Methods for the identification, assessment, and treatment of patients with cancer therapy

• US 20060281122A1
• Filed: 06/08/2006
• Published: 12/14/2006
• Est. Priority Date: 06/08/2005
• Status: Active Grant

• Current Assignee: Millennium Pharmaceuticals Incorporated (Takeda Pharmaceutical Company Limited)
• Original Assignee: Millennium Pharmaceuticals Incorporated (Takeda Pharmaceutical Company Limited)
• Inventors: Bryant, Barbara M., Mulligan, George, Damokosh, Andrew I.
• Granted Patent: US 8,278,038 B2
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: A61K 31/69 Boron compounds
• CPC Class Codes: A61P 35/00 Antineoplastic agents
• CPC Class Codes: A61P 35/02 specific for leukemia
• CPC Class Codes: C12Q 1/6886 for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 2600/106 Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
• CPC Class Codes: C12Q 2600/136 Screening for pharmacological compounds
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G01N 2800/44 Multiple drug resistance
• CPC Class Codes: G01N 33/57407 Specifically defined cancers
• CPC Class Codes: G01N 33/57415 of breast
• CPC Class Codes: G01N 33/57419 of colon
• CPC Class Codes: G01N 33/57423 of lung
• CPC Class Codes: G01N 33/57426 leukemia
• CPC Class Codes: G01N 33/57434 of prostate
• CPC Class Codes: G01N 33/57438 of liver, pancreas or kidney
• CPC Class Codes: G01N 33/57449 of ovaries
• CPC Class Codes: G06Q 20/40 Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 : Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16H 10/40 : for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The present invention is directed to the identification of predictive markers that can be used to determine whether patients with cancer are clinically responsive or non-responsive to a therapeutic regimen prior to treatment. In particular, the present invention is directed to the use of certain individual and/or combinations of predictive markers, wherein the expression of the predictive markers correlates with responsiveness or non-responsiveness to a therapeutic regimen. Thus, by examining the expression levels of individual predictive markers and/or predictive markers comprising a marker set, it is possible to determine whether a therapeutic agent, or combination of agents, will be most likely to reduce the growth rate of tumors in a clinical setting.

Claims

1. A method for determining a cancer therapy regimen for treating a tumor in a patient comprising:

a) determining the level of expression of at least one predictive marker in a patient sample;

b) comparing the level of expression of the predictive marker or markers to a control expression level to determine whether the level of expression of the predictive marker is an informative expression level; and

b) determining a cancer therapy regimen for treating the tumor based on the expression of the predictive marker or markers,

wherein the predictive marker or markers is selected from the predictive markers identified in any one of Table 1A, Table 1B, Table 2A, Table 2B, and Table 3; and

wherein an informative expression level is indicative that the patient is either a responsive patient or a non-responsive patient.

2. The method of claim 1 wherein the level of expression of the predictive marker is determined by detection of mRNA.

3. The method of claim 1 wherein the level of expression of the predictive marker is determined by detection of protein.

4. The method of claim 1 wherein the cancer therapy regimen comprises a proteasome inhibition-based regimen therapy.

5. The method of claim 1, wherein the cancer therapy comprises a glucocorticoid therapy regimen.

6. The method of claim 1 wherein determining the informative expression level is determined by comparison with a control marker or by comparison to a predetermined standard.

7. The method of claim 1, wherein the tumor is selected from liquid or solid tumors.

8. The method of claim 7 wherein the liquid tumor is selected from the group consisting of myelomas, multiple myeloma, Non-Hodgkins Lymphoma, B-cell lymphomas, Waldenstrom'"'"'s syndrome, chronic lymphocytic leukemia, and other leukemias.

9. The method of claim 7, wherein the solid tumor is selected from the group selected from lung, breast, prostate, ovary, colon, kidney, and liver tumors.

10. The method of claim 1 wherein the informative expression level is determined by a predictive marker set comprising two or more predictive markers.

11. The method of claim 4, wherein the proteasome inhibition-based regimen for treating the tumor comprises treatment with a proteasome inhibitor is selected from the group consisting of a peptidyl aldehyde, a peptidyl boronic acid, a peptidyl boronic ester, a vinyl sulfone, an epoxyketone, and a lactacystin analog.

12. The method of claim 5, wherein the glucocorticoid therapy for treating the tumor comprises treatment with a glucocorticoid selected from the group consisting of dexamethasone, hydrocortisone, predisolone, prednisone, and triamcinolone.

13. The method of claim 1, wherein the patient sample comprising tumor cells is obtained from the subject any time selected from prior to tumor therapy, concurrently with tumor therapy or after tumor therapy.

14. A marker set for use in the method of claim 1 comprising at least two probesets selected from Table 1A, Table 1B, Table 2A, Table 2B, and Table 3.

15. A method for identifying a predictive marker or a predictive marker set comprising:

a) determining in a sample the level of expression of one or more predictive markers identified in any one of Table 1A, Table 1B, Table 2A, Table 2B, and Table 3;

b) applying a statistical analysis method to the expression level of each marker to select features associated with responsiveness or non-responsiveness; and

c) identifying a marker or marker set comprising the features selected.

16. The method of claim 15 wherein the level of expression of the predictive marker is determined by detection of mRNA.

17. The method of claim 15 wherein the level of expression of the predictive marker is determined by detection of protein.

18. The method of claim 15 wherein the sample is a tumor sample.

19. The method of claim 18, wherein the tumor is selected from liquid or solid tumors.

20. A marker set identified by the method of claim 15 comprising at least two markers selected from Table 1A, Table 1B, Table 2A, Table 2B, and Table 3.

21. The method of claim 15, wherein the statistical analysis method is the linear predictive score method.

22. The method of claim 15, wherein the statistical analysis method is the k-nearest neighbors model.

23. A kit for determining a proteasome inhibition therapy and/or glucocorticoid therapy for treating a tumor in a patient comprising reagents for assessing the expression of at least one predictive marker, and instructions for use.

24. The kit of claim 23 wherein the reagents comprise one or a plurality of nucleic acid probes, wherein the probe specifically binds at least one predictive marker.

25. The kit of claim 23 wherein the reagents comprise at least one detecting reagent selected from the group consisting of an antibody, an antibody derivative, an antibody fragment, and peptide probe, wherein the antibody, antibody derivative, antibody fragment or peptide probe specifically binds to a protein corresponding to at least one predictive marker.

26. A method to identify a candidate agent useful for treating cancer comprising:

a) contacting an assay composition comprising a tumor cell with a test agent;

b) determining the informative expression level of at least one marker selected from Table 1A, Table 1B, Table 2A, Table 2B, and Table 3;

c) identifying the test agent as a candidate agent if it induces an informative expression level typical of a responsive patient.

27. A method of deciding whether to pay for the treatment of cancer comprising:

a) obtaining the informative expression level of a predictive marker or a predictive marker set selected from or derived from Table 1A, Table 1B, Table 2A, Table 2B, and Table 3;

b) authorizing payment if the informative expression level identifies a responsive patient.