Gene signatures for cancer prognosis

a gene signature and cancer prognosis technology, applied in the field of molecular classification of diseases, can solve the problems of significant morbidity, unneeded treatment and their associated side effects, and often different severity of side effects, and achieve the effect of determining the prognosis of these cancers

Inactive Publication Date: 2012-03-01
MYRIAD GENETICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Cancer is a major public health problem, accounting for roughly 25% of all deaths in the United States.
Though many treatments have been devised for various cancers, these treatments often vary in severity of side effects.
For many of these patients, however, these treatments and their associated side effects and costs are unnecessary because the cancer in these patients is not aggressive (i.e., grows slowly and is unlikely to cause mortality or significant morbidity during the patient's lifetime).
Despite these advances, however, many patients are given improper cancer treatments and there is still a serious need for novel and improved tools for predicting cancer recurrence.

Method used

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  • Gene signatures for cancer prognosis
  • Gene signatures for cancer prognosis
  • Gene signatures for cancer prognosis

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0162]The following cell cycle gene (CCG) signature was tested for predicting time to chemical recurrence after radical prostatectomy.

31-CCG Prostate Recurrence SignatureAURKABUB1CCNB1CCNB2CDC2CDC20CDC45LCDCA8CENPACKS2DLG7DTLFOXM1HMMRKIF23KPNA2MAD2L1MELKMYBL2NUSAP1PBKPRC1PTTG1RRM2TIMELESSTPX2TRIP13TTKUBE2CUBE2SZWINT

[0163]Mean mRNA expression for the above 31 CCGs was tested on 440 prostate tumor FFPE samples using a Cox Proportional Hazard model in Splus 7.1 (Insightful, Inc., Seattle Wash.). The p-value for the likelihood ratio test was 3.98×10−5.

[0164]The mean of CCG expression is robust to measurement error and individual variation between genes. In order to determine the optimal number of cell cycle genes for the signature, the predictive power of the mean was tested for randomly selected sets of from 1 to 30 of the CCGs listed above. This simulation showed that there is a threshold number of CCGs in a panel that provides significantly improved predictive power.

example 2

[0165]In a univariate analysis a set of 31 CCGs (Table 3) was found to be a significant predictor of biochemical recurrence (p-value=1.8×10−9) after RP in prostate cancer patients. This signature was further evaluated to determine whether it added to an established clinical nomogram for prostate cancer recurrence (the Kattan-Stephenson nomogram). In summary, the nomogram was a highly significant predictor of recurrence (p-value 1.6×10−10) and, after adjusting for the nomogram, the CCG signature was a significant predictor of biochemical recurrence (p-value 4.8×10−5, Table 6).

Patients and Methods

[0166]Eight hundred four consecutive RP patients were followed for a median of 9.5 years. The patient characteristics and the treatment outcomes of the entire cohort have been previously reported (Swanson et al., UROL ONCOL. (2007) 25:110-114). Tissue blocks and / or slides from the final pathological evaluation with enough tissue for analysis were available for 430 patients. The cohort was div...

example 3

[0173]The following study aimed at determining the optimal number of CCGs to include in the signature. As mentioned above, CCG expression levels are correlated to each other so it was possible that measuring a small number of genes would be sufficient to predict disease outcome. In fact, single CCGs from the 31-gene set in Table 3 (Panel C) add significantly to the Kattan-Stephenson nomogram, as shown in Table 9 below (after adjustment for the nomogram and an interaction term between the nomogram and CCG expression):

TABLE 9Gene #Genep-value*1NUSAP12.8E−072DLG75.9E−073CDC26.0E−074FOXM11.1E−065MYBL21.1E−066CDCA83.3E−067CDC203.8E−068RRM27.2E−069PTTG11.8E−0510CCNB25.2E−0511HMMR5.2E−0512BUB18.3E−0513PBK1.2E−0414TTK3.2E−0415CDC45L7.7E−0416PRC11.2E−0317DTL1.4E−0318CCNB11.5E−0319TPX21.9E−0320ZWINT9.3E−0321KIF231.1E−0222TRIP131.7E−0223KPNA22.0E−0224UBE2C2.2E−0225MELK2.5E−0226CENPA2.9E−0227CKS25.7E−0228MAD2L11.7E−0129UBE2S2.0E−0130AURKA4.8E−0131TIMELESS4.8E−01*p-value for likelihood ratio tes...

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Abstract

Biomarkers and methods using the biomarkers for the prediction of the recurrence risk of cancer in a patient are provided.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application claims the priority benefit of U.S. Provisional Application Ser. No. 61 / 362,209 (filed on Jul. 7, 2010), which is hereby incorporated by reference in its entirety.FIELD OF THE INVENTION[0002]The invention generally relates to a molecular classification of disease and particularly to molecular markers for cancer prognosis and methods of use thereof.BACKGROUND OF THE INVENTION[0003]Cancer is a major public health problem, accounting for roughly 25% of all deaths in the United States. Though many treatments have been devised for various cancers, these treatments often vary in severity of side effects. It is useful for clinicians to know how aggressive a patient's cancer is in order to determine how aggressively to treat the cancer.[0004]For example, most patients with early-stage asymptomatic prostate cancer are treated with radical prostatectomy or radiotherapy and optionally adjuvant therapy (e.g., hormone or chemotherapy)...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): A61K45/00C40B30/04C40B60/12A61P35/00
CPCC12Q1/6886C12Q2600/158C12Q2600/118A61P35/00C12Q2600/178C12Q2600/156
Inventor STONE, STEVENGUTIN, ALEXANDERWAGNER, SUSANNEREID, JULIA
Owner MYRIAD GENETICS
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