U.S. patent application number 14/875367 was filed with the patent office on 2016-04-28 for diagnosing and monitoring cns malignancies using microrna.
The applicant listed for this patent is The Brigham and Women's Hospital, Inc., The Regents of the University of California. Invention is credited to Santosh Kesari, Anna M. Krichevsky, Brit Mollenhauer, Nadiya Teplyuk.
Application Number | 20160115549 14/875367 |
Document ID | / |
Family ID | 46084626 |
Filed Date | 2016-04-28 |
United States Patent
Application |
20160115549 |
Kind Code |
A1 |
Krichevsky; Anna M. ; et
al. |
April 28, 2016 |
Diagnosing and Monitoring CNS Malignancies Using MicroRNA
Abstract
The use of specific microRNAs (miRNAs) present in CSF as
biomarkers for particular brain malignancies and disease
activity.
Inventors: |
Krichevsky; Anna M.;
(Brookline, MA) ; Teplyuk; Nadiya; (Boston,
MA) ; Kesari; Santosh; (San Diego, CA) ;
Mollenhauer; Brit; (Kassel, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Brigham and Women's Hospital, Inc.
The Regents of the University of California |
Boston
Oakland |
MA
CA |
US
US |
|
|
Family ID: |
46084626 |
Appl. No.: |
14/875367 |
Filed: |
October 5, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13885762 |
Sep 3, 2013 |
9150928 |
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PCT/US2011/061047 |
Nov 16, 2011 |
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14875367 |
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61457000 |
Nov 16, 2010 |
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Current U.S.
Class: |
514/266.4 ;
435/6.12 |
Current CPC
Class: |
C12Q 2600/118 20130101;
C12Q 1/6886 20130101; A61K 31/517 20130101; C12Q 2600/158 20130101;
C12Q 2600/178 20130101; G16B 40/00 20190201; G16B 20/00
20190201 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68; A61K 31/517 20060101 A61K031/517 |
Goverment Interests
FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] This invention was made with Government support under Grant
Nos. CA023100, CA124804, and CA138734 awarded by the National
Institutes of Health. The Government has certain rights in the
invention.
Claims
1.-8. (canceled)
9. A method of monitoring progression of a brain tumor, the method
comprising: determining, using real time polymerase chain reaction
(RT-PCR) or an RNA expression assay, levels of one or more of
miR-10b, miR-21, and a miR-200 family member in a first sample
comprising cerebrospinal fluid from a subject; and determining,
using real time polymerase chain reaction (RT-PCR) or an RNA
expression assay, levels of one or more of miR-10b, miR-21, and a
miR-200 family member in a subsequent sample comprising
cerebrospinal fluid from the subject; comparing the levels of the
one or more of miR-10b, miR-21, and a miR-200 family member in the
first sample to the levels of the one or more of miR-10b, miR-21,
and a miR-200 family member in the subsequent sample; and
determining presence of progression or recurrence of the brain
tumor based on the presence of levels of miR-10b, miR-21, or
miR-200 family member in the subsequence sample above the levels in
the first sample, or determining that the brain tumor is regressing
or is in remission based on levels of miR-10b miR-21, or miR-200
family member in the subsequent sample below the levels in the
first sample.
10. The method of claim 9, wherein: the subject has been diagnosed
with a primary brain tumor, and the method includes monitoring
levels of one or both of miR-10b and miR-21; or the subject has
been diagnosed with a metastatic brain tumor, and the method
includes monitoring levels of one or more of miR-10b, miR-21, and a
miR-200 family member.
11. The method of claim 9, wherein the method further comprises
administering a treatment to the subject after obtaining the first
sample and before obtaining the subsequent sample, and wherein a
decrease in levels of miR-10b, miR-21, or at least one miR-200
family member in the subsequence sample as compared to the level in
the first sample indicates that the treatment is effective.
12. The method of claim 11, wherein the treatment comprises
administration of one or more of surgical resection, chemotherapy,
or radiotherapy.
13.-14. (canceled)
15. The method of any of claim 9, in which the levels are
determined using RT-PCR.
16. The method of claim 9, wherein the miR-200 family member is
miR-200a, miR-200b, miR-200c, miR-141, or miR-429.
17. The method of claim 9, wherein the method comprises normalizing
the levels to a level of miR-125 or miR-24.
18. The method of claim 9, wherein the primary brain tumor is a
glioma, glioblastoma, hemangioma, or medulloblastoma.
19. (canceled)
20. A method of treating metastatic or primary brain tumors in a
subject, the method comprising: determining levels of miR-10b,
miR-21, and miR-200 in a sample comprising cerebrospinal fluid
(CSF) from a subject, and comparing the levels of miR-10b, miR-21,
and miR-200 to reference levels of miR-10b, miR-21, and at least
one miR-200 family member, and determining that the subject does
not have a metastatic or primary brain tumor when the levels of all
of miR200, miR-10b or miR-21 are below the reference levels;
diagnosing a metastatic or primary brain tumor in a subject who has
levels of miR-10b or miR-21 above the reference levels, and
administering a treatment for a metastatic or primary brain tumor
to the subject; and diagnosing a metastatic brain tumor when the
levels of the miR-200 family member are above the reference level,
and administering a treatment for metastatic brain cancer to a
subject who has levels of the miR-200 family member above the
reference level.
21. The method of claim 20, wherein determining levels of miR-10b,
miR-21, and miR-200 in a sample comprises using real time
polymerase chain reaction (RT-PCR) or an RNA expression assay.
22. The method of claim 20, wherein the treatment comprises
administration of one or more of surgical resection, chemotherapy,
or radiotherapy.
Description
CLAIM OF PRIORITY
[0001] This application is a continuation of U.S. patent
application Ser. No. 13/885,762, filed May 16, 2013, which is a
U.S. National Phase Application under 35 U.S.C. .sctn.371 of
International Patent Application No. PCT/US2011/061047, filed on
Nov. 16, 2011, which claims the benefit of U.S. Provisional Patent
Application Ser. No. 61/457,000, filed on Nov. 16, 2010. The entire
contents of the foregoing are hereby incorporated by reference in
their entireties.
TECHNICAL FIELD
[0003] The present methods relate to the use of specific microRNAs
(miRNAs) that are present in CSF as biomarkers for particular brain
malignancies and disease activity.
BACKGROUND
[0004] The most frequently occurring brain malignancies in adults
are metastatic brain cancers (e.g., from primary melanoma, lung
cancer, breast cancer, gastrointestinal cancer (e.g., pancreatic or
colorectal), kidney cancer, bladder cancer, certain sarcomas, or
testicular or germ cell tumors) followed by glioblastoma (GBM). GBM
is the most aggressive primary brain cancer, which generally has a
poor prognosis with median survival of about 14 months, despite
aggressive treatment (Filippini et al. Neuro Oncol. 2008;
10(0):79-87). Currently diagnosis of brain tumors is made with
brain biopsy if possible and the analysis of cerebrospinal fluid
(CSF) for the presence of cancer cells (cytology). CSF can be
accessed readily for longitudinal disease monitoring during and
after therapy. However, the currently used method of CSF analysis
has moderate sensitivity, is non-quantitative and technically
challenging. There is presently no routine way to subtype the
malignancy and monitor molecular changes from CSF indicating the
need for more accurate and reliable biomarkers and methods.
SUMMARY
[0005] The present invention is based on the identification of
specific miRNAs that can serve as biomarkers for particular brain
malignancies and disease activity.
[0006] Thus, in a first aspect, the invention provides methods for
detecting or making a diagnosis between metastatic and primary
brain tumors. The methods include determining levels of miR-10b,
miR-21, and miR-200 in a sample from a subject, and comparing the
levels of miR-10b, miR-21, and miR-200 to reference levels of
miR-10b, miR-21, and at least one miR-200 family member. The
presence of levels of all of miR200, miR-10b or miR-21 below the
reference levels indicates the absence of a metastatic or primary
brain tumor. The presence of levels of miR-10b or miR-21 above the
reference levels indicates the presence of a metastatic or primary
brain tumor. The presence of levels of the miR-200 family member
above the reference level indicates the presence of a metastatic
brain tumor.
[0007] In another aspect, the invention provides
computer-implemented methods for detecting or making a diagnosis
between metastatic and primary brain tumors. The methods include
determining levels of miR-10b, miR-21, and at least one miR-200
family member, in a sample from a subject, to provide a subject
dataset; downloading the dataset into a computer system having a
memory, an output device, and a processor programmed for executing
an algorithm, wherein the algorithm assigns the datasets into one
of two categories levels of miR-10b, miR-21, and at least one
miR-200 family member; assigning the subject dataset into the first
or second category; and generating an output comprising a report
indicating the assignment to the first or second category.
[0008] In some embodiments, the first category is presence of a
primary brain tumor and the second category is presence of a
metastatic brain tumor. In some embodiments, an assignment to the
first category is made based on the presence of levels of miR-10b
or miR-21 above reference levels, and the presence of levels of the
miR-200 family member below a reference level; and an assignment to
the second category is made based on the presence of levels of
miR-10b or miR-21 above reference levels, and the presence of
levels of the miR-200 family member above the reference level.
[0009] In some embodiments, the first category is presence of a
primary brain tumor or a metastatic brain tumor, and the second
category is absence of a primary brain tumor or a metastatic brain
tumor. In some embodiments, an assignment to the first category is
made based on the presence of any of miR200, miR-10b or miR-21
above reference levels, and an assignment to the second category is
made based on the presence of levels of all of miR200, miR-10b or
miR-21 below the reference levels.
[0010] In some embodiments, the algorithm is a linear algorithm or
radial basis function.
[0011] In some embodiments, the algorithm is a linear algorithm
comprising:
[0012]
(a*miR-125b)+(b*miR-10b)+(c*miR-21)+(d*miR-141)+(e*miR-200a)+(f*miR-
-200b)+(g*miR-200c)-h, wherein a-g are weights and h is a constant,
determined using a support vector machine algorithm.
[0013] In some embodiments, the methods further include selecting a
treatment for a metastatic or primary brain tumor for the subject,
based on the presence of a metastatic or primary brain tumor.
[0014] In some embodiments, the methods further include
administering the treatment to the subject.
[0015] In another aspect, the invention provides methods for
monitoring progression of a brain tumor. The methods include
determining levels of one or more of miR-10b, miR-21, and a miR-200
family member in a first sample; and determining levels of one or
more of miR-10b, miR-21, and a miR-200 family member in a
subsequent sample. The presence of levels of miR-10b, miR-21, or
miR-200 family member in the subsequence sample above the levels in
the first sample indicates the presence of progression or
recurrence of the brain tumor, and levels of miR-10b miR-21, or
miR-200 family member in the subsequent sample below the levels in
the first sample indicates that the brain tumor is regressing or is
in remission.
[0016] In some embodiments, wherein the subject has been diagnosed
with a primary brain tumor, the methods include monitoring levels
of one or both of miR-10b and miR-21. In some embodiments, wherein
the subject has been diagnosed with a metastatic brain tumor, the
methods include monitoring levels of one or more of miR-10b,
miR-21, and a miR-200 family member.
[0017] In some embodiments, the methods further include
administering a treatment to the subject, e.g., between the first
and subsequent samples, and a decrease in levels of miR-10b,
miR-21, or at least one miR-200 family member in the subsequence
sample as compared to the level in the first sample indicates that
the treatment was effective, e.g., reduced the size of the tumor.
No change indicates that the treatment either halted tumor growth
or had no effect, and an increase indicates that the treatment was
not effective.
[0018] In some embodiments, the treatment includes administration
of one or more of surgical resection, chemotherapy, or
radiotherapy.
[0019] In some embodiments of the methods described herein, the
sample comprises cerebrospinal fluid from a subject.
[0020] In some embodiments of the methods described herein, the
subject is a human who has or is suspected of having a brain
tumor.
[0021] In some embodiments of the methods described herein, the
levels are determined using RT-PCR.
[0022] In some embodiments of the methods described herein, the
miR-200 family member is miR-200a, miR-200b, miR-200c, miR-141, or
miR-429.
[0023] In some embodiments of the methods described herein, the
method comprises normalizing the levels to a level of a
housekeeping miRNA, e.g., miR-125 or miR-24.
[0024] In some embodiments of the methods described herein, the
primary brain tumor is a glioma, glioblastoma, hemangioma, or
medulloblastoma.
[0025] In some embodiments of the methods described herein, the
metastatic brain tumor is from a primary lung, breast, kidney,
bladder, testicular, germ cell or gastrointestinal cancer, or
melanoma.
[0026] 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 belongs. Methods
and materials are described herein for use in the present
invention; other, suitable methods and materials known in the art
can also be used. The materials, methods, and examples are
illustrative only and not intended to be limiting. All
publications, patent applications, patents, sequences, database
entries, and other references mentioned herein are incorporated by
reference in their entirety. In case of conflict, the present
specification, including definitions, will control.
[0027] Other features and advantages of the invention will be
apparent from the following detailed description and figures, and
from the claims.
DESCRIPTION OF DRAWINGS
[0028] FIGS. 1A-C show miR-10b and miR-21 up-regulation in GBM, and
CSF levels of miR-10b and miR-21 in patients with GBM, metastatic
brain cancer and non-neoplastic controls. (1A) miRNAs deregulated
in GBM more than two fold as compared to normal brains. miRNA
levels were obtained by the analysis of TCGA miRNA microarrays data
and error bars represent standard deviation between individual
probe sets present for each miRNA on the arrays. (1B) miR-10b and
(1C) miR-21 levels were examined by qRT-PCR in CSF samples of
neurological patients, and the relative levels are demonstrated for
individual CSF samples. The lines indicate median miRNA levels for
each group of patients: "Controls"--non-neoplastic
neuropathological cases, "GBM"--glioblastoma cases, "Breast to
Brain" and "Lung to Brain"--breast and lung cancer brain
metastasis, "Breast LM" and "Lung LM"--breast and lung cancer
leptomeningeal metastasis, respectively. Differences between group
means have been determined by non-parametric Wilcoxon Signed Rank
test and the significance is indicated by asterisks: (*)p<0.05,
(**)p<0.001, (***)p<0.0001. miR-10b and miR-21 CSF levels
normalized to miR-125b are presented in FIGS. 6A-B.
[0029] FIGS. 2A-F show the results of detection of miRNAs of
miR-200 family in metastatic brain cancer patients. (2A) miR-200b
expression levels were examined by qRT-PCR in various primary and
metastatic brain tumor tissue specimens and normalized to
ubiquitously expressed miR-125b. Error bars indicate standard
errors between technical duplicates. PNET: primitive
neuroectodermal brain tumor. MiR-200a (2B), miR-200b (2C), miR-200c
(2D) and miR-141 (2E) levels were examined by qRT-PCR in CSF
samples of neurological patients, and the relative values are
demonstrated for individual patients. Differences between group
means that reached statistical significance as determined by
non-parametric Wilcoxon Signed Rank test are indicated with
asterisks: (*)p<0.05, (**)p<0.001, (***)p<0.0001.
Corresponding values normalized to miR-125b are presented in Suppl.
FIG. 2C-F. (2F) The average levels of miR-200a/miR-200b and
miR-141/miR-200c cluster miRNAs in CSF of metastatic brain cancer
patients. The error bars represent the standard error of mean for
each group of patients.
[0030] FIG. 3A is an exemplary diagnostic decision tree showing a
method of classification of brain cancer patients based on CSF
miRNA biomarkers (miR-10b, -21, and -200).
[0031] FIG. 3B is a pair of graphs showing the correlation of
miR-10b and miR-21 levels between brain tumors and matching CSF
samples collected from the same patients. The Pearson coefficients
(r) of linear regression between two data sets were calculated for
each miRNA.
[0032] FIGS. 4A-C show CSF levels of miRNA markers in metastatic
lung cancer and GBM patients during treatment with erlotinib.
miRNAs levels were examined by qRT-PCR in CSF samples of lung
cancer patients (Patients A, C) and GBM patient (Patient B) during
the time course of erlotinib treatment. The disease progression and
the drug response were concomitantly monitored by MRI, as
following. For Patient A (shown in FIG. 4A): serial axial
post-gadolinium MRIs of lung cancer patient's brain during course
of progression of disease and stability and improvement on MRI with
escalating doses of erlotinib. A: time 0 weeks while patient on
erlotinib, there is no leptomeningeal and parenchymal enhancement
and CSF cytology was negative; B: 3 weeks progression on erlotinib
150 mg daily dosing with new cerebellar leptomeningeal enhancement
(small arrows) and nodule (large arrow), erlotinib increased to 600
mg every 4 days at 9 weeks; C: 29 weeks on showing stable
leptomeningeal enhancement and nodule; D-40 weeks showing reduction
in leptomeningeal enhancement and nodule, erlotinib increased to
900 mg every 4 days at 41 weeks; E: 64 weeks after 6 cycles of
chemotherapy with carboplatinum and pemetrexed due to lung cancer
progression showing further reduction in leptomeningeal enhancement
and nodule has disappeared. For Patient B (shown in FIG. 4B): A:
time 2 weeks for patient with GBM with predominant mass effect and
enhancement felt to be radiation changes rather than tumor based on
MM spectroscopy and PET scan on erlotinib at 600 mg every 4 days;
B: 26 weeks on treatment showing progression on MRI with new lesion
(arrow) concerning for tumor; C: 27 weeks on treatment showing
hypermetabolic area (arrow) on PET consistent with tumor and biopsy
confirmed. For Patient C (shown in FIG. 4C): had inadequate
treatment due to functional status and rapidly progressed over a
few weeks, which was reflected by an increase in levels of miR-200
family members in a short interval.
[0033] FIGS. 5A-G are graphs showing miR-NA levels in CSF of
patients with GBM, metastatic brain cancers and non-neoplastic
neurological conditions. miR-NA levels were determined in CSF
samples by qRT-PCR and relative levels calculated by .DELTA.Ct
method with expression at Ct=36 set as one unit.
[0034] FIGS. 6A-F are graphs showing miRNA levels in CSF of
randomly selected patients with GBM, metastatic brain cancers and
non-neoplastic controls are demonstrated for: (6A) miR-15b, (6B)
miR-15b normalized to miR-125b, (6C) miR-17-5p, (6D) miR-17-5p
normalized to miR-125b, (6E) miR-93, (6F) miR-93 normalized to
miR-125b. miRNA levels in CSF samples were determined by qRT-PCR
reaction. Relative miRNA levels were quantified by the .DELTA.Ct
method and normalized to miR-125b as described in Materials and
methods. Error bars represent standard error of mean between
technical duplicates.
[0035] FIGS. 7A-B are bar graphs showing miR-10b expression in
different human tissues. (7A) The normalized data on miR-10b
expression in various human tissues were obtained from qRT-PCR
based profiling (Liang, 2007). miR-10b levels were calculated
relative to miR-10b expression in brain, which was set as one unit.
(7B) The data on miR-10b expression in normal human tissues and
corresponding carcinomas were obtained from profiling based on
hybridization arrays (Lu, 2005), Gene Expression Omnibus (GEO)
accession number GSE2564. Normalized miR-10b signals were plotted
relative to miR-10b level in brain, which was set as one unit.
[0036] FIGS. 8A-B are bar graphs showing miRNA-200 family in
different human tissues. (8A) The normalized data on miR-200a,
-200b, 200c and miR-141 expression in human tissues were obtained
from qRT-PCR based profiling (Liang, 2007). miRNA levels were
calculated relative to corresponding miRNA expression levels in
brain, which were set as one unit. (8B) The data on miR-200 family
expression in normal human tissues and corresponding carcinomas
were obtained from profiling based on hybridization arrays (Lu,
2005); Gene Expression Omnibus (GEO) accession number GSE2564.
Normalized signals for specific miRNAs were plotted relative to
corresponding miRNA levels in brain, which were set as one
unit.
[0037] FIG. 9. miR-195 levels in CSF of randomly selected patients
with GBM, metastatic brain cancers and non-neoplastic controls.
miR-195 levels in CSF samples were determined by qRT-PCR reaction.
Relative miRNA levels were quantified by .DELTA.Ct method as
described. Error bars represent standard error of mean between
technical duplicates.
[0038] FIGS. 10A-F are graphs showing miRNA levels in CSF of
patients with GBM and metastatic brain cancers remissions. The
levels of (10A) miR-10b, (10B) miR-21, (10C) miR-200a, (10D)
miR-200b, (10E) miR-200c and (10F) miR-141 were determined in CSF
by qRT-PCR reaction. Relative miRNA levels were quantified by
.DELTA.Ct method and normalized to miR-125b as described in
Materials and methods. Average miRNA levels are presented for each
group of cancer patients and individual miRNA levels are presented
for patients with cancer remissions. Error bars represent standard
error of mean within groups of patients.
DETAILED DESCRIPTION
[0039] miRNAs are small endogenous mediators of RNA interference
and key regulatory components of many biological processes required
for organism development, cell specialization and homeostasis. Many
miRNAs exhibit tissue-specific patterns of expression and are
deregulated in various cancers, where they can either be oncogenic
(oncomirs) or tumor suppressive. The recent discovery of miRNAs in
the secreted membrane vesicles, exosomes.sup.2, 3, as well as in
the blood serum.sup.4, 5 and other body fluids.sup.6 suggested that
miRNAs play a role in intercellular communication in both paracrine
and endocrine manner. It had also opened a new exciting direction
for study of miRNAs as biomarkers for diseases, and cancer
diagnostics by miRNA profile in blood serum became a quickly
growing field.sup.7.
[0040] Several studies have reported miRNA detection, among several
biological fluids, in CSF.sup.8-10, raising the possibility that
miRNAs in CSF might serve as informative biomarkers of central
nervous system (CNS) disease. Such a possibility, largely
unexplored until now, is supported by the finding that different
types of brain cancer have distinct signatures of miRNA expression,
with some miRNAs species abundant in cancer while undetectable in
healthy brain.sup.11-13. Since CSF is separated from blood
circulation by blood-brain barrier, it is conceivable that CSF
might better retain a unique signature of miRNA expression specific
for brain tumors.
[0041] A recent study demonstrated the usefulness of miRNA
profiling in CSF for diagnostics of brain lymphoma.sup.10. In the
current study, levels of several candidate miRNAs were tested in
the CSF of patients with GBM and compared to those of metastatic
brain cancers and a variety of non-neoplastic CNS diseases. There
was a strong association between the particular types of brain
cancer and the presence of specific miRNAs in CSF. Using this
approach enables detection of GBM and metastatic brain cancers, and
discrimination between them with about 95% accuracy. These results
demonstrate the utility of miRNA as biomarkers of high-grade brain
malignancies and reveal their value for the development of
diagnostic and prognostic tools, as well as for monitoring of CNS
pathology in general.
[0042] Methods of Diagnosis/Detection of CNS Malignancies
[0043] Thus, the methods described herein can be used to diagnose,
i.e., detect the presence of, a CNS malignancy, based on levels of
miRNAs in CSF, e.g., levels of one or more of miR-21, miR-10b, and
or a miR-200 (as used herein, the term "miR-200" encompasses all
members of the miR-200 family, i.e., miR-200a, miR-200b, miR-200c,
miR-141, and miR-429). In some embodiments, levels of miR-10b are
determined and compared to a reference level, and the presence of
levels of miR-10b in the subject above the reference level
indicates that the subject has a metastatic or neoplastic primary
brain tumor, e.g., GBM. In some embodiments, levels of miR-200 are
determined and compared to a reference level, and the presence of
levels of miR-200 (e.g., miR-200a) in the subject above the
reference level indicates that the subject has a metastatic brain
tumor, e.g., from primary lung or breast cancer. In some
embodiments, levels of miR-21 are determined and compared to a
reference level, and the presence of levels of miR-21 in the
subject above the reference level indicates that the subject has a
metastatic or neoplastic primary brain tumor, e.g., GBM. In some
embodiments, the methods include determining levels of miR-10b or
miR-21 and miR-200 (either non-normalized or normalized to
relatively uniformly expressed miRNAs such as miR-125 or miR-24),
and comparing the levels of each miRNA to a reference level. In
this case, the presence of elevated miR-10b or miR-21 indicates the
presence of a metastatic or neoplastic primary brain tumor, e.g.,
GBM, and the presence of miR-200 indicates the presence of a
metastatic brain tumor. See, e.g., FIG. 3A.
[0044] In some embodiments, the methods are used to determine
whether a metastatic brain tumor originated from a primary breast
or lung tumor. The methods include detecting levels of miR-200a
and/or miR-200b. The presence of increased levels of miR-200a and
miR-200b (two miRNAs encoded as a cluster at chromosome 1p36.33) in
CSF indicate an increased likelihood of the presence of metastatic
breast cancer relative to lung cancer. In some embodiments, the
methods include determining CSF levels of miR-141 and -200c
(co-encoded at chromosome 12p13.31), which are expressed at similar
levels in breast and lung cancer cases, and determining a ratio
between the miRNAs of the two different miR-200 genomic clusters
(e.g., [level of miR200a+level of miR200b]/[level of
miR141+miR200c], wherein a ratio above a reference ratio indicates
an increased likelihood of the presence of metastatic breast cancer
relative to lung cancer.
[0045] In some embodiments, the methods are used to make a
differential diagnosis of GBM versus brain metastasis, or GBM and
brain metastasis versus non-neoplastic tumors on the basis of
detection of levels in a CSF sample of seven miRNAs: miR-10b,
miR-21, miR-125b, miR-141, miR-200a, miR-200b, and miR-200c as
independent variables. Each of these miRNAs is detected in the
sample, and an algorithm (e.g., a linear or radial is applied to
make a diagnosis.
[0046] Reference levels can be determined using methods known in
the art, e.g., standard epidemiological and biostatistical methods.
The reference level can represent the levels in a reference cohort,
e.g., levels in subjects who do not have GBM or metastatic brain
cancer. The reference levels can be, e.g., median levels, or levels
representing a cutoff for the highest quartile, and can be set to
provide sufficient specificity and accuracy to provide for an
optimal level of true positives/true negatives while minimizing
levels of false positives/false negatives. Appropriate methods are
known in the art. See, e.g., Fleiss, "Design and Analysis of
Clinical Experiments," (Wiley-Interscience; 1 edition (Feb. 22,
1999)); Lu and Fang, "Advanced Medical Statistics," (World
Scientific Pub Co Inc (Mar. 14, 2003)); Armitage et al.,
"Statistical Methods in Medical Research, 4.sup.th Ed", Blackwell
Science (Boston, Mass., Oxford: Blackwell Scientific Publications,
2001).
[0047] In some embodiments, the methods include determining levels
of miR-125b, and normalizing levels of other miRNAs to the levels
of miR-125b, see, e.g., FIGS. 5A-5G. The reference levels can then
be set in comparison to those normalized levels, using methods
known in the art.
[0048] In some embodiments, miRNA levels are determined after an
initial diagnosis of a brain mass, e.g., detection of a mass using
an imaging method such as MM, or after a subject has presented with
symptoms that are consistent with a brain mass, to assist in making
a differential diagnosis of GBM versus brain metastasis versus
non-neoplastic tumor. A health care provider can identify subjects
who have symptoms consistent with a brain mass based on knowledge
in the art; general signs and symptoms include new onset or change
in pattern of headaches; headaches that gradually become more
frequent and more severe; unexplained nausea or vomiting; vision
problems, such as blurred vision, double vision or loss of
peripheral vision; gradual loss of sensation or movement in an arm
or a leg; difficulty with balance; speech difficulties; confusion
in everyday matters; personality or behavior changes; seizures,
especially in someone who doesn't have a history of seizures; and
hearing problems.
[0049] In some embodiments, once a differential diagnosis is made,
the methods include the selection and optionally the administration
of a treatment for the diagnosed disease. Thus, the methods can
include selecting a treatment regimen for the subjects comprising
one or more of surgical intervention, chemotherapy, and
radiotherapy. For all brain cancers, the choice of therapy (e.g.,
surgery, radiation and/or chemotherapy) can be chosen depending on
site, size, neurological function, and systemic disease status. For
example, if the subject has GBM, then a treatment regime including
radiation, temozolamide, and avastin may be selected and optionally
administered. If the subject has metastatic brain cancer, then the
treatment may depend on the source of the metastasis, i.e., on the
primary cancer. For metastatic breast cancer, then the treatment
could include chemotherapies approved for breast cancer (e.g.,
herceptin, lapatinib, doxil, or taxanes); for lung metastases, then
lung cancer therapies can be selected (e.g., tarceva, alimta, or
carboplatin). One of skill in the art would be able to select an
appropriate treatment based on knowledge in the art. See, e.g., the
National Comprehensive Cancer Network (NCCN) Guidelines, available
on the internet at nccn.org.
[0050] For a subject who has been determined to have a
non-neoplastic lesion using a method described herein, the methods
can include monitoring the subject on a continuing basis to detect
any change in the lesion, e.g., a shift to malignancy, which would
be indicated by an increase in levels of miR-10b, miR-21, or
miR-200.
[0051] Methods of Monitoring CNS Malignancies
[0052] The methods described herein can also be used to monitor a
subject, e.g., a subject who is undergoing treatment or being
followed for progression. The methods include determining levels of
miR-10b, miR-21, and/or miR-200, wherein the presence of levels of
miR-10b, miR-21, or miR-200 above a reference level indicate the
presence of recurrence of the malignancy, and levels below the
reference level indicate that the subject is in remission.
[0053] In some embodiments, e.g., for a subject who is undergoing
treatment, levels of miR-10b, miR-21, and/or miR-200 can be
monitored over time (e.g., by comparing levels determined from
first and second, e.g., subsequent, samples taken over time; the
first sample can be, but need not be, a baseline or initial
sample); a decrease in levels of miR-10b, miR-21, and/or miR-200 in
a subject undergoing treatment indicates that the treatment is
effective. An increase in levels indicates progression. No
significant change in levels indicates that no significant change
has occurred, i.e., no significant change in a subject being
treated that the treatment is at best slowing growth of the tumor,
or is ineffective, and no significant change in a subject who is
not being treated indicates that the tumor is not progressing. The
presence of elevated levels in a subject who was previously in
remission indicates the presence of a recurrence of the tumor, and
can indicate a need for treatment.
[0054] In addition, the methods can be used to detect real
progression versus pseudoprogression (a phenomenon in which a
subject is observed to have experienced disease growth immediately
after therapy, e.g., after radiotherapy, but are later shown to
have improved or stable disease by brain imaging, see, e.g.,
Hoffman et al., J Neurosurg 50:624-628, 1979; Brandes et al., Clin
Oncol 26:2192-2197, 2008; de Witt et al., Neurology 63:535-537,
2004; Taal et al., Cancer 113:405-410, 2008), e.g., in subjects
with GBM. In the case of an apparent progression (e.g., as measured
by imaging), the presence of stable or decreasing levels of miR-10b
(or miR-200) as compared to earlier levels (e.g., pre-treatment
levels) indicates that the apparent progression is a
pseudoprogression.
[0055] The levels can be determined, e.g., before, during, or after
treatment, e.g., treatment with surgery (e.g., resection or
debulking), chemotherapy, or radiotherapy.
[0056] Methods of Detection
[0057] Any methods known in the art can be used to detect and/or
quantify levels of a miRNA as described herein. For example, the
level of a miRNA can be evaluated using methods known in the art,
e.g., RT-PCR (e.g., the TAQMAN miRNA assay or similar),
quantitative real time polymerase chain reaction (qRT-PCR),
Northern blotting, RNA in situ hybridization (RNA-ISH), RNA
expression assays, e.g., microarray analysis, deep sequencing,
cloning or molecular barcoding (e.g., NANOSTRING, as described in
U.S. Pat. No. 7,473,767). Analytical techniques to determine miRNA
levels are known. See, e.g., Sambrook et al., Molecular Cloning: A
Laboratory Manual, 3rd Ed., Cold Spring Harbor Press, Cold Spring
Harbor, N.Y. (2001).
[0058] In some embodiments, the methods include contacting an agent
that selectively binds to a biomarker, e.g., to a miRNA (such as an
oligonucleotide probe that binds specifically to the miRNA) with a
sample, to evaluate the level of the miRNA in the sample. In some
embodiments, the agent bears a detectable label. The term
"labeled," with regard to an agent encompasses direct labeling of
the agent by coupling (i.e., physically linking) a detectable
substance to the agent, as well as indirect labeling of the agent
by reactivity with a detectable substance. Examples of detectable
substances are known in the art and include chemiluminescent,
fluorescent, radioactive, or colorimetric labels. For example,
detectable substances can include various enzymes, prosthetic
groups, fluorescent materials, luminescent materials,
bioluminescent materials, and radioactive materials. Examples of
suitable enzymes include horseradish peroxidase, alkaline
phosphatase, beta-galactosidase, or acetylcholinesterase; examples
of suitable prosthetic group complexes include streptavidin/biotin
and avidin/biotin; examples of suitable fluorescent materials
include umbelliferone, fluorescein, fluorescein isothiocyanate,
rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride,
quantum dots, or phycoerythrin; an example of a luminescent
material includes luminol; examples of bioluminescent materials
include luciferase, luciferin, and aequorin, and examples of
suitable radioactive material include .sup.125I, .sup.131I,
.sup.35S or .sup.3H.
[0059] In some embodiments, high throughput methods, e.g., arrays
(e.g., TAQMAN Array MicroRNA Cards) or gene chips as are known in
the art (see, e.g., Ch. 12, "Genomics," in Griffiths et al., Eds.
Modem genetic Analysis, 1999,W. H. Freeman and Company; Ekins and
Chu, Trends in Biotechnology, 1999;17:217-218; MacBeath and
Schreiber, Science 2000, 289(5485):1760-1763; Hardiman, Microarrays
Methods and Applications: Nuts & Bolts, DNA Press, 2003), can
be used to detect the presence and/or level of a miRNA.
[0060] In some embodiments, the methods include using a modified
RNA in situ hybridization technique using a branched-chain DNA
assay to directly detect and evaluate the level of a miRNA in the
sample (see, e.g., Luo et al., U.S. Pat. No. 7,803,541B2, 2010;
Canales et al., Nature Biotechnology 24(9):1115-1122 (2006); Nguyen
et al., Single Molecule in situ Detection and Direct Quantiication
of miRNA in Cells and FFPE Tissues, poster available at
panomics.com/index.php?id=product_87). A kit for performing this
assay is commercially-available from Affymctrix (VicwRNA).
[0061] Human miRNA Sequences
[0062] The following table sets forth sequences for mature human
miRNAs useful in the present methods.
TABLE-US-00001 Micro SEQ NO RNA ID Mature Sequence miR-10b 1
UACCCUGUAGAACCGAAUUUGUG miR-21 2 UAGCUUAUCAGACUGAUGUUGA miR-24-1 3
UGCCUACUGAGCUGAUAUCAGU miR-24-2 4 UGCCUACUGAGCUGAAACACAG miR-200a 5
CAUCUUACCGGACAGUGCUGGA miR-200b 6 CAUCUUACUGGGCAGCAUUGGA miR-200c 7
CGUCUUACCCAGCAGUGUUUGG miR-141 8 CAUCUUCCAGUACAGUGUUGGA miR-429 9
UAAUACUGUCUGGUAAAACCGU miR-125 10 UCCCUGAGACCCUAACUUGUGA
[0063] Algorithms and Computer-Implemented Methods
[0064] In some embodiments, the methods include using one or more
algorithms to assign a diagnosis, based on levels of miRNAs as
described herein. For example, the methods can include the use of a
linear algorithm, in which one or more of the levels are weighted.
In another example, the methods can include the use of a radial
basis function (RBF). Appropriate linear and RBF algorithms useful
in the present methods can be generated using methods known in the
art, e.g., a support vector machine (SVM). The SVM was originally
developed by Boser, Guyon and Vapnik ("A training algorithm for
optimal margin classifiers", Fifth Annual Workshop on Computational
Learning Theory, Pittsburgh, ACM (1992) pp. 142-152). See, e.g.,
Vapnik, "Statistical Learning Theory." John Wiley & Sons, Inc.
1998; Cristianini and Shawc-Taylor, "An Introduction to Support
Vector Machines and other kernel-based learning methods." Cambridge
University Press, 2000. ISBN 0-521-78019-5; and Scholkopf and
Smola, "Learning with Kernels." MIT Press, Cambridge, Mass., 2002,
as well as U.S. Pat. Nos. 7,475,048 and 6,882,990, all of which are
incorporated herein by reference in their entirety for their
teachings relating to computer systems and SVM-based methods. For
example, the present methods can be performed using a computer
system as described in FIG. 4 of U.S. Pat. No. 7,475,048.
EXAMPLES
[0065] The invention is further described in the following
examples, which do not limit the scope of the invention described
in the claims.
[0066] Materials and Methods
[0067] The following materials and methods were used in Examples
1-5, below.
[0068] Collection of samples. CSF and brain tumor samples were
obtained from the Department of Neurosciences, UC San Diego, Moores
Cancer Center, La Jolla, Calif., Department for Neurosurgery at
Brigham and Women's Hospital, Boston, Mass., and from the
Department for Neurosurgery at Gottingen University Medical Center,
Gottingen, Germany over the period of 2-5 years. At least one ml of
each CSF sample was cleared of cells and debris immediately after
collection by brief centrifugation at 3000 rpm 5 min at 4.degree.
C. and stored in aliquots at -80.degree. C. All tumor specimens
were fresh-frozen on dry ice and stored at -80.degree. C. until
tested.
[0069] RNA isolation and miRNA profiling. CSF samples were
lyophilized and total RNA was extracted using mirVana miRNA
isolation kit (Ambion) according to the manufacturer's protocol.
The amount of RNA extracted from the CSF samples was within 50-2500
ng/ml range, consistent with the previous findings.sup.3. Total RNA
from frozen tumor tissues was isolated using Trizol reagent
(Invitrogen). The levels of individual miRNAs in CSF and tumors
were determined by TaqMan miRNA assays from Applied Biosystems.
Four ng of total RNA was used in 6 .mu.l of reverse transcription
reaction with specific miRNA RT probes, prior to TaqMan real-time
PCR reactions that were performed in duplicates. MiR-125b, which is
abundantly and uniformly expressed in brain, was detected in all
CSF samples and used as an internal control for normalization (FIG.
5). However, since miR-125b levels themselves are not uniform
across the CSF samples, both normalized and non-normalized data
were considered in this study. No miRNA marker that was less
variable across the CSF samples was identifiable, and generally
higher miRNA CSF levels were observed in neoplastic cases relative
to non-neoplastic controls. This trend may reflect a release of
miRNA-containing microvesicles by cancer cells.sup.3 and/or
destruction of the brain tissue in neoplastic conditions. miRNAs
levels were calculated relative to corresponding miR-125b levels by
the formula 2 .DELTA.Ct, where
.DELTA.Ct=Ct.sub.miR-125b-Ct.sub.miR-X. All data are mean of
technical duplicates, and the standard errors of mean were
calculated between duplicates. Normalization to another
housekeeping miRNA, miR-24, did not change the results (data not
shown).
[0070] Samples classification and data analysis. A total of 118
patients of two neurooncological clinics, and corresponding CSF
samples were analyzed in this study. 108 patients were classified
into six groups based on clinical and pathological diagnoses
(including CSF cytology and tumor histology when applicable), and
magnetic resonance imaging (MRI) findings (Table 1A, the detailed
patients' characteristics are listed in Table 1B). The first
control group referred as "Non-neoplastic" includes patients with
various neurological conditions other than brain neoplasia. The
patients in this group had no cancer at the time of CSF collection,
and no previous history of CNS malignancies. The second group "GBM"
includes patients diagnosed with active GBM. GBM was referred to as
clinically "active" when primary tumor mass was apparent by MRI
imaging at the time of CSF samples collection and was further
classified as GBM by tumor tissue histology. The two groups called
"Breast to Brain" and "Lung to Brain" comprise of samples from the
patients with parenchymal brain metastasis from breast carcinoma
and lung cancer (including SCLC and NSCLC), respectively. The
presence of metastases in these patients was confirmed by MRI
imaging at the time of CSF collection. Two additional groups
represent patients with documented leptomeningial metastasis of
these cancers (CSF or MRI positive disease). Additional seven
patients not included in the groups described above were analyzed
separately. These patients represent cases of remission of primary
and metastatic brain tumors, as indicated by no detectable brain
tumor at the time of CSF collection based on imaging features,
clinical stability and CSF cytology. The remaining three patients
were analyzed in the longitudinal study.
TABLE-US-00002 TABLE 1A Groups of patients included in this study
Group N Clinical/Pathology based diagnosis Control 15
Non-neoplastic neurological conditions: headache (4)*, trigeminal
neuralgia, memory problem, gait difficulty, dementia, Parkinson
disease, myelitis (2), normal pressure hydrocephalus, encephalitis,
neuropathy, benign cerebellal lesion, Hodgkin disease with no CNS
cancer. GBM 19 Glioblastoma multiforme (glioma grade IV) Breast to
Brain 16 Breast cancer metastasis to brain Breast LM 26 Breast
cancer leptomeningial metastasis Lung to Brain 28 Lung cancer
metastasis to brain Lung LM 4 Lung cancer leptomeningial metastasis
N = number of patients per group. *The number of patients with a
particular diagnosis, if more than one, is indicated in
parenthesis.
TABLE-US-00003 TABLE 1B Neurological diagnosis and individual
characteristics of patients included in CSF microRNA analysis Year
of Clinical/Pathology Tumor CSF sample Time/way of sample ## based
diagnosis grade cytology Age Gender collection collection Control
(Non-neoplastic neurological conditions) 1 Non-specific pain No
Negative 50 F 2005 No surgery/LP syndrome tumor 2 Headache No
Negative 33 F 2006 No surgery/LP tumor 3 Memory No Negative 77 F
2006 No surgery/LP problems, gait tumor difficulty 4 Trigeminal No
Negative 67 F 2005 No surgery/LP neuralgia tumor 5 Normal pressure
No Negative 80 M 2006 No surgery/LP hydrocephalus tumor 6 Benign
cerebellar No Negative 60 M 2006 Year after surgery/LP lesion tumor
7 Hodgkin's No Negative 33 F 2007 No surgery/LP disease, no CNS
tumor cancer 8 Neuropathy No Negative 28 F 2007 No surgery/LP tumor
9 Encephalitis in No Negative 63 M 2007 No surgery/LP patient with
tumor leukemia 10 Dementia No Negative 44 F 2007 No surgery/LP
progressive tumor 11 Headache No Negative 25 M 2005 No surgery/LP
tumor 12 Headache No Negative 40 F 2007 No surgery/LP tumor 13
Parkinson Disease No Negative 71 M 2008 No surgery/LP tumor 14
Transverse No Negative 43 F 2008 No surgery/LP myelitis tumor 15
Transverse No Negative 31 F 2008 No surgery/LP myelitis tumor GBM:
Glioblastoma multiforme 1 GBM IV Negative 55 F 2007 After
surgery/LP/ before chemoradiation 2 GBM IV Positive 27 F 2007 After
surgery/Ommaya/ after chemoradiation 3 GBM IV Positive 25 F 2008
After surgery/LP/after chemoradiation 4 GBM IV Negative 28 M 2007
After surgery/LP/after chemoradiation 5 GBM IV Positive 59 M 2007
After surgery/LP/after chemoradiation 6 GBM IV Negative 32 M 2007
After surgery/LP/after chemoradiation 7 GBM IV Negative 61 F 2008
After surgery/LP/after chemoradiation 8 GBM IV Negative 63 M 2009
After surgery/LP/after chemoradiation 9 GBM IV NA NA NA 2008 During
surgery/ Ommaya/ before chemoradiation 10 GBM IV NA NA NA 2008
During surgery/ Ommaya before chemoradiation 11 GBM IV NA NA NA
2008 During surgery/ Ommaya before chemoradiation 12 GBM IV NA NA
NA 2008 During surgery/ Ommaya before chemoradiation 13 GBM IV NA
NA NA 2008 During surgery/ Ommaya before chemoradiation 14 GBM IV
NA NA NA 2008 During surgery/ Ommaya before chemoradiation 15 GBM
IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation 16
GBM IV NA NA NA 2008 During surgery/ Ommaya before chemoradiation
17 GBM IV NA NA NA 2008 During surgery/ Ommaya before
chemoradiation 18 GBM IV Negative 61 F 2005 After surgery/LP/after
chemoradiation 19 GBM IV NA 43 F 2010 After surgery/LP/ before
chemoradiation Breast to Brain: breast cancer brain metastasis 1
Breast carcinoma IV Positive 55 F 2008 No surgery/LP/after brain
metastasis radiation and during chemotherapy 2 Breast carcinoma IV
Positive 63 F 2008 After brain metastasis surgery/Ommaya/ after
radiation and during chemotherapy 3 Breast carcinoma IV Positive 54
F 2008 No surgery/LP/after brain metastasis radiation and during
chemotherapy 4 Breast carcinoma IV Positive 60 F 2008 After brain
metastasis surgery/Ommaya/ after radiation and during chemotherapy
5 Breast carcinoma IV Positive 55 F 2008 After brain metastasis
surgery/Ommaya/ after radiation and during chemotherapy 6 Breast
carcinoma IV Positive 62 F 2008 After brain metastasis
surgery/Ommaya/ after radiation and during chemotherapy 7 Breast
carcinoma IV Positive 54 F 2008 After brain metastasis
surgery/Ommaya/ after radiation and during chemotherapy 8 Breast
carcinoma IV Positive 60 F 2008 After surgery/LP/after brain
metastasis radiation and during chemotherapy 9 Breast carcinoma IV
Positive 54 F 2008 After brain metastasis surgery/Ommaya/ after
radiation and during chemotherapy 10 Breast carcinoma IV Positive
52 F 2008 No surgery/after brain metastasis radiation and during
chemotherapy 11 Breast carcinoma IV Positive 65 F 2008 After brain
metastasis surgery/Ommaya/ after radiation and during chemotherapy
12 Breast carcinoma IV Positive 48 F 2008 After brain metastasis
surgery/Ommaya/ after radiation and during chemotherapy 13 Breast
carcinoma IV Positive 46 F 2008 After surgery/LP/after brain
metastasis radiation and during chemotherapy 14 Breast carcinoma IV
Atypical 50 F 2008 After surgery/LP/after brain metastasis
radiation and during chemotherapy 15 Breast carcinoma IV Positive
55 F 2008 After surgery/LP/after brain metastasis radiation and
during chemotherapy 16 Breast carcinoma IV Positive 57 F 2008 After
surgery/LP/after brain metastasis radiation and during chemotherapy
Breast LM: breast cancer leptomeningial metastasis 1 Breast
carcinoma IV Negative 42 F 2006 No surgery/LP/after leptomeningial
radiation metastasis 2 Breast carcinoma IV Positive 60 F 2007 After
leptomeningial surgery/Ommaya/ metastasis after radiation and
during chemotherapy 3 Breast carcinoma IV Positive 59 F 2007 After
leptomeningial surgery/Ommaya/ metastasis after radiation and
during chemotherapy 4 Breast carcinoma IV Positive 61 F 2007 After
leptomeningial surgery/Ommaya/ metastasis after radiation and
during chemotherapy 5 Breast carcinoma IV Positive 64 F 2007 After
leptomeningial surgery/Ommaya/ metastasis after radiation and
during chemotherapy 6 Breast carcinoma IV Positive 53 F 2007 After
leptomeningial surgery/Ommaya/ metastasis after radiation and
during chemotherapy 7 Breast carcinoma IV Positive 66 F 2007 No
surgery/LP/after leptomeningial radiation metastasis 8 Breast
carcinoma IV Positive 54 F 2007 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
9 Breast carcinoma IV Positive 60 F 2007 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
10 Breast carcinoma IV Positive 63 F 2007 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
11 Breast carcinoma IV Positive 66 F 2007 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
12 Breast carcinoma IV Positive 60 F 2007 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
13 Breast carcinoma IV Positive 55 F 2007 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
14 Breast carcinoma IV Positive 56 F 2007 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
15 Breast carcinoma IV Positive 44 F 2007 After leptomeningial
surgery/Ommaya/after metastasis radiation and during chemotherapy
16 Breast carcinoma IV Positive 58 F 2007 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
17 Breast carcinoma IV Positive 54 F 2007 No surgery/LP/after
leptomeningial radiation and during metastasis chemotherapy 18
Breast carcinoma IV Negative 45 F 2007 No surgery/LP/after
leptomeningial radiation and during metastasis chemotherapy 19
Breast carcinoma IV Negative 60 F 2008 No surgery/LP/after
leptomeningial radiation and during metastasis chemotherapy 20
Breast carcinoma IV Positive 51 F 2008 After leptomeningial
surgery/Ommaya/ metastasis after radiation and during chemotherapy
21 Breast carcinoma IV Positive 29 F 2008 No surgery/LP/after
leptomeningial radiation and during metastasis chemotherapy 22
Breast carcinoma IV Positive 69 F 2008 No surgery/LP/after
leptomeningial radiation and during metastasis chemotherapy 23
Breast carcinoma IV Positive 61 F 2008 NA leptomeningial metastasis
24 Breast carcinoma IV Positive 64 F 2008 No surgery/LP/after
leptomeningial radiation and during metastasis chemotherapy 25
Breast carcinoma IV Positive 63 F 2008 No surgery/LP leptomeningial
metastasis 26 Breast carcinoma IV Positive 59 F 2008 After
leptomeningial surgery/Ommaya/ metastasis after radiation and
during chemotherapy Lung to Brain: lung cancer brain metastasis 1
Lung cancer brain IV Positive 56 F 2007 No surgery/LP/after
metastasis radiation and during chemotherapy 2 Lung cancer brain IV
Positive 59 F 2007 No surgery/LP/after metastasis radiation and
during chemotherapy
3 Lung cancer brain IV Positive 56 F 2007 No surgery/LP/after
metastasis radiation and during chemotherapy 4 Lung cancer brain IV
Positive 68 F 2007 No surgery/LP metastasis 5 Lung cancer brain IV
Positive 69 M 2007 No surgery/LP/after metastasis radiation 6 Lung
cancer brain IV Positive 71 M 2007 No surgery/LP/after metastasis
radiation and during chemotherapy 7 Lung cancer brain IV Positive
66 F 2007 No surgery/LP/after metastasis radiation and during
chemotherapy 8 Lung cancer brain IV Positive 63 F 2007 No
surgery/LP/after metastasis radiation and during chemotherapy 9
Lung cancer brain IV Positive 60 F 2007 No surgery/LP/after
metastasis radiation and during chemotherapy 10 Lung cancer brain
IV Positive 59 F 2007 No surgery/LP metastasis 11 Lung cancer brain
IV Positive 55 M 2008 No surgery/LP metastasis 12 NSCLC brain IV
Negative 66 F 2008 No surgery/LP metastasis 13 Lung cancer brain IV
Positive 62 F 2007 No surgery/LP/after metastasis radiation and
during chemotherapy 14 Lung cancer brain IV Positive 64 F 2006 No
surgery/LP metastasis 15 Lung cancer brain IV Positive 64 F 2006 No
surgery/LP metastasis 16 Lung cancer brain IV Negative 46 F 2007 No
surgery/LP metastasis 17 Lung cancer brain IV Positive 64 F 2007 No
surgery/LP metastasis 18 NSLC brain IV Negative 50 M 2007 No
surgery/LP metastasis 19 NSCLC brain IV Positive 56 M 2007 No
surgery/LP/after metastasis radiation and during chemotherapy 20
NSCLC brain IV Positive 49 F 2007 No surgery/LP/after metastasis
radiation and during chemotherapy 21 Lung cancer brain IV Positive
42 M 2007 No surgery/LP/after metastasis radiation and during
chemotherapy 22 Lung cancer brain IV Positive 56 F 2007 No
surgery/LP/after metastasis radiation and during chemotherapy 23
Lung cancer brain IV Positive 58 F 2008 No surgery/LP/after
metastasis radiation and during chemotherapy 24 NSCLC brain IV
Positive 48 M 2008 No surgery/LP metastasis 25 MSCLC brain IV
Negative 54 F 2008 No surgery/LP metastasis 26 NSCLC brain IV
Negative 61 F 2008 No surgery/LP metastasis 27 NSCLC brain IV NA 51
F 2010 After surgery/ metastasis Ommaya after radiation and during
chemotherapy 28 NSCLC brain IV NA 66 F 2010 No surgery/LP after
metastasis radiation and during chemotherapy Lung LM: lung cancer
leptomeningial metastasis 1 Lung cancer IV Positive 67 F 2006 No
surgery/LP leptomeningial metastasis 2 SCLC IV Negative 52 M 2007
No surgery/LP leptomeningial metastasis 3 Lung cancer IV Negative
56 F 2008 No surgery/LP leptomeningial metastasis 4 NSCLC IV NA 63
M 2010 No surgery/LP/after leptomeningial radiation and metastasis
chemotherapy NA = not available, NSCLC--non-small cell lung
carcinoma, SCLC--small cell lung carcinoma/
[0071] Statistical Analysis and Support Vector Machine (SVM)-based
data classification. The differences in CSF miRNAs levels between
groups of samples were determined using Graph Pad Prism software by
Wilcoxon Signed Rank test, and two-tailed P-values were
calculated.
[0072] SVM was implemented within a machine learning software
package weka (Witten, "Data Mining: Practical machine learning
tools and techniques, 3rd Edition". Morgan Kaufmann, San Francisco
(2011)), available on the internet at cs.waikato.ac.nz/ml/weka. In
such an approach, a sample's miRNA levels were treated as
independent variables and the type of cancer, if any, as a variable
to be predicted. The SVM was trained and tested on such a dataset,
using standard N-fold cross-validation process. In this process the
SVM was trained on all samples, except for one, and tested on that
holdout sample. The procedure was repeated as many times as there
were samples in the dataset, hence each sample once and only once
forms the holdout set. The following choices of non-default
parameters working best: Classifier: SMO, kernel RBF, Complexity
parameter=one for all tasks, except breast vs. lung metastasis, in
which case it was 100. Ct data were used for the classification as
is, with no standardization or normalization, except "1000" was
used on the place of Ct values in the cases of undetectable
miRNA.
[0073] The Cancer Genome Atlas (TCGA) miRNA expression microarray
data for GBM patients were downloaded from
tcga-data.nci.nih.gov/tcga/homepage.htm; see Hudson et al., Nature
464:993-998 (2010). The fold difference in specific signals between
GBM (n=261) and normal brain (n=10) tissue were calculated for each
miRNA as described.sup.3.
Example 1
miR-10b is Present and miR-21 is Elevated in CSF of Glioblastoma
and Brain Metastasis Patient
[0074] To identify miRNA biomarkers for GBM, a candidate approach
was used based on previous miRNA profiling data.sup.3, 14, 15. An
additional analysis of miRNA expression in 261 GBM patients
utilized The Cancer Genome Atlas (TCGA) dataset (Hudson et al.,
Nature 464:993-998 (2010)) and revealed a panel of miRNAs
deregulated in GBM relative to normal brain tissues (FIG. 1A).
Among them, miR-10b and miR-21 were the most strongly up-regulated
(FIG. 1A). miR-10b is a unique molecule, as it is the only known
miRNA undetectable in normal brain while highly expressed in
GBM.sup.16, 17. It was therefore chosen as the top priority
candidate. Expression of miR-10b is also associated with metastatic
phenotypes of several solid cancers, including breast and lung
cancers.sup.18,19.
[0075] miR-10b levels were examined in the CSF samples of the study
cohort patients, and miR-10b-specific qRT-PCR product was detected
in CSF of 17 out of 19 GBM patients (89% cases, FIG. 1B). This is
consistent with previous finding of miR-10b expression in
.about.90% of GBM tumors.sup.15. miR-10b was also detected in CSF
of 81% of patients with brain and leptomeningeal metastasis of both
breast and lung cancer (FIG. 1B). None of the patients with various
non-neoplastic neurological conditions showed detectable levels of
miR-10b at 40 cycles of the qRT-PCR reaction. Raw qRT-PCR Ct values
representing specific CSF levels of miR-10b and other miRNAs are
shown in Table 2B. Therefore, miR-10b in CSF is a highly indicative
marker of high-grade primary and metastatic brain cancers.
[0076] Next CSF levels were assessed for another candidate miRNA,
miR-21, which is the most common miRNA elevated in GBM and other
cancers.sup.20 and also most strongly up-regulated in GBM as
compared to normal brain (FIG. 1A). miR-21 levels are significantly
increased in CSF of most GBM and metastatic patients relatively to
its levels in the control CSF samples (FIG. 1C), suggesting that it
may represent an additional CSF biomarker for both GBM and
metastatic brain cancer.
[0077] The levels of three additional candidate miRNAs upregulated
in GBM relative to normal brain, miR-15b, miR-17-5p and miR-93
(FIG. 1A), have been determined in a randomly selected set of
several CSF samples. The levels of all three miRNAs were higher in
CSF of GBM and metastatic brain cancer patients relative to the
non-neoplastic controls (FIGS. 6A, C, E); however, these
differences have not reached the significance and were abolished by
data normalization to miR-125b (FIGS. 6B, D, F).
TABLE-US-00004 TABLE 2A Accuracies of classification of brain
tumors by SVM analysis. Instances classified in the test sets
Comparison Correctly Incorrectly GBM versus non-neoplastic controls
31 (91.2%) 3 (8.8%) Metastasis versus non-neoplastic controls 88
(98.9%) 1 (1.1%) GBM and metastasis versus 105 (97.2%) 3 (2.8%)
non-neoplastic controls GBM versus metastasis 89 (95.7%) 4 (4.3%)
GBM versus non-GBM (all others) 102 (94.5%) 6 (5.5%) Metastasis
versus non-metastasis (all others) 100 (92.6%) 8 (7.4%) Breast
versus lung metastasis 51 (68.9%) 23 (31.1%)
TABLE-US-00005 TABLE 2B miRNA Type # 125b 10b 21 141 200a 200b 200c
Non-neoplastic 1 34.2697 UD 33.3324 UD UD UD UD Non-neoplastic 1
33.9405 UD 33.0829 UD UD UD UD Non-neoplastic 2 33.0152 UD 33.5002
UD UD UD UD Non-neoplastic 2 32.799 UD 32.9746 UD UD UD UD
Non-neoplastic 3 32.9036 UD 33.707 UD UD UD UD Non-neoplastic 3
33.5036 UD 33.5222 UD UD UD UD Non-neoplastic 4 32.1067 UD 32.5033
UD UD UD UD Non-neoplastic 4 32.2493 UD 32.8214 UD UD UD UD
Non-neoplastic 5 33.8516 UD 33.258 UD UD UD UD Non-neoplastic 5
35.8576 UD 32.7309 UD UD UD UD Non-neoplastic 6 32.4644 UD 28.6672
UD UD UD UD Non-neoplastic 6 32.4621 UD 28.7054 UD UD UD UD
Non-neoplastic 7 31.6864 UD 35.2616 UD 37.4531 UD UD Non-neoplastic
7 32.1712 UD 35.0806 UD 37.2431 UD UD Non-neoplastic 8 32.0006 UD
32.1841 UD UD UD UD Non-neoplastic 8 31.7911 UD 31.7029 UD UD UD UD
Non-neoplastic 9 34.5177 UD 30.3603 UD UD UD UD Non-neoplastic 9
35.5515 UD 30.6514 UD UD UD UD Non-neoplastic 10 32.5169 UD 32.9137
UD UD UD UD Non-neoplastic 10 32.781 UD 32.3816 UD UD UD UD
Non-neoplastic 11 30.661 UD 30.635 UD UD UD UD Non-neoplastic 11
30.706 UD 30.528 UD UD UD UD Non-neoplastic 12 30.396 UD 30.993 UD
UD UD UD Non-neoplastic 12 30.159 UD 31.398 UD UD UD UD
Non-neoplastic 13 29.798 UD 38.9142 UD UD UD UD Non-neoplastic 13
29.469 UD 38.9142 UD UD UD UD Non-neoplastic 14 37.111 UD 36.431 UD
UD UD UD Non-neoplastic 14 36.750 UD 35.824 UD UD UD UD
Non-neoplastic 15 32.311 UD 33.307 UD UD UD UD Non-neoplastic 15
31.782 UD 33.483 UD UD UD UD GBM 1 28.493 35.4474 24.8591 UD UD UD
UD GBM 1 28.3347 36.1669 25.0358 UD UD UD UD GBM 2 30.27 UD 28.5448
UD UD UD UD GBM 2 29.8595 UD 28.7406 UD UD UD UD GBM 3 25.5607
33.3961 22.0836 36.807 33.5488 36.6658 36.6814 GBM 3 24.3582
33.0576 22.1982 35.7105 33.2086 37.0597 37.1643 GBM 4 24.9425
37.8446 23.4126 UD 35.597 UD 34.1835 GBM 4 24.8871 37.0681 22.9477
UD 35.0309 UD 34.1049 GBM 5 34.2504 UD 33.3238 UD UD UD UD GBM 5
34.4141 UD 33.2358 UD UD UD UD GBM 6 25.9917 36.2066 21.9135 UD
35.2526 UD UD GBM 6 25.7625 36.2066 21.6147 UD 34.1246 UD UD GBM 7
29.2959 33.4857 29.3222 UD 37.1513 UD UD GBM 7 29.1532 33.1848
28.6781 UD 36.9511 UD UD GBM 8 29.7628 30.8808 33.2773 UD UD UD UD
GBM 8 29.6696 30.7112 32.7008 UD UD UD UD GBM 9 29.5463 36.926
22.4494 28.5888 UD 31.3221 UD GBM 9 29.8912 38.0723 22.4455 29.173
UD 31.7444 UD GBM 10 18.8301 28.2565 21.3035 34.1768 30.673 35.202
30.9622 GBM 10 19.1781 28.3153 20.1106 35.3052 31.3501 34.5208
32.0136 GBM 11 19.0653 25.3992 19.9446 35.7793 30.3237 34.3587
35.3505 GBM 11 19.0975 25.3985 20.5917 35.4663 29.8643 34.234
36.6375 GBM 12 21.4785 29.5007 22.5529 34.3938 32.3403 36.3228
33.6589 GBM 12 21.4785 30.5404 22.0745 35.6437 32.8565 35.9838
33.3638 GBM 13 20.6069 28.0427 22.8669 38.4408 29.7108 34.4638
31.5322 GBM 13 21.1061 27.6744 22.4195 36.4015 31.1373 33.8695
32.1085 GBM 14 20.5726 29.0133 19.8893 35.0699 31.0412 35.4186
32.4751 GBM 14 20.4409 29.2476 20.1753 36.0567 31.5226 35.4393
33.3155 GBM 15 28.0429 34.4698 31.1034 UD UD UD UD GBM 15 28.3493
34.9682 31.2799 UD UD UD UD GBM 16 18.9454 29.2594 20.2101 33.9212
UD 34.7543 30.0307 GBM 16 19.0949 29.0995 19.8017 34.5306 UD
34.0056 31.1451 GBM 17 19.0563 25.713 19.6841 35.3343 28.5198
31.2043 31.0678 GBM 17 19.3106 26.0705 19.6881 35.0194 29.3597
31.4789 31.798 GBM 18 31.138 34.459 26.774 UD UD UD UD GBM 18
31.555 35.215 26.695 UD UD UD UD GBM 19 28.157 33.496 27.861 UD UD
UD UD GBM 19 27.883 34.539 27.602 UD UD UD UD Breast to Brain 1
27.8174 32.0139 21.1639 29.5078 26.0618 31.4264 27.1292 Breast to
Brain 1 27.2568 31.706 20.675 29.3259 26.2505 30.9209 27.7123
Breast to Brain 2 32.6303 UD 28.0095 37.1365 31.0578 32.6672
31.5072 Breast to Brain 2 32.5818 UD 27.7492 37.6775 31.0501
32.4441 31.8525 Breast to Brain 3 25.7808 31.3092 20.1414 29.1359
27.1009 30.5338 28.0328 Breast to Brain 3 25.977 31.3399 20.1774
29.2168 26.8024 30.2247 28.4686 Breast to Brain 4 31.1532 38.8239
23.4787 32.0578 26.4437 29.4728 30.6951 Breast to Brain 4 31.3755
UD 23.5862 32.8802 26.9978 29.1922 31.5229 Breast to Brain 5
29.6268 36.8038 25.6345 29.8542 24.483 27.1907 28.9925 Breast to
Brain 5 30.2187 36.262 25.0105 32.3864 24.483 27.2909 29.4038
Breast to Brain 6 30.3481 UD 25.5752 30.7873 24.67 28.5216 26.9064
Breast to Brain 6 30.709 UD 27.1514 31.7873 24.7185 28.2027 26.8947
Breast to Brain 7 35.4251 36.5204 28.0536 32.7134 27.8074 30.2571
32.2786 Breast to Brain 7 35.9251 36.5204 28.2612 32.3935 28.0258
29.9113 33.1268 Breast to Brain 8 30.5423 36.5667 27.8147 32.3054
29.5245 32.3943 29.0791 Breast to Brain 8 30.1858 36.8752 27.8631
32.1674 29.9147 32.5332 28.0088 Breast to Brain 9 32.1644 UD
25.9139 31.7038 28.1264 30.3435 30.0191 Breast to Brain 9 33.1737
UD 25.8558 32.1792 28.1041 30.1035 30.2432 Breast to Brain 10
28.3774 37.1231 25.108 28.4444 27.2268 31.0834 26.2144 Breast to
Brain 10 28.8228 36.1869 25.0972 28.835 26.5499 31.1109 25.9687
Breast to Brain 11 33.2952 UD 30.864 UD 33.4073 38.1796 33.7632
Breast to Brain 11 32.6806 UD 30.8002 UD 35.7065 37.0988 33.3951
Breast to Brain 12 30.044 32.846 25.180 30.020 30.641 32.699 29.391
Breast to Brain 12 29.709 34.234 25.414 30.461 30.452 32.992 30.033
Breast to Brain 13 30.368 36.826 27.307 33.816 33.117 35.072 31.908
Breast to Brain 13 30.417 36.920 27.261 33.340 32.604 35.081 32.021
Breast to Brain 14 21.508 25.708 23.920 35.289 35.603 35.800 34.705
Breast to Brain 14 21.414 25.617 23.742 36.763 35.476 38.213 34.781
Breast to Brain 15 29.378 36.876 26.886 30.667 30.539 32.789 29.405
Breast to Brain 15 29.457 36.376 26.601 30.678 30.333 32.183 29.738
Breast to Brain 16 30.966 36.324 30.592 34.492 34.035 36.778 32.881
Breast to Brain 16 30.699 37.014 30.740 34.933 33.617 36.980 32.690
Breast LM 1 30.631 35.604 28.651 35.954 35.557 UD 35.152 Breast LM
1 30.519 35.568 28.452 37.282 35.763 38.580 UD Breast LM 2 26.997
34.318 20.781 29.000 26.659 28.954 27.883 Breast LM 2 26.886 34.178
20.395 29.111 26.412 28.871 28.265 Breast LM 3 24.423 31.054 19.165
27.767 25.225 27.433 26.237 Breast LM 3 24.284 31.130 18.992 27.967
25.008 27.407 26.622 Breast LM 4 28.283 35.548 22.324 30.800 26.526
30.470 29.647 Breast LM 4 28.123 34.502 22.095 30.900 26.425 30.638
29.759 Breast LM 5 24.748 31.465 19.238 29.508 26.466 28.156 27.618
Breast LM 5 24.735 31.253 19.162 29.591 26.347 28.039 27.623 Breast
LM 6 25.164 31.746 19.547 29.870 27.440 28.653 28.036 Breast LM 6
25.097 31.742 19.467 30.269 27.271 28.579 28.192 Breast LM 7 31.297
34.899 28.895 38.345 36.188 UD 28.182 Breast LM 7 31.275 34.054
28.710 38.815 36.763 UD 28.202 Breast LM 8 25.550 31.414 20.539
30.363 28.001 30.203 29.081 Breast LM 8 25.382 31.941 20.389 31.110
28.097 29.728 29.224 Breast LM 9 25.436 32.248 19.751 29.839 27.778
29.802 28.736 Breast LM 9 25.381 32.310 19.668 30.266 27.705 29.566
29.577 Breast LM 10 26.174 32.970 20.036 32.305 28.691 30.722
29.632 Breast LM 10 26.062 32.313 19.916 32.080 28.712 31.071
29.973 Breast LM 11 29.221 35.174 24.557 36.691 33.055 33.915
32.966 Breast LM 11 29.204 34.316 24.509 36.177 32.815 33.137
33.631 Breast LM 12 30.453 UD 27.958 33.654 30.871 33.833 31.953
Breast LM 12 30.371 UD 28.002 33.772 30.846 33.242 32.321 Breast LM
13 27.006 33.424 22.239 33.263 29.571 30.881 30.444 Breast LM 13
27.006 33.535 22.293 33.286 29.470 30.810 30.672 Breast LM 14
25.784 33.436 20.025 27.453 24.736 26.462 25.903 Breast LM 14
25.723 33.897 19.953 27.674 24.601 26.389 26.229 Breast LM 15
28.633 34.998 26.284 32.961 30.162 31.955 30.838 Breast LM 15
28.428 35.181 26.165 33.110 30.148 31.753 31.015 Breast LM 16
28.807 35.442 26.537 32.348 30.373 32.301 31.592 Breast LM 16
28.680 34.988 26.355 33.175 30.416 32.011 31.681 Breast LM 17
29.268 24.630 21.239 29.995 28.911 29.920 27.692 Breast LM 17
29.097 24.605 20.887 30.363 28.886 29.762 28.305 Breast LM 18
29.702 31.968 26.406 31.073 30.501 32.820 29.712 Breast LM 18
29.969 31.514 26.260 31.508 30.430 32.741 29.802 Breast LM 19
26.527 31.477 22.035 28.358 30.716 30.165 26.926 Breast LM 19
26.526 31.654 21.967 28.392 30.713 30.015 27.044 Breast LM 20
26.373 35.276 19.590 26.371 27.901 25.011 24.178 Breast LM 20
26.270 34.665 19.544 26.438 27.631 25.089 24.138 Breast LM 21
28.123 34.414 23.245 29.885 23.275 31.398 28.881 Breast LM 21
28.134 34.245 23.257 29.831 23.046 31.542 28.934 Breast LM 22
32.904 UD 29.293 34.773 34.715 36.438 33.616 Breast LM 22 33.028 UD
29.127 34.571 34.321 37.548 33.449 Breast LM 23 27.233 35.308
21.986 28.639 29.883 31.056 27.869 Breast LM 23 27.156 36.094
22.032 28.654 29.878 31.049 28.177 Breast LM 24 28.149 33.316
25.137 27.720 27.842 30.901 26.319 Breast LM 24 27.947 32.855
24.882 27.926 27.995 30.793 26.763 Breast LM 25 27.659 34.227
19.330 26.775 23.657 24.032 23.402 Breast LM 25 27.362 34.603
19.135 27.104 23.416 23.953 24.071 Breast LM 26 31.169 UD 25.420
28.289 25.468 30.137 26.360 Breast LM 26 30.721 UD 25.250 28.572
25.305 30.119 26.642 Lung to Brain 1 27.3027 31.5496 22.65115
25.3368 25.2186 28.9333 24.1757 Lung to Brain 1 27.2988 31.1058
23.05115 25.3807 25.1565 28.3453 23.8634 Lung to Brain 2 29.8443
34.8497 25.1519 32.1757 31.3363 34.9516 30.1594 Lung to Brain 2
29.7741 34.7253 24.5772 31.9565 31.6946 35.3302 29.1884 Lung to
Brain 3 33.0843 UD 29.3511 34.3175 34.4514 37.0247 33.1313 Lung to
Brain 3 33.5869 UD 29.4506 34.7511 34.8228 36.7855 32.3656 Lung to
Brain 4 32.6941 UD 28.2911 33.9836 31.5455 33.2976 30.4481 Lung to
Brain 4 32.6056 UD 27.1608 32.7802 31.2428 33.0444 30.4042 Lung to
Brain 5 30.2049 34.8968 24.7768 30.4436 28.4256 30.2405 27.5537
Lung to Brain 5 29.5105 34.7725 24.0629 30.5538 28.1955 29.9892
27.1272 Lung to Brain 6 32.5851 36.6255 29.7253 35.4127 33.7658
35.5324 30.491 Lung to Brain 6 32.7851 37.4443 29.7184 35.1166
33.1176 35.0508 31.0042 Lung to Brain 7 29.261 33.4991 24.232
28.9268 28.5605 30.46 27.6959 Lung to Brain 7 28.4163 33.0663
23.8848 28.9189 28.312 30.706 27.9061 Lung to Brain 8 30.4814
34.687 22.3076 28.6553 27.6452 30.3316 29.4116 Lung to Brain 8
30.776 35.2047 21.9802 29.0701 27.6333 29.272 28.661 Lung to Brain
9 30.2956 34.349 26.8941 30.8863 29.4441 31.3527 31.2236 Lung to
Brain 9 29.9115 33.5384 27.4941 31.091 29.5607 31.5945 29.1472 Lung
to Brain 10 29.1638 35.0255 22.6924 29.9554 32.817 31.0666 27.3901
Lung to Brain 10 29.4353 34.4966 23.1541 30.097 32.9107 31.0331
27.2599 Lung to Brain 11 27.4463 33.4652 21.1578 26.9988 25.7732
28.9661 25.0689 Lung to Brain 11 27.3261 34.1371 20.9667 26.3149
25.4019 28.0832 24.8732 Lung to Brain 12 32.8667 UD 30.8165 UD UD
UD 38.2814 Lung to Brain 12 32.2667 UD 30.3494 UD UD UD 37.08 Lung
to Brain 13 34.1699 UD 24.4215 30.4942 29.2874 31.5813 31.9309 Lung
to Brain 13 34.2134 UD 24.2206 30.0906 29.0842 31.5813 32.2984 Lung
to Brain 14 29.293 34.571 24.394 30.789 29.544 33.057 28.864 Lung
to Brain 14 29.009 35.563 24.532 30.838 29.377 32.956 28.902 Lung
to Brain 15 28.914 34.550 22.560 29.644 28.600 30.866 27.167 Lung
to Brain 15 28.707 34.495 22.627 29.678 28.693 30.347 27.103 Lung
to Brain 16 26.601 31.991 22.155 27.351 26.558 28.982 26.586 Lung
to Brain 16 26.458 32.220 22.243 27.760 26.265 28.980 27.004 Lung
to Brain 17 30.365 35.322 22.837 28.904 28.364 30.994 27.650 Lung
to Brain 17 30.368 35.505 22.640 28.751 27.744 31.052 27.517 Lung
to Brain 18 30.310 35.762 29.548 34.882 35.961 39.607 33.730 Lung
to Brain 18 30.162 37.352 29.501 35.203 35.808 38.411 34.555 Lung
to Brain 19 29.630 32.016 24.964 27.431 28.617 30.526 27.507 Lung
to Brain 19 29.594 31.720 24.962 27.681 28.632 30.398 27.934 Lung
to Brain 20 28.500 UD 23.147 26.762 28.607 29.801 25.805 Lung to
Brain 20 28.472 UD 23.183 26.857 28.429 29.778 25.829 Lung to Brain
21 26.383 33.937 21.266 29.484 30.964 31.936 28.164 Lung to Brain
21 26.398 33.081 21.299 29.664 30.766 31.886 28.331 Lung to Brain
22 27.589 36.414 24.198 31.107 33.120 35.063 30.855 Lung to Brain
22 27.681 36.387 24.163 31.499 32.544 34.379 30.925 Lung to Brain
23 27.335 33.311 20.275 26.183 27.803 29.310 26.190 Lung to Brain
23 27.203 32.897 20.198 26.497 27.698 29.155 26.421 Lung to Brain
24 31.188 33.761 24.351 30.843 31.061 32.678 30.078 Lung to Brain
24 31.066 34.498 24.576 31.006 30.770 32.639 29.865 Lung to Brain
25 25.438 33.677 22.276 27.030 26.485 28.167 25.754 Lung to Brain
25 25.257 32.734 22.333 27.055 26.320 28.058 25.845 Lung to Brain
26 27.957 35.622 26.272 30.664 29.900 32.145 28.598 Lung to Brain
26 27.770 35.349 25.912 30.721 29.989 32.029 28.710 Lung to Brain
27 27.791 35.924 23.314 30.597 29.887 31.737 29.783 Lung to Brain
27 27.719 36.972 22.870 31.188 29.900 32.049 30.955 Lung to Brain
28 27.600 34.338 22.529 26.370 28.088 31.174 26.558 Lung to Brain
28 27.498 34.905 21.968 26.742 27.800 31.009 26.244 Lung LM 1
28.652 30.282 22.137 25.738 24.665 27.190 25.600 Lung LM 1 28.606
30.400 21.843 26.250 24.557 27.097 25.948 Lung LM 2 27.795 33.788
24.948 39.425 36.261 37.184 37.034 Lung LM 2 27.934 32.653 24.846
38.606 36.606 37.702 36.898 Lung LM 3 27.478 37.812 31.801 29.974
29.569 31.303 28.059 Lung LM 3 27.310 37.200 31.664 30.034 29.446
31.181 28.566 Lung LM 4 27.588 32.726 19.656 24.357 24.419 27.413
24.179 Lung LM 4 27.627 32.723 19.472 24.376 24.369 27.213 24.289
UD = Undetermined
Example 2
miR-200 Family in the CSF is Indicative of Brain Metastasis
[0078] miR-10b is expressed in most extracranial tissues.sup.21, 22
(FIGS. 7A-B), and abundant in blood serum.sup.23. However, it is
not expressed in brain and not detectable in CSF of non-cancer
patients. Therefore, miR-10b and other miRNAs seem unlikely to pass
the blood-brain barrier under non-neoplastic conditions, and miRNAs
in CSF might therefore reflect a unique miRNA signature of brain.
On the other hand, miR-10b is highly expressed in breast and lung
tissues, and its presence in the CSF of lung and breast cancer
patients with CNS metastasis indicates that metastatic cells bring
their signature miRNAs to the CSF. Based on these data, other miRNA
CSF biomarkers were sought that could enable discrimination between
GBM and metastatic brain tumors. Such miRNAs should be highly
expressed in a primary carcinoma or tissues of its origin (e.g.
lung or breast) but not in brain or GBM.
[0079] According to miRNA profiling across different tissues,
miRNAs of miR-200 family are good candidates fulfilling this
criteria. All members of this family are highly expressed in lung
and breast tissues and epithelial cancers, including lung and
breast carcinomas, but are barely detectable in brain.sup.22, 24,
and FIGS. 8A-B). On the other hand the miR-200 family, unlike
miR-10b, is not expressed in GBM and other primary brain tumors,
making it a putative biomarker for metastatic brain cancer (FIG.
2A).
[0080] To explore a potential of miRNA-200 for distinguishing
between GBM and metastatic brain cancer, the levels of four miR-200
family members, miR-200a, miR-200b, miR-200c and miR-141, were
assessed in CSF of control, GBM and metastatic brain cancer
patients. Remarkably, all four miRNAs were highly expressed in the
majority of CSF samples collected from the patients with brain and
leptomeningial metastasis, but not in the control or GBM cases
(FIG. 2B-E). These data suggest miR-200 levels might be used for
discriminating between primary brain cancer and brain
metastasis.
[0081] In attempt to discriminate between metastasis from breast
vs. lung cancer, miR-195 levels were assessed in several randomly
selected CSF samples, since circulating miR-195 was proposed as a
differential biomarker of breast vs. lung cancer.sup.25. However,
no significant differences were found in miR-195 levels in CSF of
breast and lung cancer metastasis patients (FIG. 9). Another miRNA,
miR-1 is expressed at higher levels in breast versus lung tissue
according to miRNA expression profiles.sup.22 but miR-1 was
undetectable in CSF of both breast and lung cancer cohorts of
patients. Breast and lung carcinomas express strikingly similar
miRNA repertoire.sup.21. However, there were significantly higher
amounts of miR-200a and miR-200b (two miRNAs encoded as a cluster
at chromosome 1p36.33) in CSF of the patients with breast cancer
relative to lung cancer, while CSF levels of miR-141 and -200c
(co-encoded at chromosome 12p13.31) were similar in breast and lung
cancer cases (FIG. 2F). These data suggest that the ratios between
miRNAs of two different miR-200 genomic clusters in CSF may be
informative for discrimination between brain metastasis from breast
versus lung cancer.
Example 3
Computational Classification of High-Grade Brain Malignancies based
on CSF miRNA Profiling
[0082] The relationships discovered between the miRNA CSF levels
and diagnostic outcomes are illustrated by a simple diagnostic
decision tree (FIG. 3A). The next experiments tested whether the
samples can be classified into classes more accurately
(non-neoplastic control vs. GBM vs. metastasis) using a
"machine-learning technique" based on Support Vector Machine (SVM)
concept. This technique was previously applied to a wide range of
biological problems, including mRNA and miRNA expression data
analysis in cancers.sup.26-28.
[0083] Various SVM algorithms were applied for classification of
the samples. In one case (GBM vs. metastasis classification) a very
simple linear classifier provides discrimination with about 95%
accuracy. The levels of two miRNAs, miR-200a and miR-125b were used
in this case as independent variables, and a linear function of
these two Ct levels employed as a classifier with the coefficients
calculated in the process of the classifier training.
[0084] Another case that allows for a similar interpretation is the
classification of GBM and brain metastasis versus non-neoplastic
controls. In that case a linear classifier was constructed that
uses Ct levels of three miRNAs: miR-10b, miR-200a and miR-125b as
features. Accordingly, a two-dimensional plane in the space spawned
by the levels of these three miRNAs separated the space into two
domains.
[0085] Linear algorithms provided satisfactory classification for
GBM v Metastasis (using the formula
0.3364*miR-125b+0.0808*miR-10b+0.4578*miR-21+-0.0871*miR-141+0.001*miR-20-
0a+0.0213*miR-200b+-0.3419*miR-200c-7.2516); GBM and metastasis
versus non-neoplastic
(0.0003*miR-125b+-0.0021*miR-10b+-0.0002*miR-21+0*miR-141+0*miR-200a+0*mi-
R-200b+-0.0021*miR-200c+3.1536); GBM versus non-neoplastic
(0.0002*miR-125b+0.0021*miR-10b+-0.0001*miR-21+0*miR-141+0*miR-200a+0*miR-
-200b+0*miR-200c-1.0849); Metastases versus non-neoplastic
(0*miR-125b+0*miR-10b+0*miR-21+0*miR-141+0*miR-200a+0*miR-200b+0.0021*miR-
-200c-1.0744); GBM versus non-GBM (all others)
(0.2468*miR-125b+0.1816*miR-10b+0.107*miR-21+0.0007*miR-141+0.0003*miR-20-
0a+-0.0032*miR-200b+-0.1817*miR-200c-7.7752); Metastasis versus
non-metastasis (all others)
(0.3348*miR-125b+0.0838*miR-10b+0.4619*miR-21+-0.0902*miR-141+0.001*miR-2-
00a+0.0284*miR-200b+-0.3482*miR-200c-7.3231); Breast versus lung
(0.1592*miR-125b+-0.0003*miR-10b+0.0381*miR-21+-0.5325*miR-141+0.5346*miR-
-200a+-0.0014*miR-200b+-0.1282*miR-200c-1.0529). In each case, a
negative result puts the sample into the first class, and a
positive result puts the sample into the second class.
[0086] Similarly, various SVM classifiers were tested and the RBF
kernel provided good separation between all classes of samples. The
best classification accuracy was achieved using the levels of seven
miRNAs: miR-10b, miR-21, miR-125b, miR-141, miR-200a, miR-200b, and
miR-200c as independent variables.
[0087] This analysis revealed that different types of cancer are
distinguished from each other as well as from non-neoplastic
control with the average cross-validation accuracy of about 90%
(Table 2A). That means that the SVM incorrectly predicted the class
of about one of ten previously unseen samples. This analysis
suggests a possibility of computational differential diagnostics of
brain cancers using miRNA profiling.
Example 4
The origin of miRNA in CSF
[0088] miRNAs detected in the CSF of brain cancer patients may
originate from brain tumor cells, from surrounding brain tissues or
from extracranial tissues due to the blood-brain barrier disruption
associated with tumor growth. To discriminate between these
possibilities miR-10b and miR-21 expression levels were determined
in tumor biopsies obtained during brain surgery and corresponding
CSF samples from the same patients. A positive correlation was
observed between miR-10b expression level in the brain tumor and
corresponding CSF specimens, and no such correlation was observed
for miR-21 (FIG. 3B). Of note, miR-10b is expressed in tumors but
not in normal brain tissues, while miR-21 is elevated in tumors but
is also present in normal brain.sup.14, 16. Taking these expression
patterns into account, the data suggest that miRNA composition of
the CSF is established by tumor cells as well as by the cells of
surrounding brain tissues.
Example 5
miRNAs in CSF of Brain Cancer Patients as Markers of Disease
Activity
[0089] To examine whether CSF levels of miRNAs reflect a disease
status/activity, miRNA was studied in CSF of active GBM and
metastatic brain cancer versus tumor remission cases. The disease
was considered in remission if, following treatment, there were no
evidence of tumor mass detected by MRI and CSF cytological analysis
was negative. Neither miR-10b nor miR-200 family members were
detected after 40 cycles of qRT-PCR reaction in CSF samples in any
of remission cases (Table 3, FIGS. 10A-F). MiR-21 levels were
significantly lower in cancer remission cases as compared to active
GBM and metastatic brain cancer cases before treatment (FIG. 10B).
These data suggest that miRNAs analyzed in this study may reflect
the activity of brain tumors.
[0090] To further test whether the CSF levels of specific miRNAs
reflect the disease status/activity and responsiveness to therapy,
miRNA levels were determined in CSF of lung cancer and GBM patients
longitudinally during course of erlotinib treatment. miRNA analysis
was accompanied by MRI, CSF cytology, and clinical monitoring of
the disease status. A NSCLC patient (patient A) developed
parenchymal and leptomeningeal disease during course of treatment
and medication adjustment (FIG. 4A). Erlotinib, an EGFR tyrosine
kinase inhibitor, was given orally at the dose of 150 mg daily and
increased at time of progression to 600 mg every 4 days and further
to 900 mg (at 41 weeks) to achieve higher brain/CSF
concentration.sup.29, followed by a prolonged remission. The levels
of both miR-10b and miR-200 members in CSF of this patient are
consistent with the MRI results, rising during relapse and
returning back to background levels after the increase of erlotinib
dosage (significant drop by 45 weeks, FIG. 4A).
[0091] Patient B (FIG. 4B) had GBM in remission at the initial
cytological CSF analysis and MRI that was interpreted as
pseudoprogression. However, high levels of miR-10b, and significant
elevation in miR-21 levels at later time indicated disease
progression that was further confirmed by MRI, PET scan and repeat
biopsy of new lesion. Patient C (FIG. 4C) had inadequate treatment
due to functional status and rapidly progressed over a few weeks,
which was reflected by an increase in levels of miR-200 family
members.
[0092] Altogether, these data indicate for the first time that CSF
miRNA levels may serve as biomarkers of brain cancer progression
and response to therapy.
TABLE-US-00006 TABLE 3 miRNA Ct values 125b 10b 21 141 200a 200b
200c GBM remission 31.7864 UD 29.3547 UD UD UD 39.7125 31.9339 UD
29.1258 UD UD UD 39.1993 GBM remission 33.5069 UD 32.0307 UD UD UD
UD 33.8544 UD 32.6707 UD UD UD UD GBM remission 35.658 UD 34.5313
UD UD UD UD 35.5648 UD 36.6153 UD UD UD UD NSCLC remission 33.9462
UD 32.8533 UD UD UD UD 33.2768 UD 33.3858 UD UD UD UD NSCLC
remission 28.28 UD 27.57 UD UD UD UD 28.28 UD 27.57 UD UD UD UD
NSCLC remission 35.02 UD 31.35 UD UD UD UD 35.02 UD 31.35 UD UD UD
UD Breast carcinoma remission 28.28 33.51 27.03 UD UD UD UD 28.28
33.51 27.03 UD UD UD UD
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Other Embodiments
[0126] 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
10123RNAHomo sapiens 1uacccuguag aaccgaauuu gug 23222RNAHomo
sapiens 2uagcuuauca gacugauguu ga 22322RNAHomo sapiens 3ugccuacuga
gcugauauca gu 22422RNAHomo sapiens 4ugccuacuga gcugaaacac ag
22522RNAHomo sapiens 5caucuuaccg gacagugcug ga 22622RNAHomo sapiens
6caucuuacug ggcagcauug ga 22722RNAHomo sapiens 7cgucuuaccc
agcaguguuu gg 22822RNAHomo sapiens 8caucuuccag uacaguguug ga
22922RNAHomo sapiens 9uaauacuguc ugguaaaacc gu 221022RNAHomo
sapiens 10ucccugagac ccuaacuugu ga 22
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