has been diagnosed
with cancer.
[0055] In some embodiments, the sample comprises a whole blood,
serum, plasma, urine, nipple aspirate, lymph, saliva, fine needle
aspirate, and/or tumor tissue sample. In certain instances, the
whole blood sample is separated into a plasma or serum fraction and
a cellular fraction (i.e., cell pellet). The cellular fraction
typically contains red blood cells, white blood cells, and/or
circulating cells of a solid tumor such as circulating tumor cells
(CTCs) and circulating endothelial cells (CECs). The plasma or
serum fraction usually contains, inter alia, nucleic acids (e.g.,
DNA, RNA) and proteins that are released by CTCs and/or CECs. The
circulating cells can be isolated using one or more separation
methods including, for example, immunomagnetic separation (see,
e.g., Racila et al., Proc. Natl. Acad. Sci. USA, 95:4589-4594
(1998); Bilkenroth et al., Int. J. Cancer, 92:577-582 (2001)),
microfluidic separation (see, e.g., Mohamed et al., IEEE Trans.
Nanobiosci., 3:251-256 (2004)), FACS (see, e.g., Mancuso et al.,
Blood, 97:3658-3661 (2001)), density gradient centrifugation (see,
e.g., Baker et al., Clin. Cancer Res., 13:4865-4871 (2003)), and
depletion methods (see, e.g., Meye et al., Int. J. Oncol.,
21:521-530 (2002)). In some instances, the isolated circulating
cells can be stimulated in vitro with one or more growth factors
before, during, and/or after incubation with one or more tyrosine
kinase inhibitors of interest. In other instances, the isolated
circulating cells can be lysed, e.g., following growth factor
stimulation, to produce a cellular extract (e.g., tumor cell
lysate) using any technique known in the art.
[0056] In other embodiments, the tyrosine kinase inhibitor
comprises an epidermal growth factor receptor (EGFR) inhibitor,
vascular endothelial cell growth factor receptor (VEGFR) inhibitor,
platelet-derived growth factor receptor (PDGFR) inhibitor, c-KIT
inhibitor, FMS-like tyrosine kinase 3 (FLT-3) inhibitor, BCR-ABL
inhibitor, and combinations thereof. Examples of EGFR inhibitors
include, but are not limited to, gefitinib, erlotinib, lapatinib,
canertinib, sorafenib, vandetanib, pharmaceutically acceptable
salts thereof, and combinations thereof. Non-limiting examples of
VEGFR inhibitors include sunitinib, semaxinib, vatalanib,
sorafenib, vandetanib, pharmaceutically acceptable salts thereof,
and combinations thereof. Examples of PDGFR inhibitors include,
without limitation, sunitinib, imatinib, sorafenib, leflunomide,
pharmaceutically acceptable salts thereof, and combinations
thereof. Non-limiting examples of c-KIT inhibitors include
sunitinib, imatinib, semaxinib, pharmaceutically acceptable salts
thereof, and combinations thereof. Examples of FLT-3 inhibitors
include, but are not limited to, sunitinib, semaxinib,
pharmaceutically acceptable salts thereof, and combinations
thereof. Examples of BCR-ABL inhibitors include, without
limitation, imatinib and a pharmaceutically acceptable salt
thereof.
[0057] In another embodiment, the nucleic acid profile comprises a
genotypic profile, gene copy number profile, gene expression
profile, DNA methylation profile, and combinations thereof.
[0058] In certain instances, the genotypic profile comprises
determining the genotype of at least one gene selected from the
group consisting of a tyrosine kinase gene, small GTPase gene, and
combinations thereof. The genotype can be determined at a
polymorphic site such as a single nucleotide polymorphism (SNP). In
a preferred embodiment, the tyrosine kinase gene is selected from
the group consisting of an EGFR gene, VEGFR gene, PDGFR gene, c-KIT
gene, FLT-3 gene, BCR-ABL gene, and combinations thereof. Examples
of EGFR genes include, but are not limited to, an EGFR (HER1/ErbB1)
gene, HER2 (Neu/ErbB2) gene, HER3 (ErbB3) gene, HER4 (ErbB4) gene,
and combinations thereof. Non-limiting examples of small GTPase
genes include a Ras gene, Rho gene, Rac1 gene, Cdc42 gene, and
combinations thereof. Preferably, the Ras gene is selected from the
group consisting of a K-Ras gene, N-Ras gene, H-Ras gene, and
combinations thereof.
[0059] In certain instances, the gene copy number profile comprises
determining the number of copies of at least one tyrosine kinase
gene. In a preferred embodiment, the at least one tyrosine kinase
gene is selected from the group consisting of an EGFR gene, VEGFR
gene, PDGFR gene, c-KIT gene, FLT-3 gene, BCR-ABL gene, and
combinations thereof. As a non-limiting example, gene amplification
can be detected in one or more members of the EGFR family of genes,
e.g., EGFR (HER1/ErbB1), HER2 (Neu/ErbB2), HER3 (ErbB3), and/or
HER4 (ErbB4). Preferably, the number of copies of said at least one
tyrosine kinase gene is determined by fluorescence in situ
hybridization (FISH). Alternatively, gene amplification can be
measured using chromogenic in situ hybridization (CISH) or
immunohistochemistry (IHC).
[0060] In certain instances, the gene expression profile comprises
determining the expression level of at least one tyrosine kinase
gene. The expression level of any of the tyrosine kinase genes
described herein can be analyzed. Preferably, the expression level
of the at least one tyrosine kinase gene is determined by measuring
mRNA levels.
[0061] In certain instances, the DNA methylation profile comprises
determining the methylation state of at least one tumor suppressor
gene. As a non-limiting example, DNA methylation can be detected in
tumor suppressor genes such as PTEN, DMBT1, LGI1, p53, CDKN2B, ESR1
(human estrogen receptor 1), ICSBP (interferon consensus-binding
protein), ETV3 (Ets variant 3), DDX20 (DEAD box polypeptide), and
combinations thereof. The level of DNA methylation can be measured
using any method known to one of skill in the art, such as those
techniques described below.
[0062] In a further embodiment, the protein profile comprises a
protein expression profile, protein activation profile, and
combinations thereof.
[0063] In certain instances, the protein expression profile
comprises determining the expression level of at least one protein
selected from the group consisting of a tyrosine kinase, growth
factor, tumor suppressor, and combinations thereof. In a preferred
embodiment, the tyrosine kinase is selected from the group
consisting of EGFR, VEGFR, PDGFR, c-KIT, FLT-3, BCR-ABL, and
combinations thereof. As a non-limiting example, an expression
level can be measured for one or more members of the EGFR family,
e.g., EGFR (HER1/ErbB1), HER2 (Neu/ErbB2), HER3 (ErbB3), and/or
HER4 (ErbB4). Examples of growth factors include, but are not
limited to, TGF-.alpha., EGF, VEGF, PDGF, and combinations thereof.
Non-limiting examples of tumor suppressors include PTEN, DMBT1,
LGI1, and combinations thereof. Preferably, the expression level of
the at least one protein is determined by IHC or an immunoassay
such as an enzyme-linked immunosorbent assay (ELISA).
[0064] In certain instances, the protein activation profile
comprises determining the phosphorylation state, ubiquitination
state, and/or complexation state of at least one protein selected
from the group consisting of a tyrosine kinase, tyrosine kinase
signaling component, and combinations thereof. In one embodiment,
the tyrosine kinase is selected from the group consisting of EGFR,
VEGFR, PDGFR, c-KIT, FLT-3, BCR-ABL, and combinations thereof. As a
non-limiting example, the complexation state of members of the EGFR
family, e.g., EGFR (HER1/ErbB1), HER2 (Neu/ErbB2), HER3 (ErbB3),
and/or HER4 (ErbB4), can be determined by detecting the presence or
level of one or more EGFR heterodimeric complexes (e.g.,
ErbB2:EGFR, ErbB2:ErbB3, ErbB2:ErbB4, etc.). In another embodiment,
the at least one tyrosine kinase signaling component is selected
from the group consisting of Akt, MAPK/ERK, MEK, RAF, PLA2, MEKK,
JNKK, JNK, p38, PI3K, Ras, Rho, PLC, PKC, p70 S6 kinase, p53,
cyclin D1, STAT1, STAT3, PIP2, PIP3, PDK, mTOR, BAD, p21, p27,
ROCK, IP3, TSP-1, NOS, and combinations thereof. In some instances,
the phosphorylation state of the tyrosine kinase and/or tyrosine
kinase signaling component can be determined by IHC. Other
techniques include performing an immunoassay such as an ELISA to
assess the phosphorylation state of one or more proteins of
interest.
[0065] In another embodiment, the algorithm is used to calculate an
index value. In certain instances, the index value comprises a
cumulative index value. Typically, the cumulative index value is
compared to an index cutoff value. In certain instances, a
cumulative index value that is greater than or equal to the index
cutoff value indicates that the subject is responsive or has an
increased likelihood of responding to treatment with the tyrosine
kinase inhibitor. In these instances, the method can further
comprise recommending a dose (e.g., a therapeutically effective
dose) of the tyrosine kinase inhibitor to be administered to the
subject. In certain other instances, a cumulative index value that
is greater than or equal to the index cutoff value indicates that
the subject is non-responsive or has a decreased likelihood of
responding to treatment with the tyrosine kinase inhibitor. In
these instances, the method can further comprise recommending a
dose of another tyrosine kinase inhibitor or an alternative cancer
therapy to be administered to the subject.
[0066] In some embodiments, identifying a tumor as responsive or
non-responsive to treatment with a tyrosine kinase inhibitor is
based upon determining at least one nucleic acid and/or protein
profile in conjunction with the use of a learning statistical
classifier system. The learning statistical classifier system can
be selected from the group consisting of a random forest (RF),
classification and regression tree (C&RT), boosted tree, neural
network (NN), support vector machine (SVM), general chi-squared
automatic interaction detector model, interactive tree,
multiadaptive regression spline, machine learning classifier, and
combinations thereof. Preferably, the learning statistical
classifier system is a tree-based statistical algorithm (e.g., RF,
C&RT, etc.) and/or a neural network (e.g., artificial NN (ANN),
etc.).
[0067] In certain instances, the algorithm comprises a single
learning statistical classifier system. Preferably, the single
learning statistical classifier system comprises a tree-based
statistical algorithm such as a RF or C&RT or a neural network
such as an ANN. As a non-limiting example, a single learning
statistical classifier system can be used to identify the tumor as
responsive or non-responsive to treatment based upon a prediction
or probability value and the at least one nucleic acid and/or
protein profile. The use of a single learning statistical
classifier system typically identifies the tumor as sensitive or
resistant to the tyrosine kinase inhibitor of interest with a
sensitivity, specificity, positive predictive value, negative
predictive value, and/or overall accuracy of at least about 60%,
65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,
86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or
99%.
[0068] In certain other instances, the algorithm comprises a
combination of at least two learning statistical classifier
systems. Preferably, the combination of learning statistical
classifier systems comprises a RF and a NN, e.g., used in tandem or
parallel. As a non-limiting example, a RF can first be used to
generate a prediction or probability value based upon the at least
one-nucleic acid and/or protein profile, and a NN can then be used
to identify the tumor as responsive or non-responsive to treatment
with a tyrosine kinase inhibitor based upon the prediction or
probability value and the at least one nucleic acid and/or protein
profile. Advantageously, the hybrid RF/NN learning statistical
classifier system of the present invention identifies the tumor as
sensitive or resistant to the tyrosine kinase inhibitor of interest
with a sensitivity, specificity, positive predictive value,
negative predictive value, and/or overall accuracy of at least
about 60%, 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%,
84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,
97%, 98%, or 99%.
[0069] In some instances, the data obtained from using the learning
statistical classifier system or systems can be processed using a
processing algorithm. Such a processing algorithm can be selected,
for example, from the group consisting of a multilayer perceptron,
backpropagation network, and Levenberg-Marquardt algorithm. In
other instances, a combination of such processing algorithms can be
used, such as in a parallel or serial fashion.
[0070] In certain embodiments, the methods of the present invention
further comprise sending the identification results (i.e., whether
the tumor is responsive or non-responsive to treatment with the
tyrosine kinase inhibitor) to a clinician, e.g., an oncologist or a
general practitioner.
[0071] In another aspect, the present invention provides an assay
method for predicting the response of a subject to treatment with a
tyrosine kinase inhibitor, the method comprising: [0072] (a)
determining at least one profile selected from the group consisting
of a nucleic acid profile, protein profile, and combinations
thereof in a sample from the subject; and [0073] (b) predicting the
likelihood that the subject will respond to treatment with the
tyrosine kinase inhibitor using an algorithm based upon the at
least one profile.
[0074] In one embodiment, the subject has been diagnosed with
cancer, e.g., a solid tumor of a tissue selected from the group
consisting of lung, colon, rectum, gall bladder, brain, breast,
kidney, pancreas, stomach, liver, bone, skin, spleen, ovary,
testis, prostate, and muscle. Preferably, the cancer is non-small
cell lung cancer, a gastrointestinal stromal tumor, colorectal
cancer, or renal cell carcinoma.
[0075] In some embodiments, the sample comprises a whole blood,
serum, plasma, urine, nipple aspirate, lymph, saliva, fine needle
aspirate, and/or tumor tissue sample. In certain instances, the
whole blood sample is separated into a plasma or serum fraction and
a cellular fraction (i.e., cell pellet). The circulating cells of
the solid tumor can be isolated from the cellular fraction using
one or more of the separation methods described above. In some
instances, the isolated circulating cells can be stimulated in
vitro with one or more growth factors before, during, and/or after
incubation with one or more tyrosine kinase inhibitors of interest.
In other instances, the isolated circulating cells can be lysed,
e.g., following growth factor stimulation, to produce a cellular
extract (e.g., tumor cell lysate) using any technique known in the
art.
[0076] In other embodiments, the tyrosine kinase inhibitor is
selected from the group consisting of an EGFR inhibitor, VEGFR
inhibitor, PDGFR inhibitor, c-KIT inhibitor, FLT-3 inhibitor,
BCR-ABL inhibitor, and combinations thereof. Examples of inhibitors
belonging to each class are described above.
[0077] In another embodiment, the nucleic acid profile is selected
from the group consisting of a genotypic profile, gene copy number
profile, gene expression profile, DNA methylation profile, and
combinations thereof. In a further embodiment, the protein profile
is selected from the group consisting of a protein expression
profile, protein activation profile, and combinations thereof.
Non-limiting examples of techniques that can be used to determine
these nucleic acid and protein profiles are described above.
[0078] In some embodiments, the algorithm is used to calculate an
index value. In certain instances, the index value comprises a
cumulative index value. Typically, the cumulative index value is
compared to an index cutoff value. In certain instances, a
cumulative index value that is greater than or equal to the index
cutoff value indicates that the subject has an increased likelihood
of responding to treatment with the tyrosine kinase inhibitor. In
these instances, the method can further comprise recommending a
dose (e.g., a therapeutically effective dose) of the tyrosine
kinase inhibitor to be administered to the subject. In certain
other instances, a cumulative index value that is greater than or
equal to the index cutoff value indicates that the subject has a
decreased likelihood of responding to treatment with the tyrosine
kinase inhibitor. In these instances, the method can further
comprise recommending a dose of another tyrosine kinase inhibitor
or an alternative cancer therapy to be administered to the
subject.
[0079] In some embodiments, predicting the likelihood that a
subject will respond to treatment with a tyrosine kinase inhibitor
is based upon determining at least one nucleic acid and/or protein
profile in conjunction with the use of a learning statistical
classifier system. The learning statistical classifier system can
be selected from the group consisting of a random forest (RF),
classification and regression tree (C&RT), boosted tree, neural
network (NN), support vector machine (SVM), general chi-squared
automatic interaction detector model, interactive tree,
multiadaptive regression spline, machine learning classifier, and
combinations thereof. Preferably, the learning statistical
classifier system is a tree-based statistical algorithm (e.g., RF,
C&RT, etc.) and/or a neural network (e.g., artificial NN (ANN),
etc.).
[0080] In certain instances, the algorithm comprises a single
learning statistical classifier system. As a non-limiting example,
a single learning statistical classifier system can be used to
predict the likelihood that the subject will respond to treatment
based upon a prediction or probability value and the at least one
nucleic acid and/or protein profile. The use of a single learning
statistical classifier system typically predicts the likelihood
that the subject will respond to the tyrosine kinase inhibitor of
interest with a sensitivity, specificity, positive predictive
value, negative predictive value, and/or overall accuracy of at
least about 60%, 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%,
83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%,
96%, 97%, 98%, or 99%.
[0081] In certain other instances, the algorithm comprises a
combination of at least two learning statistical classifier
systems. Preferably, the combination of learning statistical
classifier systems comprises a RF and a NN, e.g., used in tandem or
parallel. As a non-limiting example, a RF can first be used to
generate a prediction or probability value based upon the at least
one nucleic acid and/or protein profile, and a NN can then be used
to predict the likelihood that the subject will respond to
treatment with a tyrosine kinase inhibitor based upon the
prediction or probability value and the at least one nucleic acid
and/or protein profile. Advantageously, the hybrid RF/NN learning
statistical classifier system of the present invention predicts the
likelihood that the subject will respond to the tyrosine kinase
inhibitor of interest with a sensitivity, specificity, positive
predictive value, negative predictive value, and/or overall
accuracy of at least about 60%, 65%, 70%, 75%, 76%, 77%, 78%, 79%,
80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%,
93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0082] In some instances, the data obtained from using the learning
statistical classifier system or systems can be processed using a
processing algorithm. Such a processing algorithm can be selected,
for example, from the group consisting of a multilayer perceptron,
backpropagation network, and Levenberg-Marquardt algorithm. In
other instances, a combination of such processing algorithms can be
used, such as in a parallel or serial fashion.
[0083] In certain embodiments, the methods of the present invention
further comprise sending the prediction results (i.e., whether the
subject is likely to respond to treatment with the tyrosine kinase
inhibitor) to a clinician, e.g., an oncologist or a general
practitioner.
[0084] In yet another aspect, the present invention provides an
assay method for monitoring treatment with a tyrosine kinase
inhibitor in a subject, the method comprising: [0085] (a)
determining at least one profile selected from the group consisting
of a nucleic acid profile, protein profile, and combinations
thereof in a sample from the subject; and [0086] (b) monitoring the
likelihood that the subject will develop resistance to treatment
with the tyrosine kinase inhibitor using an algorithm based upon
the at least one profile.
[0087] In one embodiment, the subject has been diagnosed with
cancer, e.g., a solid tumor of a tissue selected from the group
consisting of lung, colon, rectum, gall bladder, brain, breast,
kidney, pancreas, stomach, liver, bone, skin, spleen, ovary,
testis, prostate, and muscle. Preferably, the cancer is non-small
cell lung cancer, a gastrointestinal stromal tumor, colorectal
cancer, or renal cell carcinoma.
[0088] In some embodiments, the sample comprises a whole blood,
serum, plasma, urine, nipple aspirate, lymph, saliva, fine needle
aspirate, and/or tumor tissue sample. In certain instances, the
whole blood sample is separated into a plasma or serum fraction and
a cellular fraction (i.e., cell pellet). The circulating cells of
the solid tumor can be isolated from the cellular fraction using
one or more of the separation methods described above. In some
instances, the isolated circulating cells can be stimulated in
vitro with one or more growth factors before, during, and/or after
incubation with one or more tyrosine kinase inhibitors of interest.
In other instances, the isolated circulating cells can be lysed,
e.g., following growth factor stimulation, to produce a cellular
extract (e.g., tumor cell lysate) using any technique known in the
art.
[0089] In other embodiments, the tyrosine kinase inhibitor is
selected from the group consisting of an EGFR inhibitor, VEGFR
inhibitor, PDGFR inhibitor, c-KIT inhibitor, FLT-3 inhibitor,
BCR-ABL inhibitor, and combinations thereof. Examples of inhibitors
belonging to each class are described above.
[0090] In another embodiment, the nucleic acid profile is selected
from the group consisting of a genotypic profile, gene copy number
profile, gene expression profile, DNA methylation profile, and
combinations thereof. In a further embodiment, the protein profile
is selected from the group consisting of a protein expression
profile, protein activation profile, and combinations thereof.
Non-limiting examples of techniques that can be used to determine
these nucleic acid and protein profiles are described above.
[0091] In some embodiments, the algorithm is used to calculate an
index value. In certain instances, the index value comprises a
cumulative index value. Typically, the cumulative index value is
compared to an index cutoff value. In certain instances, a
cumulative index value that is greater than or equal to the index
cutoff value indicates that the subject has an increased likelihood
of developing or has developed resistance to treatment with the
tyrosine kinase inhibitor. In these instances, the method can
further comprise recommending a dose of another tyrosine kinase
inhibitor or an alternative therapy to be administered to the
subject. In certain other instances, a cumulative index value that
is greater than or equal to the index cutoff value indicates that
the subject has a decreased likelihood of developing or has not
developed resistance to treatment with the tyrosine kinase
inhibitor. In these instances, the method can further comprise
recommending that a subsequent dose of the tyrosine kinase
inhibitor be maintained.
[0092] In other embodiments, the method can further comprise
comparing the cumulative index value to a cumulative index value
generated at an earlier time. In certain instances, an increase in
the cumulative index value indicates that the subject has an
increased likelihood of developing or has developed resistance to
treatment with the tyrosine kinase inhibitor. In certain other
instances, an increase in the cumulative index value indicates that
the subject has a decreased likelihood of developing or has not
developed resistance to treatment with the tyrosine kinase
inhibitor.
[0093] In some embodiments, monitoring the likelihood that a
subject will develop resistance to treatment with a tyrosine kinase
inhibitor is based upon determining at least one nucleic acid
and/or protein profile in conjunction with the use of a learning
statistical classifier system. The learning statistical classifier
system can be selected from the group consisting of a random forest
(RF), classification and regression tree (C&RT), boosted tree,
neural network (NN), support vector machine (SVM), general
chi-squared automatic interaction detector model, interactive tree,
multiadaptive regression spline, machine learning classifier, and
combinations thereof. Preferably, the learning statistical
classifier system is a tree-based statistical algorithm (e.g., RF,
C&RT, etc.) and/or a neural network (e.g., artificial NN (ANN),
etc.).
[0094] In certain instances, the algorithm comprises a single
learning statistical classifier system. As a non-limiting example,
a single learning statistical classifier system can be used to
monitor the likelihood that the subject will develop resistance to
treatment based upon a prediction or probability value and the at
least one nucleic acid and/or protein profile. The use of a single
learning statistical classifier system typically monitors the
likelihood that the subject will develop resistance to the tyrosine
kinase inhibitor of interest with a sensitivity, specificity,
positive predictive value, negative predictive value, and/or
overall accuracy of at least about 60%, 65%, 70%, 75%, 76%, 77%,
78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,
91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0095] In certain other instances, the algorithm comprises a
combination of at least two learning statistical classifier
systems. Preferably, the combination of learning statistical
classifier systems comprises a RF and a NN, e.g., used in tandem or
parallel. As a non-limiting example, a RF can first be used to
generate a prediction or probability value based upon the at least
one nucleic acid and/or protein profile, and a NN can then be used
to monitor the likelihood that the subject will develop resistance
to treatment with a tyrosine kinase inhibitor based upon the
prediction or probability value and the at least one nucleic acid
and/or protein profile. Advantageously, the hybrid RF/NN learning
statistical classifier system of the present invention monitors the
likelihood that the subject will develop resistance to the tyrosine
kinase inhibitor of interest with a sensitivity, specificity,
positive predictive value, negative predictive value, and/or
overall accuracy of at least about 60%, 65%, 70%, 75%, 76%, 77%,
78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,
91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0096] In some instances, the data obtained from using the learning
statistical classifier system or systems can be processed using a
processing algorithm. Such a processing algorithm can be selected,
for example, from the group consisting of a multilayer perceptron,
backpropagation network, and Levenberg-Marquardt algorithm. In
other instances, a combination of such processing algorithms can be
used, such as in a parallel or serial fashion.
[0097] In certain embodiments, the methods of the present invention
further comprise sending the monitoring results (i.e., whether the
subject is likely to develop resistance to treatment with the
tyrosine kinase inhibitor) to a clinician, e.g., an oncologist or a
general practitioner.
[0098] In a further aspect, the present invention provides an assay
method for optimizing dose efficacy in a subject receiving a
tyrosine kinase inhibitor, the method comprising: [0099] (a)
determining at least one profile selected from the group consisting
of a nucleic acid profile, protein profile, and combinations
thereof in a sample from the subject; and [0100] (b) recommending a
subsequent dose of the tyrosine kinase inhibitor using an algorithm
based upon the at least one profile.
[0101] In one embodiment, the subject has been diagnosed with
cancer, e.g., a solid tumor of a tissue selected from the group
consisting of lung, colon, rectum, gall bladder, brain, breast,
kidney, pancreas, stomach, liver, bone, skin, spleen, ovary,
testis, prostate, and muscle. Preferably, the cancer is non-small
cell lung cancer, a gastrointestinal stromal tumor, colorectal
cancer, or renal cell carcinoma.
[0102] In some embodiments, the sample comprises a whole blood,
serum, plasma, urine, nipple aspirate, lymph, saliva, fine needle
aspirate, and/or tumor tissue sample. In certain instances, the
whole blood sample is separated into a plasma or serum fraction and
a cellular fraction (i.e., cell pellet). The circulating cells of
the solid tumor can be isolated from the cellular fraction using
one or more of the separation methods described above. In some
instances, the isolated circulating cells can be stimulated in
vitro with one or more growth factors before, during, and/or after
incubation with one or more tyrosine kinase inhibitors of interest.
In other instances, the isolated circulating cells can be lysed,
e.g., following growth factor stimulation, to produce a cellular
extract (e.g., tumor cell lysate) using any technique known in the
art.
[0103] In other embodiments, the tyrosine kinase inhibitor is
selected from the group consisting of an EGFR inhibitor, VEGFR
inhibitor, PDGFR inhibitor, c-KIT inhibitor, FLT-3 inhibitor,
BCR-ABL inhibitor, and combinations thereof. Examples of inhibitors
belonging to each class are described above.
[0104] In another embodiment, the nucleic acid profile is selected
from the group consisting of a genotypic profile, gene copy number
profile, gene expression profile, DNA methylation profile, and
combinations thereof. In a further embodiment, the protein profile
is selected from the group consisting of a protein expression
profile, protein activation profile, and combinations thereof.
Non-limiting examples of techniques that can be used to determine
these nucleic acid and protein profiles are described above.
[0105] In some embodiments, the algorithm is used to calculate an
index value. In certain instances, the index value comprises a
cumulative index value. Typically, the cumulative index value is
compared to an index cutoff value. In certain instances, a
cumulative index value that is greater than or equal to the index
cutoff value indicates that the subsequent dose of the tyrosine
kinase inhibitor should be increased. In certain other instances, a
cumulative index value that is greater than or equal to the index
cutoff value indicates that the subsequent dose of the tyrosine
kinase inhibitor should be decreased or an alternative cancer
therapy should be administered. The method can further comprise
recommending the subsequent dose (i.e., higher, lower, or the same)
of the tyrosine kinase inhibitor to be administered or a dose of
the alternative cancer therapy to be administered to the
subject.
[0106] In some embodiments, recommending a subsequent dose of a
tyrosine kinase inhibitor is based upon determining at least one
nucleic acid and/or protein profile in conjunction with the use of
a learning statistical classifier system. The learning statistical
classifier system can be selected from the group consisting of a
random forest (RF), classification and regression tree (C&RT),
boosted tree, neural network (NN), support vector machine (SVM),
general chi-squared automatic interaction detector model,
interactive tree, multiadaptive regression spline, machine learning
classifier, and combinations thereof. Preferably, the learning
statistical classifier system is a tree-based statistical algorithm
(e.g., RF, C&RT, etc.) and/or a neural network (e.g.,
artificial NN (ANN), etc.).
[0107] In certain instances, the algorithm comprises a single
learning statistical classifier system. In certain other instances,
the algorithm comprises a combination of at least two learning
statistical classifier systems. Preferably, the combination of
learning statistical classifier systems comprises a RF and a NN,
e.g., used in tandem or parallel.
[0108] In some instances, the data obtained from using the learning
statistical classifier system or systems can be processed using a
processing algorithm. Such a processing algorithm can be selected,
for example, from the group consisting of a multilayer perceptron,
backpropagation network, and Levenberg-Marquardt algorithm. In
other instances, a combination of such processing algorithms can be
used, such as in a parallel or serial fashion.
[0109] In certain embodiments, the methods of the present invention
further comprise sending the recommendation results to a clinician,
e.g., an oncologist or a general practitioner.
IV. Tyrosine Kinase Inhibitors
[0110] Tyrosine kinase inhibitors represent a class of therapeutic
agents or drugs that target receptor and/or non-receptor tyrosine
kinases in cells such as tumor cells. In certain instances, the
tyrosine kinase inhibitor is an antibody-based (e.g., anti-tyrosine
kinase monoclonal antibody, etc.) or polynucleotide-based (e.g.,
tyrosine kinase antisense oligonucleotide, small interfering
ribonucleic acid, etc.) form of targeted therapy. Preferably, the
tyrosine kinase inhibitor is a small molecule that inhibits target
tyrosine kinases by binding to the ATP-binding site of the enzyme.
Examples of small molecule tyrosine kinase inhibitors include, but
are not limited to, gefitinib (Iressa.RTM.), sunitinib
(Sutent.RTM.; SU11248), erlotinib (Tarceva.RTM.; OSI-1774),
lapatinib (GW572016; GW2016), canertinib (CI 1033), semaxinib
(SU5416), vatalanib (PTK787/ZK222584), sorafenib (BAY 43-9006),
imatinib (Gleevec.RTM.; STI571), dasatinib (BMS-354825),
leflunomide (SU101), vandetanib (Zactima.TM.; ZD6474),
pharmaceutically acceptable salts thereof, derivatives thereof,
analogs thereof, and combinations thereof. Additional examples of
tyrosine kinase inhibitors suitable for use in the present
invention include quinazolines (e.g., PD
153035,4-(3-chloroanilino)quinazoline, etc.), pyridopyrimidines,
pyrimidopyrimidines, pyrrolopyrimidines (e.g., CGP 59326, CGP
60261, CGP 62706, etc.), pyrazolopyrimidines,
4-(phenylamino)-7H-pyrrolo[2,3-d]pyrimidines, curcumin (diferuloyl
methane), 4,5-bis(4-fluoroanilino)phthalimide, tyrphostines
containing nitrothiophene moieties, quinoxalines (see, e.g., U.S.
Pat. No. 5,804,396), tryphostins (see, e.g., U.S. Pat. No.
5,804,396), PD0183805, PKI-166, EKB-569, IMC-1C11, Affinitac.TM.
(LY900003; ISIS 3521), and the tyrosine kinase inhibitors described
in PCT Publication Nos. WO 99/09016, WO 98/43960, WO 97/38983, WO
99/06378, WO 99/06396, WO 96/30347, WO 96/33978, WO 96/33979, and
WO 96/33980.
[0111] As described herein, tyrosine kinase inhibitor therapy is
generally limited by low response rates, the development of
acquired resistance, and/or toxic side-effects. As a result,
tyrosine kinase inhibitors currently find use only as second- or
third-line cancer therapies. However, the methods of the present
invention for predicting or identifying response and/or toxicity to
tyrosine kinase inhibitors and monitoring resistance to tyrosine
kinase inhibitor therapy advantageously enable tyrosine kinase
inhibitors to be used in the first-line treatment of cancer.
[0112] Gefitinib (Iressa.RTM.) is a selective EGFR (HER1/ErbB1)
tyrosine kinase inhibitor, exhibiting a 200-fold greater affinity
for EGFR than for HER2 (Neu/ErbB2) (Thomas et al., Cancer Treat.
Revs., 30:255-268 (2004)). It prevents autophosphorylation of EGFR
in a variety of tumor cell lines and xenografts (Arteaga et al.,
Curr. Opin. Oncol., 6:491-498 (2001)). Gefitinib can also inhibit
the growth of some HER2-overexpressing tumor cells (e.g., breast
cancer cells) (Moulder et al., Cancer Res., 61:8887-8895 (2001);
Normanno et al., Ann. Oncol., 13:65-72 (2002)) and tumor
neoangiogenesis (Arteaga et al., supra).
[0113] Gefitinib is currently approved for the treatment of
patients with non-small cell lung cancer after failure of both
platinum-based and docetaxel chemotherapies. However, most patients
with non-small cell lung cancer have no response to gefitinib. In
fact, the response rate was only about 10% in large scale Phase II
trials of patients with refractory disease (Fukuoka et al., J.
Clin. Oncol., 21:2237-2246 (2003); Kris et al., JAMA, 290:2149-2158
(2003)). Side-effects observed after gefitinib administration are
generally mild and resolve after discontinuation of the drug. The
most common adverse effects associated with gefitinib therapy
include diarrhea, rash, acne, dry skin, nausea, vomiting, pruritus,
anorexia, and asthenia (Dancey et al., Lancet, 362:62-64 (2003)).
Other toxic side-effects include fatigue, elevated serum
transaminase levels, stomatitis, bone pain, dyspnea, and pulmonary
toxicity such as interstitial lung disease (i.e., alveolitis),
pneumonitis, and interstitial pneumonia (Cersosimo, Am. J.
Health-Syst. Pharm., 61:889-898 (2004)).
[0114] Erlotinib (Tarceva.RTM.; OSI-1774) is another selective EGFR
(HER1/ErbB1) tyrosine kinase inhibitor (Ranson, Br. J. Cancer,
90:2250-2255 (2004); Moyer et al., Cancer Res., 57:4838-4848
(1997)). It inhibits EGF-dependent cell proliferation at nanomolar
concentrations and blocks cell cycle progression in the G1 phase
(Moyer et al., supra). Erlotinib was approved by the FDA in
November, 2004. In a placebo-controlled trial, patients randomized
to erlotinib with advanced stage III or IV non-small cell lung
cancer and who had progressive disease after standard
chemotherapies showed a low response rate of only 12% and a median
survival of 8.4 months (Perez-Soler, Clin. Cancer Res.,
10:4238s-4240s (2004)). The most common side-effects observed with
erlotinib include an acneiform skin rash and diarrhea. In fact,
diarrhea is a dose-limiting adverse event. Other side-effects
include headache, mucositis, hyperbilirubinemia, neutropenia, and
anemia (Ranson et al., J. Clin. Oncol., 20:2240-2250 (2002);
Ranson, Br. J. Cancer, 90:2250-2255 (2004)).
[0115] Lapatinib (GW572016; GW2016) is a tyrosine kinase inhibitor
of both EGFR (HER1/ErbB1) and HER2 (Neu/ErbB2). It has been shown
to have activity against EGFR--, HER2-, and Akt-overexpressing
human tumor xenografts (Rusnak et al., Mol. Cancer. Ther., 1:85-94
(2001)). In fact, its non-selective inhibition of several receptor
tyrosine kinases may account for a broader spectrum of antitumor
activity and improved efficacy, with a lower likelihood of
developing resistance. The most common side-effects observed with
lapatinib include diarrhea and skin rash.
[0116] Canertinib (CI 1033) is a non-selective tyrosine kinase
inhibitor that produces irreversible inhibition of all members of
the EGFR family (Ranson, Br. J. Cancer, 90:2250-2255 (2004)). It
has been shown to have activity against a variety of human breast
carcinomas in tumor xenograft models (Allen et al., Semin. Oncol.,
29:11-21 (2002)). However, one Phase II trial in patients with
refractory ovarian cancer has revealed that canertinib only
possesses minimal antitumor activity (Campos et al., J. Clin.
Oncol. ASCO Annual Meeting Proc., 22:5054 (2004)).
[0117] Sunitinib (Sutent.RTM.; SU11248) is a broad spectrum orally
available multi-targeted tyrosine kinase inhibitor of VEGFR, PDGFR,
c-KIT, and FLT-3 (Mendel et al., Proc. Am. Soc. Clin. Oncol., 21:94
(2002)). It inhibits the growth of a variety of mouse tumor cells
and xenograft models (Bergsland, Am. J. Health-Syst. Pharm.,
61:S4-S11 (2004); Traxler et al., Cancer Res., 64:4931-4941
(2004)). Tumor regression and antiangiogenic activity have been
observed in Phase I trials, and Phase II studies in patients with
metastatic kidney cancer have revealed that 33% of patients had a
partial response and 37% had stable disease for longer than 3
months on sunitinib therapy (Eskens, Br. J. Cancer, 90:1-7 (2004);
Motzer et al., J. Clin. Oncol. ASCO Annual Meeting Proc., 22:4500
(2004)). Sunitinib has also been shown to delay the time of tumor
progression and significantly reduce the death rate of
imatinib-resistant gastrointestinal stromal tumors (Demetri et al.,
J. Clin. Oncol. ASCO Annual Meeting Proc., 23:4000 (2005)).
[0118] Semaxinib (SU5416) is a non-selective tyrosine kinase
inhibitor of VEGFR-2, c-KIT, and FLT-3 (Mendel et al., Clin. Cancer
Res., 6:4848-4858 (2000)). In a multi-center Phase II trial with
twice weekly administration of semaxinib, only 1 complete and 7
partial responses were observed in patients with refractory acute
myeloid leukemia (Fiedler et al., Blood, 102:2763-2767 (2003)). In
addition, minimal objective response rates were observed in Phase
II studies of patients with prostate cancer, renal cell carcinoma,
or multiple myeloma. Toxic side-effects of semaxinib therapy
include headache, nausea, vomiting, asthenia, pain at the infusion
site, phlebitis, change in voice, and fever.
[0119] Vatalanib (PTK787/ZK222584) is a selective inhibitor of
VEGF-1 (FLT-1) and VEGFR-2 (FLK-1/KDR). At higher concentrations,
it also inhibits other tyrosine kinases such as PDGFR-.beta.,
c-KIT, and C-FMS (Lin et al., Cancer Res., 2:5019-5026 (2002)).
Studies on vatalanib have focused on its use in treating colorectal
cancer, liver cancer, advanced prostate cancer, advanced renal cell
carcinoma, and relapsed/refractory glioblastoma (Steward et al.,
Proc. Am. Soc. Clin. Oncol., 22:1098 (2003); George et al., Clin.
Cancer Res., 7:548 (2001); Bergsland, Am. J. Health-Syst. Pharm.,
61:S4-S11 (2004)). However, partial and minor responses to
vatalanib were observed in only 5% and 15% of patients with renal
cell carcinoma, respectively (Rini et al., J. Clin. Oncol.,
23:1028-1043 (2005)). Toxic side-effects of vatalanib therapy
include ataxia, vertigo, hypertension, and venous thromboembolism
(Eskens, Br. J. Cancer, 90:1-7 (2004)).
[0120] Sorafenib (BAY 43-9006) is a RAF kinase and VEGFR, EFGR, and
PDGFR tyrosine kinase inhibitor that blocks tumor cell
proliferation and angiogenesis (Wilhelm et al., Cancer Res.,
64:7099-7109 (2004); Strumberg et al., J. Clin. Oncol., 23:965-972
(2005)). It has significant activity in renal, colon, pancreatic,
lung, and ovarian tumors (Wilhelm et al., supra). A Phase II
randomized clinical trial in patients with advanced kidney cancer
showed a statistically higher percentage of patients whose disease
did not progress after a 12-week treatment period with sorafenib
compared to the placebo group (Ratain et al., J. Clin. Oncol. ASCO
Annual Meeting Proc., 22:4501 (2004)). The most common side-effects
of sorafenib therapy include skin reactions such as hand-foot
syndrome and rash, diarrhea, fatigue, weight loss, and
hypertension.
[0121] Imatinib (Gleeve.RTM.; STI571) is an inhibitor of the ABL,
BCR-ABL, c-KIT, and PDGFR tyrosine kinases (Druker et al., Nat.
Med., 5:561-566 (1996)). It is used for the treatment of
Philadelphia chromosome-positive patients with chronic myeloid
leukemia who are either newly diagnosed or have failed
interferon-.alpha. therapy (Kantarjian et al., N. Engl. J. Med.,
346:645-652 (2002); Druker et al., N. Engl. J. Med., 344:1038-1042
(2001)). For example, imatinib therapy induced major cytogenetic
responses in patients with chronic myeloid leukemia and is also
effective in the treatment of adult acute lymphoblastic leukemia
(Kantarjian et al., Clin. Cancer Res., 8:2177-2187 (2002); Druker
et al., N. Engl. J. Med., 344:1038-1042 (2001)). In some patients,
however, white blood cells become resistant to imatinib, resulting
in relapse. Several clinical trials have also shown a significant
response to imatinib in patients with advanced gastrointestinal
stromal tumors (Druker, Adv. Cancer Res., 91:1-35 (2004)). In fact,
imatinib is now approved for the treatment of patients with
c-KIT-positive unresectable and/or malignant gastrointestinal
stromal tumors. Toxic side-effects associated with imatinib therapy
include neutropenia, thrombocytopenia, anemia, nausea, skin rash,
peripheral edema, muscle cramps, and elevated liver transaminase
levels (Kantarjian et al., N. Engl. J. Med., 346:645-652
(2002)).
[0122] Leflunomide (SU101) is a small molecule inhibitor of
PDGFR-mediated phosphorylation and thus inhibits PDGF-mediated cell
signaling (Shawver et al., Clin. Cancer Res., 3:1167-1177 (1997)).
A Phase II study in patients with hormone refractory prostate
cancer indicated that administration of leflunomide resulted in
partial responses in less than 5% of patients and a decrease in
prostate specific antigen of greater than 50% in only about 7% of
patients (Ko et al., Clin. Cancer Res., 4:800-805 (2001)). The most
common side-effects include asthenia, nausea, anorexia, and
anemia.
[0123] Although the dose of a tyrosine kinase inhibitor
administered to a patient varies with the cancer being treated, the
dose should generally be between about 1 mg/day to about 800
mg/day, and preferably, between about 100 mg/day to about 400
mg/day. For example, the recommended dose of orally administered
gefitinib for patients with non-small cell lung cancer is between
about 200 mg/day to about 300 mg/day, and preferably about 250
mg/day. As another example, the recommended dose of orally
administered sunitinib for patients with gastrointestinal stromal
tumors or renal cell carcinoma is between about 20 mg/day to about
100 mg/day, and preferably about 50 mg/day. Higher doses may be
required in patients with more advanced tumors. Doses can be given
at any time of the day, with or without food. Adjustments of
dosage, if necessary, can be made according to the methods of the
present invention to optimize therapeutic efficacy and/or reduce
toxicity. In particular, the methods of the present invention
provide algorithms useful for determining whether a subsequent dose
of a tyrosine kinase inhibitor should be increased or decreased in
order to reach a therapeutic threshold and/or minimize toxicity
(e.g., side-effects). The methods of the present invention also
provide algorithms useful for determining whether a suitable dose
of an alternative cancer therapy should be administered due to the
development of resistance to tyrosine kinase inhibitor therapy.
V. Profiles
[0124] The present invention provides assay methods for predicting,
monitoring, or optimizing tyrosine kinase inhibitor therapy in a
subject using an algorithmic approach by determining at least one
nucleic acid and/or protein profile in a sample from the subject.
Examples of nucleic acid profiles include, but are not limited to,
a genotypic profile, gene copy number profile, gene expression
profile, DNA methylation profile, and combinations thereof.
Non-limiting examples of protein profiles include a protein
expression profile, protein activation profile, and combinations
thereof. Nucleic acid profiling typically comprises analyzing one
or more genetic biomarkers, while protein profiling generally
comprises analyzing one or more biochemical or serological
biomarkers.
[0125] Several biomarkers may be combined into one test for
efficient processing of multiple samples. In addition, one of skill
in the art would recognize the value of testing multiple samples
(e.g., at successive time points, before and after administration
of a tyrosine kinase inhibitor, etc.) from the same subject. Such
testing of serial samples can allow the identification of changes
in biomarker levels over time. Increases or decreases in biomarker
levels, as well as the absence of change in biomarker levels, can
provide useful information to create a specific tyrosine kinase
inhibitor dosing regimen for a subject diagnosed with cancer by
determining the initial and/or subsequent doses of the drug that
should be administered to the subject.
[0126] A panel consisting of one or more of the biomarkers
described herein may be constructed to provide relevant information
related to predicting, identifying, or monitoring efficacy and/or
toxicity to tyrosine kinase inhibitor therapy. Such a panel may be
constructed using 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more individual
biomarkers. The analysis of a single biomarker or subsets of
biomarkers can also be carried out by one skilled in the art to
optimize dose efficacy or reduce toxicity to tyrosine kinase
inhibitor therapy in various clinical settings. These include, but
are not limited to, ambulatory, urgent care, critical care,
intensive care, monitoring unit, inpatient, outpatient, physician
office, medical clinic, and health screening settings.
[0127] The analysis of biomarkers could be carried out in a variety
of physical formats as well. For example, the use of microtiter
plates or automation could be used to facilitate the processing of
large numbers of test samples. Alternatively, single sample formats
could be developed to facilitate diagnosis, prognosis, and/or
treatment in a timely fashion.
[0128] A. Genotypic Profiling
[0129] A variety of techniques can be used for genotypic analysis
of a polymorphic site in determining a genotypic profile according
to the methods of the present invention. For example, enzymatic
amplification of nucleic acid from a sample can be conveniently
used to obtain nucleic acid for subsequent analysis. However, the
presence or absence of a variant allele can also be determined
directly from a nucleic acid sample without enzymatic amplification
(e.g., using hybridization techniques). Genotyping of nucleic acid,
whether amplified or not, can be performed using any of various
techniques known to one of skill in the art. Useful techniques
include, without limitation, polymerase chain reaction (PCR)-based
analysis, sequence analysis, and electrophoretic analysis, which
can be used alone or in combination.
[0130] A nucleic acid sample can be obtained from a subject using
routine methods. Such samples comprise any biological matter from
which nucleic acid can be prepared. As non-limiting examples,
suitable samples include whole blood, serum, plasma, saliva, cheek
swab, urine, or other bodily fluid or tissue that contains nucleic
acid. In one embodiment, the methods of the present invention are
performed using whole blood or fractions thereof such as serum or
plasma, which can be obtained readily by non-invasive means and
used to prepare genomic DNA. In another embodiment, genotyping
involves the amplification of a subject's nucleic acid using PCR.
Use of PCR for the amplification of nucleic acids is well known in
the art (see, e.g., Mullis et al., The Polymerase Chain Reaction,
Birkhauser, Boston, (1994)). In yet another embodiment, PCR
amplification is performed using one or more fluorescently labeled
primers. In a further embodiment, PCR amplification is performed
using one or more labeled or unlabeled primers containing a DNA
minor grove binder. Generally, protocols for the use of PCR in
identifying mutations and polymorphisms in a gene of interest are
described in Theophilus et al., "PCR Mutation Detection Protocols,"
Humana Press (2002). Further protocols are provided in Innis et
al., "PCR Applications: Protocols for Functional Genomics," 1st
Edition, Academic Press (1999).
[0131] Any of a variety of different primers can be used to PCR
amplify a subject's nucleic acid. One skilled in the art
understands that primers for PCR analysis can be designed based on
the sequence flanking the polymorphic site of interest. As a
non-limiting example, a PCR primer can contain between about 15 to
about 60 nucleotides (e.g., 15-50, 15-40, or 15-30 nucleotides) of
a sequence upstream or downstream of the polymorphic site of
interest. Such primers generally are designed to have sufficient
guanine and cytosine content to attain a high melting temperature
which allows for a stable annealing step in the amplification
reaction. Several computer programs, such as Primer Select, are
available to aid in the design of PCR primers.
[0132] Primer sequences and amplification protocols for evaluating
EGFR (HER1/ErbB1) mutations are known to those in the art and have
been published in, e.g., Lynch et al., New Eng. J. Med.,
350:2129-2139 (2004); Paez et al., Science, 304:1497-1500 (2004);
Pao et al., Proc. Natl. Acad. Sci., 101:13306-13311 (2004); and Pao
et al., PLoS Med., 2:57-61 (2005). Preferably, the subject is
genotyped to determine the presence or absence of an activating
mutation (i.e., gain-of-function allele) in the tyrosine kinase
domain of the EGFR gene. Such mutations include, but are not
limited to, a T.fwdarw.G mutation at nucleotide 2573 in the EGFR
gene, which results in a substitution of arginine for leucine at
position 858 (L858R); a T.fwdarw.A mutation at nucleotide 2582 in
the EGFR gene, which results in a substitution of glutamine for
leucine at position 861 (L858Q); a G.fwdarw.T mutation at
nucleotide 2155 in the EGFR gene, which results in a substitution
of cysteine for glycine at position 719 (G719C); a deletion of
nucleotides 2235-2249 in the EGFR gene, which results in an
in-frame deletion of amino acids 746-750; a deletion of nucleotides
2240-2251 in the EGFR gene, which results in an in-frame deletion
of amino acids 747-751 and the insertion of a serine; and a
deletion of nucleotides 2240-2257 in the EGFR gene, which results
in an in-frame deletion of amino acids 747-753 and the insertion of
a serine. Other deletions, insertions, and/or single nucleotide
substitutions in exons 18, 19, and/or 21 of the EGFR gene can also
be determined according to the methods of the present invention.
Alternatively, the subject can be genotyped to determine the
presence or absence of a loss-of-function mutation in the EGFR
gene.
[0133] Primer sequences and amplification protocols for evaluating
K-Ras mutations are known to those in the art and have been
published in, e.g., Pao et al., PLoS Med., 2:57-61 (2005).
Preferably, the subject is genotyped to determine the presence or
absence of an activating mutation in the K-Ras gene. Such mutations
include, but are not limited to, a G.fwdarw.T mutation at
nucleotide 34 in the K-Ras gene, which results in a substitution of
cysteine for glycine at position 12 (G12C); a G.fwdarw.T mutation
at nucleotide 37 in the K-Ras gene, which results in a substitution
of cysteine for glycine at position 13 (G13C); a G.fwdarw.A
mutation at nucleotide 35 in the K-Ras gene, which results in a
substitution of aspartic acid for glycine at position 12 (G12D); a
G.fwdarw.A mutation at nucleotide 34 in the K-Ras gene, which
results in a substitution of serine for glycine at position 12
(G12S); and a G.fwdarw.T mutation at nucleotide 35 in the K-Ras
gene, which results in a substitution of valine for glycine at
position 12 (G12V). Alternatively, the subject can be genotyped to
determine the presence or absence of a loss-of-function mutation in
the K-Ras gene.
[0134] Primer sequences and amplification protocols for evaluating
c-KIT mutations are known to those in the art and have been
published in, e.g., Corless et al., J. Mol. Diagn., 6:366-70
(2004); Hirota et al., Science, 279:577-580 (1998); Hirota et al.,
J. Pathol., 193:505-510 (2001); Antonescu et al., Clin. Cancer
Res., 9:3329-3337 (2003); Emile et al., Diagn. Mol. Pathol.,
11:107-112 (2002); Lasota et al., Am. J. Pathol., 157:1091-1095
(2000); Lee et al., Am. J. Surg. Pathol., 25:979-987 (2001); Lux et
al., Am. J. Pathol., 156:791-795 (2000); and Taniguchi et al.,
Cancer Res., 59:4297-4300 (1999). Preferably, the subject is
genotyped to determine the presence or absence of an activating
mutation in the c-KIT gene. Such mutations include, but are not
limited to, any of a variety of deletions, insertions, and/or
single nucleotide substitutions in exons 9, 11, 13, and/or 17 of
the c-KIT gene. Alternatively, the subject can be genotyped to
determine the presence or absence of a loss-of-function mutation in
the c-KIT gene.
[0135] Primer sequences and amplification protocols for evaluating
PDGFRA mutations are known to those in the art and have been
published in, e.g., Hirota et al., Pathol. Int., 56:1-9 (2006);
Corless et al., J. Clin. Oncol., 23:5357-5364 (2005); and Penzel et
al., J. Clin. Pathol., 58:634-639 (2005). Preferably, the subject
is genotyped to determine the presence or absence of an activating
mutation in the PDGFRA gene. Such mutations include, but are not
limited to, any of a variety of deletions, insertions, and/or
single nucleotide substitutions in exons 12, 14, and/or 18 of the
PDGFRA gene. Alternatively, the subject can be genotyped to
determine the presence or absence of a loss-of-function mutation in
the PDGFRA gene.
[0136] Primer sequences and amplification protocols for evaluating
VEGFR-1 (FLT-1) mutations are known to those in the art and have
been published in, e.g., Meshinchi et al., Blood, 102:1474-1479
(2003). Preferably, the subject is genotyped to determine the
presence or absence of an activating mutation in the VEGFR-1 gene.
Such mutations include, but are not limited to, any of a variety of
deletions, insertions, and/or single nucleotide substitutions in
the juxtamembrane domain or tyrosine kinase domain of the VEGFR-1
gene. Alternatively, the subject can be genotyped to determine the
presence or absence of a loss-of-function mutation in the VEGFR-1
gene.
[0137] Primer sequences and amplification protocols for evaluating
VEGFR-2 (FLK-1/KDR) mutations are known to those in the art and
have been published in, e.g., Walter et al., Genes Chromosomes
Cancer, 33:295-303 (2002); and Meshinchi et al., Blood,
102:1474-1479 (2003). Preferably, the subject is genotyped to
determine the presence or absence of an activating mutation in the
VEGFR-2 gene. Such mutations include, but are not limited to, any
of a variety of deletions, insertions, and/or single nucleotide
substitutions in the juxtamembrane domain or tyrosine kinase domain
of the VEGFR-2 gene, such as a missense mutation (P1147S) in the
kinase domain. Alternatively, the subject can be genotyped to
determine the presence or absence of a loss-of-function mutation in
the VEGFR-2 gene.
[0138] Primer sequences and amplification protocols for evaluating
VEGFR-3 (FLT-4) mutations are known to those in the art and have
been published in, e.g., Walter et al., Genes Chromosomes Cancer,
33:295-303 (2002). Preferably, the subject is genotyped to
determine the presence or absence of an activating mutation in the
VEGFR-3 gene. Such mutations include, but are not limited to, any
of a variety of deletions, insertions, and/or single nucleotide
substitutions in the juxtamembrane domain or tyrosine kinase domain
of the VEGFR-3 gene, such as a missense mutation (P954S) in the
kinase domain. Alternatively, the subject can be genotyped to
determine the presence or absence of a loss-of-function mutation in
the VEGFR-3 gene.
[0139] Primer sequences and amplification protocols for evaluating
FLT-3 (FLK-2) mutations are known to those in the art and have been
published in, e.g., Gilliland et al., Curr. Opin., Hematol.,
9:274-281 (2002); and Gilliland et al., Blood, 100:1532-1542
(2002). Preferably, the subject is genotyped to determine the
presence or absence of an activating mutation in the FLT-3 gene.
Such mutations include, but are not limited to, an internal tandem
duplication in the juxtamembrane domain of the FLT-3 gene; and an
activating loop mutation in the tyrosine kinase domain of the FLT-3
gene, which results in a substitution of aspartic acid for another
amino acid at position 835 (D835X). Alternatively, the subject can
be genotyped to determine the presence or absence of a
loss-of-function mutation in the FLT-3 gene.
[0140] A Taqman.RTM. allelic discrimination assay available from
Applied Biosystems (Foster City, Calif.) can be useful for
genotypic analysis of a polymorphic site to determine the presence
or absence of a variant allele. In a Taqman.RTM. allelic
discrimination assay, a specific, fluorescent dye-labeled probe for
each allele is constructed. The probes contain different
fluorescent reporter dyes such as FAM and VIC to differentiate the
amplification of each allele. In addition, each probe has a
quencher dye at one end which quenches fluorescence by fluorescence
resonance energy transfer. During PCR, each probe anneals
specifically to complementary sequences in the nucleic acid from
the subject. The 5' nuclease activity of Taq polymerase is used to
cleave only probe that hybridizes to the allele. Cleavage separates
the reporter dye from the quencher dye, resulting in increased
fluorescence by the reporter dye. Thus, the fluorescence signal
generated by PCR amplification indicates which alleles are present
in the sample. Mismatches between a probe and allele reduce the
efficiency of both probe hybridization and cleavage by Taq
polymerase, resulting in little to no fluorescent signal. Those
skilled in the art understand that improved specificity in allelic
discrimination assays can be achieved by conjugating a DNA minor
grove binder (MGB) group to a DNA probe as described, e.g., in
Kutyavin et al., Nuc. Acids Res., 28:655-661 (2000). Suitable minor
grove binders for use in the present invention include, but are not
limited to, compounds such as dihydrocyclopyrroloindole tripeptide
(DPI3).
[0141] Sequence analysis can also be useful for genotyping at a
polymorphic site in a gene. In one embodiment, a variant allele can
be detected by sequence analysis using the appropriate primers,
which are designed based on the sequence flanking the polymorphic
site of interest, as is known by those skilled in the art. As a
non-limiting example, a sequencing primer can contain between about
15 to about 60 nucleotides (e.g., 15-50, 15-40, or 15-30
nucleotides) of a sequence between about 40 to about 400 base pairs
upstream or downstream of the polymorphic site of interest. Such
primers are generally designed to have sufficient guanine and
cytosine content to attain a high melting temperature which allows
for a stable annealing step in the sequencing reaction.
[0142] As used herein, the term "sequence analysis" includes any
manual or automated process by which the order of nucleotides in a
nucleic acid is determined. As an example, sequence analysis can be
used to determine the nucleotide sequence of a sample of DNA. The
term encompasses, without limitation, chemical and enzymatic
methods such as dideoxy enzymatic methods including, for example,
Maxam-Gilbert and Sanger sequencing as well as variations thereof.
The term also encompasses, without limitation, capillary array DNA
sequencing, which relies on capillary electrophoresis and
laser-induced fluorescence detection and can be performed using
instruments such as the MegaBACE 1000 or ABI 3700. As additional
non-limiting examples, the term encompasses thermal cycle
sequencing (Sears et al., Biotechniques, 13:626-633 (1992));
solid-phase sequencing (Zimmerman et al., Methods Mol. Cell. Biol.,
3:39-42 (1992); and sequencing with mass spectrometry, such as
matrix-assisted laser desorption/ionization time-of-flight mass
spectrometry (MALDI-TOF MS; Fu et al., Nature Biotech., 16:381-384
(1998)). The term further includes, without limitation, sequencing
by hybridization (SBH), which relies on an array of all possible
short oligonucleotides to identify a segment of sequence (Chee et
al., Science, 274:610-614 (1996); Drmanac et al., Science,
260:1649-1652 (1993); Drmanac et al., Nature Biotech., 16:54-58
(1998)). One skilled in the art understands that these and
additional variations are encompassed by the term as defined
herein. See, in general, Ausubel et al., Current Protocols in
Molecular Biology, Chapter 7 and Supplement 47, John Wiley &
Sons, Inc., New York (1999).
[0143] In addition, electrophoretic analysis can be useful for
genotyping at a polymorphic site in a gene. The term
"electrophoretic analysis," as used herein in reference to one or
more nucleic acids such as amplified fragments, includes a process
whereby charged molecules are moved through a stationary medium
under the influence of an electric field. Electrophoretic migration
separates nucleic acids primarily on the basis of their charge,
which is in proportion to their size, with smaller molecules
migrating more quickly. The term includes, without limitation,
analysis using slab gel electrophoresis such as agarose or
polyacrylamide gel electrophoresis, or capillary electrophoresis.
Capillary electrophoretic analysis generally occurs inside a
small-diameter quartz capillary in the presence of high
(kilovolt-level) separating voltages with separation times of a few
minutes. Using capillary electrophoretic analysis, nucleic acids
are conveniently detected by UV absorption or fluorescent labeling,
and single-base resolution can be obtained on fragments up to
several hundred base pairs in length. Such methods of
electrophoretic analysis, and variations thereof, are well known in
the art, as described, for example, in Ausubel et al., Current
Protocols in Molecular Biology, Chapter 2 and Supplement 45, John
Wiley & Sons, Inc., New York (1999).
[0144] Restriction fragment length polymorphism (RFLP) analysis can
also be useful for genotypic analysis of a polymorphic site in a
gene (see, e.g., Jarcho et al., Current Protocols in Human
Genetics, pages 2.7.1-2.7.5, John Wiley & Sons, Inc., New York;
Innis et al., PCR Protocols, San Diego, Academic Press, Inc.
(1990)). As used herein, "restriction fragment length polymorphism
analysis" includes any method for distinguishing polymorphic
alleles using a restriction enzyme, which is an endonuclease that
catalyzes degradation of nucleic acid following recognition of a
specific base sequence, generally a palindrome or inverted repeat.
One skilled in the art understands that the use of RFLP analysis
depends upon an enzyme that can differentiate a variant allele from
a wild-type or other allele at a polymorphic site.
[0145] Furthermore, allele-specific oligonucleotide hybridization
can be useful for genotyping at a polymorphic site in a gene.
Allele-specific oligonucleotide hybridization is based on the use
of a labeled oligonucleotide probe having a sequence perfectly
complementary, for example, to the sequence encompassing the
variant allele. Under appropriate conditions, the variant
allele-specific probe hybridizes to a nucleic acid containing the
variant allele but does not hybridize to the one or more other
alleles, which have one or more nucleotide mismatches as compared
to the probe. If desired, a second allele-specific oligonucleotide
probe that matches an alternate (e.g., wild-type) allele can also
be used. Similarly, the technique of allele-specific
oligonucleotide amplification can be used to selectively amplify,
for example, a variant allele by using an allele-specific
oligonucleotide primer that is perfectly complementary to the
nucleotide sequence of the variant allele but which has one or more
mismatches as compared to other alleles (Mullis et al., The
Polymerase Chain Reaction, Birkhauser, Boston, (1994)). One skilled
in the art understands that the one or more nucleotide mismatches
that distinguish between the variant allele and other alleles are
often located in the center of an allele-specific oligonucleotide
primer to be used in the allele-specific oligonucleotide
hybridization. In contrast, an allele-specific oligonucleotide
primer to be used in PCR amplification generally contains the one
or more nucleotide mismatches that distinguish between the variant
allele and other alleles at the 3' end of the primer.
[0146] A heteroduplex mobility assay (HMA) is another well-known
assay that can be used for genotyping at a polymorphic site in a
gene. HMA is useful for detecting the presence of a variant allele
since a DNA duplex carrying a mismatch has reduced mobility in a
polyacrylamide gel compared to the mobility of a perfectly
base-paired duplex (see, e.g., Delwart et al., Science,
262:1257-1261 (1993); White et al., Genomics, 12:301-306
(1992)).
[0147] The technique of single strand conformational polymorphism
(SSCP) can also be useful for genotypic analysis of a polymorphic
site in a gene according to the methods of the present invention
(see, e.g., Hayashi, Methods Applic., 1:34-38 (1991)). This
technique is used to detect variant alleles based on differences in
the secondary structure of single-stranded DNA that produce an
altered electrophoretic mobility upon non-denaturing gel
electrophoresis. Variant alleles are detected by comparison of the
electrophoretic pattern of the test fragment to corresponding
standard fragments containing known alleles.
[0148] Denaturing gradient gel electrophoresis (DGGE) is another
useful technique for genotyping at a polymorphic site in a gene. In
DGGE, double-stranded DNA is electrophoresed in a gel containing an
increasing concentration of denaturant. Because double-stranded
fragments comprising mismatched alleles have segments that melt
more rapidly, such fragments migrate differently as compared to
perfectly complementary sequences (Sheffield et al., "Identifying
DNA Polymorphisms by Denaturing Gradient Gel Electrophoresis," in
Innis et al., PCR Protocols, San Diego, Academic Press, Inc.
(1990)).
[0149] Other molecular techniques useful for genotypic analysis of
a polymorphic site in a gene are also known in the art and useful
in the methods of the present invention. Other well-known
genotyping techniques include, without limitation, automated
sequencing and RNAase mismatch techniques (Winter et al., Proc.
Natl. Acad. Sci., 82:7575-7579 (1985)). Furthermore, one skilled in
the art understands that, where the presence or absence of multiple
variant alleles is to be determined, individual variant alleles can
be detected by any combination of molecular techniques. See, in
general, Birren et al., Genome Analysis: A Laboratory Manual,
Volume 1 (Analyzing DNA), New York, Cold Spring Harbor Laboratory
Press (1997). In addition, one skilled in the art understands that
multiple variant alleles can be detected in individual reactions or
in a single reaction, e.g., using a multiplex real-time PCR assay.
Kits for performing multiplex real-time PCR of cDNA or genomic DNA
targets using sequence-specific probes are available from QIAGEN
Inc. (Valencia, Calif.), e.g., the QuantiTect Multiplex PCR Kit.
Systems for performing multiplex real-time PCR are available from
Applied Biosystems (Foster City, Calif.), e.g., the 7300 or 7500
Real-Time PCR Systems.
[0150] In view of the above, one skilled in the art will readily
appreciate that the methods of the present invention for
determining a genotypic profile in a sample can be practiced using
one or any combination of the well-known techniques described above
or other techniques known in the art.
[0151] B. Gene Expression Profiling
[0152] A gene expression profile is typically evaluated in vitro on
a sample collected from a subject in comparison to a normal or
reference sample. Determination of a transcriptional expression
profile can be accomplished, e.g., using hybridization techniques
well-known to those skilled in the art such as Northern analysis
and slot blot hybridization or by performing reverse-transcriptase
(RT)-PCR amplification followed by gel electrophoresis. Applicable
PCR amplification techniques are described in Ausubel et al.,
Current Protocols in Molecular Biology, John Wiley & Sons,
Inc., New York (1999); Theophilus et al., "PCR Mutation Detection
Protocols," Humana Press (2002); and Innis et al., "PCR
Applications: Protocols for Functional Genomics," 1st Edition,
Academic Press (1999). General nucleic acid hybridization methods
are described in Anderson, "Nucleic Acid Hybridization," BIOS
Scientific Publishers (1999). Amplification or hybridization of a
plurality of transcribed nucleic acid sequences (e.g., mRNA or
cDNA) can also be performed using mRNA or cDNA sequences arranged
in a microarray. Microarray methods are generally described in
Hardiman, "Microarrays Methods and Applications: Nuts & Bolts,"
DNA Press (2003) and Baldi et al., "DNA Microarrays and Gene
Expression: From Experiments to Data Analysis and Modeling,"
Cambridge University Press (2002).
[0153] Comparing patterns of gene expression is a widely used means
of identifying novel genes, investigating gene function, and
finding potential new therapeutic targets (Shiue et al., Drug
Devel. Res., 41:142-159 (1997)). Many techniques have been used to
identify and clone differentially expressed genes (Liang et al.,
Science, 257:967-971 (1992); Welsh et al., Nucleic Acids Res.,
20:4965-4970 (1992); Tedder et al., Proc. Natl. Acad. Sci.,
85:208-212 (1988); Davis et al., Proc. Natl. Acad. Sci.,
81:2194-2198 (1984); Lisitsyn et al., Science, 259:946-951 (1993);
Velculescu et al., Science, 270:484-487 (1995); Diatchenko et al.,
Proc. Natl. Acad. Sci., 93:6025-6030 (1996); Jiang et al., Proc.
Natl. Acad. Sci., 97:12684-12689 (2000); Yang et al., Nucleic Acids
Res., 27: 517-523 (1999)).
[0154] Recently, it has become routine to use the technique of cDNA
microarray hybridization to quantify the expression of many
thousands of discrete mRNA or cDNA sequences in a single assay
known as expression profiling (van't Veer et al., Nature,
415:530-536 (2002); Hughes et al., Nature Biotech., 19:342-347
(2001); Hughes et al., Cell, 102:109-126 (2000); Lockhart and
Winzeler, Nature, 405:827-836 (2000); Roberts et al., Science,
287:873-880 (2000); Wang et al., Gene, 229:101-108 (1999); Lockhart
et al., Nat. Biotech., 14:1675-1680 (1996); Schena et al., Science,
270:467-470 (1995); U.S. Pat. No. 6,040,138). For example, EGFR
mRNA levels can be measured in tumor samples by microarray
hybridization as described in Bhargava et al., Mod. Pathol.,
18:1027-1033 (2005). In certain embodiments, a gene expression
microarray groups genes according to similarities in patterns of
gene expression in expression profiling experiments.
[0155] In addition, gene expression profiles can be used to
identify pathway-specific reporters and target genes for a
particular biological pathway of interest. Such reporter genes and
probes directed to them can be used to measure the activity of a
particular biological pathway and may be further used in the design
of drugs, drug therapies, or other biological agents to target a
particular biological pathway. Gene expression profiles can also be
used to determine protein activity levels of a target protein using
the methods described in U.S. Pat. No. 6,324,479.
[0156] The measurement of gene expression profiles using
microarrays also has many important applications to the monitoring
of disease states and therapies (see, e.g., U.S. Pat. Nos.
6,218,122 and 6,222,093), the identification of drug targets, the
identification of pathways of drug action, and drug design (see,
e.g., U.S. Pat. Nos. 6,303,291, 6,165,709, 6,146,830, 5,965,352,
and 5,777,888). For example, van't Veer et al., supra, identified
"good prognosis" and "poor prognosis" gene expression signatures
that could be used to predict the clinical outcome of breast cancer
patients. Similarly, U.S. Pat. No. 5,777,888 discloses the utility
of microarray gene expression profiles to evaluate the target
specificity of a candidate drug by comparison of an expression
profile obtained from cells treated with the candidate drug to a
database of expression profiles obtained from cells treated with
known drugs. U.S. Pat. No. 6,218,122 provides methods for
monitoring the disease state of a subject and determining the
effect of a therapy upon the subject through the use of gene
expression profiles (see, also, U.S. Pat. No. 6,266,093). In
addition, Shoemaker et al., Nature, 409:922-927 (2000), discloses
methods for using microarray gene expression profiles to detect
splice variants.
[0157] In view of the above, one skilled in the art will readily
appreciate that the methods of the present invention for
determining a gene expression profile from a sample of a subject
can be practiced using one or any combination of the well-known
techniques described above or other techniques known in the
art.
[0158] C. Gene Copy Number Profiling
[0159] Analysis of biomarker gene amplification levels can also be
used alone or in combination with other markers to predict,
monitor, or optimize tyrosine kinase inhibitor therapy in a
subject. Any method known in the art for detecting or determining a
level of gene amplification of one or more of the biomarkers
described herein is suitable for use in the present invention.
[0160] In some embodiments, the level of gene amplification of a
biomarker can be determined by DNA-based techniques such as PCR or
Southern blot analysis or by molecular cytogenetic techniques such
as fluorescence in situ hybridization (FISH), chromogenic in situ
hybridization (CISH), and immunohistochemistry (IHC). For example,
the level of EGFR gene amplification in cancer cells can be
determined using FISH as described in Cappuzzo et al., J. Natl.
Caner Inst., 97:643-655 (2005). Similarly, the level of HER2 gene
amplification in cancer cells can be determined using FISH as
described in Cappuzzo et al., J. Clin. Oncol., 23:5007-5018 (2005).
Likewise, the level of c-KIT, PDGFRA, and/or VEGFR2 gene
amplification in cancer cells can be determined using FISH as
described in Joensuu et al., J. Pathol., 207:224-231 (2005). EGFR
gene copy number can also be determined using real-time
quantitative PCR as described in Bell et al., J. Clin. Oncol.,
23:8081-8092 (2005) or CISH as described in Bhargava et al., Mod.
Pathol., 18:1027-1033 (2005). Other techniques include genome-wide
scanning of amplified chromosomal regions with comparative genomic
hybridization for the detection of amplified regions in tumor DNA
(see, e.g., Kallioniemi et al., Science, 258:818-821 (1992)) and
the detection of gene amplification by genomic hybridization to
cDNA microarrays (see, e.g., Heiskanen et al., Cancer Res.,
60:799-802 (2000)). One skilled in the art will know of additional
gene amplification techniques that can be used to detect or
determine a level of an amplified gene that corresponds to a
biomarker of the present invention.
[0161] D. DNA Methylation Profiling
[0162] Analysis of biomarker DNA methylation levels can also be
used alone or in combination with other markers to predict,
monitor, or optimize tyrosine kinase inhibitor therapy in a
subject.
[0163] The regulation of gene expression by epigenetic mechanisms
such as methylation contributes to various biological processes
including genomic imprinting, X-chromosomal inactivation, cellular
differentiation, and aging, as well as the development of malignant
diseases such as cancer (see, e.g., Ferguson-Smith et al., Science,
293:1086-1089 (2001); Lee, Curr. Biol., 13:R242-254 (2003); Issa,
Clin. Immunol., 109:103-108 (2003); and Robertson, Nat. Rev.
Genet., 6:597-610 (2005)). In mammals, methylation of DNA typically
occurs at specific cytosine residues which precede a guanosine
residue (i.e., CpG dinucleotides) and generally correlates with
stable transcriptional repression (see, e.g., Bestor, Hum. Mol.
Genet., 9:2395-2402 (2000); Ng et al., Curr. Opin. Genet. Dev.,
9:158-163 (1999); and Razin, EMBO J., 17, 4905-4908 (1998)). The
aberrant gain of DNA methylation (i.e., hypermethylation) in
neoplastic cells frequently affects DNA sequences with a relatively
high content of CpG dinucleotides, known as CpG islands. These
regions often contain transcription initiation sites and promoters
and are generally not methylated in noijual cells (see, e.g.,
Costello et al., J. Med. Genet., 38:285-303 (2001); Tycko, Mutat.
Res., 386:131-140 (1997); and Wolffe et al., Proc. Natl. Acad. Sci.
USA, 96:5894-5896 (1999)). However, hypermethylation of CpG islands
causes transcriptional repression and, in cancer, leads to the
abnormal silencing of genes such as tumor suppressor genes (see,
e.g., Esteller et al., Science, 297:1807-1808 (2002); Herman et
al., N. Engl. J. Med., 349:2042-2054 (2003); Momparler, Oncogene,
22:6479-6483 (2003); and Plass, Hum. Mol. Genet., 11:2479-2488
(2002)). As a result, analyzing the level of DNA methylation in,
e.g., the genomic regulatory sequences of biomarkers such as tumor
suppressor genes (e.g., PTEN, DMBT1, LGI1, p53, ESR1, CDKN2B,
ICSBP, ETV3, DDX20, etc.) can be useful in the methods of the
present invention.
[0164] Any technique known in the art can be used for detecting or
determining the CpG methylation state of one or more of the
biomarkers described herein. For example, the level of DNA
methylation of a biomarker can be determined by chromatographic
separation, use of methylation-sensitive restriction enzymes, and
bisulfite-driven conversion of non-methylated cytosine to uracil
(see, e.g., Ushijima, Nat. Rev. Cancer, 5:223-231 (2005)).
Biomarker DNA methylation levels can also be determined by a system
designed for the application of immunofluorescence using a
monoclonal antibody that specifically recognizes 5'-methyl-cytosine
residues in single-stranded DNA hybridized to oligonucleotide
microarrays (see, e.g., Proll et al., DNA Res., 13:37-42 (2006)).
Alternatively, the level of DNA methylation of a biomarker can be
determined using the methyl-binding PCR technique described in
Gebhard et al., Nuc. Acids Res., 34:e82 (2006). One skilled in the
art will know of additional techniques that can be used to detect
or determine a level of methylation in the genomic regulatory
sequences of the biomarkers described herein.
[0165] E. Protein Expression Profiling
[0166] A variety of techniques can be used to detect the presence
or level of an expressed protein for determining a protein
expression profile according to the methods of the present
invention. For example, a proteinaceous biomarker can be analyzed
using an immunoassay. A protein expression profile can also be
evaluated using electrophoresis, e.g., Western blotting, as well as
any other technique known to those skilled in the art. Immunoassay
techniques and protocols are generally described in Price and
Newman, "Principles and Practice of Immunoassay," 2nd Edition,
Grove's Dictionaries (1997); and Gosling, "Immunoassays: A
Practical Approach," Oxford University Press (2000). The presence
or amount of the proteinaceous biomarker is typically determined
using antibodies specific for the biomarker and detecting specific
binding. For example, a monoclonal antibody directed to EGFR can be
obtained from Zymed Laboratories (San Francisco, Calif.) and a
monoclonal antibody directed to TGF-.alpha. can be obtained from
Oncogene Science (Manhasset, N.Y.). Antibodies directed to other
antigens of interest such as receptor tyrosine kinases (e.g., EGFR,
HER2, ErbB3, ErbB4, c-KIT, PDGFA, PDGFB, FLT-3/FLK-2, FLK-1, FLT-1,
FLT-4, ROS, ALK, LTK, RET, etc.), tumor suppressors (e.g., PTEN,
DMBT1, LGI1, p53, etc.), and growth factors (e.g., TGF-.alpha.,
EGF, HB-EGF, VEGF, PDGF, FGF, etc.) can be obtained from Santa Cruz
Biotechnology (Santa Cruz, Calif.).
[0167] Any suitable immunoassay can be utilized for determining the
presence of level of one or more proteinaceous biomarkers in a
sample. A variety of immunoassay techniques, including competitive
and non-competitive immunoassays, can be used (see, e.g., Self et
al., Curr. Opin. Biotechnol., 7:60-65 (1996)). The term immunoassay
encompasses techniques including, without limitation, enzyme
immunoassays (EIA) such as enzyme multiplied immunoassay technique
(EMIT), enzyme-linked immunosorbent assay (ELISA), IgM antibody
capture ELISA (MAC ELISA), and microparticle enzyme immunoassay
(MEIA); capillary electrophoresis immunoassays (CEIA);
radioimmunoassays (RIA); immunoradiometric assays (IRMA);
fluorescence polarization immunoassays (FPIA); and
chemiluminescence assays (CL). If desired, such immunoassays can be
automated. Preferably, the expression level of proteins such as
EGFR and TGF-.alpha. are determined using an enzyme immunoassay
such as ELISA. For example, TGF-.alpha. concentration in serum or
plasma can be measured using an ELISA kit available from R&D
Systems (Minneapolis, Minn.), and EGFR levels can be determined
using an ELISA kit from Biosource International (Camarillo, Calif.)
or Calbiochem (San Diego, Calif.).
[0168] Immunoassays can also be used in conjunction with laser
induced fluorescence (see, e.g., Schmalzing et al.,
Electrophoresis, 18:2184-93 (1997); Bao, J. Chromatogr. B. Biomed.
Sci., 699:463-80 (1997)). Liposome immunoassays, such as
flow-injection liposome immunoassays and liposome immunosensors,
are also suitable for use in the present invention (see, e.g.,
Rongen et al., J. Immunol. Methods, 204:105-133 (1997)). In
addition, nephelometry assays, in which the formation of
protein/antibody complexes results in increased light scatter that
is converted to a peak rate signal as a function of the marker
concentration, are suitable for use in the methods of the present
invention. Nephelometry assays are commercially available from
Beckman Coulter (Brea, Calif.; Kit #449430) and can be performed
using a Behring Nephelometer Analyzer (Fink et al., J. Clin. Chem.
Clin. Biochem., 27:261-276 (1989)).
[0169] Specific immunological binding of the antibody to the
proteinaceous biomarker can be detected directly or indirectly.
Direct labels include fluorescent or luminescent tags, metals,
dyes, radionuclides, and the like, attached to the antibody. An
antibody labeled with iodine-125 (.sup.125I) can be used for
determining the level of one or more biomarkers in a sample. A
chemiluminescence assay using a chemiluminescent antibody specific
for the biomarker is suitable for sensitive, non-radioactive
detection of biomarker levels. An antibody labeled with
fluorochrome is also suitable for determining the level of one or
more biomarkers in a sample. Examples of fluorochromes include,
without limitation, DAPI, fluorescein, Hoechst 33258,
R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas
red, and lissamine. Indirect labels include various enzymes well
known in the art, such as horseradish peroxidase (HRP), alkaline
phosphatase (AP), .beta.-galactosidase, urease, and the like. A
horseradish-peroxidase detection system can be used, for example,
with the chromogenic substrate tetramethylbenzidine (TMB), which
yields a soluble product in the presence of hydrogen peroxide that
is detectable at 450 nm. An alkaline phosphatase detection system
can be used with the chromogenic substrate p-nitrophenyl phosphate,
for example, which yields a soluble product readily detectable at
405 nm. Similarly, a .beta.-galactosidase detection system can be
used with the chromogenic substrate
o-nitrophenyl-.beta.-D-galactopyranoside (ONPG), which yields a
soluble product detectable at 410 nm. An urease detection system
can be used with a substrate such as urea-bromocresol purple (Sigma
Immunochemicals; St. Louis, Mo.).
[0170] A signal from the direct or indirect label can be analyzed,
for example, using a spectrophotometer to detect color from a
chromogenic substrate; a radiation counter to detect radiation such
as a gamma counter for detection of .sup.125I; or a fluorometer to
detect fluorescence in the presence of light of a certain
wavelength. For detection of enzyme-linked antibodies, a
quantitative analysis of the amount of marker levels can be made
using a spectrophotometer such as an EMAX Microplate Reader
(Molecular Devices; Menlo Park, Calif.) in accordance with the
manufacturer's instructions. If desired, the assays of the present
invention can be automated or performed robotically, and the signal
from multiple samples can be detected simultaneously.
[0171] Antigen capture assays can be useful in the methods of the
present invention. For example, in an antigen capture assay, an
antibody directed to a proteinaceous biomarker of interest is bound
to a solid phase and sample is added such that the biomarker is
bound by the antibody. After unbound proteins are removed by
washing, the amount of bound marker can be quantitated using, for
example, a radioimmunoassay (see, e.g., Harlow and Lane,
Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, New
York (1988)). Sandwich enzyme immunoassays can also be useful in
the methods of the present invention. For example, in a
two-antibody sandwich assay, a first antibody is bound to a solid
support, and the biomarker is allowed to bind to the first
antibody. The amount of the biomarker is quantitated by measuring
the amount of a second antibody that binds the biomarker. The
antibodies can be immobilized onto a variety of solid supports,
such as magnetic or chromatographic matrix particles, the surface
of an assay plate (e.g., microtiter wells), pieces of a solid
substrate material or membrane (e.g., plastic, nylon, paper), and
the like. An assay strip can be prepared by coating the antibody or
a plurality of antibodies in an array on a solid support. This
strip can then be dipped into the test sample and processed quickly
through washes and detection steps to generate a measurable signal,
such as a colored spot.
[0172] Quantitative Western blotting also can be used to detect or
determine the level of one or more proteinaceous biomarkers in a
sample. Western blots can be quantitated by well-known methods such
as scanning densitometry or phosphorimaging. In certain instances,
autoradiographs of the blots are analyzed using a scanning
densitometer (Molecular Dynamics; Sunnyvale, Calif.) and normalized
to a positive control. Values are reported, for example, as a ratio
between the actual value to the positive control (densitometric
index). Such methods are well known in the art as described, e.g.,
in Parra et al., J. Vasc. Surg., 28:669-675 (1998).
[0173] Alternatively, a variety of immunohistochemistry (IHC)
techniques can be used to determine the level of one or more
proteinaceous biomarkers in a sample. As used herein, the term
"immunohistochemistry" or "IHC" encompasses techniques that utilize
the visual detection of fluorescent dyes or enzymes coupled (i.e.,
conjugated) to antibodies that react with the biomarker using
fluorescent microscopy or light microscopy and includes, without
limitation, direct fluorescent antibody, indirect fluorescent
antibody (IFA), anticomplement immunofluorescence, avidin-biotin
immunofluorescence, and immunoperoxidase assays. An IFA assay, for
example, is useful for determining whether a sample is positive for
a particular marker of interest, the level of that marker, and/or
the staining pattern of that marker. The concentration of the
marker in a sample can be quantitated, e.g., through endpoint
titration or through measuring the visual intensity of fluorescence
compared to a known reference standard.
[0174] Any IHC technique known to one of skill in the art is
suitable for use in the assay methods of the present invention. As
a non-limiting example, IHC can be performed according to the
following protocol: (1) slides containing the sample (e.g., tumor
tissue) are deparaffinized with xylene/70% ethanol into phosphate
buffered saline (PBS) at pH 7.4; (2) the slides are then immersed
in 10 mM citric acid at pH 6.0, microwaved for about 37 minutes,
and cooled down at room temperature (RT) for about 30-60 minutes;
(3) endogenous peroxidases are quenched for about 10 minutes in 1
part 30% H.sub.2O.sub.2 and 9 parts methanol and the slides are
washed 3 times for 3 minutes in PBS; (4) the slides are blocked
with blocking reagent at RT for about 30 minutes; (5) antibodies
against the biomarker of interest are added and the slides are
incubated at 4.degree. C. overnight; (6) the slides are washed in
PBS at RT for about 30 minutes, changing the wash buffer every 5
minutes; (7) secondary antibodies such as biotinylated antibodies
are added and the slides are incubated at RT for about 60 minutes;
(8) the slides are washed in PBS at RT for about 30 minutes,
changing the wash buffer every 5 minutes; (9) streptavidin is added
and the slides are incubated at RT for about 30 minutes; (10)
3,3'-diaminobenzidine (DAB) is added, the slides are incubated for
5 minutes, the DAB is neutralized with bleach, and the slides are
washed for 5 minutes with water; (11) the slides are counterstained
with methylgreen for 3 minutes and washed 3 times with water; (12)
the slides are dipped in 95% ethanol, followed by a 100% ethanol
and xylene series; and (13) a coverslip is placed onto the
slide.
[0175] Examples of IHC protocols for determining the presence or
level of specific antigens of interest are known in the art. These
include the IHC protocols described in, e.g., Ishikawa et al.,
Cancer Res., 65:9176-9184 (2005) for TGF-.alpha. and amphiregulin;
Cappuzzo et al., J. Clin. Oncol., 23:5007-5018 (2005) for HER2;
Cappuzzo et al., J. Natl. Caner Inst., 97:643-655 (2005) for EGFR;
Abrams et al., Mol. Cancer. Ther., 2:471-478 (2003) for c-KIT and
PDGFRB; and Lee et al., Anal. Quant. Cytol. Histol., 27:202-210
(2005) for PTEN. Tissue staining can be visualized using
peroxidase-based immunostaining kits available from Vector
Laboratories (Burlingame, Calif.) and DAKO (Glostrup, Denmark).
[0176] The presence or level of a proteinaceous biomarker can also
be determined by detecting or quantifying the amount of the
purified marker. Purification of the marker can be achieved, for
example, by high pressure liquid chromatography (HPLC), alone or in
combination with mass spectrometry (e.g., MALDI/MS, MALDI-TOF/MS,
tandem MS, etc.). Qualitative or quantitative detection of a
biomarker can also be determined by well-known methods including,
without limitation, Bradford assays, Coomassie blue staining,
silver staining, assays for radiolabeled protein, and mass
spectrometry.
[0177] The analysis of a plurality of proteinaceous biomarkers may
be carried out separately or simultaneously with one test sample.
For separate or sequential assay of biomarkers, suitable
apparatuses include clinical laboratory analyzers such as the
ElecSys (Roche), the AxSym (Abbott), the Access (Beckman), the
ADVIA.RTM., the CENTAUR.RTM. (Bayer), and the NICHOLS
ADVANTAGE.RTM. (Nichols Institute) immunoassay systems. Preferred
apparatuses or protein chips perform simultaneous assays of a
plurality of biomarkers on a single surface. Particularly useful
physical formats comprise surfaces having a plurality of discrete,
addressable locations for the detection of a plurality of different
biomarkers. Such formats include protein microarrays, or "protein
chips" (see, e.g., Ng et al., J. Cell Mol. Med., 6:329-340 (2002))
and certain capillary devices (see, e.g., U.S. Pat. No. 6,019,944).
In these embodiments, each discrete surface location may comprise
antibodies to immobilize one or more biomarkers for detection at
each location. Surfaces may alternatively comprise one or more
discrete particles (e.g., microparticles or nanoparticles)
immobilized at discrete locations of a surface, where the
microparticles comprise antibodies to immobilize one or more
biomarkers for detection.
[0178] In view of the above, one skilled in the art will readily
appreciate that the methods of the present invention for
determining a protein expression profile from a sample of a subject
can be practiced using one or any combination of the well-known
techniques described above or other techniques known in the
art.
[0179] F. Protein Activation Profiling
[0180] Analysis of the activation or inhibition of proteinaceous
biomarkers of interest can be used alone or in combination with
other markers to predict, monitor, or optimize tyrosine kinase
inhibitor therapy in a subject according to the methods of the
present invention. Any method known in the art for detecting or
determining the activity or activation state of one or more of the
biomarkers described herein is suitable for use in the present
invention.
[0181] In some embodiments, the activation or inhibition of a
proteinaceous biomarker can be determined by molecular cytogenetic
techniques such as immunohistochemistry (IHC). An IHC assay is
particularly useful for determining the phosphorylation state of
proteins that are activated or inhibited by phosphorylation at
specific tyrosine, serine, and/or threonine residues. In
particular, IHC can be performed to determine whether a sample is
positive for a particular phosphorylated marker of interest, the
level of that phosphorylated marker, and/or the staining pattern of
that phosphorylated marker. As a non-limiting example,
paraffin-embedded tumor tissue sections can be stained with
antibodies against phospho-Akt (P-Akt) and phospho-MAPK (P-MAPK) as
described in Cappuzzo et al., J. Natl. Caner Inst., 96:1133-1141
(2004) to determine their activation state. Negative to weak P-MAPK
staining typically indicates the absence of active MAPK, whereas
moderate to strong staining generally indicates the presence of
active MAPK. Since activation of Akt by phosphorylation results in
the translocation of P-Akt from the cytoplasm to the nucleus, the
presence of P-Akt staining in the nucleus indicates the presence of
active Akt, whereas the absence of nuclear staining indicates the
absence of active Akt. An additional IHC protocol for detecting
P-Akt in tumor cells is described in Cappuzzo et al., J. Natl.
Caner Inst., 97:643-655 (2005).
[0182] Phospho-specific antibodies against various phosphorylated
forms of proteins such as Akt, MAPK, EGFR, c-KIT, c-Src, FLK-1,
PDGFRA, PDGFRB, PTEN, Raf, and MEK are available from Santa Cruz
Biotechnology (Santa Cruz, Calif.). Such phospho-specific
antibodies can also be used in techniques including any of the
immunoassays described above (e.g., ELISA) as well as Western
blotting and immunoprecipitation assays to determine the activation
state of a protein of interest according to the methods of the
present invention. For example, specific tyrosine phosphorylated
forms of EGFR can be detected using EGFR Phospho ELISA kits
available from Sigma-Aldrich (St. Louis, Mo.).
[0183] Other methods for detecting the phosphorylation state of a
protein of interest include, but are not limited to, KIRA ELISA
(see, e.g., U.S. Pat. Nos. 5,766,863; 5,891,650; 5,914,237;
6,025,145; and 6,287,784), mass spectrometry (comparing size of
phosphorylated and unphosphorylated protein), and the eTag.TM.
assay system.
[0184] When at least one of the proteinaceous biomarkers of
interest is an enzyme, a level of enzymatic activity can be
determined to assess the activation state of the enzyme. For
example, any of the receptor or non-receptor tyrosine kinases
described herein can be assayed for the presence or level of kinase
activity using an appropriate substrate. Similarly, any tyrosine or
serine/threonine kinase or phosphatase involved in the downstream
signaling of receptor tyrosine kinases can be assayed for the
presence or level of kinase activity using a suitable substrate.
Tyrosine kinase activity can be determined using kits available
from Chemicon International, Inc. (Temecula, Calif.) and QIAGEN
Inc. (Valencia, Calif.). A fluorescent-based tyrosine kinase or
tyrosine phosphatase activity assay is available from Promega
Corporation (Madison, Wis.) and is described in Goueli et al., Cell
Notes, 8:15-20 (2004). The activity of serine/threonine kinases
such as Akt and MAPK can be determined using a kit available from
Stressgen Bioreagents (Victoria, BC, Canada) and Chemicon
International, Inc., respectively.
[0185] In some embodiments, the activation state of interest
corresponds to the phosphorylation state of a proteinaceous
biomarker, the ubiquitination state of the biomarker, or the
complexation state of the biomarker with another cellular molecule.
Non-limiting examples of activation states (listed in parentheses)
of tyrosine kinases and their signaling components that are
suitable for detection include: EGFR (EGFRvIII, phosphorylated (p-)
EGFR, EGFR:Shc, ubiquitinated (u-) EGFR, p-EGFRvIII); ErbB2
(p85:truncated (Tr)-ErbB2, p-ErbB2, p85:Tr-p-ErbB2, Her2:Shc,
ErbB2:PI3K, ErbB2:EGFR, ErbB2:ErbB3, ErbB2:ErbB4); ErbB3 (p-ErbB3,
ErbB3:PI3K, p-ErbB3:PI3K, ErbB3:Shc); ErbB4 (p-ErbB4, ErbB4:Shc);
IGF-1R (p-IGF-1R, IGF-1R:IRS, IRS:PI3K, p-IRS, IGF-1R:PI3K); INSR
(p-INSR); KIT (p-KIT); FLT3 (p-FLT3); HGFRI (p-HGFRI); HGFR2
(p-HGFR2); RET (p-RET); PDGFRa (p-PDGFRa); PDGFRP (p-PDGFRP);
VEGFRI (p-VEGFRI, VEGFRI:PLCg, VEGFRI:Src); VEGFR2 (p-VEGFR2,
VEGFR2:PLCy, VEGFR2:Src, VEGFR2:heparin sulphate,
VEGFR2:VE-cadherin); VEGFR3 (p-VEGFR3); FGFR1 (p-FGFR1); FGFR2
(p-FGFR2); FGFR3 (p-FGFR3); FGFR4 (p-FGFR4); Tie1 (p-Tie1); Tie2
(p-Tie2); EphA (p-EphA); EphB (p-EphB); NFKB and/or IKB (p-IK
(S32), p-NFKB (5536), p-P65:IKBa); Akt (p-Akt (T308, 5473)); PTEN
(p-PTEN); Bad (p-Bad (5112, S136), Bad: 14-3-3); mTor (p-mTor
(52448)); p70S6K (p-p70S6K (T229, T389)); Mek (p-Mek (5217, 5221));
Erk (p-Erk (T202, Y204)); Rsk-1 (p-Rsk-1 (T357, 5363)); Jnk (p-Jnk
(T183, Y185)); P38 (p-P38 (T180, Y182)); Stat3 (p-Stat-3 (Y705,
5727)); Fak (p-Fak (Y576)); Rb (p-Rb (5249, T252, 5780)); Ki67; p53
(p-p53 (5392, S20)); CREB (p-CREB (5133)); c-Jun (p-c-Jun (S63));
cSrc (p-cSrc (Y416)); and paxillin (p-paxillin (Y118)).
[0186] Any method known in the art can be used to detect the
complexation state of a proteinaceous biomarker of interest with
another cellular molecule. Preferably, the formation of
heterodimeric complexes between members of the EGFR family of
receptor tyrosine kinases are detected in tumors or tumor cells.
Several preferred methods are described below. These methods
generally detect noncovalent protein-protein interactions between
proteins of interest.
[0187] Immunoaffinity-based methods, such as immunoprecipitation or
ELISA, can be used to detect heterodimeric complexes between
proteins of interest (e.g., EGFR heterodimers). In one embodiment,
antibodies against a particular EGFR subtype are used to
immunoprecipitate complexes comprising that EGFR subtype from tumor
cells, and the resulting immunoprecipitant is then probed for the
presence of one or more additional EGFR subtypes by immunoblotting.
In another embodiment, EGFR ligands specific to one or more types
of EGFR heterodimers can be used to precipitate complexes, which
are then probed for the presence of each EGFR subtype present in
the complexes. In certain instances, the EGFR ligands can be
conjugated to avidin and EGFR heterodimeric complexes purified on a
biotin column.
[0188] In other embodiments, such as ELISA or antibody
sandwich-type assays, antibodies against a particular EGFR subtype
are immobilized on a solid support, contacted with tumor cells or
tumor cell lysate, washed, and then exposed to antibodies against
one or more additional EGFR subtypes. Binding of the latter
antibody, which may be detected directly or by a secondary antibody
conjugated to a detectable label, indicates the presence of EGFR
heterodimers. In certain instances, EGFR ligands may be used in
place of, or in combination with, antibodies against EGFR
subtypes.
[0189] Immunoprecipitation with antibodies against EGFR subtypes
can be followed by a functional assay for heterodimers, as an
alternative or supplement to immunoblotting. In one embodiment,
immunoprecipitation with antibodies against a particular EGFR
subtype is followed by an assay for receptor tyrosine kinase
activity in the immunoprecipitant. As a non-limiting example
involving the detection of ErbB2:ErbB3 heterodimers, the presence
of tyrosine kinase activity in the immunoprecipitant indicates that
ErbB3 is most likely associated with ErbB2 because ErbB3 does not
have intrinsic tyrosine kinase activity (see, e.g., Graus-Porta et
al., EMBO J., 16:1647-1655 (1997); Klapper et al., Proc. Natl.
Acad. Sci. USA, 96:4995-5000 (1999)). As another non-limiting
example involving the detection of ErbB2:EGFR heterodimers,
immunoprecipitation with ErbB2 antibody can be followed by an assay
for EGFR kinase activity. In this assay, the immunoprecipitant can
be contacted with radioactive ATP and a peptide substrate that
mimics the in vivo site of transphosphorylation of ErbB2 by EGFR.
Phosphorylation of the peptide indicates co-immunoprecipitation and
thus heterodimerization of EGFR with ErbB2. Receptor tyrosine
kinase activity assays are well known in the art and include assays
that detect phosphorylation of target substrates, for example, by
phosphotyrosine antibody, and activation of cognate signal
transduction pathways, such as the MAPK pathway.
[0190] Chemical or UV cross-linking can also be used to covalently
join heterodimers on the surface of living tumor cells (see, e.g.,
Hunter et al., Biochem. Jr., 320:847-853 (1996)). Examples of
chemical cross-linkers include, but are not limited to,
dithiobis(succinimidyl) propionate (DSP) and
3,3'-dithiobis(sulphosuccinim-idyl) propionate (DTS SP). In one
embodiment, cell extracts from chemically cross-linked tumor cells
are analyzed by SDS-PAGE and immunoblotted with antibodies to one
or more antibodies against EGFR subtypes. A supershifted band of
the appropriate molecular weight most likely represents specific
EGFR heterodimers. This result may be confirmed by subsequent
immunoblotting with the appropriate antibodies.
[0191] Fluorescence resonance energy transfer (FRET) can also be
used to detect heterodimers between members of the EGFR family of
receptor tyrosine kinases. FRET detects protein conformational
changes and protein-protein interactions in vivo and in vitro based
on the transfer of energy from a donor fluorophore to an acceptor
fluorophore (see, e.g., Selvin, Nat. Struct. Biol., 7:730-734
(2000)). Energy transfer takes place only if the donor fluorophore
is in sufficient proximity to the acceptor fluorophore. In a
typical FRET experiment, two proteins or two sites on a single
protein are labeled with different fluorescent probes. One of the
probes, the donor probe, is excited to a higher energy state by
incident light of a specified wavelength. The donor probe then
transmits its energy to the second probe, the acceptor probe,
resulting in a reduction in the donor's fluorescence intensity and
an increase in the acceptor's fluorescence emission. To measure the
extent of energy transfer, the donor's intensity in a sample
labeled with donor and acceptor probes is compared with its
intensity in a sample labeled with donor probe only. Optionally,
acceptor intensity is compared in donor/acceptor and acceptor only
samples. Suitable probes are known in the art and include, for
example, membrane permeant dyes (e.g., fluorescein, rhodamine,
etc.), organic dyes (e.g., cyanine dyes, etc.), and lanthanide
atoms. Methods and instrumentation for detecting and measuring
energy transfer are known in the art.
[0192] FRET-based techniques suitable for detecting and measuring
protein-protein interactions in individual cells are also known in
the art. For example, donor photobleaching fluorescence resonance
energy transfer (pbFRET) microscopy and fluorescence lifetime
imaging microscopy (FLIM) may be used to detect the dimerization of
cell surface receptors (see, e.g., Selvin, supra; Gadella et al.,
J. Cell Biol., 129:1543-1558 (1995)). In one embodiment, pbFRET is
used on cells either "in suspension" or "in situ" to detect and
measure the formation of EGFR heterodimers, as described, e.g., in
Nagy et al., Cytometry, 32:120-131 (1998). These techniques measure
the reduction in a donor's fluorescence lifetime due to energy
transfer. In a particular embodiment, a flow cytometric
Foerster-type FRET technique (FCET) may be used to investigate EGFR
heterodimerization, as described, e.g., in Nagy et al., supra, and
Brockhoff et al., Cytometry, 44:33848 (2001).
[0193] FRET is preferably used in conjunction with standard
immunohistochemical labeling techniques (see, e.g., Kenworthy,
Methods, 24:289-296 (2001). For example, antibodies conjugated to
suitable fluorescent dyes can be used as probes for labeling two
different proteins. If the proteins are within proximity of one
another, the fluorescent dyes act as donors and acceptors for FRET.
Energy transfer can be detected by standard means. Energy transfer
may be detected by flow cytometric means or by digital microscopy
systems, such as confocal microscopy or wide-field fluorescence
microscopy coupled to a charge-coupled device (CCD) camera.
[0194] In one embodiment of the present invention, antibodies
against different EGFR subtypes are directly labeled with two
different fluorophores. Tumor cells or tumor cell lysates are
contacted with the differentially labeled antibodies, which act as
donors and acceptors for FRET in the presence of particular EGFR
heterodimers. Alternatively, unlabeled antibodies against the
different EGFR subtypes are used along with differentially labeled
secondary antibodies that serve as donors and acceptors. Energy
transfer can be detected and the presence of EGFR heterodimers
determined if the labels are found to be in close proximity.
[0195] In another embodiment, the presence of EGFR heterodimers on
the surface of tumor cells is demonstrated by co-localization of
EGFR subtypes using standard direct or indirect immunofluorescence
techniques and confocal laser scanning microscopy. Alternatively,
laser scanning imaging (LSI) can be used to detect antibody binding
and co-localization of EGFR subtypes in a high-throughput format,
such as a microwell plate, as described, e.g., in Zuck et al.,
Proc. Natl. Acad. Sci. USA, 96:11122-11127 (1999).
[0196] In further embodiments, the presence of EGFR heterodimers is
determined by identifying enzymatic activity that is dependent upon
the proximity of the heterodimer components. Antibodies against an
EGFR subtype are conjugated with one enzyme and antibodies against
another EGFR subtype are conjugated with a second enzyme. A first
substrate for the first enzyme is added and the reaction produces a
second substrate for the second enzyme. This leads to a reaction
with another molecule to produce a detectable compound, such as a
dye. The presence of another chemical breaks down the second
substrate, so that reaction with the second enzyme is prevented
unless the first and second enzymes, and thus the two antibodies,
are in close proximity. In a particular embodiment, tumor cells or
tumor cell lysates are contacted with an ErbB2 antibody that is
conjugated with glucose oxidase and an ErbB3 or EGFR antibody that
is conjugated with horseradish peroxidase. Glucose is added to the
reaction, along with a dye precursor, such as DAB, and catalase.
The presence of EGFR heterodimers is determined by the development
of color upon staining for DAB.
[0197] Heterodimers may also be detected using methods based on the
eTag.TM. assay system as described, e.g., in U.S. Pat. No.
6,673,550. An eTag.TM., or "electrophoretic tag," comprises a
detectable reporter moiety, such as a fluorescent group. It may
also comprise a "mobility modifier," which comprises a moiety
having a unique electrophoretic mobility. These moieties allow for
separation and detection of the eTag.TM. from a complex mixture
under defined electrophoretic conditions, such as capillary
electrophoresis (CE). The portion of the eTag.TM. containing the
reporter moiety and, optionally, the mobility modifier is linked to
a first target binding moiety by a cleavable linking group to
produce a first binding compound. The first target binding moiety
specifically recognizes a particular first target, such as a
nucleic acid or protein. The first target binding moiety is not
limited in any way, and may be, for example, a polynucleotide or a
polypeptide. Preferably, the first target binding moiety is an
antibody or antibody fragment. Alternatively, the first target
binding moiety may be an EGFR ligand or binding-competent fragment
thereof.
[0198] The linking group preferably comprises a cleavable moiety,
such as an enzyme substrate, or any chemical bond that may be
cleaved under defined conditions. When the first target binding
moiety binds to its target, the cleaving agent is introduced and/or
activated, and the linking group is cleaved, thus releasing the
portion of the eTag.TM. containing the reporter moiety and mobility
modifier. Thus, the presence of a "free" eTag.TM. indicates the
binding of the target binding moiety to its target.
[0199] Preferably, a second binding compound comprises the cleaving
agent and a second target binding moiety that specifically
recognizes a second target. The second target binding moiety is
also not limited in any way and may be, for example, an antibody or
antibody fragment or an EGFR ligand or binding-competent fragment
thereof. The cleaving agent is such that it will only cleave the
linking group in the first binding compound If the first binding
compound and the second binding compound are in close
proximity.
[0200] As a non-limiting example, a first binding compound
comprises an eTag.TM. in which antibodies against ErbB2 serve as
the first target binding moiety. A second binding compound
comprises antibodies against EGFR or ErbB3 joined to a cleaving
agent capable of cleaving the linking group of the eTag.TM..
Preferably, the cleaving agent must be activated in order to be
able to cleave the linking group. Tumor cells or tumor cell lysates
are contacted with the eTag.TM., which binds to ErbB2, and with the
modified EGFR or ErbB3 antibodies, which binds to EGFR or ErbB3 on
the cell surface. Unbound binding compound is preferably removed,
and the cleaving agent is activated, if necessary. If EGFR:ErbB2 or
ErbB2:ErbB3 heterodimers are present, the cleaving agent will
cleave the linking group and release the eTag.TM. due to the
proximity of the cleaving agent to the linking group. Free eTag.TM.
may then be detected by any method known in the art, such as
capillary electrophoresis. In certain instances, the cleaving agent
is an activatable chemical species that acts on the linking group.
For example, the cleaving agent may be activated by exposing the
sample to light.
[0201] In view of the above, one skilled in the art will readily
appreciate that the methods of the present invention for
determining a protein activation profile from a sample of a subject
can be practiced using one or any combination of the well-known
techniques described above or other techniques known in the
art.
VI. Selection of Antibodies
[0202] The generation and selection of antibodies not already
commercially available for detecting or determining the level of
proteinaceous biomarkers may be accomplished several ways. For
example, one way is to purify polypeptides of interest or to
synthesize the polypeptides of interest using, e.g., solid phase
peptide synthesis methods well known in the art. See, e.g., Guide
to Protein Purification, Murray P. Deutcher, ed., Meth. Enzymol.,
Vol. 182, 1990; Solid Phase Peptide Synthesis, Greg B. Fields, ed.,
Meth. Enzymol., Vol. 289, 1997; Kiso et al., Chem. Pharm. Bull.,
38:1192-99 (1990); Mostafavi et al., Biomed. Pept. Proteins Nucleic
Acids, 1:255-60, (1995); Fujiwara et al., Chem. Pharm. Bull.,
44:1326-31 (1996). The selected polypeptides may then be injected,
for example, into mice or rabbits, to generate polyclonal or
monoclonal antibodies. One skilled in the art will recognize that
many procedures are available for the production of antibodies, for
example, as described in Antibodies, A Laboratory Manual, Harlow
and Lane, Eds., Cold Spring Harbor Laboratory, Cold Spring Harbor,
N.Y. (1988). One skilled in the art will also appreciate that
binding fragments or Fab fragments which mimic antibodies can also
be prepared from genetic information by various procedures (see,
e.g., Antibody Engineering: A Practical Approach, Borrebaeck, Ed.,
Oxford University Press, Oxford (1995); J. Immunol., 149:3914-3920
(1992)).
[0203] In addition, numerous publications have reported the use of
phage display technology to produce and screen libraries of
polypeptides for binding to a selected target (see, e.g, Cwirla et
al., Proc. Natl. Acad. Sci. USA, 87:6378-6382 (1990); Devlin et
al., Science, 249:404-406 (1990); Scott et al., Science,
249:386-388 (1990); and Ladner et al., U.S. Pat. No. 5,571,698). A
basic concept of phage display methods is the establishment of a
physical association between DNA encoding a polypeptide to be
screened and the polypeptide. This physical association is provided
by the phage particle, which displays a polypeptide as part of a
capsid enclosing the phage genome which encodes the polypeptide.
The establishment of a physical association between polypeptides
and their genetic material allows simultaneous mass screening of
very large numbers of phage bearing different polypeptides. Phage
displaying a polypeptide with affinity to a target bind to the
target and these phage are enriched by affinity screening to the
target. The identity of polypeptides displayed from these phage can
be determined from their respective genomes. Using these methods a
polypeptide identified as having a binding affinity for a desired
target can then be synthesized in bulk by conventional means (see,
e.g., U.S. Pat. No. 6,057,098).
[0204] The antibodies that are generated by these methods may then
be selected by first screening for affinity and specificity with
the purified polypeptide of interest and, if required, comparing
the results to the affinity and specificity of the antibodies with
polypeptides that are desired to be excluded from binding. The
screening procedure can involve immobilization of the purified
polypeptides in separate wells of microtiter plates. The solution
containing a potential antibody or group of antibodies is then
placed into the respective microtiter wells and incubated for about
30 min to 2 h. The microtiter wells are then washed and a labeled
secondary antibody (e.g., an anti-mouse antibody conjugated to
alkaline phosphatase if the raised antibodies are mouse antibodies)
is added to the wells and incubated for about 30 min and then
washed. Substrate is added to the wells and a color reaction will
appear where antibody to the immobilized polypeptide(s) are
present.
[0205] The antibodies so identified may then be further analyzed
for affinity and specificity in the assay design selected. In the
development of immunoassays for a target protein, the purified
target protein acts as a standard with which to judge the
sensitivity and specificity of the immunoassay using the antibodies
that have been selected. Because the binding affinity of various
antibodies may differ, certain antibody pairs (e.g., in sandwich
assays) may interfere with one another sterically, etc., assay
performance of an antibody may be a more important measure than
absolute affinity and specificity of an antibody.
[0206] Those skilled in the art will recognize that many approaches
can be taken in producing antibodies or binding fragments and
screening and selecting for affinity and specificity for the
various polypeptides, but these approaches do not change the scope
of the present invention.
VII. Algorithms
[0207] The present invention provides assay methods for predicting,
monitoring, or optimizing tyrosine kinase inhibitor therapy in a
subject using an algorithmic analysis of a panel of biomarkers in a
sample from the subject. In particular, the algorithms described
herein can advantageously provide improved sensitivity,
specificity, negative predictive value, positive predictive value,
and/or overall accuracy for carrying out the methods of the present
invention.
[0208] The term "algorithm" includes any of a variety of
statistical analyses used to determine relationships between
variables. In some embodiments of the present invention, the
variables are profiles such as nucleic acid and/or protein
profiles. In these embodiments, the algorithm is used, e.g., to
predict, identify, monitor, and/or optimize tyrosine kinase
inhibitor efficacy, toxicity, and/or resistance in a tumor, tumor
cell, or patient. Any number of profiles can be analyzed using an
algorithm according to the methods of the present invention. For
example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 25, 30, 35, 40, 45, 50, or more profiles can be
included in an algorithm. In one embodiment, logistic regression is
used. In another embodiment, linear regression is used. In certain
instances, the algorithms of the present invention can use a
quantile measurement of a particular profile within a given
population as a variable. Quantiles are a set of "cut points" that
divide a sample of data into groups containing (as far as possible)
equal numbers of observations. For example, quartiles are values
that divide a sample of data into four groups containing (as far as
possible) equal numbers of observations. The lower quartile is the
data value a quarter way up through the ordered data set; the upper
quartile is the data value a quarter way down through the ordered
data set. Quintiles are values that divide a sample of data into
five groups containing (as far as possible) equal numbers of
observations. The present invention can also include the use of
percentile ranges of profiles (e.g., tertiles, quartile, quintiles,
etc.), or their cumulative indices (e.g., quartile sums of
profiles, etc.) as variables in the algorithms (just as with
continuous variables).
[0209] As used herein, the term "index value" refers to a number
for a subject that is determined using an algorithm according to
the methods of the present invention. For example, the index value
may be determined using logistic regression and correspond to a
number between 0 and 1, e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,
0.8, 0.9, and further divisions thereof such as 0.25 or 0.225.
Preferably, the index value is presented as a "cumulative index
value," which represents a summation of those values determined
from the assessment of at least one nucleic acid and/or protein
profile (see, e.g., Examples 1 and 2 below). The cumulative index
value can be compared to an index cut-off value, or the ratio of
cumulative index values of all tested profiles can be divided by an
index cut-off value, e.g., to predict, identify, monitor, and/or
optimize tyrosine kinase inhibitor efficacy, toxicity, and/or
resistance in a tumor, tumor cell, or patient.
[0210] The term "index cutoff value" refers to a number chosen on
the basis of population analysis that is used for comparison to an
index value calculated for a subject. Thus, the index cutoff value
is based on analysis of index values determined using an algorithm.
Those of skill in the art will recognize that an index cutoff value
can be determined according to the needs of the user and
characteristics of the analyzed population. When the algorithm is
logistic regression, the index cutoff value will, of necessity, be
between 0 and 1, e.g., between 0.1 to 0.9, 0.2 to 0.8, 0.3 to 0.7,
or 0.4 to 0.6. Preferably, the index cutoff value is calculated
according to the formulas set forth in Examples 1 and 2 below.
[0211] The term "iterative approach" refers to the analysis of at
least one profile associated with cancer from a subject using more
than one algorithm and/or index cutoff value. For example, two or
more algorithms could be used to analyze different sets of
profiles. As another example, a single algorithm could be used to
analyze at least one profile, but more than one index cutoff value
based on the algorithm could be used in the methods of the present
invention.
[0212] In certain instances, cut-off values can be determined and
independently adjusted for each of a number of biomarkers to
observe the effects of the adjustments on clinical parameters such
as sensitivity, specificity, negative predictive value, positive
predictive value, and overall accuracy. In particular, Design of
Experiments (DOE) methodology can be used to simultaneously vary
the cut-off values and to determine the effects on the resulting
clinical parameters of sensitivity, specificity, negative
predictive value, positive predictive value, and overall accuracy.
The DOE methodology is advantageous in that variables are tested in
a nested array requiring fewer runs and cooperative interactions
among the cut-off variables can be identified. Optimization
software such as DOE Keep It Simple Statistically (KISS) can be
obtained from Air Academy Associates (Colorado Springs, Colo.) and
can be used to assign experimental runs and perform the
simultaneous equation calculations. Using the DOE KISS program, an
optimized set of cut-off values for a given clinical parameter and
a given set of biomarkers can be calculated. ECHIP optimization
software, available from ECHIP, Inc. (Hockessin, Del.), and
Statgraphics optimization software, available from STSC, Inc.
(Rockville, Md.), are also useful for determining cut-off values
for a given set of biomarkers. Alternatively, cut-off values can be
determined using Receiver Operating Characteristic (ROC) curves and
adjusted to achieve the desired clinical parameter values.
[0213] In some embodiments, the algorithms of the present invention
comprise one or more learning statistical classifier systems. As
used herein, the term "learning statistical classifier system"
refers to a machine learning algorithmic technique capable of
adapting to complex data sets and making decisions based upon such
data sets. In some embodiments, one or more learning statistical
classifier systems are used, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or
more learning statistical classifier systems are used, preferably
in tandem. Examples of learning statistical classifier systems
include, but are not limited to, those using inductive learning
(e.g., decision/classification trees such as random forests,
classification and regression trees (CART), boosted trees, etc.),
Probably Approximately Correct (PAC) learning, connectionist
learning (e.g., neural networks (NN), artificial neural networks
(ANN), neuro fuzzy networks (NFN), network structures, perceptrons
such as multi-layer perceptrons, multi-layer feed-forward networks,
applications of neural networks, Bayesian learning in belief
networks, etc.), reinforcement learning (e.g., passive learning in
a known environment such as naive learning, adaptive dynamic
learning, and temporal difference learning, passive learning in an
unknown environment, active learning in an unknown environment,
learning action-value functions, applications of reinforcement
learning, etc.), and genetic algorithms and evolutionary
programming. Other learning statistical classifier systems include
support vector machines (e.g., Kernel methods), multivariate
adaptive regression splines (MARS), Levenberg-Marquardt algorithms,
Gauss-Newton algorithms, mixtures of Gaussians, gradient descent
algorithms, and learning vector quantization (LVQ).
[0214] Random forests are learning statistical classifier systems
that are constructed using an algorithm developed by Leo Breiman
and Adele Cutler. Random forests use a large number of individual
decision trees and decide the class by choosing the mode (i.e.,
most frequently occurring) of the classes as determined by the
individual trees. Random forest analysis can be performed, e.g.,
using the RandomForests software available from Salford Systems
(San Diego, Calif.). See, e.g., Breiman, Machine Learning, 45:5-32
(2001); and
http://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm,
for a description of random forests.
[0215] Classification and regression trees represent a computer
intensive alternative to fitting classical regression models and
are typically used to determine the best possible model for a
categorical or continuous response of interest based upon one or
more predictors. Classification and regression tree analysis can be
performed, e.g., using the CART software available from Salford
Systems or the Statistica data analysis software available from
StatSoft, Inc. (Tulsa, Okla.). A description of classification and
regression trees is found, e.g., in Breiman et al. "Classification
and Regression Trees," Chapman and Hall, New York (1984); and
Steinberg et al., "CART: Tree-Structured Non-Parametric Data
Analysis," Salford Systems, San Diego, (1995).
[0216] Neural networks are interconnected groups of artificial
neurons that use a mathematical or computational model for
information processing based on a connectionist approach to
computation. Typically, neural networks are adaptive systems that
change their structure based on external or internal information
that flows through the network. Specific examples of neural
networks include feed-forward neural networks such as perceptrons,
single-layer perceptrons, multi-layer perceptrons, backpropagation
networks, ADALINE networks, MADALINE networks, Leammatrix networks,
radial basis function (RBF) networks, and self-organizing maps or
Kohonen self-organizing networks; recurrent neural networks such as
simple recurrent networks and Hopfield networks; stochastic neural
networks such as Boltzmann machines; modular neural networks such
as committee of machines and associative neural networks; and other
types of networks such as instantaneously trained neural networks,
spiking neural networks, dynamic neural networks, and cascading
neural networks. Neural network analysis can be performed, e.g.,
using the Statistica data analysis software available from
StatSoft, Inc. See, e.g., Freeman et al., In "Neural Networks:
Algorithms, Applications and Programming Techniques,"
Addison-Wesley Publishing Company (1991); Zadeh, Information and
Control, 8:338-353 (1965); Zadeh, "IEEE Trans. on Systems, Man and
Cybernetics," 3:28-44 (1973); Gersho et al., In "Vector
Quantization and Signal Compression," Kluywer Academic Publishers,
Boston, Dordrecht, London (1992); and Hassoun, "Fundamentals of
Artificial Neural Networks," MIT Press, Cambridge, Mass., London
(1995), for a description of neural networks.
[0217] Support vector machines are a set of related supervised
learning techniques used for classification and regression and are
described, e.g., in Cristianini et al., "An Introduction to Support
Vector Machines and Other Kernel-Based Learning Methods," Cambridge
University Press (2000). Support vector machine analysis can be
performed, e.g., using the SVM.sup.light software developed by
Thorsten Joachims (Cornell University) or using the LIBSVM software
developed by Chih-Chung Chang and Chih-Jen Lin (National Taiwan
University).
[0218] The learning statistical classifier systems described herein
can be trained and tested using a cohort of samples from healthy
individuals, cancer patients, cancer cell lines, and the like. For
example, samples from patients diagnosed by a physician, and
preferably by an oncologist, as having cancer are suitable for use
in training and testing the learning statistical classifier systems
of the present invention. Samples from healthy individuals can
include those that were not identified as having cancer. In certain
embodiments, samples from cancer cell lines can be used in training
and testing the learning statistical classifier systems described
herein (see, e.g., Example 4 below). One skilled in the art will
know of additional techniques and diagnostic criteria for obtaining
a cohort of samples that can be used in training and testing the
learning statistical classifier systems of the present
invention.
[0219] As used herein, the term "sensitivity" refers to the
probability that an algorithm of the present invention gives a
positive result when the sample is positive. Sensitivity is
calculated as the number of true positive results divided by the
sum of the true positives and false negatives. Sensitivity
essentially is a measure of how well an algorithm of the present
invention correctly identifies responders (e.g., subjects likely to
respond to tyrosine kinase inhibitor therapy, subjects without
acquired resistance to tyrosine kinase inhibitor therapy, etc.)
from non-responders. The marker values or learning statistical
classifier models (e.g., random forest or neural network models)
can be selected such that the sensitivity is at least about 60%,
and can be, for example, at least about 65%, 70%, 75%, 76%, 77%,
78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,
91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0220] The term "specificity" as used herein refers to the
probability that an algorithm of the present invention gives a
negative result when the sample is not positive. Specificity is
calculated as the number of true negative results divided by the
sum of the true negatives and false positives. Specificity
essentially is a measure of how well an algorithm of the present
invention excludes non-responders from responders. The marker
values or learning statistical classifier models can be selected
such that the specificity is at least about 70%, for example, at
least about 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%,
94%, 95%, 96%, 97%, 98%, or 99%.
[0221] As used herein, the term "negative predictive value" or
"NPV" refers to the probability that a subject classified as a
non-responder is actually unlikely to respond to tyrosine kinase
inhibitor therapy or has developed acquired resistance to tyrosine
kinase inhibitor therapy. Negative predictive value can be
calculated as the number of true negatives divided by the sum of
the true negatives and false negatives. Negative predictive value
is determined by the characteristics of the algorithm as well as
the prevalence of the disease in the population analyzed. The
marker values or learning statistical classifier models can be
selected such that the negative predictive value in a population
having a disease prevalence is at least about 70% and can be, for
example, at least about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%,
83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%,
96%, 97%, 98%, or 99%.
[0222] The term "positive predictive value" or "PPV" as used herein
refers to the probability that an individual classified as a
responder is actually likely to respond to tyrosine kinase
inhibitor therapy or has not developed acquired resistance to
tyrosine kinase inhibitor therapy. Positive predictive value can be
calculated as the number of true positives divided by the sum of
the true positives and false positives. Positive predictive value
is determined by the characteristics of the algorithm as well as
the prevalence of the disease in the population analyzed. The
marker values or learning statistical classifier models can be
selected such that the positive predictive value in a population
having a disease prevalence is at least about 25% and can be, for
example, at least about 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%,
70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%,
95%, 96%, 97%, 98%, or 99%.
[0223] Predictive values, including negative and positive
predictive values, are influenced by the prevalence of the disease
in the population analyzed. In the algorithms of the present
invention, the marker values or learning statistical classifier
models can be selected to produce a desired clinical parameter for
a clinical population with a particular cancer prevalence. For
example, marker values or learning statistical classifier models
can be selected for a cancer prevalence of at least about 1%, 5%,
10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, or 70%,
which can be seen, e.g., in a clinician's office or a general
practitioner's office.
[0224] As used herein, the term "overall accuracy" refers to the
accuracy with which an algorithm of the present invention
classifies responders and non-responders. Overall accuracy is
calculated as the sum of the true positives and true negatives
divided by the total number of sample results and is affected by
the prevalence of the disease in the population analyzed. For
example, the marker values or learning statistical classifier
models can be selected such that the overall accuracy in a patient
population having a disease prevalence is at least about 60%, and
can be, for example, at least about 65%, 70%, 75%, 76%, 77%, 78%,
79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 95%,
96%, 97%, 98%, or 99%.
VIII. Methods of Administration
[0225] According to the methods of the present invention, the
compounds described herein (e.g., tyrosine kinase inhibitors such
as gefitinib and sunitinib) are administered to a subject by any
convenient means known in the art. The assay methods of the present
invention can be used to optimize dosage of tyrosine kinase
inhibitors in subjects who have not received any tyrosine kinase
inhibitor therapy as well as subjects who are currently undergoing
tyrosine kinase inhibitor therapy. The assay methods of the present
invention can also be used to reduce toxicity to tyrosine kinase
inhibitors in subjects who have not received any tyrosine kinase
inhibitor therapy as well as subjects who are currently undergoing
tyrosine kinase inhibitor therapy. One skilled in the art will
appreciate that tyrosine kinase inhibitors can be administered
alone or as part of a combined therapeutic approach with
conventional chemotherapy, radiotherapy, hormonal therapy,
immunotherapy, and/or surgery.
[0226] Tyrosine kinase inhibitors can be administered with a
suitable pharmaceutical excipient as necessary and can be carried
out via any of the accepted modes of administration. Thus,
administration can be, for example, oral, buccal, sublingual,
gingival, palatal, intravenous, topical, subcutaneous,
transcutaneous, transdermal, intramuscular, intra-joint,
parenteral, intra-arteriole, intradermal, intraventricular,
intracranial, intraperitoneal, intravesical, intrathecal,
intralesional, intranasal, rectal, vaginal, or by inhalation. By
"co-administer" it is meant that a tyrosine kinase inhibitor is
administered at the same time, just prior to, or just after the
administration of a second drug (e.g., another tyrosine kinase
inhibitor, a drug useful for reducing the side-effects associated
with tyrosine kinase inhibitor therapy, a chemotherapeutic agent, a
radiotherapeutic agent, a hormonal therapeutic agent, an
immunotherapeutic agent, etc.).
[0227] As a non-limiting example, the tyrosine kinase inhibitors
described herein can be co-administered with conventional
chemotherapeutic agents including platinum-based drugs (e.g.,
oxaliplatin, cisplatin, carboplatin, spiroplatin, iproplatin,
satraplatin, etc.), alkylating agents (e.g., cyclophosphamide,
ifosfamide, chlorambucil, busulfan, melphalan, mechlorethamine,
uramustine, thiotepa, nitrosoureas, etc.), anti-metabolites (e.g.,
5-fluorouracil, azathioprine, methotrexate, leucovorin,
capecitabine, cytarabine, floxuridine, fludarabine, gemcitabine,
pemetrexed, raltitrexed, etc.), plant alkaloids (e.g., vincristine,
vinblastine, vinorelbine, vindesine, podophyllotoxin, paclitaxel,
docetaxel, etc.), topoisomerase inhibitors (e.g., irinotecan,
topotecan, amsacrine, etoposide (VP16), etoposide phosphate,
teniposide, etc.), antitumor antibiotics (e.g., doxorubicin,
adriamycin, daunorubicin, epirubicin, actinomycin, bleomycin,
mitomycin, mitoxantrone, plicamycin, etc.), pharmaceutically
acceptable salts thereof, stereoisomers thereof, derivatives
thereof, analogs thereof, and combinations thereof.
[0228] The tyrosine kinase inhibitors described herein can also be
co-administered with conventional hormonal therapaeutic agents
including, but not limited to, steroids (e.g., dexamethasone),
finasteride, aromatase inhibitors, tamoxifen, and
gonadotropin-releasing hormone agonists (GnRH) such as
goserelin.
[0229] Additionally, the tyrosine kinase inhibitors described
herein can be co-administered with conventional immunotherapeutic
agents including, but not limited to, immunostimulants (e.g.,
Bacillus Calmette-Guerin (BCG), levamisole, interleukin-2,
alpha-interferon, etc.), monoclonal antibodies (e.g., anti-CD20,
anti-HER2, anti-CD52, anti-HLA-DR, and anti-VEGF monoclonal
antibodies), immunotoxins (e.g., anti-CD33 monoclonal
antibody-calicheamicin conjugate, anti-CD22 monoclonal
antibody-pseudomonas exotoxin conjugate, etc.), and
radioimmunotherapy (e.g., anti-CD20 monoclonal antibody conjugated
to .sup.111In, .sup.90Y, or .sup.131I, etc.).
[0230] In a further embodiment, the tyrosine kinase inhibitors
described herein can be co-administered with conventional
radiotherapeutic agents including, but not limited to,
radionuclides such as .sup.74Sc, .sup.64Cu, .sup.67Cu, .sup.89Sr,
.sup.86Y, .sup.87Y, .sup.90Y, .sup.105Rh, .sup.111Ag, .sup.111In,
.sup.117mSn, .sup.149Pm, .sup.153Sm, .sup.166Ho, .sup.177Lu,
.sup.186Re, .sup.188Re, .sup.211At, and .sup.212Bi, optionally
conjugated to antibodies directed against tumor antigens.
[0231] A therapeutically effective amount of a tyrosine kinase
inhibitor may be administered repeatedly, e.g., at least 2, 3, 4,
5, 6, 7, 8, or more times, or the dose may be administered by
continuous infusion. The dose may take the form of solid,
semi-solid, lyophilized powder, or liquid dosage forms, such as,
for example, tablets, pills, pellets, capsules, powders, solutions,
suspensions, emulsions, suppositories, retention enemas, creams,
ointments, lotions, gels, aerosols, foams, or the like, preferably
in unit dosage forms suitable for simple administration of precise
dosages.
[0232] As used herein, the term "unit dosage form" refers to
physically discrete units suitable as unitary dosages for human
subjects and other mammals, each unit containing a predetermined
quantity of a tyrosine kinase inhibitor calculated to produce the
desired onset, tolerability, and/or therapeutic effects, in
association with a suitable pharmaceutical excipient (e.g., an
ampoule). In addition, more concentrated dosage forms may be
prepared, from which the more dilute unit dosage forms may then be
produced. The more concentrated dosage forms thus will contain
substantially more than, e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, or more times the amount of the tyrosine kinase inhibitor.
[0233] Methods for preparing such dosage forms are known to those
skilled in the art (see, e.g., REMINGTON'S PHARMACEUTICAL SCIENCES,
18TH ED., Mack Publishing Co., Easton, Pa. (1990)). The dosage
forms typically include a conventional pharmaceutical carrier or
excipient and may additionally include other medicinal agents,
carriers, adjuvants, diluents, tissue permeation enhancers,
solubilizers, and the like. Appropriate excipients can be tailored
to the particular dosage form and route of administration by
methods well known in the art (see, e.g., REMINGTON'S
PHARMACEUTICAL SCIENCES, supra).
[0234] Examples of suitable excipients include, but are not limited
to, lactose, dextrose, sucrose, sorbitol, mannitol, starches, gum
acacia, calcium phosphate, alginates, tragacanth, gelatin, calcium
silicate, microcrystalline cellulose, polyvinylpyrrolidone,
cellulose, water, saline, syrup, methylcellulose, ethylcellulose,
hydroxypropylmethylcellulose, and polyacrylic acids such as
Carbopols, e.g., Carbopol 941, Carbopol 980, Carbopol 981, etc. The
dosage forms can additionally include lubricating agents such as
talc, magnesium stearate, and mineral oil; wetting agents;
emulsifying agents; suspending agents; preserving agents such as
methyl-, ethyl-, and propyl-hydroxy-benzoates (i.e., the parabens);
pH adjusting agents such as inorganic and organic acids and bases;
sweetening agents; and flavoring agents. The dosage forms may also
comprise biodegradable polymer beads, dextran, and cyclodextrin
inclusion complexes.
[0235] For oral administration, the therapeutically effective dose
can be in the form of tablets, capsules, emulsions, suspensions,
solutions, syrups, sprays, lozenges, powders, and sustained-release
formulations. Suitable excipients for oral administration include
pharmaceutical grades of mannitol, lactose, starch, magnesium
stearate, sodium saccharine, talcum, cellulose, glucose, gelatin,
sucrose, magnesium carbonate, and the like.
[0236] In some embodiments, the therapeutically effective dose
takes the form of a pill, tablet, or capsule, and thus, the dosage
form can contain, along with a tyrosine kinase inhibitor, any of
the following: a diluent such as lactose, sucrose, dicalcium
phosphate, and the like; a disintegrant such as starch or
derivatives thereof; a lubricant such as magnesium stearate and the
like; and a binder such a starch, gum acacia, polyvinylpyrrolidone,
gelatin, cellulose and derivatives thereof. A tyrosine kinase
inhibitor can also be formulated into a suppository disposed, for
example, in a polyethylene glycol (PEG) carrier.
[0237] Liquid dosage forms can be prepared by dissolving or
dispersing a tyrosine kinase inhibitor and optionally one or more
pharmaceutically acceptable adjuvants in a carrier such as, for
example, aqueous saline (e.g., 0.9% w/v sodium chloride), aqueous
dextrose, glycerol, ethanol, and the like, to form a solution or
suspension, e.g., for oral, topical, or intravenous administration.
A tyrosine kinase inhibitor can also be formulated into a retention
enema.
[0238] For topical administration, the therapeutically effective
dose can be in the form of emulsions, lotions, gels, foams, creams,
jellies, solutions, suspensions, ointments, and transdermal
patches. For administration by inhalation, a tyrosine kinase
inhibitor can be delivered as a dry powder or in liquid form via a
nebulizer. For parenteral administration, the therapeutically
effective dose can be in the form of sterile injectable solutions
and sterile packaged powders. Preferably, injectable solutions are
formulated at a pH of from about 4.5 to about 7.5.
[0239] The therapeutically effective dose can also be provided in a
lyophilized form. Such dosage forms may include a buffer, e.g.,
bicarbonate, for reconstitution prior to administration, or the
buffer may be included in the lyophilized dosage form for
reconstitution with, e.g., water. The lyophilized dosage form may
further comprise a suitable vasoconstrictor, e.g., epinephrine. The
lyophilized dosage form can be provided in a syringe, optionally
packaged in combination with the buffer for reconstitution, such
that the reconstituted dosage form can be immediately administered
to a subject.
[0240] A subject can also be monitored at periodic time intervals
to assess the efficacy of a certain therapeutic regimen. For
example, the levels of certain biomarkers can change based on the
therapeutic effect of a treatment such as a tyrosine kinase
inhibitor. The subject can be monitored to assess response and
understand the effects of certain drugs or treatments in an
individualized approach. Additionally, subjects who initially
respond to a tyrosine kinase inhibitor may become refractory to the
drug, indicating that these subjects have developed acquired
resistance to the drug. These subjects can be discontinued on their
current therapy and alternative treatments prescribed.
IX. Examples
[0241] The following examples are offered to illustrate, but not to
limit, the claimed invention.
Example 1
Algorithm for Predicting Response to Gefitinib (Iressa.RTM.)
Therapy
[0242] This example illustrates an algorithm that was developed to
predict response to gefitinib therapy. In particular, a
representative panel of genetic, serological, and biochemical tests
was performed on tumor, plasma, and/or serum samples to calculate a
cumulative index value for a subject diagnosed with a solid tumor
such as non-small cell lung cancer. In this example, representative
index values predictive of gefitinib sensitivity were assigned a
value of 1. However, one skilled in the art will appreciate that
the index values need not be integers. The final individual profile
assessment can be presented as a cumulative index value, which
represents a summation of the representative index values
determined for each biomarker. In certain instances, one or more
biomarkers can have a weighted representative index value.
[0243] As shown in FIG. 1, a subject diagnosed with a cancer such
as non-small cell lung cancer is first genotyped at a polymorphic
site in EGFR to determine the presence or absence of an EGFR
activating mutation (100). The presence of an EGFR activating
mutation indicates that administration of gefitinib should be
recommended. Subjects who do not have an EGFR activating mutation
are then genotyped at a polymorphic site in K-Ras to determine the
presence or absence of a K-Ras activating mutation (110). The
presence of a K-Ras activating mutation indicates that
administration of another tyrosine kinase inhibitor or an
alternative cancer therapy should be recommended. One skilled in
the art will appreciate that genotyping K-Ras can be performed at
the same time, just prior to, or just after genotyping EGFR.
[0244] Various nucleic acid and/or protein profiles are then
determined for those subjects who do not have EGFR and K-Ras
activating mutations using a panel of biomarkers (120). For
example, a gene copy number profile can be determined by analyzing
EGFR copy number and HER2 copy number; a protein expression profile
can be determined by measuring EGFR expression, TGF-.alpha.
expression, and PTEN expression; and a protein activation profile
can be determined by assessing Erk (MAPK) activation and Akt
activation. A cumulative index value (CIV) based upon the sum of
the representative index values for each of these biomarkers can be
calculated according to the following formula (130):
CIV=(2.times.EGFR copy number)+(2.times.EGFR expression)+Erk (MAPK)
activation+Akt activation+TGF-.alpha. expression+HER2 copy
number+PTEN expression,
[0245] wherein
TABLE-US-00001 Representative Index Values Marker Sample Assay 0 1
EGFR copy number Tumor FISH No or low genomic gain High polysomy or
gene amplification Tumor PCR .ltoreq.4-fold amplification
>4-fold amplification EGFR expression Tumor IHC Negative to weak
staining Moderate to strong staining Serum/ ELISA .ltoreq.850 ng/ml
>850 ng/ml Plasma Erk (MAPK) activation Tumor IHC Negative to
weak staining Moderate to strong staining Akt activation Tumor IHC
Nuclear staining absent Nuclear staining present TGF-.alpha.
expression Tumor IHC Moderate to strong staining Negative to weak
staining Serum/ ELISA >15 pg/ml .ltoreq.15 pg/ml Plasma HER2
copy number Tumor FISH No or low genomic gain High polysomy or gene
amplification PTEN expression Tumor IHC Negative to weak staining
Moderate to strong staining
[0246] Here, a cumulative index value greater than or equal to an
index cut-off value of 4 is predictive of gefitinib sensitivity or
an increased likelihood of responding to gefitinib (140).
Administration of gefitinib should be recommended. However, a
cumulative index value less than an index cut-off value of 4 is
predictive of gefitinib insensitivity or a decreased likelihood of
responding to gefitinib. Administration of another tyrosine kinase
inhibitor or an alternative cancer therapy should be
recommended.
Example 2
Algorithm for Predicting Response to Sunitinib (Sutent.RTM.)
Therapy
[0247] This example illustrates an algorithm that was developed to
predict response to sunitinib therapy. In particular, a
representative panel of genetic, serological, and biochemical tests
was performed on tumor, plasma, and/or serum samples to calculate a
cumulative index value for a subject diagnosed with a solid tumor
such as a gastrointestinal stromal tumor or renal cell carcinoma.
In this example, representative index values predictive of
sunitinib sensitivity were assigned a value of 1. However, one
skilled in the art will appreciate that the index values need not
be integers. The final individual profile assessment can be
presented as a cumulative index value, which represents a summation
of the representative index values determined for each biomarker.
In certain instances, one or more biomarkers can have a weighted
representative index value.
[0248] As shown in FIG. 2, a subject diagnosed with a cancer such
as a gastrointestinal stromal tumor or renal cell carcinoma is
first genotyped at a polymorphic site in c-KIT, PDGFR, and/or VEGFR
to determine the presence or absence of activating mutations in
these genes (200). In addition, VEGFR and/or PDGFR expression is
measured in a serum or plasma sample and c-KIT expression is
determined by immunohistochemistry (IHC). The presence of a c-KIT,
PDGFR, and/or VEGFR activating mutation, in combination with VEGFR
and/or PDGFR overexpression and c-KIT overexpression, indicates
that administration of sunitinib should be recommended. Subjects
negative for these biomarkers are then genotyped at a polymorphic
site in K-Ras to determine the presence or absence of a K-Ras
activating mutation (210). The presence of a K-Ras activating
mutation indicates that administration of another tyrosine kinase
inhibitor or an alternative cancer therapy should be recommended.
One skilled in the art will appreciate that genotyping K-Ras can be
performed at the same time, just prior to, or just after genotyping
c-KIT, PDGFR, and/or VEGFR.
[0249] Various nucleic acid and/or protein profiles are then
determined for those subjects who are negative for c-KIT, PDGFR,
VEGFR, and K-Ras activating mutations and do not overexpress VEGFR,
PDGFR, and c-KIT using a panel of biomarkers (220). For example, a
protein expression profile can be determined by measuring PTEN
expression; and a protein activation profile can be determined by
assessing Erk (MAPK) activation and Akt activation. A cumulative
index value (CIV) based upon the sum of the representative index
values for each of these biomarkers can be calculated according to
the following formula (230):
CIV=Erk (MAPK) activation+Akt activation+PTEN expression,
wherein
TABLE-US-00002 Representative Index Values Marker Sample Assay 0 1
Erk (MAPK) Tumor IHC Negative to weak Moderate to strong activation
staining staining Akt Tumor IHC Nuclear staining Nuclear staining
activation absent present PTEN Tumor IHC Negative to weak Moderate
to strong expression staining staining
[0250] Here, a cumulative index value greater than or equal to an
index cut-off value of 2 is predictive of sunitinib sensitivity or
an increased likelihood of responding to sunitinib (240).
Administration of sunitinib should be recommended. However, a
cumulative index value less than an index cut-off value of 2 is
predictive of sunitinib insensitivity or a decreased likelihood of
responding to sunitinib. Administration of another tyrosine kinase
inhibitor or an alternative cancer therapy should be
recommended.
Example 3
Biomarker Analysis in Fractionated Whole Blood
[0251] This example illustrates the use of fractionated whole blood
for determining a spectrum of profiles including a genotypic
profile, gene copy number profile, gene expression profile, DNA
methylation profile, protein expression profile, protein activation
profile, and combinations thereof. Whole blood which has been
separated into its liquid and cellular components can also be used
for determining the localization of proteinaceous biomarkers of
interest, the morphology of cells of interest, and the number of
circulating tumor and/or endothelial cells in a subject diagnosed
with a solid tumor.
[0252] Circulating tumor and/or endothelial cells can act as a
surrogate for biomarker analysis of the primary or metastatic
tumor. In addition to releasing intact viable cells into the
circulation, tumors also release freely circulating DNA, RNA, and
shed proteins at levels that can be analyzed with current
technologies. By segregating a whole blood sample into its fluid
and cellular components, an entire spectrum of biomarkers can be
analyzed using a single sample. As a result, all of the biomarkers
in Examples 1 and 2 that are typically analyzed in tumor tissue can
alternatively be analyzed in a fractional component of whole blood.
FIG. 3 shows a flow diagram illustrating the analyses applicable
for each whole blood fraction.
[0253] Whole blood is typically fractionated into a plasma or serum
component and a cellular component using an art-recognized method
such as centrifugation. As a non-limiting example, whole blood can
be collected according to standard procedures in tubes containing
an anticoagulant such as EDTA and fractionated by centrifuging at
about 1500-2000.times.g for about 10-15 min at room temperature.
This protocol is useful for separating whole blood into an upper
plasma layer (i.e., plasma fraction) and a lower cellular layer
(i.e., cell pellet fraction).
[0254] As shown in FIG. 3, DNA, RNA, and proteins secreted by tumor
cells can be analyzed in the plasma fraction of whole blood. For
example, DNA can be isolated from the plasma fraction using any
method known in the art and a mutational analysis performed to
determine the genotype of genes such as tyrosine kinase genes
and/or a small GTPase genes. The level of DNA methylation in
genomic regulatory sequences can also be detected in isolated DNA
using any of the techniques described above. In addition, RNA can
be isolated from the plasma fraction using any method known in the
art and a gene expression analysis can be performed to determine
the level of expression of cancer and/or tissue-specific genes
using any of the above-described techniques. Moreover, the
expression level of one or more proteinaceous biomarkers such as
tyrosine kinases, growth factors, and/or tumor suppressors can be
determined in a plasma fraction using immunoassays or other
art-recognized techniques as described above.
[0255] FIG. 3 also shows that circulating tumor cells (CTCs) and
circulating endothelial cells (CECs) can be analyzed in the cell
pellet fraction of whole blood. Since CTCs and CECs are relatively
rare, they can first be enriched using an immunomagnetic assay
available from, e.g., Immunicon Corp. (Huntingdon Valley, Pa.), or
any other magnetic-activated cell separation technique known in the
art. A negative selection can also be performed to remove red blood
cells and white blood cells from the cell pellet fraction. The
enriched CTCs and CECs can be analyzed using any of a variety of
microscopic techniques including, for example, in situ
hybridization, immunohistochemistry, and immunofluorescence, to
determine cell surface protein expression and/or localization, cell
morphology, and CTC/CEC number. Proximity-based assays such as
scintillation proximity assays (see, e.g., McDonald et al., Anal,
Biochem., 268:318-329 (1999), fluorescence polarization assays
(see, e.g., Scott et al., Anal. Biochem., 316:82-91 (2003)), and
luminescent proximity assays (see, e.g., U.S. Patent Publication
No. 20060063219), as well as any of the techniques described above,
can be used to determine the phosphorylation state of at least one
tyrosine kinase signaling component in CTCs and CECs.
Example 4
Prediction of Gefitinib-Sensitive Cell Lines Using Artificial
Intelligence
[0256] This example illustrates that the use of learning
statistical classifier systems to combine the information from
disparate sample sets results in greater diagnostic power than each
set provides alone.
Samples
[0257] A nucleic acid and/or protein profile of one or more
biomarkers in a set of cancer cell lines was obtained to generate a
data file of input values for use in an algorithm to predict
whether a particular cell line would be responsive to treatment
with gefitinib (Iressa.RTM.). As a non-limiting example, the level
of EGFR family kinase expression (i.e., HER1-4) and Akt
phosphorylation in the breast cancer cell lines described by
Moasser et al. (Cancer Res., 61:7184-7188 (2001)) was used to
generate a data file of input values for statistical analysis
(Table 1). The breast cancer cell lines were BT474, MDA-MB-361,
ZR75, T47D, MDA-MIB-231, SkBr3, MDA-MB-453, A431, MDA-MB-468, A549,
PC3, SkOv3, DU145, MCF-7, Colo205, T24, and DU4475. One skilled in
the art will appreciate that the nucleic acid and/or protein
profile of biomarkers of interest can be obtained either
prospectively or retrospectively from one or more patient sample
sets and used in the algorithms described herein for predicting
whether a particular type of tumor would be responsive to gefitinib
therapy.
TABLE-US-00003 TABLE 1 Data file of input values used in
algorithmic analysis of breast cancer cell lines. Cell Line HER1
HER2 HER3 HER4 IC.sub.50 CATAG Akt SUB1 SUB2 BT474 1 5000 150 10
0.8 1 10 Train Train MDA-MB-361 1 1000 100 2 8 1 20 Train Test ZR75
0 100 1 3 16 0 85 Train Train T47D 1 50 200 1000 12 0 85 Train Test
MDA-MIB-231 100 30 0 1 15 0 70 Train Train SkBr3 50 5000 50 3 1 1 2
Train Test MDA-MB-453 0.5 1000 200 5 7 1 40 Train Test A431 2000
120 100 0 1 1 2 Test Test MDA-MB-468 2000 0 100 0 13.5 0 90 Train
Train A549 75 90 2 0 12 0 85 Test Train PC3 75 30 0 0 14 0 55 Test
Train SkOv3 200 2000 1 20 2.5 1 40 Test Train DU145 80 40 1 0 7 1
35 Test Test MCF-7 1 40 200 200 15 0 70 Train Train Co1o205 1 200 5
5 12 0 105 Test Test T24 50 90 0 0 18 0 90 Test Train DU4475 0.5 0
1 1 10 0 105 Test Test The densities of the HER1, HER2, HER3, and
HER4 bands in the Western blot from Figure 1 of Moasser et al. were
estimated by naked eye observation and given a relative value
ranging from 0 to 5000. The level of Akt activity for each cell
line was determined using the data from FIG. 3 of Moasser et al. at
a gefitinib concentration of 10 .mu.M. An IC.sub.50 < 9 .mu.M
was considered gefitinib-sensitive ("CATAG" = 1).
Statistical Analyses
[0258] In this study, two different learning statistical
classifiers were used (e.g., random forests and artificial neural
networks) to predict sensitivity of the cell lines to gefitinib.
These learning statistical classifiers use multivariate statistical
methods like, for example, multilayer perceptrons with feed-forward
backpropagation that can adapt to complex data and make decisions
based strictly on the data presented, without the constraints of
regular statistical classifiers.
Random Forests
[0259] Each breast cancer cell line sample was randomly selected
for random forest (RF) prediction. Out-of-the-bag data was used for
testing. Multiple RF models using commercially available software
(Salford Systems; San Diego, Calif.) were created and analyzed for
accuracy of prediction using the test cohort. The best predictive
RF models were selected and tested for accuracy of prediction using
data from the validation cohort. The success of the RF prediction
is shown in Table 2. Table 3 shows a ranking of the importance of
the variables.
TABLE-US-00004 TABLE 2 Random forest prediction success. Sensitive
Insensitive Actual Cell Line Total Cases Percent Correct N = 7 N =
10 Sensitive 7 100 7 0 Insensitive 10 100 0 10
TABLE-US-00005 TABLE 3 Random forest variable importance. Variable
Score Akt 100.00 |||||||||||||||||||||||||||||||||||||||||| HER2
30.03 |||||||||||| HER3 3.58 | HER1 0.00 HER4 0.00
Artificial Neural Networks
[0260] Each breast cancer cell line sample was randomly selected
for neural network prediction, with a total of 9 for training and 8
for validation. Different samples were used for training, testing,
and for validation purposes. The Intelligent Problem Solver module
of the neural networks software package (Statistica; StatSoft,
Inc.; Tulsa, Okla.) was used to create artificial neural network
(ANN) models in a feed-forward, backpropagation, and classification
mode with the training cohort. Linear, multi-layer perceptron
(MLP), and probabilistic neural networks (PNN) models are shown in
Table 4. The best models were selected based on the lowest error of
prediction on the test dataset.
TABLE-US-00006 TABLE 4 Neural network prediction accuracy. Model
Subset Sensitive* Insensitive* Linear 1 3/3 5/5 MLP 1 3/3 5/5 PNN 2
4/5 3/3 Linear 3 4/4 4/4 MLP 4 2/2 6/6 *= Predicted
numbers/observed numbers.
[0261] All publications and patent applications cited in this
specification are herein incorporated by reference as if each
individual publication or patent application were specifically and
individually indicated to be incorporated by reference. Although
the foregoing invention has been described in some detail by way of
illustration and example for purposes of clarity of understanding,
it will be readily apparent to those of ordinary skill in the art
in light of the teachings of this invention that certain changes
and modifications may be made thereto without departing from the
spirit or scope of the appended claims.
* * * * *
References