Title:
Diagnosing and Monitoring CNS Malignancies Using MicroRNA
Kind Code:
A1


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, US)
Teplyuk, Nadiya (Boston, MA, US)
Kesari, Santosh (San Diego, CA, US)
Mollenhauer, Brit (Kassel, DE)
Application Number:
14/875367
Publication Date:
04/28/2016
Filing Date:
10/05/2015
Assignee:
The Brigham and Women's Hospital, Inc. (Boston, MA, US)
The Regents of the University of California (Oakland, CA, US)
Primary Class:
Other Classes:
435/6.12
International Classes:
C12Q1/68; A61K31/517
View Patent Images:



Other References:
Korpal et al, J. Biol. Chem. 283 (2), 14910 (2008)
Primary Examiner:
MARTINELL, JAMES
Attorney, Agent or Firm:
FISH & RICHARDSON P.C. (BO) (MINNEAPOLIS, MN, US)
Claims:
1. 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. 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

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. §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.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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.

TECHNICAL FIELD

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

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

The present invention is based on the identification of specific miRNAs that can serve as biomarkers for particular brain malignancies and disease activity.

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.

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.

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.

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.

In some embodiments, the algorithm is a linear algorithm or radial basis function.

In some embodiments, the algorithm is a linear algorithm comprising:

(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.

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.

In some embodiments, the methods further include administering the treatment to the subject.

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.

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.

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.

In some embodiments, the treatment includes administration of one or more of surgical resection, chemotherapy, or radiotherapy.

In some embodiments of the methods described herein, the sample comprises cerebrospinal fluid from a subject.

In some embodiments of the methods described herein, the subject is a human who has or is suspected of having a brain tumor.

In some embodiments of the methods described herein, the levels are determined using RT-PCR.

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.

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.

In some embodiments of the methods described herein, the primary brain tumor is a glioma, glioblastoma, hemangioma, or medulloblastoma.

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.

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.

Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

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.

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.

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).

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.

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.

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 ΔCt method with expression at Ct=36 set as one unit.

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 ΔCt method and normalized to miR-125b as described in Materials and methods. Error bars represent standard error of mean between technical duplicates.

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.

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.

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 ΔCt method as described. Error bars represent standard error of mean between technical duplicates.

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 ΔCt 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

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, exosomes2, 3, as well as in the blood serum4, 5 and other body fluids6 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 field7.

Several studies have reported miRNA detection, among several biological fluids, in CSF8-10, 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 brain11-13. 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.

A recent study demonstrated the usefulness of miRNA profiling in CSF for diagnostics of brain lymphoma10. 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.

Methods of Diagnosis/Detection of CNS Malignancies

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.

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.

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.

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, 4th Ed”, Blackwell Science (Boston, Mass., Oxford: Blackwell Scientific Publications, 2001).

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.

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.

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.

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.

Methods of Monitoring CNS Malignancies

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.

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.

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.

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.

Methods of Detection

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).

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 125I, 131I, 35S or 3H.

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.

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).

Human miRNA Sequences

The following table sets forth sequences for mature human miRNAs useful in the present methods.

MicroSEQ NO
RNAIDMature Sequence
miR-10b1UACCCUGUAGAACCGAAUUUGUG
miR-212UAGCUUAUCAGACUGAUGUUGA
miR-24-13UGCCUACUGAGCUGAUAUCAGU
miR-24-24UGCCUACUGAGCUGAAACACAG
miR-200a5CAUCUUACCGGACAGUGCUGGA
miR-200b6CAUCUUACUGGGCAGCAUUGGA
miR-200c7CGUCUUACCCAGCAGUGUUUGG
miR-1418CAUCUUCCAGUACAGUGUUGGA
miR-4299UAAUACUGUCUGGUAAAACCGU
miR-12510UCCCUGAGACCCUAACUUGUGA

Algorithms and Computer-Implemented Methods

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

The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

Materials and Methods

The following materials and methods were used in Examples 1-5, below.

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 Göttingen University Medical Center, Göttingen, 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° C. and stored in aliquots at −80° C. All tumor specimens were fresh-frozen on dry ice and stored at −80° C. until tested.

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 findings3. 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 μl 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 cells3 and/or destruction of the brain tissue in neoplastic conditions. miRNAs levels were calculated relative to corresponding miR-125b levels by the formula 2̂ΔCt, where ΔCt=CtmiR-125b−CtmiR-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).

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 1A
Groups of patients included in this study
GroupN Clinical/Pathology based diagnosis
Control15Non-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.
GBM19Glioblastoma multiforme (glioma grade IV)
Breast to Brain16Breast cancer metastasis to brain
Breast LM26Breast cancer leptomeningial metastasis
Lung to Brain28Lung cancer metastasis to brain
Lung LM4Lung 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 1B
Neurological diagnosis and individual characteristics
of patients included in CSF microRNA analysis
Year of
Clinical/PathologyTumorCSFsampleTime/way of sample
##based diagnosisgradecytologyAgeGendercollectioncollection
Control (Non-neoplastic neurological conditions)
1Non-specific painNoNegative50F2005No surgery/LP
syndrometumor
2HeadacheNoNegative33F2006No surgery/LP
tumor
3MemoryNoNegative77F2006No surgery/LP
problems, gaittumor
difficulty
4TrigeminalNoNegative67F2005No surgery/LP
neuralgiatumor
5Normal pressureNoNegative80M2006No surgery/LP
hydrocephalustumor
6Benign cerebellarNoNegative60M2006Year after surgery/LP
lesiontumor
7Hodgkin'sNoNegative33F2007No surgery/LP
disease, no CNStumor
cancer
8NeuropathyNoNegative28F2007No surgery/LP
tumor
9Encephalitis inNoNegative63M2007No surgery/LP
patient withtumor
leukemia
10DementiaNoNegative44F2007No surgery/LP
progressivetumor
11HeadacheNoNegative25M2005No surgery/LP
tumor
12HeadacheNoNegative40F2007No surgery/LP
tumor
13Parkinson DiseaseNoNegative71M2008No surgery/LP
tumor
14TransverseNoNegative43F2008No surgery/LP
myelitistumor
15TransverseNoNegative31F2008No surgery/LP
myelitistumor
GBM: Glioblastoma multiforme
1GBMIVNegative55F2007After surgery/LP/
before chemoradiation
2GBMIVPositive27F2007After
surgery/Ommaya/
after chemoradiation
3GBMIVPositive25F2008After surgery/LP/after
chemoradiation
4GBMIVNegative28M2007After surgery/LP/after
chemoradiation
5GBMIVPositive59M2007After surgery/LP/after
chemoradiation
6GBMIVNegative32M2007After surgery/LP/after
chemoradiation
7GBMIVNegative61F2008After surgery/LP/after
chemoradiation
8GBMIVNegative63M2009After surgery/LP/after
chemoradiation
9GBMIVNANANA2008During surgery/
Ommaya/
before chemoradiation
10GBMIVNANANA2008During surgery/
Ommaya
before chemoradiation
11GBMIVNANANA2008During surgery/
Ommaya
before chemoradiation
12GBMIVNANANA2008During surgery/
Ommaya
before chemoradiation
13GBMIVNANANA2008During surgery/
Ommaya
before chemoradiation
14GBMIVNANANA2008During surgery/
Ommaya
before chemoradiation
15GBMIVNANANA2008During surgery/
Ommaya
before chemoradiation
16GBMIVNANANA2008During surgery/
Ommaya
before chemoradiation
17GBMIVNANANA2008During surgery/
Ommaya
before chemoradiation
18GBMIVNegative61F2005After surgery/LP/after
chemoradiation
19GBMIVNA43F2010After surgery/LP/
before chemoradiation
Breast to Brain: breast cancer brain metastasis
1Breast carcinomaIVPositive55F2008No surgery/LP/after
brain metastasisradiation and during
chemotherapy
2Breast carcinomaIVPositive63F2008After
brain metastasissurgery/Ommaya/
after radiation and
during chemotherapy
3Breast carcinomaIVPositive54F2008No surgery/LP/after
brain metastasisradiation and during
chemotherapy
4Breast carcinomaIVPositive60F2008After
brain metastasissurgery/Ommaya/
after radiation and
during chemotherapy
5Breast carcinomaIVPositive55F2008After
brain metastasissurgery/Ommaya/
after radiation and
during chemotherapy
6Breast carcinomaIVPositive62F2008After
brain metastasissurgery/Ommaya/
after radiation and
during chemotherapy
7Breast carcinomaIVPositive54F2008After
brain metastasissurgery/Ommaya/
after radiation and
during chemotherapy
8Breast carcinomaIVPositive60F2008After surgery/LP/after
brain metastasisradiation and during
chemotherapy
9Breast carcinomaIVPositive54F2008After
brain metastasissurgery/Ommaya/
after radiation and
during chemotherapy
10Breast carcinomaIVPositive52F2008No surgery/after
brain metastasisradiation and during
chemotherapy
11Breast carcinomaIVPositive65F2008After
brain metastasissurgery/Ommaya/
after radiation and
during chemotherapy
12Breast carcinomaIVPositive48F2008After
brain metastasissurgery/Ommaya/
after radiation and
during chemotherapy
13Breast carcinomaIVPositive46F2008After surgery/LP/after
brain metastasisradiation and during
chemotherapy
14Breast carcinomaIVAtypical50F2008After surgery/LP/after
brain metastasisradiation and during
chemotherapy
15Breast carcinomaIVPositive55F2008After surgery/LP/after
brain metastasisradiation and during
chemotherapy
16Breast carcinomaIVPositive57F2008After surgery/LP/after
brain metastasisradiation and during
chemotherapy
Breast LM: breast cancer leptomeningial metastasis
1Breast carcinomaIVNegative42F2006No surgery/LP/after
leptomeningialradiation
metastasis
2Breast carcinomaIVPositive60F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
3Breast carcinomaIVPositive59F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
4Breast carcinomaIVPositive61F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
5Breast carcinomaIVPositive64F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
6Breast carcinomaIVPositive53F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
7Breast carcinomaIVPositive66F2007No surgery/LP/after
leptomeningialradiation
metastasis
8Breast carcinomaIVPositive54F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
9Breast carcinomaIVPositive60F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
10Breast carcinomaIVPositive63F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
11Breast carcinomaIVPositive66F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
12Breast carcinomaIVPositive60F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
13Breast carcinomaIVPositive55F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
14Breast carcinomaIVPositive56F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
15Breast carcinomaIVPositive44F2007After
leptomeningialsurgery/Ommaya/after
metastasisradiation and during
chemotherapy
16Breast carcinomaIVPositive58F2007After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
17Breast carcinomaIVPositive54F2007No surgery/LP/after
leptomeningialradiation and during
metastasischemotherapy
18Breast carcinomaIVNegative45F2007No surgery/LP/after
leptomeningialradiation and during
metastasischemotherapy
19Breast carcinomaIVNegative60F2008No surgery/LP/after
leptomeningialradiation and during
metastasischemotherapy
20Breast carcinomaIVPositive51F2008After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
21Breast carcinomaIVPositive29F2008No surgery/LP/after
leptomeningialradiation and during
metastasischemotherapy
22Breast carcinomaIVPositive69F2008No surgery/LP/after
leptomeningialradiation and during
metastasischemotherapy
23Breast carcinomaIVPositive61F2008NA
leptomeningial
metastasis
24Breast carcinomaIVPositive64F2008No surgery/LP/after
leptomeningialradiation and during
metastasischemotherapy
25Breast carcinomaIVPositive63F2008No surgery/LP
leptomeningial
metastasis
26Breast carcinomaIVPositive59F2008After
leptomeningialsurgery/Ommaya/
metastasisafter radiation and
during chemotherapy
Lung to Brain: lung cancer brain metastasis
1Lung cancer brainIVPositive56F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
2Lung cancer brainIVPositive59F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
3Lung cancer brainIVPositive56F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
4Lung cancer brainIVPositive68F2007No surgery/LP
metastasis
5Lung cancer brainIVPositive69M2007No surgery/LP/after
metastasisradiation
6Lung cancer brainIVPositive71M2007No surgery/LP/after
metastasisradiation and during
chemotherapy
7Lung cancer brainIVPositive66F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
8Lung cancer brainIVPositive63F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
9Lung cancer brainIVPositive60F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
10Lung cancer brainIVPositive59F2007No surgery/LP
metastasis
11Lung cancer brainIVPositive55M2008No surgery/LP
metastasis
12NSCLC brainIVNegative66F2008No surgery/LP
metastasis
13Lung cancer brainIVPositive62F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
14Lung cancer brainIVPositive64F2006No surgery/LP
metastasis
15Lung cancer brainIVPositive64F2006No surgery/LP
metastasis
16Lung cancer brainIVNegative46F2007No surgery/LP
metastasis
17Lung cancer brainIVPositive64F2007No surgery/LP
metastasis
18NSLC brainIVNegative50M2007No surgery/LP
metastasis
19NSCLC brainIVPositive56M2007No surgery/LP/after
metastasisradiation and during
chemotherapy
20NSCLC brainIVPositive49F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
21Lung cancer brainIVPositive42M2007No surgery/LP/after
metastasisradiation and during
chemotherapy
22Lung cancer brainIVPositive56F2007No surgery/LP/after
metastasisradiation and during
chemotherapy
23Lung cancer brainIVPositive58F2008No surgery/LP/after
metastasisradiation and during
chemotherapy
24NSCLC brainIVPositive48M2008No surgery/LP
metastasis
25MSCLC brainIVNegative54F2008No surgery/LP
metastasis
26NSCLC brainIVNegative61F2008No surgery/LP
metastasis
27NSCLC brainIVNA51F2010After surgery/
metastasisOmmaya after
radiation and during
chemotherapy
28NSCLC brainIVNA66F2010No surgery/LP after
metastasisradiation and during
chemotherapy
Lung LM: lung cancer leptomeningial metastasis
1Lung cancerIVPositive67F2006No surgery/LP
leptomeningial
metastasis
2SCLCIVNegative52M2007No surgery/LP
leptomeningial
metastasis
3Lung cancerIVNegative56F2008No surgery/LP
leptomeningial
metastasis
4NSCLCIVNA63M2010No surgery/LP/after
leptomeningialradiation and
metastasischemotherapy
NA = not available,
NSCLC—non-small cell lung carcinoma,
SCLC—small cell lung carcinoma/

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.

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.

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 described3.

Example 1

miR-10b is Present and miR-21 is Elevated in CSF of Glioblastoma and Brain Metastasis Patient

To identify miRNA biomarkers for GBM, a candidate approach was used based on previous miRNA profiling data3, 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 GBM16, 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 cancers18,19.

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 ˜90% of GBM tumors15. 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.

Next CSF levels were assessed for another candidate miRNA, miR-21, which is the most common miRNA elevated in GBM and other cancers20 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.

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 2A
Accuracies of classification of brain tumors by SVM analysis.
Instances classified
in the test sets
ComparisonCorrectlyIncorrectly
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 versus105 (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 2B
miRNA
Type#125b10b21141200a200b200c
Non-neoplastic134.2697UD33.3324UDUDUDUD
Non-neoplastic133.9405UD33.0829UDUDUDUD
Non-neoplastic233.0152UD33.5002UDUDUDUD
Non-neoplastic232.799UD32.9746UDUDUDUD
Non-neoplastic332.9036UD33.707UDUDUDUD
Non-neoplastic333.5036UD33.5222UDUDUDUD
Non-neoplastic432.1067UD32.5033UDUDUDUD
Non-neoplastic432.2493UD32.8214UDUDUDUD
Non-neoplastic533.8516UD33.258UDUDUDUD
Non-neoplastic535.8576UD32.7309UDUDUDUD
Non-neoplastic632.4644UD28.6672UDUDUDUD
Non-neoplastic632.4621UD28.7054UDUDUDUD
Non-neoplastic731.6864UD35.2616UD37.4531UDUD
Non-neoplastic732.1712UD35.0806UD37.2431UDUD
Non-neoplastic832.0006UD32.1841UDUDUDUD
Non-neoplastic831.7911UD31.7029UDUDUDUD
Non-neoplastic934.5177UD30.3603UDUDUDUD
Non-neoplastic935.5515UD30.6514UDUDUDUD
Non-neoplastic1032.5169UD32.9137UDUDUDUD
Non-neoplastic1032.781UD32.3816UDUDUDUD
Non-neoplastic1130.661UD30.635UDUDUDUD
Non-neoplastic1130.706UD30.528UDUDUDUD
Non-neoplastic1230.396UD30.993UDUDUDUD
Non-neoplastic1230.159UD31.398UDUDUDUD
Non-neoplastic1329.798UD38.9142UDUDUDUD
Non-neoplastic1329.469UD38.9142UDUDUDUD
Non-neoplastic1437.111UD36.431UDUDUDUD
Non-neoplastic1436.750UD35.824UDUDUDUD
Non-neoplastic1532.311UD33.307UDUDUDUD
Non-neoplastic1531.782UD33.483UDUDUDUD
GBM128.49335.447424.8591UDUDUDUD
GBM128.334736.166925.0358UDUDUDUD
GBM230.27UD28.5448UDUDUDUD
GBM229.8595UD28.7406UDUDUDUD
GBM325.560733.396122.083636.80733.548836.665836.6814
GBM324.358233.057622.198235.710533.208637.059737.1643
GBM424.942537.844623.4126UD35.597UD34.1835
GBM424.887137.068122.9477UD35.0309UD34.1049
GBM534.2504UD33.3238UDUDUDUD
GBM534.4141UD33.2358UDUDUDUD
GBM625.991736.206621.9135UD35.2526UDUD
GBM625.762536.206621.6147UD34.1246UDUD
GBM729.295933.485729.3222UD37.1513UDUD
GBM729.153233.184828.6781UD36.9511UDUD
GBM829.762830.880833.2773UDUDUDUD
GBM829.669630.711232.7008UDUDUDUD
GBM929.546336.92622.449428.5888UD31.3221UD
GBM929.891238.072322.445529.173UD31.7444UD
GBM1018.830128.256521.303534.176830.67335.20230.9622
GBM1019.178128.315320.110635.305231.350134.520832.0136
GBM1119.065325.399219.944635.779330.323734.358735.3505
GBM1119.097525.398520.591735.466329.864334.23436.6375
GBM1221.478529.500722.552934.393832.340336.322833.6589
GBM1221.478530.540422.074535.643732.856535.983833.3638
GBM1320.606928.042722.866938.440829.710834.463831.5322
GBM1321.106127.674422.419536.401531.137333.869532.1085
GBM1420.572629.013319.889335.069931.041235.418632.4751
GBM1420.440929.247620.175336.056731.522635.439333.3155
GBM1528.042934.469831.1034UDUDUDUD
GBM1528.349334.968231.2799UDUDUDUD
GBM1618.945429.259420.210133.9212UD34.754330.0307
GBM1619.094929.099519.801734.5306UD34.005631.1451
GBM1719.056325.71319.684135.334328.519831.204331.0678
GBM1719.310626.070519.688135.019429.359731.478931.798
GBM1831.13834.45926.774UDUDUDUD
GBM1831.55535.21526.695UDUDUDUD
GBM1928.15733.49627.861UDUDUDUD
GBM1927.88334.53927.602UDUDUDUD
Breast to Brain127.817432.013921.163929.507826.061831.426427.1292
Breast to Brain127.256831.70620.67529.325926.250530.920927.7123
Breast to Brain232.6303UD28.009537.136531.057832.667231.5072
Breast to Brain232.5818UD27.749237.677531.050132.444131.8525
Breast to Brain325.780831.309220.141429.135927.100930.533828.0328
Breast to Brain325.97731.339920.177429.216826.802430.224728.4686
Breast to Brain431.153238.823923.478732.057826.443729.472830.6951
Breast to Brain431.3755UD23.586232.880226.997829.192231.5229
Breast to Brain529.626836.803825.634529.854224.48327.190728.9925
Breast to Brain530.218736.26225.010532.386424.48327.290929.4038
Breast to Brain630.3481UD25.575230.787324.6728.521626.9064
Breast to Brain630.709UD27.151431.787324.718528.202726.8947
Breast to Brain735.425136.520428.053632.713427.807430.257132.2786
Breast to Brain735.925136.520428.261232.393528.025829.911333.1268
Breast to Brain830.542336.566727.814732.305429.524532.394329.0791
Breast to Brain830.185836.875227.863132.167429.914732.533228.0088
Breast to Brain932.1644UD25.913931.703828.126430.343530.0191
Breast to Brain933.1737UD25.855832.179228.104130.103530.2432
Breast to Brain1028.377437.123125.10828.444427.226831.083426.2144
Breast to Brain1028.822836.186925.097228.83526.549931.110925.9687
Breast to Brain1133.2952UD30.864UD33.407338.179633.7632
Breast to Brain1132.6806UD30.8002UD35.706537.098833.3951
Breast to Brain1230.04432.84625.18030.02030.64132.69929.391
Breast to Brain1229.70934.23425.41430.46130.45232.99230.033
Breast to Brain1330.36836.82627.30733.81633.11735.07231.908
Breast to Brain1330.41736.92027.26133.34032.60435.08132.021
Breast to Brain1421.50825.70823.92035.28935.60335.80034.705
Breast to Brain1421.41425.61723.74236.76335.47638.21334.781
Breast to Brain1529.37836.87626.88630.66730.53932.78929.405
Breast to Brain1529.45736.37626.60130.67830.33332.18329.738
Breast to Brain1630.96636.32430.59234.49234.03536.77832.881
Breast to Brain1630.69937.01430.74034.93333.61736.98032.690
Breast LM130.63135.60428.65135.95435.557UD35.152
Breast LM130.51935.56828.45237.28235.76338.580UD
Breast LM226.99734.31820.78129.00026.65928.95427.883
Breast LM226.88634.17820.39529.11126.41228.87128.265
Breast LM324.42331.05419.16527.76725.22527.43326.237
Breast LM324.28431.13018.99227.96725.00827.40726.622
Breast LM428.28335.54822.32430.80026.52630.47029.647
Breast LM428.12334.50222.09530.90026.42530.63829.759
Breast LM524.74831.46519.23829.50826.46628.15627.618
Breast LM524.73531.25319.16229.59126.34728.03927.623
Breast LM625.16431.74619.54729.87027.44028.65328.036
Breast LM625.09731.74219.46730.26927.27128.57928.192
Breast LM731.29734.89928.89538.34536.188UD28.182
Breast LM731.27534.05428.71038.81536.763UD28.202
Breast LM825.55031.41420.53930.36328.00130.20329.081
Breast LM825.38231.94120.38931.11028.09729.72829.224
Breast LM925.43632.24819.75129.83927.77829.80228.736
Breast LM925.38132.31019.66830.26627.70529.56629.577
Breast LM1026.17432.97020.03632.30528.69130.72229.632
Breast LM1026.06232.31319.91632.08028.71231.07129.973
Breast LM1129.22135.17424.55736.69133.05533.91532.966
Breast LM1129.20434.31624.50936.17732.81533.13733.631
Breast LM1230.453UD27.95833.65430.87133.83331.953
Breast LM1230.371UD28.00233.77230.84633.24232.321
Breast LM1327.00633.42422.23933.26329.57130.88130.444
Breast LM1327.00633.53522.29333.28629.47030.81030.672
Breast LM1425.78433.43620.02527.45324.73626.46225.903
Breast LM1425.72333.89719.95327.67424.60126.38926.229
Breast LM1528.63334.99826.28432.96130.16231.95530.838
Breast LM1528.42835.18126.16533.11030.14831.75331.015
Breast LM1628.80735.44226.53732.34830.37332.30131.592
Breast LM1628.68034.98826.35533.17530.41632.01131.681
Breast LM1729.26824.63021.23929.99528.91129.92027.692
Breast LM1729.09724.60520.88730.36328.88629.76228.305
Breast LM1829.70231.96826.40631.07330.50132.82029.712
Breast LM1829.96931.51426.26031.50830.43032.74129.802
Breast LM1926.52731.47722.03528.35830.71630.16526.926
Breast LM1926.52631.65421.96728.39230.71330.01527.044
Breast LM2026.37335.27619.59026.37127.90125.01124.178
Breast LM2026.27034.66519.54426.43827.63125.08924.138
Breast LM2128.12334.41423.24529.88523.27531.39828.881
Breast LM2128.13434.24523.25729.83123.04631.54228.934
Breast LM2232.904UD29.29334.77334.71536.43833.616
Breast LM2233.028UD29.12734.57134.32137.54833.449
Breast LM2327.23335.30821.98628.63929.88331.05627.869
Breast LM2327.15636.09422.03228.65429.87831.04928.177
Breast LM2428.14933.31625.13727.72027.84230.90126.319
Breast LM2427.94732.85524.88227.92627.99530.79326.763
Breast LM2527.65934.22719.33026.77523.65724.03223.402
Breast LM2527.36234.60319.13527.10423.41623.95324.071
Breast LM2631.169UD25.42028.28925.46830.13726.360
Breast LM2630.721UD25.25028.57225.30530.11926.642
Lung to Brain127.302731.549622.6511525.336825.218628.933324.1757
Lung to Brain127.298831.105823.0511525.380725.156528.345323.8634
Lung to Brain229.844334.849725.151932.175731.336334.951630.1594
Lung to Brain229.774134.725324.577231.956531.694635.330229.1884
Lung to Brain333.0843UD29.351134.317534.451437.024733.1313
Lung to Brain333.5869UD29.450634.751134.822836.785532.3656
Lung to Brain432.6941UD28.291133.983631.545533.297630.4481
Lung to Brain432.6056UD27.160832.780231.242833.044430.4042
Lung to Brain530.204934.896824.776830.443628.425630.240527.5537
Lung to Brain529.510534.772524.062930.553828.195529.989227.1272
Lung to Brain632.585136.625529.725335.412733.765835.532430.491
Lung to Brain632.785137.444329.718435.116633.117635.050831.0042
Lung to Brain729.26133.499124.23228.926828.560530.4627.6959
Lung to Brain728.416333.066323.884828.918928.31230.70627.9061
Lung to Brain830.481434.68722.307628.655327.645230.331629.4116
Lung to Brain830.77635.204721.980229.070127.633329.27228.661
Lung to Brain930.295634.34926.894130.886329.444131.352731.2236
Lung to Brain929.911533.538427.494131.09129.560731.594529.1472
Lung to Brain1029.163835.025522.692429.955432.81731.066627.3901
Lung to Brain1029.435334.496623.154130.09732.910731.033127.2599
Lung to Brain1127.446333.465221.157826.998825.773228.966125.0689
Lung to Brain1127.326134.137120.966726.314925.401928.083224.8732
Lung to Brain1232.8667UD30.8165UDUDUD38.2814
Lung to Brain1232.2667UD30.3494UDUDUD37.08
Lung to Brain1334.1699UD24.421530.494229.287431.581331.9309
Lung to Brain1334.2134UD24.220630.090629.084231.581332.2984
Lung to Brain1429.29334.57124.39430.78929.54433.05728.864
Lung to Brain1429.00935.56324.53230.83829.37732.95628.902
Lung to Brain1528.91434.55022.56029.64428.60030.86627.167
Lung to Brain1528.70734.49522.62729.67828.69330.34727.103
Lung to Brain1626.60131.99122.15527.35126.55828.98226.586
Lung to Brain1626.45832.22022.24327.76026.26528.98027.004
Lung to Brain1730.36535.32222.83728.90428.36430.99427.650
Lung to Brain1730.36835.50522.64028.75127.74431.05227.517
Lung to Brain1830.31035.76229.54834.88235.96139.60733.730
Lung to Brain1830.16237.35229.50135.20335.80838.41134.555
Lung to Brain1929.63032.01624.96427.43128.61730.52627.507
Lung to Brain1929.59431.72024.96227.68128.63230.39827.934
Lung to Brain2028.500UD23.14726.76228.60729.80125.805
Lung to Brain2028.472UD23.18326.85728.42929.77825.829
Lung to Brain2126.38333.93721.26629.48430.96431.93628.164
Lung to Brain2126.39833.08121.29929.66430.76631.88628.331
Lung to Brain2227.58936.41424.19831.10733.12035.06330.855
Lung to Brain2227.68136.38724.16331.49932.54434.37930.925
Lung to Brain2327.33533.31120.27526.18327.80329.31026.190
Lung to Brain2327.20332.89720.19826.49727.69829.15526.421
Lung to Brain2431.18833.76124.35130.84331.06132.67830.078
Lung to Brain2431.06634.49824.57631.00630.77032.63929.865
Lung to Brain2525.43833.67722.27627.03026.48528.16725.754
Lung to Brain2525.25732.73422.33327.05526.32028.05825.845
Lung to Brain2627.95735.62226.27230.66429.90032.14528.598
Lung to Brain2627.77035.34925.91230.72129.98932.02928.710
Lung to Brain2727.79135.92423.31430.59729.88731.73729.783
Lung to Brain2727.71936.97222.87031.18829.90032.04930.955
Lung to Brain2827.60034.33822.52926.37028.08831.17426.558
Lung to Brain2827.49834.90521.96826.74227.80031.00926.244
Lung LM128.65230.28222.13725.73824.66527.19025.600
Lung LM128.60630.40021.84326.25024.55727.09725.948
Lung LM227.79533.78824.94839.42536.26137.18437.034
Lung LM227.93432.65324.84638.60636.60637.70236.898
Lung LM327.47837.81231.80129.97429.56931.30328.059
Lung LM327.31037.20031.66430.03429.44631.18128.566
Lung LM427.58832.72619.65624.35724.41927.41324.179
Lung LM427.62732.72319.47224.37624.36927.21324.289
UD = Undetermined

Example 2

miR-200 Family in the CSF is Indicative of Brain Metastasis

miR-10b is expressed in most extracranial tissues21, 22 (FIGS. 7A-B), and abundant in blood serum23. 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.

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 brain22, 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).

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.

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 cancer25. 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 profiles22 but miR-1 was undetectable in CSF of both breast and lung cancer cohorts of patients. Breast and lung carcinomas express strikingly similar miRNA repertoire21. 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

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 cancers26-28.

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.

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.

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-200a+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*miR-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-200a+−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-200a+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.

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.

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

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 brain14, 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

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.

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 concentration29, 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).

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.

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 3
miRNA Ct values125b10b21141200a200b200c
GBM remission31.7864UD29.3547UDUDUD39.7125
31.9339UD29.1258UDUDUD39.1993
GBM remission33.5069UD32.0307UDUDUDUD
33.8544UD32.6707UDUDUDUD
GBM remission35.658UD34.5313UDUDUDUD
35.5648UD36.6153UDUDUDUD
NSCLC remission33.9462UD32.8533UDUDUDUD
33.2768UD33.3858UDUDUDUD
NSCLC remission28.28UD27.57UDUDUDUD
28.28UD27.57UDUDUDUD
NSCLC remission35.02UD31.35UDUDUDUD
35.02UD31.35UDUDUDUD
Breast carcinoma remission28.2833.5127.03UDUDUDUD
28.2833.5127.03UDUDUDUD

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Other Embodiments

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.