Exploring the Impact of Treatment Switching on Overall Survival from the PROfound Study in Homologous Recombination Repair (HRR)-Mutated Metastatic Castration-Resistant Prostate Cancer (mCRPC)
Abstract:Background In oncology trials, treatment switching from the comparator to the experimental regimen is often allowed but may lead to underestimating overall survival (OS) of an experimental therapy. Objective This study evaluates the impact of treatment switching from control to olaparib on OS using the final survival data from the PROfound study and compares validated adjustment methods to estimate the magnitude of OS benefit with olaparib. Patients and methods The primary population from PROfound (Cohort A) w… Show more
“…For decades, cancer genomists have struggled to identify gene biomarkers whose altered sequence (e.g., Reference [1]) or/and expression (e.g., Reference [2]) is/are indicative for the prostate cancer (PCa) and could serve in active surveillance [3] of PCa development. The skillful handling of biomarkers was hoped to increase the survival rate (e.g., Reference [4]), destroy the cancer cells (e.g., Reference [5]) and reduce their proliferation (e.g., Reference [6]) and spreading (e.g., Reference [7]). Biomarkers' "smart" manipulation was also thought to block the PCa recurrence after various types of treatments (e.g., References [8,9]).…”
Many years and billions spent for research did not yet produce an effective answer to prostate cancer (PCa). Not only each human, but even each cancer nodule in the same tumor, has unique transcriptome topology. The differences go beyond the expression level to the expression control and networking of individual genes. The unrepeatable heterogeneous transcriptomic organization among men makes the quest for universal biomarkers and “fit-for-all” treatments unrealistic. We present a bioinformatics procedure to identify each patient’s unique triplet of PCa Gene Master Regulators (GMRs) and predict consequences of their experimental manipulation. The procedure is based on the Genomic Fabric Paradigm (GFP), which characterizes each individual gene by the independent expression level, expression variability and expression coordination with each other gene. GFP can identify the GMRs whose controlled alteration would selectively kill the cancer cells with little consequence on the normal tissue. The method was applied to microarray data on surgically removed prostates from two men with metastatic PCas (each with three distinct cancer nodules), and DU145 and LNCaP PCa cell lines. The applications verified that each PCa case is unique and predicted the consequences of the GMRs’ manipulation. The predictions are theoretical and need further experimental validation.
“…For decades, cancer genomists have struggled to identify gene biomarkers whose altered sequence (e.g., Reference [1]) or/and expression (e.g., Reference [2]) is/are indicative for the prostate cancer (PCa) and could serve in active surveillance [3] of PCa development. The skillful handling of biomarkers was hoped to increase the survival rate (e.g., Reference [4]), destroy the cancer cells (e.g., Reference [5]) and reduce their proliferation (e.g., Reference [6]) and spreading (e.g., Reference [7]). Biomarkers' "smart" manipulation was also thought to block the PCa recurrence after various types of treatments (e.g., References [8,9]).…”
Many years and billions spent for research did not yet produce an effective answer to prostate cancer (PCa). Not only each human, but even each cancer nodule in the same tumor, has unique transcriptome topology. The differences go beyond the expression level to the expression control and networking of individual genes. The unrepeatable heterogeneous transcriptomic organization among men makes the quest for universal biomarkers and “fit-for-all” treatments unrealistic. We present a bioinformatics procedure to identify each patient’s unique triplet of PCa Gene Master Regulators (GMRs) and predict consequences of their experimental manipulation. The procedure is based on the Genomic Fabric Paradigm (GFP), which characterizes each individual gene by the independent expression level, expression variability and expression coordination with each other gene. GFP can identify the GMRs whose controlled alteration would selectively kill the cancer cells with little consequence on the normal tissue. The method was applied to microarray data on surgically removed prostates from two men with metastatic PCas (each with three distinct cancer nodules), and DU145 and LNCaP PCa cell lines. The applications verified that each PCa case is unique and predicted the consequences of the GMRs’ manipulation. The predictions are theoretical and need further experimental validation.
“…Sensitivity analyses using RPSFTMs, a method used to adjust for crossover, showed that the HR for olaparib versus control was between 0.27 (95% CI, 0.19 to 0.75) and 0.40 (95% CI, 0.27 to 0.90), depending on the model selected (Appendix Fig A3A without recensoring, and Fig A3B with recensoring). 23…”
“…7 Recently, poly-ADP ribose polymerase inhibitors (PARPis) have been approved for the treatment of metastatic prostate cancer carrying HRR gene mutations. 8 Furthermore, HRR gene mutations are rendered sensitive to chemotherapy and immunotherapy. 9,10 Nonetheless, the molecular mechanisms and tumor immune profiles of HRR genes and their relevance to the response to clinical therapy in UTUC remain to be clarified.…”
Section: Introductionmentioning
confidence: 99%
“…Homologous recombination repair (HRR) participates in DNA repair mechanisms, which might contribute to the reorganization of chromosomes, genome instability, and cell apoptosis 7 . Recently, poly‐ADP ribose polymerase inhibitors (PARPis) have been approved for the treatment of metastatic prostate cancer carrying HRR gene mutations 8 . Furthermore, HRR gene mutations are rendered sensitive to chemotherapy and immunotherapy 9,10 .…”
BackgroundHomologous recombination (HR) repair (HRR) has been indicated to be a biomarker for immunotherapy, chemotherapy, and poly‐ADP ribose polymerase inhibitors inhibitors (PARPis). Nonetheless, their molecular correlates in upper tract urothelial carcinoma (UTUC) have not been well studied. This study aimed to explore the molecular mechanism and tumor immune profile of HRR genes and the relevance of their prognostic value in patients with UTUC.Materials and MethodsOne hundred and ninety‐seven tumors and matched blood samples from Chinese UTUC were subjected to next‐generation sequencing. A total of 186 patients from The Cancer Genome Atlas were included. Comprehensive analysis was performed.ResultsIn Chinese patients with UTUC, 5.01% harbored germline HRR gene mutations, and 1.01% had Lynch syndrome‐related genes. A total of 37.6% (74/197) of patients carried somatic or germline HRR gene mutations. There was marked discrepancy in the mutation landscapes, genetic interactions, and driver genes between the HRR‐mut cohorts and HRR‐wt cohorts. Aristolochic acid signatures and defective DNA mismatch repair signatures only existed in individuals in the HRR‐mut cohorts. Inversely, the unknown signature (signature A) and signature SBS55 only existed in patients in the HRR‐wt cohorts. HRR gene mutations regulated immune activities by NKT cells, plasmacytoid dendritic cells, hematopoietic stem cell, and M1 macrophages. In patients with local recurrence, patients with HRR gene mutations had poorer DFS rates than patients with wild‐type HRR genes.ConclusionsOur results imply that the detection of HRR gene mutations can predict recurrence in patients with UC. In addition, this study provides a path to explore the role of HRR‐directed therapies, including PARPis, chemotherapy, and immunotherapy.
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