U.S. patent application number 14/773488 was filed with the patent office on 2016-01-21 for methods and materials for identifying and treating mammals having lung adenocarcinoma characterized by neuroendocrine differentiation.
The applicant listed for this patent is MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH. Invention is credited to Marie-Christine Aubry, Cristiane M. Ida, Farhad Kosari, George Vasmatzis.
Application Number | 20160018399 14/773488 |
Document ID | / |
Family ID | 51492020 |
Filed Date | 2016-01-21 |
United States Patent
Application |
20160018399 |
Kind Code |
A1 |
Kosari; Farhad ; et
al. |
January 21, 2016 |
METHODS AND MATERIALS FOR IDENTIFYING AND TREATING MAMMALS HAVING
LUNG ADENOCARCINOMA CHARACTERIZED BY NEUROENDOCRINE
DIFFERENTIATION
Abstract
This document provides methods and materials involved in
identifying mammals having lung adenocarcinoma characterized by
neuroendocrine differentiation as well as methods and materials
involved in treating mammals having lung adenocarcinoma
characterized by neuroendocrine differentiation. For example,
methods and materials for using ASCL1 and RET expression levels to
identify lung cancer patients having lung adenocarcinoma
characterized by neuroendocrine differentiation are provided.
Inventors: |
Kosari; Farhad; (Ellsworth,
WI) ; Vasmatzis; George; (Oronoco, MN) ;
Aubry; Marie-Christine; (Pine Island, MN) ; Ida;
Cristiane M.; (Rochester, MN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH |
Rochester, |
MN |
US |
|
|
Family ID: |
51492020 |
Appl. No.: |
14/773488 |
Filed: |
March 7, 2014 |
PCT Filed: |
March 7, 2014 |
PCT NO: |
PCT/US14/22037 |
371 Date: |
September 8, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61775316 |
Mar 8, 2013 |
|
|
|
Current U.S.
Class: |
424/94.64 ;
514/252.19; 514/254.06; 514/266.22; 514/346; 514/367; 514/414;
514/53 |
Current CPC
Class: |
A61K 31/225 20130101;
A61K 31/26 20130101; A61K 31/4709 20130101; A61K 31/4439 20130101;
A61K 31/428 20130101; A61K 31/198 20130101; C12Y 304/21 20130101;
C12Q 2600/158 20130101; A61K 31/13 20130101; A61K 31/404 20130101;
G01N 33/57423 20130101; A61K 38/482 20130101; A61K 31/517 20130101;
A61K 31/44 20130101; G01N 2333/4703 20130101; A61K 31/506 20130101;
A61K 31/5025 20130101; A61K 31/7135 20130101; C12Q 1/6886 20130101;
C12Y 304/21068 20130101; A61K 31/47 20130101; C12Y 304/21007
20130101; G01N 2333/71 20130101 |
International
Class: |
G01N 33/574 20060101
G01N033/574; A61K 31/404 20060101 A61K031/404; A61K 31/44 20060101
A61K031/44; A61K 31/7135 20060101 A61K031/7135; A61K 31/517
20060101 A61K031/517; A61K 31/428 20060101 A61K031/428; A61K 38/48
20060101 A61K038/48; A61K 31/506 20060101 A61K031/506; A61K 31/47
20060101 A61K031/47; A61K 31/4709 20060101 A61K031/4709; A61K
31/5025 20060101 A61K031/5025; A61K 31/198 20060101 A61K031/198;
A61K 31/26 20060101 A61K031/26; A61K 31/4439 20060101 A61K031/4439;
A61K 31/13 20060101 A61K031/13; A61K 31/225 20060101 A61K031/225;
C12Q 1/68 20060101 C12Q001/68 |
Claims
1-12. (canceled)
13. A method for treating lung cancer, wherein said method
comprises: (a) detecting the presence of an elevated level of ASCL1
expression and an elevated level of RET expression in lung cancer
cells from a mammal, and (b) administering a molecule to said
mammal under conditions wherein the number of lung cancer cells
within said mammal is reduced, wherein said molecule is selected
from the group consisting of sunitinib, vandetanib, riluzole,
alteplase, anistreplase, tenecteplase, sucralfate, dasatinib,
pazopanib, tivozanib, OSI-930, telatinib, tandutinib, imatinib,
sorafenib, levodopa, carbidopa, entacapone orion, L-dopa, ABT-089,
mecamylamine, and succinylcholine.
14. The method of claim 13, wherein said lung cancer is lung
adenocarcinoma characterized by neuroendocrine differentiation.
15. The method of claim 13, wherein said mammal is a human.
16. The method of claim 13, wherein said molecule is sunitinib.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application Ser. No. 61/775,316, filed Mar. 8, 2013. The disclosure
of the prior application is considered part of (and is incorporated
by reference in) the disclosure of this application.
BACKGROUND
[0002] 1. Technical Field
[0003] This document relates to methods and materials involved in
identifying mammals having lung adenocarcinoma characterized by
neuroendocrine differentiation as well as methods and materials
involved in treating mammals having lung adenocarcinoma
characterized by neuroendocrine differentiation. For example, this
document provides methods and materials for using achaete-scute
homolog 1 (ASCL1) and RET expression levels to identify lung cancer
patients having lung adenocarcinoma characterized by neuroendocrine
differentiation.
[0004] 2. Background Information
[0005] The clinical significance of neuroendocrine (NE)
differentiation in lung adenocarcinoma, and the most appropriate
biomarkers for this assessment, has been long debated. In the
absence of a gold standard, investigators have most commonly used
immunohistochemistry (IHC) of one or a combination of
neuroendocrine markers, such as chromogranin (CHGA), synaptophysin
(SYP), neuron-specific enolase (NSE), or neural cell adhesion
molecule (CD56/NCAM) to assess the role of neuroendocrine
differentiation in lung cancer survival.
SUMMARY
[0006] This document provides methods and materials involved in
identifying mammals having lung adenocarcinoma characterized by
neuroendocrine differentiation as well as methods and materials
involved in treating mammals having lung adenocarcinoma
characterized by neuroendocrine differentiation. For example, this
document provides methods and materials for using ASCL1 and RET
expression levels to identify lung cancer patients having lung
adenocarcinoma characterized by neuroendocrine differentiation. As
described herein, the presence of an elevated level of ASCL1
expression and an elevated level of RET within a lung cancer sample
can indicate that a mammal (e.g., a human) has lung adenocarcinoma
characterized by neuroendocrine differentiation. In some cases, the
absence of an elevated level of ASCL1 expression and an elevated
level of RET within a lung cancer sample can indicate that a mammal
(e.g., a human) does not have lung adenocarcinoma characterized by
neuroendocrine differentiation.
[0007] Having the ability to identify mammals as having lung
adenocarcinoma characterized by neuroendocrine differentiation as
described herein can allow those lung cancer patients to be
properly identified and treated in an effective and reliable
manner. For example, the lung cancer treatments provided herein can
be used to treat lung cancer patients identified as having lung
adenocarcinoma characterized by neuroendocrine differentiation.
[0008] In general, one aspect of this document features a method
for identifying a mammal as having lung adenocarcinoma
characterized by neuroendocrine differentiation. The method
comprises, or consist essentially of, determining whether or not
cancer cells from the mammal contain an elevated level of ASCL1
expression and an elevated level of RET expression, wherein the
presence of the elevated level of ASCL1 expression and the presence
of the elevated level of RET expression indicates that the mammal
has lung adenocarcinoma characterized by neuroendocrine
differentiation, and wherein the absence of the elevated level of
ASCL1 expression and the absence of the elevated level of RET
expression indicates that the mammal does not have lung
adenocarcinoma characterized by neuroendocrine differentiation. The
mammal can be a human. The elevated level can be determined using
PCR. The elevated level can be determined using
immunohistochemistry.
[0009] In another aspect, this document features a method for
identifying a mammal as having lung adenocarcinoma characterized by
neuroendocrine differentiation. The method comprises, or consists
essentially of, (a) determining whether or not a lung cancer cells
from the mammal contain an elevated level of ASCL1 expression and
an elevated level of RET expression, (b) classifying the mammal as
having lung adenocarcinoma characterized by neuroendocrine
differentiation if the sample contains the elevated level of ASCL1
expression and the elevated level of RET expression, and (c)
classifying the mammal as not having lung adenocarcinoma
characterized by neuroendocrine differentiation if the sample lacks
the elevated level of ASCL1 expression and the elevated level of
RET expression. The mammal can be a human. The elevated level can
be determined using PCR. The elevated level can be determined using
immunohistochemistry.
[0010] In another aspect, this document features a method for
identifying a mammal as having lung adenocarcinoma characterized by
neuroendocrine differentiation, wherein the method comprises, or
consists essentially of, (a) detecting the presence of an elevated
level of ASCL1 expression and an elevated level of RET expression
in lung cancer cells from the mammal, and (b) classifying the
mammal as having lung adenocarcinoma characterized by
neuroendocrine differentiation based at least in part on the
presence of the elevated level of ASCL1 expression and the elevated
level of RET expression. The mammal can be a human. The elevated
level can be detecting using PCR. The elevated level can be
detecting using immunohistochemistry.
[0011] In another aspect, this document features a method for
treating lung cancer, wherein the method comprises, or consists
essentially of, (a) detecting the presence of an elevated level of
ASCL1 expression and an elevated level of RET expression in lung
cancer cells from a mammal, and (b) administering a molecule to the
mammal under conditions wherein the number of lung cancer cells
within the mammal is reduced, wherein the molecule is selected from
the group consisting of sunitinib, vandetanib, riluzole, alteplase,
anistreplase, tenecteplase, sucralfate, dasatinib, pazopanib,
tivozanib, OSI-930, telatinib, tandutinib, imatinib, sorafenib,
levodopa, carbidopa, entacapone orion, L-dopa, ABT-089,
mecamylamine, and succinylcholine.
[0012] Unless otherwise defined, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which this invention pertains.
Although methods and materials similar or equivalent to those
described herein can be used to practice the invention, suitable
methods and materials are described below. All publications, patent
applications, patents, and other references mentioned herein are
incorporated by reference in their entirety. In case of conflict,
the present specification, including definitions, will control. In
addition, the materials, methods, and examples are illustrative
only and not intended to be limiting.
[0013] The details of one or more embodiments of the invention are
set forth in the accompanying drawings and the description below.
Other features, objects, and advantages of the invention will be
apparent from the description and drawings, and from the
claims.
DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 is a schematic showing the composition of Datasets 1
and 2. Boxes with dotted shadings in Dataset 1 denote samples
collected by laser capture microdissection (LCM). Numbers in
parenthesis in Dataset 2 are all stages and stage 1 only sample
sizes, respectively. Dataset 2 was used in survival analysis by
RET. All samples in this dataset were collected in bulk.
[0015] FIG. 2 is a graph plotting the threshold selection for ASCL1
positive and negative status. With minor exceptions, signal
intensities at or above 8 produced a positive staining on the IHC.
There was a strong Pearson's correlation (rho=0.89) between the
Labeling index (LI) and microarray signal intensities
(Log.sub.e).
[0016] FIG. 3 contains photographs and a heat graph of an
immunohistochemical analysis. (A-E) AD characterized by ASCL1 mRNA
expression (ASCL1.sup.+ AD): (A) Adenocarcinoma with an acinar
pattern. H&E. 400.times.; (B) ASCL1 protein expression (nuclear
pattern, 70%, 2+). 400.times.; (C) CHGA (cytoplasmic pattern, 5%,
3+). 400.times.; (D) SYP (cytoplasmic pattern, 5%, 2+) 400.times.;
and (E) CD56/NCAM (membranous pattern, 5%, 2+) 400.times.. (F-J)
SCLC: (F) High-grade tumor characterized by extensive areas of
necrosis and cells with high nuclear to cytoplasmic ratio, delicate
nuclear chromatin and inconspicuous nucleoli. H&E 400.times.;
(G) ASCL1 protein expression (nuclear pattern, 95%, 3+) 400.times.;
(H) CHGA (cytoplasmic pattern, 100%, 2+) 400.times.; (I) SYP
(cytoplasmic pattern, 100%, 1+) 400.times.; and (J) CD56/NCAM
(membranous pattern, 100%, 2+) 400.times.. (K-O) LCNEC: (K) Poorly
differentiated tumor with high mitotic activity (>10 mitotic
figures/2 mm.sup.2) and organoid nesting composed by cells with
vesicular nuclei, evident nucleoli and moderate amount of
cytoplasm. H&E, 400.times.; (L) ASCL1 protein expression
(nuclear pattern, 95%, 3+) 400.times.; (M) CHGA (cytoplasmic
pattern, 90%, 3+) 400.times.; (N) SYP (cytoplasmic pattern, 95%,
2+) 400.times.; and (0) CD56/NCAM (membranous pattern, 90%, 3+)
400.times.. (P) Heat map of IHC protein expression of ASCL1, CHGA,
SYP, and CD56/NCAM.
[0017] FIG. 4 is a heat map of the microarray Log.sub.2 expression
values using known NE genes and RET. The map also includes SQCC and
AD specific genes (DSG3 and NKX2.1, respectively). ASCL1 expression
is much more frequent in AD than in SQCC. Each gene is represented
by the most variable probeset with the highest standard
deviation.
[0018] FIG. 5 contains graphs demonstrating that NE differentiation
in never smoker AD is rare. In contrast with typical carcinoid
(CT), no subset of AD has a distinguishably higher expression of
the NE markers than non-neoplastic lung. Similar observations can
be made about SQCC, but the number of samples is small.
[0019] FIG. 6 is a graph of a KM plot of stage I AD in Dataset 2
based on ASCL1 status. The drop off for the ASCL1.sup.+ tumors is
sharper than the ASCL1.sup.- tumors, suggesting nonproportional
hazard.
[0020] FIGS. 7A and 7B are graphs demonstrating the over
representation of tumors expressing RET (211421_s_at probe set) in
ASCL1.sup.+ compared with ASCL1.sup.- in (A) stage I ADs (Dataset
2), and in (B) all lung cancers (Dataset 1). This
over-representation was consistent in all datasets. Few samples
(shown in black circles) expressed RET while ASCL1 was below noise
level (arbitrary Log.sub.2 signal intensity of 3.5). Data points
correspond to the samples from the Mayo Clinic unless stated in the
figure legends.
[0021] FIGS. 8A and 8B are graphs plotting overall survival in
ASCL1.sup.+ stage I (A) and all (B) AD as a function of the RET
mRNA expression level by microarrays.
[0022] FIG. 9 contains photographs and graphs of RET protein
expression by IHC. RET staining in fatal adenocarcinoma was
typically much less intense in ASCL1.sup.- (A) than in ASCL1.sup.+
(B) tumors. (C) Co-IHC of ASCL1 (nuclear brown staining) and RET
(cytoplasmic red staining) identified areas with overlapping
expression of the two proteins. (D) KM plot of 14 ASCL1.sup.+ AD
samples indicate a significant association with OS (p=0.05). (E)
When samples were not stratified by the ASCL1 expression, RET IHC
was not significant in predicting OS (overall survival).
[0023] FIG. 10 is a Kaplan Meir plot of the overall survival (OS)
in ASCL1.sup.+ stage I AD in patients with low (n=38) and high
(n=15) expression levels of RET. A significant association with the
overall survival was identified (p=0.007).
[0024] FIG. 11 is a plot of ASCL1 and RET expression versus
promoter methylation of the ASCL1 promoter in the GSE32867 dataset.
This data indicates that promoter hypo-methylation increases
expression levels of the ASCL1 transcript. Interestingly, high
expression level of ASCL1 is significantly associated with high
level of RET. In the plot, samples with high transcript levels of
RET are indicated by circles around the solid circles. RET and
ASCL1 expression levels are based on the Illumina
ILMN.sub.--1655610 and ILMN.sub.--1701653 probesets, respectively.
ASCL1 promoter methylation is assessed by the cg20053158 probeset.
High RET expression is marked by circles.
[0025] FIG. 12 is a Kaplan Meir plot of the ASCL1.sup.+ AD patients
overall survival based on the levels of the RET protein by IHC.
Samples are from Mayo patients and were used in the discovery step
by microarrays. A significant association between RET IHC and the
overall survival was identified (p=0.038).
[0026] FIG. 13 contains three graphs of an in vitro analysis that
confirms that ASCL1 regulates RET expression.
[0027] FIG. 14 contains photographs of cells from a filling the gap
scratch assay (see, e.g., Liang et al., Nature Protocols, 2:329-333
(2007)). Wild-type HCC1833 cells and HCC1833 cells transfected with
a control vector filled most of the gap by day 3, while HCC1833
cells transfected with an ASCL1 knock down vector (ASCL1-sh2 cells)
did not fill as much of the gap, indicating that wild-type HCC1833
cells and cells transfected with empty vector (VEC) have a higher
migration capacity than cells transfected with ASCL1 shRNA
(ASCL1-sh2).
[0028] FIG. 15 contains a bar graph plotting the clonogenic number
of cells that do not express ASCL1 (A549 cells) and cells that
express ASCL1 (A549-ASCL1 cells) after treatment with the indicated
amount of cisplatin (e.g., 0 .mu.M, 3 .mu.M, or 9 .mu.M) for 14
days. FIG. 15 also contains a line graph plotting the viability of
A549 cells and A549-ASCL1 cells 72 hours after treatment with the
indicated amount of cisplatin. These results demonstrate that
adenocarcinomas that express ASCL1 appear to be more resistant to
treatment by cisplatin.
[0029] FIG. 16 is a line graph plotting the viability of A549 cells
and A549-ASCL1 cells 72 hours after treatment with the indicated
amount of sunitinib (marketed as Sutent by Pfizer, and previously
known as SU11248). The vertical line is drawn at about 3 .mu.M.
These results demonstrate that adenocarcinomas that express
ASCL1/RET are more susceptible to treatment by sunitinib.
DETAILED DESCRIPTION
[0030] This document provides methods and materials related to
identifying mammals having lung adenocarcinoma characterized by
neuroendocrine differentiation. For example, this document provides
methods and materials for identifying mammals (e.g., humans) as
having lung adenocarcinoma characterized by neuroendocrine
differentiation by determining whether or not a lung cancer sample
(e.g., lung tissue biopsy) from the mammal contains cancer cells
having an elevated level of ASCL1 expression and/or an elevated
level of RET expression. As described herein, if a mammal contains
lung cancer cells with an elevated level of ASCL1 expression and/or
an elevated level of RET expression, then that mammal can be
classified as having lung adenocarcinoma characterized by
neuroendocrine differentiation. If a mammal contains a lung cancer
cells that lack an elevated level of ASCL1 expression and lack an
elevated level of RET expression, then that mammal can be
classified as not having lung adenocarcinoma characterized by
neuroendocrine differentiation.
[0031] The term "elevated level" as used herein with respect to a
level of expression (e.g., ASCL1 and/or RET expression) refers to
any level that is greater than a reference level for that molecule
(e.g., a reference level of ASCL1 and/or RET expression). The term
"reference level" as used herein with respect to a particular
molecule (e.g., a reference level of ASCL1 and/or RET expression)
refers to the level of expression that is typically observed with
normal healthy lung cells or lung adenocarcinoma characterized by a
lack of neuroendocrine differentiation from mammals (e.g., humans).
For example, a reference level of ASCL1 expression can be the
average level of ASCL1 expression that is present in lung cells
obtained from a random sampling of 50 humans free of lung cancer.
In some cases, an elevated level of expression (e.g., ASCL1 and/or
RET expression) can be a level that is at least 10, 25, or 50
percent greater than a reference level for that molecule (e.g., a
reference level of ASCL1 and/or RET expression). In some cases, an
elevated level of ASCL1 expression or RET expression can be a
detectable level (e.g., an expression level detectable by
immunocytochemistry). It will be appreciated that levels from
comparable samples are used when determining whether or not a
particular level is an elevated level.
[0032] As described herein, the level of ASCL1 and/or RET
expression within lung cancer cells can be used to determine
whether or not a particular mammal has lung adenocarcinoma
characterized by neuroendocrine differentiation. Any appropriate
lung cancer sample can be used as described herein to identify
mammals having lung adenocarcinoma characterized by neuroendocrine
differentiation. For example, lung cancer tissue samples, lung
cancer cell samples, and lung cancer needle biopsy specimen can be
used to determine whether or not a mammal has lung adenocarcinoma
characterized by neuroendocrine differentiation.
[0033] In addition, any appropriate method can be used to obtain
lung cancer cells. For example, a lung cancer sample can be
obtained by a tissue biopsy or following a surgical resection. Once
obtained, a sample can be processed prior to measuring a level of
expression. For example, a lung cancer sample can be processed to
extract RNA from the sample. Once obtained, the RNA can be
evaluated to determine the level of an mRNA of interest. In some
embodiments, nucleic acids present within a sample can be amplified
(e.g., linearly amplified) prior to determining the level of
expression (e.g., using array technology). In another example, a
lung cancer sample can be frozen, and sections of the frozen tissue
sample can be prepared on glass slides. The frozen tissue sections
can be stored (e.g., at -80.degree. C.) prior to analysis, or they
can be analyzed immediately (e.g., by immunohistochemistry with an
antibody specific for a particular polypeptide of interest).
[0034] Any appropriate methods can be used to determine the level
of ASCL1 and/or RET expression within lung cancer cells. For
example, quantitative real time PCR, in situ hybridization, or
microarray technology can be used to determine whether or not a
particular sample contains an elevated level of mRNA expression for
a particular nucleic acid or lacks an elevated level of mRNA
expression for a particular nucleic acid. In some cases, the level
of expression can be determined using polypeptide detection methods
such as immunochemistry techniques. For example, antibodies
specific for ASCL1 and/or RET polypeptides can be used to determine
the polypeptide level in a sample. In some cases, polypeptide-based
techniques such as ELISAs and immunocytochemistry techniques can be
used to determine whether or not a particular sample contains an
elevated level of polypeptide expression for a particular nucleic
acid or lacks an elevated level of polypeptide expression for a
particular nucleic acid.
[0035] Examples of a human ASCL1 nucleic acid can have the sequence
set forth in GenBank.RTM. Accession No. NM.sub.--004316 (GI No.
190343011), and a human ASCL1 polypeptide can have the sequence set
forth in GenBank.RTM. Accession No. NP.sub.--004307 (GI No.
55743094). Examples of a human RET nucleic acid can have the
sequence set forth in GenBank.RTM. Accession No. NM.sub.--020630
(GI No. 126273513) or NM.sub.--020975 (GI No. 126273511), and a
human RET polypeptide can have the sequence set forth in
GenBank.RTM. Accession No. NP.sub.--065681 (GI No. 10862701) or
NP.sub.--066124 (GI No. 10862703).
[0036] Once the level of ASCL1 and/or RET expression within lung
cancer cells from a mammal is determined, the level(s) can be
compared to reference level(s) and used to classify the mammal as
having or lacking lung adenocarcinoma characterized by
neuroendocrine differentiation as described herein.
[0037] This document also provides methods and materials to assist
medical or research professionals in identifying a mammal as having
lung adenocarcinoma characterized by neuroendocrine
differentiation. Medical professionals can be, for example,
doctors, nurses, medical laboratory technologists, and pharmacists.
Research professionals can be, for example, principle
investigators, research technicians, postdoctoral trainees, and
graduate students. A professional can be assisted by (a)
determining the level of ASCL1 and/or RET expression within lung
cancer cells, and (b) communicating information about that the
level(s) to that professional.
[0038] Any method can be used to communicate information to another
person (e.g., a professional). For example, information can be
given directly or indirectly to a professional. In addition, any
type of communication can be used to communicate the information.
For example, mail, e-mail, telephone, and face-to-face interactions
can be used. The information also can be communicated to a
professional by making that information electronically available to
the professional. For example, the information can be communicated
to a professional by placing the information on a computer database
such that the professional can access the information. In addition,
the information can be communicated to a hospital, clinic, or
research facility serving as an agent for the professional.
[0039] This document also provides methods and materials for
treating lung adenocarcinoma characterized by neuroendocrine
differentiation. For example, one or more molecules listed in Table
1, 2, or 3 can be administered to a mammal (e.g., a human) having
lung adenocarcinoma characterized by neuroendocrine differentiation
under conditions wherein the presence or progression of the lung
adenocarcinoma characterized by neuroendocrine differentiation is
reduced. For example, a molecule listed in Table 1 such as
tedisamil can be administered to a human having lung adenocarcinoma
characterized by neuroendocrine differentiation such that the
number of lung adenocarcinoma cells within the human is reduced. In
some cases, one or more of molecules listed in Table 2A or 2B can
be administered in combination with one or more of molecules listed
in Table 1 to treat lung adenocarcinoma characterized by
neuroendocrine differentiation. For example, tedisamil can be
administered in combination with riluzole to a human having lung
adenocarcinoma characterized by neuroendocrine differentiation.
TABLE-US-00001 TABLE 1 Molecules for treating lung adenocarcinoma
characterized by neuroendocrine differentiation. Gene Molecule
Dosage Range (mg/kg) ADRA2A Paliperidone 6-12 mg daily oral, 117
monthly if injectable but can vary 39-234 mg FGB sucralfate 1 g
(e.g., 10 mL/2 teaspoonfuls) four times per day TUBB2B Brentixumab
vedotin 1.8 mg/kg administered as an intravenous infusion over 30
minutes every 3 weeks TUBB2B cabazitaxel 20 to 25 mg/m.sup.2
administered as a one-hour intravenous infusion every three weeks
KCNMB4 tedisamil 0.5-4 mg/kg, i.v.
TABLE-US-00002 TABLE 2A Molecules that can be used in combination
with one or more molecules listed in Table 1 for treating lung
adenocarcinoma characterized by neuroendocrine differentiation.
Gene Molecule Dosage Range (mg/kg) RET sunitinib 30 mg/kg RET
vandetanib 60 mg/kg SCN3A riluzole 40-60 mg twice daily FGA
Alteplase 0.9 mg/kg, and a total not exceeding 90 mg FGA
Anistreplase IV 30 units over 2 to 5 min into IV line or vein FGA
Tenecteplase less than 60 kg: 30 mg IV bolus administered over 5
seconds. 60 to less than 70 kg: 35 mg IV bolus administered over 5
seconds 70 to less than 80 kg: 40 mg IV bolus administered over 5
seconds 80 to less than 90 kg: 45 mg IV bolus administered over 5
seconds 90 kg or greater: 50 mg IV bolus administered over 5
seconds) FGA Sucralfate 1 g (10 mL/2 teaspoonfuls) four times per
day KIT Dasatinib 100-180 mg once daily KIT sunitinib 30 mg/kg KIT
pazopanib 400-800 mg orally once daily KIT tivozanib 1-2 mg daily
KIT OSI-930 500 mg twice a day KIT Telatinib 20 mg once daily to
1,500 mg twice daily KIT tandutinib 50 mg to 700 mg twice daily KIT
imatinib 400-800 mg a day KIT sorafenib 400-800 mg a day DDC
Levodopa/Carbidopa/ 200 mg/50 mg/200 mg dose is Entacapone Orion 7
tablets per day (maximum dose a day) DDC carbidopa/levodopa 1
tablet of carbidopa 25 mg/ levodopa 100 mg orally 3 times a day, or
1 tablet of 10 mg carbidopa/100 mg levodopa orally 3 to 4 times a
day. The dose may be increased by 1 tablet orally every 1 to 2 days
to a dose of 8 tablets/day (2 tablets orally 4 times a day) DDC
Carbidopa 70-100 mg a day DDC L-Dopa 100-500 mg CHRNA9 ABT-089 1-50
mg CHRNA9 mecamylamine 2-25 mg CHRNA9 succinylcholine 0.3-2.0
mg/kg
TABLE-US-00003 TABLE 2B Molecules that can be used to reduce or
inhibit the activity of polypeptides encoded by the listed genes.
Gene Molecule KCNMB4 tedisamil RET sunitinib, vandetanib SCN3A
riluzole ADRA2A paliperidone, risperidone, antazoline/naphazoline,
acetaminophen/clemastine/pseudoephedrine, articaine/epinephrine,
bupivacaine/epinephrine, caffeine/ergotamine,
acetaminophen/dexbrompheniramine/pseudoephedrine, dapiprazole,
dexbrompheniramine/pseudoephedrine,
chlorpheniramine/ibuprofen/pseudoephedrine, dipivefrin,
cetirizine/pseudoephedrine, asenapine, epinephrine/prilocaine,
epinephrine/lidocaine, PYM- 50018, V2006, lurasidone, paliperidone
palmitate, fexofenadine/pseudoephedrine,
guaifenesin/phenylpropanolamine, oxymetazoline, prazosin,
phenylpropanolamine, ephedrine, tolazoline, guanfacine, guanabenz,
guanethidine, phenoxybenzamine, dexmedetomidine, UK 14304,
clonidine, dexefaroxan, quinidine, polythiazide/prazosin,
chlorothiazide/methyldopa, chlorthalidone/clonidine, propafenone,
guanadrel, hydrochlorothiazide/methyldopa, metaraminol, tizanidine,
quetiapine, D-pseudoephedrine, apraclonidine, venlafaxine,
phentolamine, labetalol, mephentermine, propylhexedrine, yohimbine,
dihydroergotamine, ergotamine, norepinephrine, alpha- methyl dopa,
epinephrine, dopamine, chlorpheniramine/phenylpropanolamine,
desloratadine/pseudoephedrine, acrivastine/pseudoephedrine,
carbinoxamine/pseudoephedrine,
brompheniramine/codeine/phenylpropanolamine,
pseudoephedrine/triprolidine, codeine/pseudoephedrine/triprolidine,
carbetapentane/chlorpheniramine/ephedrine/phenylephrine,
brompheniramine/dextromethorphan/pseudoephedrine,
chlorpheniramine/hydrocodone/pseudoephedrine,
azatadine/pseudoephedrine, naphazoline,
carbinoxamine/dextromethorphan/pseudoephedrine FGA F2 FGB F2 KIT
dasatinib, sunitinib, pazopanib, tivozanib, OSI-930, telatinib,
tandutinib, imatinib, sorafenib carbidopa/entacapone/levodopa,
carbidopa/levodopa, S(-)- DDC carbidopa, L-dopa TUBB2B brentuximab
vedotin, cabazitaxel CHRNA9 ABT-089, isoflurane, mecamylamine,
succinylcholine, rocuronium, doxacurium, amobarbital, mivacurium,
pipecuronium, rapacuronium, metocurine, atracurium, cisatracurium,
acetylcholine, nicotine, D-tubocurarine, arecoline, enflurane,
pancuronium, vecuronium
[0040] The invention will be further described in the following
examples, which do not limit the scope of the invention described
in the claims.
EXAMPLES
Example 1
Elevated ASCL1 and RET Expression can be Used to Identify Patients
with Lung Adenocarcinoma Characterized by Neuroendocrine
Differentiation
Patient Sample Population
[0041] Using the Mayo Clinic frozen tumor bank, lung specimens
resected from 303 patients between 1997 and 2007 were selected.
Neoadjuvant therapy was not given to any patient included in this
study. Formalin-fixed paraffin-embedded H&E sections from the
corresponding surgical specimens were reviewed, and the diagnoses
were confirmed according to the 2004 World Health Organization
classification of tumors. Bronchioloalveolar carcinoma variant of
lung adenocarcinoma (AD) was excluded; hence, all ADs analyzed were
clearly and predominantly invasive tumors. Never-smokers (NS) were
characterized by less than 100 cigarettes per lifetime. Samples
exclusively from NS (n=130) were analyzed on the Illumina platform,
and samples from former and current smokers (S) patients (n=186)
and NS (n=18) were analyzed on the Affymetrix platform. Table 3
describes the clinicopathologic features of the samples.
TABLE-US-00004 TABLE 3 Clinicopathologic features of samples used.
Samples arrayed on the Samples arrayed Affymetrix on the DASL
platform platform Age at diagnosis (median, range) 69 yrs, 31-93
yrs 67 yrs, 17-91 yrs Sex Male 94 24 Female 92 82 Smoking status
Smoker 166 0 Never-smoker 18 106 Not Available 2 0 Histological
analysis Adenocarcinoma (AD) 132 70 Adeno-squamous 0 6 Squamous
cell carcinoma (SQCC) 24 2 Small cell carcinoma (SCLC) 15 0 Large
cell carcinoma (LCC) 5 0 Typical carcinoid (TC) 10 24 Atypical
carcinoid (AC) 0 4 Non-neoplastic lung tissue (N) 12 118
Immunohistochemical (IHC) Analysis
[0042] IHC procedures for ASCL1, CHGA, SYP, CD56/NCAM, and RET were
as follows. A representative formalin fixed paraffin embedded
(FFPE) block from a subset of gene expression profiled lung tumors
of smokers (S), consisting of adenocarcinoma (AD) (n=83), small
cell lung carcinoma (SCLC) (n=12), large cell carcinoma (LCC)
(n=4), and large cell neuroendocrine carcinoma (LCNEC) (n=2), was
selected. The analysis was limited to S as NE differentiation was
significantly more prevalent in this group of tumors. IHC studies
using antibodies directed against ASCL1/MASH1 (monoclonal, clone
24B72D11.1, 1:50 dilution, BD/Pharmingen, San Diego, Calif.), CHGA
(monoclonal, clone LK2H10, 1:500 dilution, Chemicon/Millipore,
Billerica, Mass.), SYP (monoclonal, clone SY38, 1:40 dilution, ICN,
Irvine, Calif.), and CD56/NCAM (monoclonal clone 123C3, 1:25
dilution, Monosan, Uden, the Netherlands) were performed. The IHC
stains were detected by the Dako Advance polymer-based detection
system (Dako, Carpenteria, Calif., U.S.) using the Dako
Autostainer. For each IHC assay, a positive control and negative
control were performed. Immunostained slides were reviewed and
scored by two pathologists, who were blinded to the corresponding
microarray data. A consensus score was achieved for all cases.
Cases were considered immunoreactive when exhibiting 5% or more
tumor cells showing a nuclear staining pattern for ASCL1, a clear
granular cytoplasmic staining pattern for CHGA and SYP, and a
distinct membranous staining pattern for CD56/NCAM.
[0043] Similarly, twenty nine AD samples (14 ASCL1.sup.+ and 15
ASCL1.sup.-) with microarray expression data were selected for RET
IHC using 1:500 dilutions of Epitomics 3454-1 rabbit monoclonal
antibody. An ASCL1/RET co-IHC was developed by DAB staining for
ASCL1 first (1:100 dilution monoclonal, clone 24B72D11.1,
BD/Pharmingen, San Diego, Calif.) and then Fast Red staining (1:500
dilutions of Epitomics 3454-1 rabbit monoclonal antibody) for
RET.
[0044] Immunoreactivity was semi-quantitatively scored based on a)
the percentage of positive tumor cells (Labeling index, LI),
ranging from 0 to 100%, in increments of 5%; and b) the intensity
of staining, graded as: weak +1, moderate +2, and strong +3. For a
comparative analysis of NE markers (ASCL1, CHGA, SYP, and
CD56/NCAM), the Log.sub.2 of the product of the percentage of
positive tumor cells (Labeling index, LI) multiplied by the
intensity of staining was determined for each IHC NE marker and
used to generate a heat map of the IHC NE markers using `heatmap`
function in the open source package R version 2.12.2 (World Wide
Web at "r-project.org/"). RET IHC frequently had areas with
different intensity of stains. In each case, RET IHC score was
computed as the summation of Log.sub.2 (LI) x intensity for each
stained area.
Preparation of Samples for Expression Profiling on the Affymetrix
Platform
[0045] Lung tumor cells and non-neoplastic cells were collected by
either laser capture microdissection (LCM=86) or macrodissection
(M=112) to assure high tumor content (>80%) as described
elsewhere (Klee et al., BMC Med. Genomics, 2:13 (2009)). Total RNA
from samples collected by LCM was isolated using the Micropure kit
(Qiagen Corp, Valencia, Calif.) as described elsewhere
(Savci-Heijink et al., Am. J. Pathol., 174(5):1629-37 (2009)).
Briefly, RNA quality and quantity were controlled by the Agilent
bioanalyzer and the Ribogreen assay or by a quantitative PCR assay
based on the ratio of concentration of 3' to middle transcript of
.beta.-actin. Total RNA (10 ng) from these LCM-collected samples
were labeled in a two round linear amplification/labeling process
according to the Small Sample Preparation protocol (Affymetrix
Corp, Santa Clara, Calif.). Affymetrix arrays were scanned
according to the manufacturer's protocol. Total RNA from samples
obtained by macrodissection was isolated using the RNeasy kit
(Qiagen). The quality and quantity of RNA samples were controlled
by the Agilent bioanalyzer and a NanoDrop spectrophotometer. Total
RNA (1.2 .mu.g) was labeled according to the standard Affymetrix
protocol. Labeled cRNA was hybridized to U133PLUS2 chipset.
Preparation of Samples for Expression Profiling on the Illumina
Platform
[0046] RNA from macrodissected samples were purified by the RNeasy
kit (Qiagen) and analyzed by the Agilent bioanalyzer and a NanoDrop
spectrophotometer. For the WG-DASL assay (Illumina, San Diego,
Calif.), total RNA (100 ng) was reverse transcribed with
biotinylated primers. The resulting cDNA was annealed to chimeric
query oligonucleotides, which contained a gene-specific region and
a universal primer sequence for PCR amplification, and then bound
to streptavidin-conjugated paramagnetic particles. The
gene-specific oligonucleotides were extended by second-strand cDNA
synthesis and then ligated. Subsequently, the products were
sequestered by magnetic separation, washed to remove unbound
molecules, and then amplified by PCR with fluorophore-labeled
universal primers. The resulting PCR products were purified,
applied to HumanRef-8 v3 beadchips, and then hybridized for 16
hours at 58.degree. C. The beadchips were washed and scanned in a
BeadArray Reader using BeadScan v3 software (Illumina).
Microarray Data Analysis
[0047] Normalized expression values from WG-DASL experiments were
generated by the Bead Studio software (Illumina). Affymetrix
intensity files (.CEL files) were processed and normalized by the
`gcrma` package in R. All subsequent analyses of DASL and
Affymetrix data were carried out in R. Other than data generated at
Mayo, expression analysis included various publically available
Affymetrix datasets. Two major datasets which were a compendium of
smaller datasets and frequently used in this study were named
Dataset 1 and Dataset 2. Compositions of these two sets are shown
in FIG. 1. AD and LCC samples with high expression of either DSG3
or KRT5 (squamous differentiation markers) were excluded.
Similarly, squamous cell carcinoma (SQCC) samples with low
expression of DSG3 and KRT5 were excluded. Pearson correlation
coefficients between various NE markers were calculated by
`contest,` and a heatmap of all samples in Dataset 1 was generated
by the `heatmap` function.
Differentially expressed transcripts between ASCL1.sup.+ and
ASCL1.sup.- tumors
[0048] Dataset 2 (FIG. 1) was used to examine the expression
differences between ASCL1.sup.+ and ASCL1.sup.- tumors in stage I
AD. All files had follow up information. Array files (n=593) with
more than 22,000 common Affymetrix probesets were included in this
dataset. The microarray signal intensity (.CEL) files were
normalized and processed by the "gcrma" package in R. Threshold for
ASCL1 status (+ or -) was chosen as before using 209988_s_at
probeset at Log.sub.2 intensity of 8. To identify most
differentially expressed genes in ASCL1.sup.+ versus ASCL1.sup.-
tumors, probesets were ranked by signal to noise ratio calculated
as
SNR=(.mu..sub.ASCL1+-.mu..sub.ASCL1-)/(.sigma..sub.ASCL1++.sigma..sub.ASC-
L1-) where .mu.'s were mean expression values and a's were maximum
of 0.2.times..mu. and standard deviation (Golub et al., Science,
286(5439):531-7 (1999)). SNR values greater than and less than zero
potentially indicate over and under expression in ASCL1.sup.+
compared with ASCL1.sup.- tumors, respectively. It was also
required in this example that the average expression in samples
over-expressing a gene had greater than 3.5 Log.sub.2 intensities.
Log.sub.2 expression intensities for the gcrma normalized data
ranged from 2 to 15. Based on experience with quantitative RT-PCR,
gene expression intensities below 3.5 were not reliable and
frequently not detected. Significant figures for over-expression in
ASCL1.sup.+ compared with ASCL1.sup.- tumors were calculated by
t-test and then corrected for multiple comparison correction using
the `qvalue` package in R (Storey et al., Proc. Natl. Acad. Sci.
USA, 100(16):9440-5 (2003)).
Survival Analysis
[0049] Given that only 15-20% of AD expresses ASCL1, any one
dataset by itself did not provide sufficient samples for
statistical analysis. Therefore, the Mayo dataset (n=132) was
combined with four other lung AD microarray datasets that had
follow up information available. These included the Director's
Challenge dataset (Shedden et al., Nat. Med., 14(8):822-7 (2008))
(n=420), Bhattarcharjee dataset (Bhattacharjee et al., Proc. Natl.
Acad. Sci. USA, 98(24):13790-5 (2001)) (n=139), Kune dataset (GEO
dataset GSE10245, n=40), and Hou dataset (GEO dataset GSE19188,
n=45). With the exception of Bhattarcharjee dataset, all other
array files had common probesets for ASCL1, and the most variable
probeset in all sets (209988_s_at) was chosen to determine the
expression levels of ASCL1. Based on the IHC data, expression
levels above signal intensity 8 (Log.sub.2) were chosen as the
threshold for ASCL1.sup.+ and ASCL1.sup.- status (FIG. 2). The
range of microarray Log.sub.2 signal intensities for this probeset
was 2-15. Therefore, signal intensity of 8 or higher corresponded
to a moderate to high expression level in the 85th percentile and
higher. The ASCL1 status in the Bhattarcharjee dataset was
determined by inspecting ASCL1 expression histograms and selecting
thresholds breakpoints at the 85 percentile. Survival analyses used
the "survival" package in R (http at "//cran.at.r-project.org") and
included time to progression and overall survival stratified by
stage. In the combined dataset, differences in survival times
between ASCL1.sup.- and ASCL1.sup.+ tumors for stage I patients who
died was assessed by a group t-test. A subset of 11 AD in the
Director's Challenge data with high expression of CHGA, SCG2, CHGB,
NCAM1 (CD56), or SYP were identified as large cell NE carcinomas
(LCNEC) after histologic review (Bryant et al., PLoS One,
5(7):e11712 (2010)). In light of this finding, the cases of AD in
this study were re-reviewed, and the diagnosis confirmed by
morphology. Furthermore, in the Director's Challenge data, 19 cases
were identified with high expression of at least one of these NE
markers which could have represented LCNEC and thus NSCLC with
poorer prognosis. A group t-test was repeated after excluding these
samples, but did not alter the overall results and conclusions. In
the analysis of overall survival, the proportional hazard
assumption for stage I tumors was tested by the "cox.zph" routine
in the "survival" package. A small p-value indicated
non-proportional hazard. Proportional hazard assumption was tested
after censoring follow up times at five and more years.
Associations Between RET Expression and Survival in ASC1.sup.+
Tumors
[0050] By Cox proportional hazards regression analysis in R
(coxph), two probesets corresponding to the RET oncogene
(215771_x_at, 205879_x_at) had significant associations with
overall survival in stage I AD after the follow up data at 5 years
was censored. To visualize this association by a Kaplan Meir (KM)
plot, varying the threshold for "low" and "high" expression levels
of RET (215771_x_at) was examined. Values in 3.0 to 6.5 were
significant with p values ranging 0.0005 to 0.029. Excluding AD
samples where an alternative diagnosis of LCNEC was possible did
not appreciably change these results (Bryant et al., PLoS One,
5(7):e11712 (2010)). The reported KM plot used a threshold of 3.5,
as signal intensities below this threshold are usually not detected
by RT-PCR. If the data was not censored at 5 years, p-values ranged
from 0.00053 to 0.037 as the threshold changed from 3.0 to 6.5.
Same probeset and threshold was used in the KM plot of all AD
stages. Also, a KM plot for RET stains was generated by using the
mean of all RET IHC scores as the threshold for selecting "low" and
"high" levels.
Gene Set Analysis
[0051] To find gene sets enriched in ASCL1.sup.+ tumors compared
with ASCL1.sup.- tumors, probesets (13166) with SNR greater or less
than zero were used in the GSA package in R and using Molecular
Signatures Database (MSigDB) version 3.0. The analysis used 500
permutations and an FDR default value of 0.05. For robustness, 20
iterations were performed, and gene sets identified in at least 16
iterations (80%) were reported. To find gene sets associated with
aggressive behavior in ASCL1.sup.+ tumors, these tumors were
divided into aggressive and non-aggressive groups. Aggressive
tumors were from patients who died in less than 3.5 years after
surgery (n=21) and non-aggressive tumors were from patients who
survived 6 or more years after surgery (n=20). Probesets (13126)
with SNR greater or less than zero in comparisons of aggressive
versus non-aggressive tumors were used in the GSA program with the
same selection criteria as above.
Comparative IHC Analysis of NE Markers (ASCL1, CHGA, SYP and
CD56/NCAM)
[0052] Immunostaining quality of ASCL1, CHGA, SYP, and CD56/NCAM
was comparable, and all slides were interpretable. Scattered
immunoreactive bronchiolar basal-located NE cells were considered
as positive internal controls for the IHC reaction. Labeling
indices (LIs) and immunoreactivity for ASCL1, CHGA, SYP, and
CD56/NCAM for AD, SCLC, LCNEC and LCC are shown in Table 4.
TABLE-US-00005 TABLE 4 Detailed results of the immunohistochemical
study for ASCL1 and other NE markers in all lung cancer subtypes.
SUBTYPE AD SCLC LCNEC LCC No. of 83 12 2 4 patients ASCL1 LI mean
+/- SD 54.4 +/- 29 84.6 +/- 25.6 95 +/- 0 0 Range 5-95 5-95 95 0
Immunoreactive 15/83 (18%) 12/12 (100%) 2/2 (100%) 0/4 (0%) cases
CHGA LI mean +/- SD 42.5 +/- 40.2 74.6 +/- 31.4 55 +/- 49.5 0 Range
5-85 50-100 20-90 0 Immunoreactive 4/83 (5%) 11/12 (92%) 2/2 (100%)
0/4 (0%) cases SYP LI mean +/- SD 31.3 +/- 32.8 90.4 +/- 20.5 95
+/- 0 0 Range 5-100 20-100 95 0 Immunoreactive 20/83 (24%) 12/12
(100%) 2/2 (100%) 0/4 (0%) cases CD56/ LI mean +/- SD 26 +/- 29
92.9 +/- 13.9 92.5 +/- 3.5 0 NCAM Range 5-75 50-100 90-95 0
Immunoreactive 5/83 (6%) 12/12 (100%) 2/2 (100%) 0/4 (0%) cases
[0053] The pattern of ASCL1 immunoreactivity varied according to
tumor histological subtype. In AD showing ASCL1 immunoreactivity
(ASCL1+AD), ASCL1.sup.+ cells were focal and admixed with
ASCL1.sup.- cells (FIGS. 3A and 3B), resulting in low to moderate
LIs (Table 4). In SCLC (FIGS. 3F and 3G) and LCNEC examples (FIGS.
3K and 3L), ASCL1 immunostaining was diffuse, resulting in moderate
to high LIs (Table 4). In addition, ASCL1 immunoreactivity in AD
was more frequent than for CHGA (FIG. 3C) and CD56/NCAM (FIG. 3D);
however, SYP (FIG. 3E) was the most common IHC NE marker expressed
in this group as shown in Table 5 and illustrated in FIG. 3P. One
ASCL1.sup.+ AD was not immunoreactive for any of the other IHC NE
markers, whereas six ADs exhibiting reactivity for at least one of
the other NE markers were ASCL1-AD (Table 6). Among SCLC (FIGS. 3H,
3I and 3J) and LCNEC (FIGS. 3M, 3N and 3O), all cases were
immunoreactive for ASCL1 as well as for most other IHC NE markers
(CHGA, SYP, and CD56/NCAM); whereas the remaining 4 LCC examples
were not immunoreactive for any IHC NE marker, including ASCL1
(FIG. 3P).
TABLE-US-00006 TABLE 5 Detailed results of correlation between
ASLC1 and other NE markers in all lung cancer subtypes SUB- CD56/
CD56/ TYPE CHGA.sup.+ CHGA.sup.- SYP.sup.+ SYP.sup.- NCAM.sup.+
NCAM.sup.- AD No 4 79 20 63 5 78 (n = 83) ASCL1.sup.+ 3 12 14 1 4
11 (n = 15) ASCL1.sup.- 1 67 6 62 1 67 (n = 68) SCLC No 11 1 12 0
12 0 (n = 12) ASCL1.sup.+ 11 1 12 0 12 0 (n = 12) ASCL1.sup.- 0 0 0
0 0 0 (n = 0) LCNEC No 2 0 2 0 2 0 (n = 2) ASCL1.sup.+ 2 0 2 0 2 0
(n = 2) ASCL1.sup.- 0 0 0 0 0 0 (n = 0) LCC No 0 4 0 4 0 4 (n = 4)
ASCL1.sup.+ 0 0 0 0 0 0 (n = 0) ASCL1.sup.- 0 4 0 4 0 4 (n = 4)
TABLE-US-00007 TABLE 6 Correlation of ASCL1 with other NE markers
in AD AD SUBTYPE CHGA/SYP/CD56*.sup.+ CHGA/SYP/CD56*.sup.-
ASCL1.sup.+ AD (n = 15) 14 (94%) 1 (6%) ASCL1.sup.- AD (n = 68) 6
(9%) 62 (91%)
ASCL1 mRNA Expression is More Prevalent in AD than in SQCC
[0054] The expression of ASCL1 and other known NE markers in
Dataset 1 (FIG. 1) consisting of AD (n=232), SQCC (n=100), SCLC
(n=15), adjacent non-neoplastic lung (N, n=12), and LCC and LCNEC
(n=9) is shown as a heatmap in FIG. 4. The heatmap also includes
the SQCC and AD differentiation genes DSG3 and NKX2.1/TTF1,
respectively, and RET. ASCL1 had the highest correlation with
calcitonin (CPRG/CALCA) (correlation coefficient=0.65). However, in
general, there was not a high correlation between the mRNA
expression levels of the NE markers (Table 7). Most NE markers had
similar frequency of expression in the AD and SQCC samples. In
contrast, ASCL1 was much more specific to AD than to SQCC. For a
quantitative analysis, a threshold was selected for ASCL1 that
corresponded to a positive IHC stain (FIG. 2). Microarray signal
intensity levels above this threshold had excellent correlation
with the IHC staining (correlation coefficient=0.89, FIG. 2). In
AD, 44 of 232 cases (19.0%) were ASCL1.sup.+. On the other hand, in
100 SQCC only 1 of 100 cases (1.0%) was ASCL1.sup.+. Of the nine
LCC, two had strong expression of ASCL1 and all other NE markers
and were classified as LCNEC. Importantly, ASCL1 also was highly
prevalent in other NE lung tumors, including SCLC and carcinoid
tumors (CT). Six of 10 (60%) and 14 of 15 (93%) CT and SCLC,
respectively, were ASCL1.sup.+.
TABLE-US-00008 TABLE 7 Correlation between any two NE markers in AD
and SQCC that express either or both markers. CHGA CHGB SCG2 INSM1
PCSK1 SYP NCAM1 ASCL1 CALCA CHGA 1 CHGB 0.219 1 SCG2 0.395 0.207 1
INSM1 NS* 0.259 0.439 1 PCSK1 NS* 0.3 0.33 0.308 1 SYP NS* NS* NS*
NS* NS* 1 NCAM1 NS* NS* NS* NS* NS* NS* 1 ASCL1 NS* NS* NS* NS*
0.497 NS* NS* 1 CALCA NS* NS* 0.177 NS* 0.593 NS* NS* 0.65 1 *NS:
Pearson correlation p-value >0.05
Neuroendocrine Differentiation is Rare in Non-Smoker
Adenocarcinomas
[0055] Expression levels of known NE markers were examined in the
Mayo Clinic lung cancer samples from NS, which included 75 AD, 32
CT, 8 adenosquamous carcinomas and SQCC, and 125 adjacent
non-neoplastic (N) samples. Compared with N, all NE markers were
over-expressed in a majority of CT as expected (FIG. 5). In
contrast, the expression levels of NE markers in AD were within the
range of N. A subset of AD was not identified with marked
over-expression of any of the NE markers. These data suggest that
NE differentiation in lung AD is largely restricted to smokers.
Survival Analysis of Lung AD in Relation to ASCL1 mRNA
Expression
[0056] Given that ASCL1 is expressed in about 20% of AD, to obtain
sufficient statistical power for survival analysis, the Mayo Clinic
AD microarray data was combined with four publicly available AD
datasets for which outcome data was available. An association was
not identified between the ASCL1 expression status and survival or
time to progression in stage I tumors nor in combined stage II-IV
tumors (p.gtoreq.0.28). However, the Kaplan Meir (KM) survival
curves for ASCL1.sup.+ and ASCL1.sup.- stage I tumors had different
drop off profiles (FIG. 6). ASCL1.sup.+ patients who died had
significantly shorter survival times causing a sharp drop in the
survival curve. Upon further investigation, this pattern was found
to be consistent in all five data sets (Table 8). In the combined
datasets, ASCL1.sup.+ patients who died had statistically shorter
survival times than ASCL1.sup.- patients who died
(p<5.times.10.sup.-6). This trend did not change after excluding
samples suspected of LCNEC in the Director's Challenge dataset. A
statistical test (cox.zph) indicated non-proportional hazards in
ASCL1.sup.-/ASCL1.sup.+ tumors when censoring times of 6.5 or more
years (p<0.05) were used. These observations suggested that
ASCL1.sup.+ status might reflect a different underlying biology for
these tumors. The roles of traditional prognostic markers such as
age, gender, tumor grade, race, smoking status (former or current),
tumor "T" stage, and tumor grade were assessed by cox analysis. The
only significant parameters were age (p=10.sup.-6) and gender
(p=0.045). When stage I AD were stratified by ASCL1 status,
differences in age and gender between the ASCL1.sup.+ and
ASCL1.sup.- patients were not identified (group t-test and
chi-square p.gtoreq.0.09).
TABLE-US-00009 TABLE 8 Survival times of patients with fatal stage
I AD according to the ASCL1 status. ASCL1.sup.+ ASCL1.sup.- Dataset
n median mean n median mean Mayo Clinic 5 16.0 17.6 41 25.3 33.5
Director Challenge 14 31.9 29.4 93 45.8 50.3 Bhattacharjee et al. 4
20.3 20.0 27 25.5 31.5 Kune et al. 3 21.9 23.3 7 31.2 26.5 Hou et
al. 1 4.9 4.9 11 24.2 38.4 All sets 27 23.6 24.3 179 38.2 41.9
RET mRNA Expression in ASCL1.sup.+ AD is Predictive of Overall
Survival
[0057] To gain further insight into the biology of ASCL1.sup.+
tumors, gene expression data for ASCL1.sup.+ and ASCL1.sup.- tumors
were compared. Gene expression analysis used Dataset 2 (FIG. 1),
which was a compendium of four sets of microarray data with follow
up information and more than 22,000 common Affymetrix probesets
from 593 AD including 367 stage I AD. Genes (probesets)
over-expressed in ASCL1.sup.+ compared with ASCL1.sup.- tumors were
identified by signal to noise ratio (SNR). The top 12 genes (16
probesets) following ASCL1 are listed in Table 9. All genes in the
list were significantly over-expressed in ASCL1.sup.+ tumors after
correcting for multiple comparisons (q-value<10.sup.-6) (Storey
and Tibshirani, Proc. Natl. Acad. Sci. USA, 100(16):9440-5 (2003)).
RET was the fourth most over-expressed gene after ASCL1 followed by
CALCA and Clorf95 (Table 9). FIG. 7A illustrates the expression of
RET in ASCL1.sup.- and ASCL1.sup.+ stage I AD. The over-expression
of RET in ASCL1.sup.+ tumors was consistent in all four datasets
(FIG. 7A). RET expression was more consistent with ASCL1 than other
NE markers (FIG. 4). Depending on the microarray signal threshold
for calling a transcript present (Log.sub.e signal intensity of 3.5
or 4.5), 91 to 95% of samples that expressed RET also expressed
ASCL1. In contrast, only 0% to 55% of samples that expressed RET
also expressed CHGA, CHGB, SCG2, SYP, INSM1, PCSK1, or NCAM1. Also
noted was a small portion of samples with high levels of RET in the
absence of ASCL1 (black circles in FIG. 7A), indicating that in
rare cases RET is expressed independent of ASCL1.
TABLE-US-00010 TABLE 9 Probesets (20) with highest signal to noise
ratio (SNR) in ASCL1.sup.+ compared with ASCL1.sup.- tumors in
Dataset 2. Affy Probeset Symbol SNR q-value Drug 209988_s_at ASCL1
2.78 1.4E-39 209987_s_at ASCL1 2.70 7.5E-27 213768_s_at ASCL1 1.51
4.5E-15 217561_at CALCA 1.40 1.2E-15 210728_s_at CALCA 1.33 4.5E-15
210727_at CALCA 1.32 5.0E-15 217495_x_at CALCA 1.16 4.4E-11
209985_s_at ASCL1 1.15 7.0E-11 213925_at C1orf95 1.09 9.1E-13
211421_s_at RET 1.06 1.4E-10 sunitinib, vandetanib 205549_at PCP4
1.05 6.5E-20 220782_x_at KLK12 1.03 3.2E-11 209617_s_at CTNND2 0.97
3.2E-11 214023_x_at TUBB2B 0.93 1.2E-15 brentuximab vedotin,
cabazitaxel 205305_at FGL1 0.91 2.7E-13 204623_at TFF3 0.85 4.8E-18
214058_at MYCL1 0.85 7.0E-11 205879_x_at RET 0.82 5.6E-08
sunitinib, vandetanib 210432_s_at SCN3A 0.80 3.1E-07 riluzole
209228_x_at TUSC3 0.77 1.3E-13
RET Expression Coinciding with ASCL1 was not Limited to Stage I
AD
[0058] A similar ASCL1/RET co-expression was observed in all stages
of AD and other lung cancer subtypes. FIG. 7B illustrates the
expression of RET in Dataset 1. In SQCC where ASCL1 was largely
absent, RET was also rarely expressed. In SCLC, CT, and LCC, RET
expression was largely restricted to ASCL1.sup.+ tumors.
[0059] As in FIG. 7A, RET mRNA was detectable in a limited number
of lung cancers that did not express ASCL1 (black circles in FIG.
7B).
[0060] Two probesets corresponding to RET were significant in
predicting the overall survival (OS) in stage I ASCL1.sup.+ tumors
by cox analysis (p values of 0.029 and 0.006). High expression of
RET was associated with shorter survival. In contrast, an
association was not identified between the OS and RET expression
level in ASCL1.sup.- tumors. For illustration, a threshold for
`low` and `high` expression of RET in a Kaplan Meir (KM) plot was
selected as shown in FIG. 8A. The results did not appreciably
change after excluding samples where an alternative diagnosis of
LCNEC was possible. Using the same threshold, RET mRNA also was
significant in predicting OS in all AD (FIG. 8B).
RET Protein Expression Analysis by IHC
[0061] A select set of Mayo AD samples with expression data by the
microarrays were immunostained for RET. RET protein expression by
IHC was more prevalent than expected from the microarrays, perhaps
due to the sensitivity of antibody to multiple variants of RET. A
blush staining was observed in some ASCL1.sup.- cases with RET mRNA
expression below detection levels by microarrays (FIG. 9A).
However, ASCL1.sup.+ cases from fatal tumors often had intensely
stained areas (FIG. 9B). There was a significant correlation
between RET IHC scores and microarray signal intensity by the RET
probeset used in OS analysis (rho=0.43, p<0.02). An ASCL1/RET
co-IHC assay was developed, and overlapping tumor areas with
positive staining for both proteins were frequently found (FIG.
9C). However, because of the discrepancies in sensitivity and
specificity of RET and ASCL1 antibodies or because of ASCL1
independent activation of RET, areas with positive RET staining
without ASCL1 expression also were encountered.
[0062] RET protein level by IHC was predictive of OS in the Mayo AD
samples, which also were positive for ASCL1 by IHC (log-rank test p
value=0.05, FIG. 9D). When cases were not stratified by the ASCL1
expression, RET IHC was not predictive of OS (FIG. 9E). In this
situation, median survival times of tumors with `low` levels of RET
was slightly less than tumors with `high` levels of RET, but this
difference was statistically insignificant. These results indicate
that RET protein is predictive of OS survival only in the context
of ASCL1 expression.
Gene Set Analysis of ASCL1.sup.+ Tumors
[0063] To gain further insight in the biology of ASCL1.sup.+
tumors, gene set enrichment analysis was performed by the GSA
program and MSigDB version 3 with close to 7000 gene sets. The
results are shown in Table 10. Notably, positively and negatively
associated gene sets included OSADA_ASCL1_TARGETS_UP and_DN,
respectively. These sets contained genes that were up and down
regulated by ASCL1 in a study of ASCL1-transduced A549 lung AD
cells (Osada et al., Cancer Res., 68(6):1647-55 (2008)).
Importantly, RET was among the target genes up regulated by ASCL1
in the OSADA_ASCL1_TARGETS_UP set corroborating the observations in
patient data. In the module corresponding to human chromosome and
cytogenetic bands, 12q22 and 8p22 were enriched. Ten of 37 genes
(including ASCL1) on chr12q22 and twelve of 41 genes on chr 8p22
were significantly over-expressed in ASCL1.sup.+ tumors. The high
concentration of over-expressed genes in these regions suggested
potential copy number changes.
TABLE-US-00011 TABLE 10 Gene sets positively and negatively
associated with ASCL1.sup.+ compared with ASCL1.sup.- stage I
tumors. Pathway Names Frequency Score Positive Associations
TATCTGG, MIR-488 100 0.76 BONE_REMODELING 90 1.2 TISSUE_REMODELING
85 1.16 module_382 85 2.46 chr12q22 85 1.64 OSADA_ASCL1_TARGETS_UP
80 1.16 chr8p22 80 1.72 HANN_RESISTANCE_TO_BCL2_INHIBITOR_DN 80
0.95 Negative Associations
CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_DN 100 -0.85
HUANG_DASATINIB_RESISTANCE_UP 100 -1.34
BOYLAN_MULTIPLE_MYELOMA_D_DN 100 -0.92 MARSON_FOXP3_TARGETS_UP 100
-1.06 module_543 100 -2.66 HUMMEL_BURKITTS_LYMPHOMA_DN 95 -1.48
WANG_BARRETTS_ESOPHAGUS_AND_ESOPHAGUS_CA 95 -1.34
LEE_EARLY_T_LYMPHOCYTE_DN 95 -1.49 KEGG_VIRAL_MYOCARDITIS 95 -1.55
module_411 95 -0.68 FUJII_YBX1_TARGETS_UP 90 -1.51 GNF2_RAP1B 90
-1.41 module_223 90 -0.96 KEGG_CELL_ADHESION_MOLECULES_CAMS 90
-1.18 BIOCARTA_TH1TH2_PATHWAY 90 -2.42 module_341 90 -0.65
WU_CELL_MIGRATION 85 -0.74 OSADA_ASCL1_TARGETS_DN 80 -1.29
KIM_LRRC3B_TARGETS 80 -1.94 CASTELLANO_NRAS_TARGETS_UP 80 -0.86
[0064] mRNA correlates of aggressive behavior in stage I
ASCL1.sup.+ AD also were examined. Tumors from patients who died in
less than 3.5 years following surgery (n=21) and from patients who
survived more than 6 years following surgery (n=20) were designated
as aggressive and non-aggressive tumors, respectively. When
probesets were ranked by SNR in aggressive versus non-aggressive
tumors, two probesets for RET were among the list of top 10
probesets. GSA analysis in these tumors identified six gene sets
(Table 11). Most notably, KANG-CISPLATIN-RESISTANCE-UP was
positively associated with aggressive tumors. This set included
genes that were up-regulated in gastric cancer cell lines resistant
to cisplatin (Kang et al., Clin. Cancer Res., 10(1 Pt 1):272-84
(2004)).
TABLE-US-00012 TABLE 11 Gene sets positively and negatively
associated with aggressive behavior in ASCL1.sup.+ stage I AD.
Pathway Names Frequency Score Positive Associations
CHIANG_LIVER_CANCER_SUB- 100 1.39 CLASS_POLYSOMY7_UP V$AP1_Q4 85
0.35 module_94 85 0.4 KANG_CISPLATIN_RESISTANCE_UP 80 0.97
ENZYME_INHIBITOR_ACTIVITY 80 0.53 Negative Associations V$MAX_01 85
-0.3
[0065] In summary, the results provided herein demonstrate that
lung cancer patients can be examined for the presence of lung
cancer cells expressing ASCL1 (e.g., an elevated level ASCL1) and
RET (e.g., an elevated level RET). If the presence of lung cancer
cells expressing ASCL1 and RET is detected in a particular lung
cancer patient, then that lung cancer patient can be classified as
having lung adenocarcinoma characterized by neuroendocrine
differentiation and/or as having a poor survival prognosis. In some
cases, lung cancer patients classified as having lung
adenocarcinoma characterized by neuroendocrine differentiation can
be treated as described herein.
Example 2
Genes Over Expressed in Lung Adenocarcinoma Expressing ASCL1 and
RET
[0066] The genes listed in Table 12 were found to be overexpressed
in lung adenocarcinoma samples that express ASCL1 and RET.
Additional information about each of these ten genes is provided in
Table 13. Possible drugs for treating lung adenocarcinoma
characterized by neuroendocrine differentiation are listed in Table
14, Table 1, Table 2A, or Table 2B.
TABLE-US-00013 TABLE 12 Symbol Entrez Gene Name KCNMB4 potassium
large conductance calcium-activated channel, subfamily M, beta
member 4 RET ret proto-oncogene SCN3A sodium channel,
voltage-gated, type III, alpha subunit ADRA2A adrenoceptor alpha 2A
FGA fibrinogen alpha chain FGB fibrinogen beta chain KIT v-kit
Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog DDC dopa
decarboxylase (aromatic L-amino acid decarboxylase) TUBB2B tubulin,
beta 2B class IIb CHRNA9 cholinergic receptor, nicotinic, alpha 9
(neuronal)
TABLE-US-00014 TABLE 13 Entrez Entrez Gene ID Gene ID Entrez for
for Gene ID Symbol Affymetrix Location Type(s) Human Mouse for Rat
KCNMB4 219287_at Plasma ion channel 27345 58802 66016 Membrane RET
205879_x_at Plasma kinase 5979 19713 24716 Membrane SCN3A
210432_s_at Plasma ion channel 6328 20269 497770 Membrane ADRA2A
209869_at Plasma G-protein 150 11551 25083 Membrane coupled
receptor FGA 205649_s_at Extracellular other 2243 14161 361969
Space FGB 204988_at Extracellular other 2244 110135 24366 Space KIT
205051_s_at Plasma kinase 3815 16590 64030 Membrane DDC 205311_at
Cytoplasm enzyme 1644 13195 24311 TUBB2B 214023_x_at Cytoplasm
other 347733 73710 291081 CHRNA9 221107_at Plasma transmembrane
55584 231252 65024 Membrane receptor
TABLE-US-00015 TABLE 14 Drug(s) for treating lung adenocarcinoma
characterized by Symbol neuroendocrine differentiation KCNMB4
tedisamil RET sunitinib, vandetanib SCN3A riluzole ADRA2A
paliperidone, risperidone, antazoline/naphazoline,
acetaminophen/clemastine/pseudoephedrine, articaine/epinephrine,
bupivacaine/epinephrine, caffeine/ergotamine,
acetaminophen/dexbrompheniramine/pseudoephedrine, dapiprazole,
dexbrompheniramine/pseudoephedrine,
chlorpheniramine/ibuprofen/pseudoephedrine, dipivefrin,
cetirizine/pseudoephedrine, asenapine, epinephrine/prilocaine,
epinephrine/lidocaine, PYM-50018, V2006, lurasidone, paliperidone
palmitate, fexofenadine/pseudoephedrine,
guaifenesin/phenylpropanolamine, oxymetazoline, prazosin,
phenylpropanolamine, ephedrine, tolazoline, guanfacine, guanabenz,
guanethidine, phenoxybenzamine, dexmedetomidine, UK 14304,
clonidine, dexefaroxan, quinidine, polythiazide/prazosin,
chlorothiazide/methyldopa, chlorthalidone/clonidine, propafenone,
guanadrel, hydrochlorothiazide/methyldopa, metaraminol, tizanidine,
quetiapine, D- pseudoephedrine, apraclonidine, venlafaxine,
phentolamine, labetalol, mephentermine, propylhexedrine, yohimbine,
dihydroergotamine, ergotamine, norepinephrine, alpha-methyl dopa,
epinephrine, dopamine, chlorpheniramine/phenylpropanolamine,
desloratadine/pseudoephedrine, acrivastine/pseudoephedrine,
carbinoxamine/pseudoephedrine,
brompheniramine/codeine/phenylpropanolamine,
pseudoephedrine/triprolidine, codeine/pseudoephedrine/triprolidine,
carbetapentane/chlorpheniramine/ephedrine/phenylephrine,
brompheniramine/dextromethorphan/pseudoephedrine,
chlorpheniramine/hydrocodone/pseudoephedrine,
azatadine/pseudoephedrine, naphazoline,
carbinoxamine/dextromethorphan/pseudoephedrine FGA F2 FGB F2 KIT
dasatinib, sunitinib, pazopanib, tivozanib, OSI-930, telatinib,
tandutinib, imatinib, sorafenib DDC carbidopa/entacapone/levodopa,
carbidopa/levodopa, S(-)-carbidopa, L- dopa TUBB2B brentuximab
vedotin, cabazitaxel CHRNA9 ABT-089, isoflurane, mecamylamine,
succinylcholine, rocuronium, doxacurium, amobarbital, mivacurium,
pipecuronium, rapacuronium, metocurine, atracurium, cisatracurium,
acetylcholine, nicotine, D- tubocurarine, arecoline, enflurane,
pancuronium, vecuronium
Example 3
Methods for Confirming Effectiveness of Drugs for Treating Lung
Adenocarcinoma Characterized by Neuroendocrine Differentiation
[0067] Two cell lines are used to confirm the effectiveness of
drugs for treating lung adenocarcinoma characterized by
neuroendocrine differentiation. The first is the HCC1833 cell line,
which was derived from lung AD and has high expression levels of
ASCL1 and RET. The second in the A549 cell line which has low
endogenous expression of ASCL1 and is stably transfected with ASCL1
(A549-As.sup.+). A549-As.sup.+ captures salient features of
ASCL1.sup.+ lung AD from patients, including increased expression
of RET. HCC1833 are stably transfected with ASCL1 siRNA to knock
down ASCL1 and produce a HCC1833-AsKD cell line. Lowering ASCL1
expression leads to low levels of RET expression.
[0068] Selected drugs such as sunitinib, sorafinib, or others
listed in Table 14 are incubated with A549 and A549-As.sup.+ cells
in vitro and HCC1833 and HCC1833-AsKD cells in vitro. The cells are
treated in culture at various concentrations (e.g., 10 to 100 nM or
2 to 10 .mu.M concentrations). The treated cell lines are examined
for sensitivity to the selected drugs. Cell viability and apoptosis
are assessed using standard assays to compare sensitivity of A549,
A549-As.sup.+, HCC1833, and HCC1833-AsKD cells to the selected
drugs.
[0069] In vivo methods are performed as follows. HCC1833 or
A549-As.sup.+ cells are transplanted into Nude mice subcutaneously
or by IP injections. Tumors are allowed to grow, and the animals
are treated to receive daily treatments of a selected drug (e.g.,
sunitinib and/or sorafinib) given by oral administration at a
particular dose (e.g., 30 mg/kg or 60 mg/kg). Tumor growth is
evaluated twice-weekly by measurement of tumor volume, and
histology of the tumors is assessed at the end of the treatment or
after mice become moribund.
[0070] The HCC1833 adenocarcinoma cell line expressed high
endogenous levels of ASCL1 and RET (FIG. 13, left). Transfection
with sh1 (a small interfering RNA construct that includes:
GCCAACAAGAAGATGAGTAAG (SEQ ID NO:1)) or sh2 (a small interfering
RNA construct that includes: CAACCGCGTCAAGTTGGTCAA (SEQ ID NO:2))
reduced ASCL1 expression as well as RET expression (FIG. 13, left).
These results suggest that ASCL1 is an upstream regulator of RET.
In addition, STAT3 expression (in JAK/STAT3 pathway) went down.
Knocking down ASCL1 expression also caused reduced cell
proliferation (FIG. 13, right). Also, HCC1833 cells with reduced
ASCL1 expression (ASCL1-sh2 cells) exhibited a much slower ability
to filling the gap in a scratch assay (FIG. 14).
[0071] A549 cells expressed little ASCL1 and RET, while A549 cells
transfected with ASCL1 lentivirus (A549-ASCL1 cells or
A549-As.sup.+ cells) exhibited much more ASCL1 expression (FIG. 13,
center). Importantly, A549-As.sup.+ cells also expressed high
levels of RET, again suggesting that ASCL1 is an up-stream
regulator of RET. Also, the A549-As.sup.+ cells exhibited increased
resistance to cisplatin-induced cytotoxicity (FIG. 15).
Furthermore, after cisplatin treatment, the remaining clonogenic
potential in ASCL1 over-expressing cells was higher than in cells
that did not express ASCL1. The effects of sunitinib, a tyrosine
kinase inhibitor, also were examined. A549-As.sup.+ cells were more
susceptible to sunitinib than the wild type A549 cells (FIG.
16).
Example 4
Treating Lung Adenocarcinoma Characterized by Neuroendocrine
Differentiation with Brentuximab Vedotin
[0072] A patient is identified as having lung adenocarcinoma
characterized by neuroendocrine differentiation and is administered
Brentuximab vedotin at a dose that is between 1.5 and 2.0 mg/kg
(1.8 mg/kg) via intravenous infusion over 30 minutes every 3
weeks.
Example 5
Treating Lung Adenocarcinoma Characterized by Neuroendocrine
Differentiation with Sucralfate
[0073] A patient is identified as having lung adenocarcinoma
characterized by neuroendocrine differentiation and is administered
sucralfate at a dose of about 1 g (10 mL/2 teaspoonfuls) four times
per day.
Example 6
Treating Lung Adenocarcinoma Characterized by Neuroendocrine
Differentiation with Paliperidone
[0074] A patient is identified as having lung adenocarcinoma
characterized by neuroendocrine differentiation and is administered
paliperidone at a dose of 6 to 12 mg daily orally.
Other Embodiments
[0075] It is to be understood that while the invention has been
described in conjunction with the detailed description thereof, the
foregoing description is intended to illustrate and not limit the
scope of the invention, which is defined by the scope of the
appended claims. Other aspects, advantages, and modifications are
within the scope of the following claims.
Sequence CWU 1
1
2121DNAArtificial SequenceSynthetically generated oligonucleotide
1gccaacaaga agatgagtaa g 21221DNAArtificial SequenceSynthetically
generated oligonucleotide 2caaccgcgtc aagttggtca a 21
* * * * *