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Association of genetic variation of the six gene prognostic model for castration-resistant prostate cancer with survival.

ABSTRACT: BACKGROUND:We previously identified a blood RNA transcript-based model consisting of six immune or inflammatory response genes (ABL2, SEMA4D, ITGAL, C1QA, TIMP1, and CDKN1A) that was prognostic for survival in cohorts of men with castration-resistant prostate cancer (CRPC). We investigated whether inherited variation in these six genes was associated with overall survival (OS) in men with CRPC. METHODS:The test cohort comprised 600 patients diagnosed with CRPC between 1996 and 2011 at Dana-Farber Cancer Institute. Genotyping of 66 tagging single nucleotide polymorphisms (SNPs) spanning the six genes was performed on blood derived DNAs. For the top four SNPs (P

SUBMITTER: Xie W

PROVIDER: S-EPMC6476182 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Association of genetic variation of the six gene prognostic model for castration-resistant prostate cancer with survival.

Xie Wanling W Stopsack Konrad H KH Drouin Sarah J SJ Fu Henry H Pomerantz Mark M MM Mucci Lorelei A LA Lee Gwo-Shu M GM Kantoff Philip W PW

The Prostate 20180823 1

<h4>Background</h4>We previously identified a blood RNA transcript-based model consisting of six immune or inflammatory response genes (ABL2, SEMA4D, ITGAL, C1QA, TIMP1, and CDKN1A) that was prognostic for survival in cohorts of men with castration-resistant prostate cancer (CRPC). We investigated whether inherited variation in these six genes was associated with overall survival (OS) in men with CRPC.<h4>Methods</h4>The test cohort comprised 600 patients diagnosed with CRPC between 1996 and 201 ...[more]

PMID: 30141208

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Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer.

Project description:PurposePrognostic models for overall survival (OS) for patients with metastatic castration-resistant prostate cancer (mCRPC) are dated and do not reflect significant advances in treatment options available for these patients. This work developed and validated an updated prognostic model to predict OS in patients receiving first-line chemotherapy.MethodsData from a phase III trial of 1,050 patients with mCRPC were used (Cancer and Leukemia Group B CALGB-90401 [Alliance]). The data were randomly split into training and testing sets. A separate phase III trial served as an independent validation set. Adaptive least absolute shrinkage and selection operator selected eight factors prognostic for OS. A predictive score was computed from the regression coefficients and used to classify patients into low- and high-risk groups. The model was assessed for its predictive accuracy using the time-dependent area under the curve (tAUC).ResultsThe model included Eastern Cooperative Oncology Group performance status, disease site, lactate dehydrogenase, opioid analgesic use, albumin, hemoglobin, prostate-specific antigen, and alkaline phosphatase. Median OS values in the high- and low-risk groups, respectively, in the testing set were 17 and 30 months (hazard ratio [HR], 2.2; P < .001); in the validation set they were 14 and 26 months (HR, 2.9; P < .001). The tAUCs were 0.73 (95% CI, 0.70 to 0.73) and 0.76 (95% CI, 0.72 to 0.76) in the testing and validation sets, respectively.ConclusionAn updated prognostic model for OS in patients with mCRPC receiving first-line chemotherapy was developed and validated on an external set. This model can be used to predict OS, as well as to better select patients to participate in trials on the basis of their prognosis.

| S-EPMC3927736 | biostudies-literature

A robust blood gene expression-based prognostic model for castration-resistant prostate cancer.

Project description:BackgroundCastration-resistant prostate cancer (CRPC) is associated with wide variations in survival. Recent studies of whole blood mRNA expression-based biomarkers strongly predicted survival but the genes used in these biomarker models were non-overlapping and their relationship was unknown. We developed a biomarker model for CRPC that is robust, but also captures underlying biological processes that drive prostate cancer lethality.MethodsUsing three independent cohorts of CRPC patients, we developed an integrative genomic approach for understanding the biological processes underlying genes associated with cancer progression, constructed a novel four-gene model that captured these changes, and compared the performance of the new model with existing gene models and other clinical parameters.ResultsOur analysis revealed striking patterns of myeloid- and lymphoid-specific distribution of genes that were differentially expressed in whole blood mRNA profiles: up-regulated genes in patients with worse survival were overexpressed in myeloid cells, whereas down-regulated genes were noted in lymphocytes. A resulting novel four-gene model showed significant prognostic power independent of known clinical predictors in two independent datasets totaling 90 patients with CRPC, and was superior to the two existing gene models.ConclusionsWhole blood mRNA profiling provides clinically relevant information in patients with CRPC. Integrative genomic analysis revealed patterns of differential mRNA expression with changes in gene expression in immune cell components which robustly predicted the survival of CRPC patients. The next step would be validation in a cohort of suitable size to quantify the prognostic improvement by the gene score upon the standard set of clinical parameters.

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Development and validation of a prognostic model for overall survival in chemotherapy-naive men with metastatic castration-resistant prostate cancer.

Project description:Background:Prognostic models are needed that reflect contemporary practice for men with metastatic castration-resistant prostate cancer (mCRPC). We sought to identify predictive and prognostic variables for overall survival (OS) in chemotherapy-naïve men with mCRPC treated with enzalutamide. Patients and methods:Patients from the PREVAIL trial database (enzalutamide versus placebo) were randomly split 2 : 1 into training (n?=?1159) and testing (n?=?550) sets. Using the training set, 23 predefined variables were analyzed and a multivariable model predicting OS was developed and validated in an independent testing set. Results:Patient characteristics and outcomes were well balanced between training and testing sets; median OS was 32.7?months in each. The final validated multivariable model included 11 independent prognostic variables. Median OS for low-, intermediate-, and high-risk groups (testing set) defined by prognostic risk tertiles were not yet reached (NYR) (95% CI NYR-NYR), 34.2?months (31.5-NYR), and 21.1?months (17.5-25.0), respectively. Hazard ratios (95% CI) for OS in the low- and intermediate-risk groups versus high-risk group were 0.20 (0.14-0.29) and 0.40 (0.30-0.53), respectively. Secondary outcomes of response and progression differed widely in model-defined risk groups. Enzalutamide improved outcomes in all prognostic risk groups. Conclusions:Our validated prognostic model incorporates variables routinely collected in chemotherapy-naïve men with mCRPC treated with enzalutamide, identifying subsets of patients with widely differing survival outcomes that provide useful information for external validation, patient care, and clinical trial design. Trial registration:ClinicalTrials.gov: NCT01212991.

| S-EPMC6888025 | biostudies-literature

Association of Body Composition With Survival and Treatment Efficacy in Castration-Resistant Prostate Cancer.

Project description:Objectives: The association of body composition with survival and the efficacy of first-line treatment was investigated in patients with castration-resistant prostate cancer (CRPC). Methods: The records of CRPC patients treated with docetaxel or androgen receptor signaling inhibitors (ARSi) between 2005 and 2018 were reviewed. Skeletal muscle index (SMI), visceral fat index, and subcutaneous fat index were evaluated using pretreatment computed tomography images. Results: Of 230 eligible patients, 144 received docetaxel, and 86 received ARSi as the first-line treatment for CRPC. The SMIhi (based on median values) group had higher prostate-specific antigen (PSA) progression-free survival (median 13.5 vs. 8.3 months, p = 0.030), radiologic progression-free survival (14.9 vs. 9.1 months, p < 0.001), and overall survival (24.1 vs. 16.9 months, p = 0.015) than the SMIlo group. In docetaxel-treated patients, the SMIhi group had higher PSA progression-free survival (13.5 vs. 5.9 months, p = 0.016) and radiologic progression-free survival (14.6 vs. 6.7 months, p < 0.001) than the SMIlo group. However, PSA progression-free survival and radiologic progression-free survival were comparable between the two groups in ARSi-treated patients. SMI was independently associated with the risk of radiologic progression in patients treated with docetaxel but not in those treated with ARSi. Conclusions: High skeletal muscle mass may be associated with reduced risk of disease progression and mortality in patients with CRPC. However, the significance of these relationships is limited in patients treated with docetaxel. These results suggest that assessing skeletal muscle mass may be worthwhile when selecting treatments for CRPC; however, further prospective validation and large-scale studies are needed.

| S-EPMC7180747 | biostudies-literature

A whole-blood RNA transcript based prognostic model in castration-resistant prostate cancer

Project description:Peripheral blood from 62 men with castration resistant prostate cancer was collected between 8/2006 and 6/2008. A panel of 168 inflammation-related and prostate cancer related genes was assessed with quantitative PCR to assess biomarkers predictive of survival. qPCR profiling of whole blood from patients with castration-resistant prostate cancer.

2013-04-02 | E-GEOD-45705 | biostudies-arrayexpress

New Prognostic Biomarkers in Metastatic Castration-Resistant Prostate Cancer.

Project description:Prostate cancer is one of the most frequent cancers in men and is a common cause of cancer-related death. Despite significant progress in the diagnosis and treatment of this tumor, patients who relapse after radical treatments inevitably develop metastatic disease. Patient stratification is therefore key in this type of cancer, and there is an urgent need for prognostic biomarkers that can define patients' risk of cancer-related death. In the last 10 years, multiple prognostic factors have been identified and studied. Here, we review the literature available and discuss the most common aberrant genomic pathways in metastatic castration-resistant prostate cancer shown to have a prognostic relevance in this setting.

| S-EPMC7835961 | biostudies-literature

Prognostic Index Model for Progression-Free Survival in Chemotherapy-Naive Metastatic Castration-Resistant Prostate Cancer Treated With Abiraterone Acetate Plus Prednisone.

Project description:BACKGROUND:Radiographic progression-free survival (rPFS) is associated with overall survival (OS) in chemotherapy-naïve metastatic castration-resistant prostate cancer (mCRPC) patients. Using readily assessable baseline clinical and laboratory parameters, we developed a prognostic index model for rPFS in chemotherapy-naïve mCRPC patients without visceral disease who were treated with abiraterone acetate plus prednisone. METHODS:Data from the abiraterone acetate plus prednisone arm of COU-AA-302 were used. rPFS was defined based on modified Prostate Cancer Working Group 2 criteria. Baseline variables were assessed for association with rPFS through univariate Cox modeling. The lower (LLN) and upper (ULN) limits of laboratory normal were used to dichotomize most laboratory parameters; baseline median was used to dichotomize prostate-specific antigen (PSA). Prognostic factors for rPFS were identified by multivariate Cox modeling. Model accuracy was estimated by the C-index. RESULTS:Presence of lymph node metastasis (hazard ratio [HR] = 1.76, P < .0001), lactate dehydrogenase > ULN (234 IU/L) (HR = 1.71, P = .0001), ? 10 bone metastases (HR = 1.71, P = .0015), hemoglobin ? LLN (12.7 g/dL) (HR = 1.47, P = .0030) and PSA > 39.5 ng/mL (HR = 1.42, P = .0078) were associated with poor outcome. Patients were categorized into 3 prognostic groups (good, n = 230; intermediate, n = 152; poor, n = 164) based on number of risk factors. Median rPFS was calculated (27.6, 16.6, and 8.3 months for good, intermediate, and poor, respectively). The C-index was 0.83 (95% confidence interval = 0.73-0.91). CONCLUSIONS:The prognostic index model for rPFS reveals differential outcomes based on factors readily available in clinical practice. If validated, this model can be integrated into clinical practice and design of risk-stratified trials.

| S-EPMC5785489 | biostudies-literature

A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer.

Project description:Metastatic castration resistant prostate cancer (mCRPC) is one of the most common cancers with a poor prognosis. To improve prognostic models of mCRPC, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Consortium organized a crowdsourced competition known as the Prostate Cancer DREAM Challenge. In the competition, data from four phase III clinical trials were utilized. A total of 1600 patients' clinical information across three of the trials was used to generate prognostic models, whereas one of the datasets (313 patients) was held out for blinded validation. The previously introduced prognostic model of overall survival of chemotherapy-naive mCRPC patients treated with docetaxel or prednisone (so called Halabi model) was used as a performance baseline. This paper presents the model developed by the team TYTDreamChallenge and its improved version to predict the prognosis of mCRPC patients within the first 30 months after starting the treatment based on available clinical features of each patient. In particular, by replacing our original larger set of eleven features with a smaller more carefully selected set of only five features the prediction performance on the independent validation cohort increased up to 5.4 percent. While the original TYTDreamChallenge model (iAUC=0.748) performed similarly as the performance-baseline model developed by Halabi et al. (iAUC=0.743), the improved post-challenge model (iAUC=0.779) showed markedly improved performance by using only PSA, ALP, AST, HB, and LESIONS as features. This highlights the importance of the selection of the clinical features when developing the predictive models.

| S-EPMC6556990 | biostudies-literature

A whole-blood RNA transcript based prognostic model in castration-resistant prostate cancer

2013-04-02 | GSE45705 | GEO

Time to castration resistance is a novel prognostic factor of cancer-specific survival in patients with nonmetastatic castration-resistant prostate cancer.

Project description:We aimed to identify prognostic factors of cancer-specific survival (CSS) in non-metastatic castration-resistant prostate cancer (M0CRPC) patients. The final analysis of this retrospective cohort included 82 patients who were diagnosed as M0CRPC between 1998 and 2018 at the University of Tokyo Hospital. CRPC was defined as prostate-specific antigen (PSA) progression (increased PSA ≥ 25% and ≥ 2 ng/mL above the nadir or detection of a metastatic lesion). The median value of age and PSA at the time of CRPC were 76 (range 55-94) years and 2.84 (range 2.04-22.5) ng/mL, respectively. The median follow-up time from CRPC diagnosis was 38 (range 3-188) months. The prognostic factors of CSS were 'PSA doubling time (PSADT) ≤ 3 months', 'time to CRPC diagnosis from the start of androgen deprivation therapy (TTCRPC) ≤ 12 months', of which TTCRPC was a novel risk factor of CSS. In the multivariate analysis, 'PSADT ≤ 3 months' and TTCRPC ≤ 12 months' remained as statistically significant predictors of CSS. Novel risk stratification was developed based on the number of these risk factors. The high-risk group showed a hazard ratio of 4.416 (95% confidence interval 1.701-11.47, C-index = 0.727).

| S-EPMC9519913 | biostudies-literature

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