RNA Biomarkers as a Response Measure for Survival in Patients with Metastatic Castration-Resistant Prostate Cancer.
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ABSTRACT:
SUBMITTER: Boerrigter E
PROVIDER: S-EPMC8699291 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MicroRNAs (miRNAs) have emerged as key regulators of numerous biological processes, and increasing evidence suggests that circulating miRNAs may be useful biomarkers of clinical disease. In this study, we sought to identify plasma miRNAs that differentiate patients with metastatic castration resistant prostate cancer (mCRPC) from those with localized prostate cancer (PCa). Pooled plasma samples from patients with localized PCa or mCRPC (25 per group) were assayed using the Exiqon miRNA qPCR panel, and the differential expression of selected candidates was validated using qRT-PCR. We identified 63 miRNAs upregulated in mCRPC versus localized PCa, while only four were downregulated. Pearson's correlation analysis revealed two highly correlated groups: one consisting of miR-141, miR375 and miR-200c and the other including miR151-3p, miR423-3p, miR-126, miR152 and miR-21. A third group, containing miR-16 and miR-205, showed less correlation. One miRNA from each group (miR-141, miR151-3p and miR-16) was used for logistic regression analysis and proved to increase the sensitivity of the prostate-specific antigen (PSA) test alone. While no miRNA alone differentiated localized PCa and mCRPC, combinations had greater sensitivity and specificity. The expression of these 10 candidates was assayed for association with clinical parameters of disease progression through the cBio portal. Our results demonstrate that plasma levels of selected miRNAs are potential biomarkers to differentiate localized PCa and mCRPC.
| S-EPMC3645714 | biostudies-other
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
Project description:Radium-223 dichloride ([223Ra]RaCl2; Ra-223) is a targeted alpha-emitting radiopharmaceutical which results in an overall survival and health related quality of life (HRQoL) benefit in symptomatic patients with metastatic castration resistant prostate cancer (mCRPC) and predominantly bone metastasis. Although effective, options to select patients who will derive treatment benefit and to monitor and predict treatment outcomes are limited. PSA response and radiographic evaluation are commonly used in mCRPC treatment assessment but are not informative in Ra-223 treated patients. Consequently, there is a clear need for predictive and prognostic tools. In this review, we discuss the physiology of bone metastases and the mechanism of action and efficacy of Ra-223 treatment, as well as offering an outline of current innovative prognostic and predictive biomarkers.
| S-EPMC8431634 | biostudies-literature
Project description:To review and evaluate current literature on the US Food and Drug Administration (FDA)-approved drug enzalutamide (XTANDI(®)) in metastatic castration-resistant prostate cancer.Literature search was done through PubMed using the terms enzalutamide, MDV3100, abiraterone, and castration-resistant prostate cancer. Data from FDA product labels were also used.Recent and relevant studies were included in the review. Collected clinical trials were screened and evaluated.Enzalutamide is an androgen receptor (AR) inhibitor with high selectivity and affinity to the AR. It was approved by the FDA to treat metastatic castration-resistant prostate cancer in patients previously treated with docetaxel, after a Phase III trial (AFFIRM) that showed a 4.8-month survival benefit in this population. Recently, the FDA expanded the approval of enzalutamide as first-line therapy for metastatic castration-resistant prostate cancer (mCRPC) who did not receive chemotherapy. Moreover, enzalutamide is shown to be associated with an acceptable safety profile.Enzalutamide has been shown to be both safe and effective in improving overall survival in metastatic castration-resistant prostate cancer postchemotherapy with docetaxel and as a first line treatment before initiation of chemotherapy. However, additional studies and head-to-head trials are needed.
| S-EPMC4407758 | biostudies-other
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
Project description:BackgroundThe administration of docetaxel chemotherapy is one therapeutic option to delay disease progression and increase overall survival in metastatic castration resistant prostate cancer (mCRPC). However, about 15% of patients are primary resistant to chemotherapy and hence would benefit from an alternative mCRPC treatment. Despite intensive research, there are no robust clinical validated biomarkers to predict mCRPC therapy response. Thus, the aim of the study was to determine KDM5D expression in archival radical prostatectomy specimens of patients medicated with docetaxel at time of mCRPC development in order to correlate KMD5D expression with treatment response.MethodsWe used in situ hybridization (ISH) (RNA scope 2.5 HD) to determine KDM5D expression in tissue samples of 28 prostate cancer patients. KDM5D status was correlated to chemotherapy response (PSA and radiographic response).ResultsData revealed that KDM5D is significantly overexpressed in tumor cells (P<0.0001) but also in benign cells (P<0.02) of those patients who responded to chemotherapy compared to non-responders.ConclusionsTo summarize, KDM5D is a promising novel biomarker predicting response to docetaxel chemotherapy already at the time of localized disease and thus potentially avoiding metastatic biopsies in the mCRPC stage of disease.
| S-EPMC8575572 | biostudies-literature
Project description:BACKGROUND:In the SPARTAN study, compared with placebo, apalutamide added to ongoing androgen deprivation therapy significantly prolonged metastasis-free survival (MFS) and time to symptomatic progression in patients with high-risk non-metastatic castration-resistant prostate cancer (nmCRPC). Overall survival (OS) results at the first interim analysis (IA1) were immature, with 104 of 427 (24%) events required for planned final OS analysis. Here, we report the results of a second pre-specified interim analysis (IA2). METHODS:One thousand two hundred and seven patients with nmCRPC were randomized 2?:?1 to apalutamide (240?mg daily) or placebo. The primary end point of the study was MFS. Subsequent therapy for metastatic CRPC was permitted. When the primary end point was met, the study was unblinded. Patients receiving placebo who had not yet developed metastases were offered open-label apalutamide. At IA2, pre-specified analysis of OS was undertaken, using a group-sequential testing procedure with O'Brien-Fleming-type alpha spending function. Safety and second progression-free survival (PFS2) were assessed. RESULTS:Median follow-up was 41?months. With 285 (67% of required) OS events, apalutamide was associated with an improved OS compared with placebo (HR 0.75; 95% CI 0.59-0.96; P?=?0.0197), although the P-value did not cross the pre-specified O'Brien-Fleming boundary of 0.0121. Apalutamide improved PFS2 (HR 0.55; 95% CI 0.45-0.68). At IA2, 69% of placebo-treated and 40% of apalutamide-treated patients had received subsequent life-prolonging therapy for metastatic CRPC. No new safety signals were observed. CONCLUSION:In patients with nmCRPC, apalutamide was associated with a 25% reduction in risk of death compared with placebo. This OS benefit was observed despite crossover of placebo-treated patients and higher rates of subsequent life-prolonging therapy for the placebo group.
| S-EPMC6927320 | biostudies-literature
Project description:BackgroundPre-treatment androgen levels are associated with overall survival (OS) in patients with metastatic castration-resistant prostate cancer (CRPC) treated with androgen synthesis inhibitors. The current study sought to determine whether pre-treatment serum androgens predict clinical outcome among patients with metastatic CRPC treated with docetaxel chemotherapy.Materials and methodsData were obtained from 1050 men who were chemotherapy-naive prior to treatment with docetaxel, prednisone, and either bevacizumab or placebo (CALGB 90401). Pretreatment serum assays for testosterone, androstenedione, and dehydroepiandrosterone (DHEA) were performed with tandem liquid chromatography-mass spectrometry.ResultsMedian values for testosterone, androstenedione, and DHEA were 1.00, 13.50, and 8.12 ng/dL, respectively. The median was used to define the midpoint between low and high values. In univariate analysis, median OS for low versus high levels was 21.4 and 24.2 months for testosterone, 23.8 and 21.9 months for androstenedione, and 20.2 and 25.2 months for DHEA (P = NS). In multivariable analysis of all androgens, baseline DHEA was prognostic of ≥ 50% PSA decline from baseline (P = .008). In multivariable analysis adjusting for 10 known prognostic values and prior ketoconazole use for metastatic CRPC, a 10-unit increase in baseline testosterone increased risk of death (hazard ratio, 1.11; 95% confidence interval, 1.01-1.23; P = .039), whereas a 10-unit increase in androstenedione lowered risk of death (hazard ratio, 0.92; 95% confidence interval, 0.88-0.97; P = .001).ConclusionConsistent with prior studies, higher androstenedione levels in patients with metastatic CRPC treated with docetaxel are associated with improved survival. However pretreatment levels of other androgen levels are associated with varied effects on clinical outcome in chemotherapy-treated patients.
| S-EPMC7252587 | biostudies-literature
Project description:In this paper, we present our winning method for survival time prediction in the 2015 Prostate Cancer DREAM Challenge, a recent crowdsourced competition focused on risk and survival time predictions for patients with metastatic castration-resistant prostate cancer (mCRPC). We are interested in using a patient's covariates to predict his or her time until death after initiating standard therapy. We propose an iterative algorithm to multiply impute right-censored survival times and use ensemble learning methods to characterize the dependence of these imputed survival times on possibly many covariates. We show that by iterating over imputation and ensemble learning steps, we guide imputation with patient covariates and, subsequently, optimize the accuracy of survival time prediction. This method is generally applicable to time-to-event prediction problems in the presence of right-censoring. We demonstrate the proposed method's performance with training and validation results from the DREAM Challenge and compare its accuracy with existing methods.
| S-EPMC5321124 | biostudies-other
Project description:ImportanceRandomized clinical trials (RCTs) are considered the criterion standard for clinical evidence. Despite their many benefits, RCTs have limitations, such as costliness, that may reduce the generalizability of their findings among diverse populations and routine care settings.ObjectiveTo assess the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic castration-resistant prostate cancer (CRPC) when the model is applied to real-world data from electronic health records (EHRs).Design, setting, and participantsThe RCT-trained model and patient data from the RCTs were obtained from the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge for prostate cancer, which occurred from March 16 to July 27, 2015. This challenge included 4 phase 3 clinical trials of patients with metastatic CRPC. Real-world data were obtained from the EHRs of a tertiary care academic medical center that includes a comprehensive cancer center. In this study, the DREAM challenge RCT-trained model was applied to real-world data from January 1, 2008, to December 31, 2019; the model was then retrained using EHR data with optimized feature selection. Patients with metastatic CRPC were divided into RCT and EHR cohorts based on data source. Data were analyzed from March 23, 2018, to October 22, 2020.ExposuresPatients who received treatment for metastatic CRPC.Main outcomes and measuresThe primary outcome was the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic CRPC when the model is applied to real-world data. Model performance was compared using 10-fold cross-validation according to time-dependent integrated area under the curve (iAUC) statistics.ResultsAmong 2113 participants with metastatic CRPC, 1600 participants were included in the RCT cohort, and 513 participants were included in the EHR cohort. The RCT cohort comprised a larger proportion of White participants (1390 patients [86.9%] vs 337 patients [65.7%]) and a smaller proportion of Hispanic participants (14 patients [0.9%] vs 42 patients [8.2%]), Asian participants (41 patients [2.6%] vs 88 patients [17.2%]), and participants older than 75 years (388 patients [24.3%] vs 191 patients [37.2%]) compared with the EHR cohort. Participants in the RCT cohort also had fewer comorbidities (mean [SD], 1.6 [1.8] comorbidities vs 2.5 [2.6] comorbidities, respectively) compared with those in the EHR cohort. Of the 101 variables used in the RCT-derived model, 10 were not available in the EHR data set, 3 of which were among the top 10 features in the DREAM challenge RCT model. The best-performing EHR-trained model included only 25 of the 101 variables included in the RCT-trained model. The performance of the RCT-trained and EHR-trained models was adequate in the EHR cohort (mean [SD] iAUC, 0.722 [0.118] and 0.762 [0.106], respectively); model optimization was associated with improved performance of the best-performing EHR model (mean [SD] iAUC, 0.792 [0.097]). The EHR-trained model classified 256 patients as having a high risk of mortality and 256 patients as having a low risk of mortality (hazard ratio, 2.7; 95% CI, 2.0-3.7; log-rank P < .001).Conclusions and relevanceIn this study, although the RCT-trained models did not perform well when applied to real-world EHR data, retraining the models using real-world EHR data and optimizing variable selection was beneficial for model performance. As clinical evidence evolves to include more real-world data, both industry and academia will likely search for ways to balance model optimization with generalizability. This study provides a pragmatic approach to applying RCT-trained models to real-world data.
| S-EPMC7823224 | biostudies-literature