ena-STUDY-VANCOUVER PROSTATE CENTRE-31-01-2017-14:53:19:747-459
Stromal gene signature predictive for PCa metastasis
Design, setting, and participantsTo develop the signature, whole genome and transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models derived from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate risk cohort were used to validate the biomarkers. Outcome measurements and statistical analysisThe outcome measurements defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stromal metastasis signature (SMS). The association of SMS with metastasis was tested using the Wilcoxon rank sum. Performance was assessed using the area under the curve (AUC) of receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SMS, alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SMS is additive or synergistic to previously reported signatures.Results and limitationsA close association between stromal gene expression and metastatic phenotype was revealed. Accordingly, the SMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SMS scores were found to have worse prognoses. Furthermore, the SMS was found to be an independent prognostic factor, can stratify risk in intermediate risk PCa and improve performance of other previously reported biomarkers.Conclusions The profiling of stromal gene expression led to the development of a multi-gene signature that was validated to be an independently prognostic for the metastatic potential of prostate tumors. Patient summary A prognostic signature derived from stromal cells may lessen the problems associated with tumor heterogeneity and biopsy under-sampling. The significance will be PCa patients, and their physicians, making improved decisions regarding selection of active surveillance versus surgery and/or radiation therapy.
Design, setting, and participantsTo develop the signature, whole genome and transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models derived from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate risk cohort were used to validate the biomarkers. Outcome measurements and statistical analysisThe outcome measurements defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stromal metastasis signature (SMS). The association of SMS with metastasis was tested using the Wilcoxon rank sum. Performance was assessed using the area under the curve (AUC) of receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SMS, alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SMS is additive or synergistic to previously reported signatures.Results and limitationsA close association between stromal gene expression and metastatic phenotype was revealed. Accordingly, the SMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SMS scores were found to have worse prognoses. Furthermore, the SMS was found to be an independent prognostic factor, can stratify risk in intermediate risk PCa and improve performance of other previously reported biomarkers.Conclusions The profiling of stromal gene expression led to the development of a multi-gene signature that was validated to be an independently prognostic for the metastatic potential of prostate tumors. Patient summary A prognostic signature derived from stromal cells may lessen the problems associated with tumor heterogeneity and biopsy under-sampling. The significance will be PCa patients, and their physicians, making improved decisions regarding selection of active surveillance versus surgery and/or radiation therapy.