Multimodal biomarkers overcome sampling bias to predict presence of aggressive localized prostate cancer.

209 Background: Histopathologic investigation of diagnostic prostate biopsies both confirms the presence of disease and estimates its potential for distal spread via tumour grade. The accuracy of biopsy grading is limited by intra-tumoral heterogeneity, inter-observer variability, and other factors. To improve risk stratification at the time of diagnosis, we sought to create objective molecular biomarkers of radical prostatectomy grade that are resistant to sampling error and should be useful when applied to biopsy tissue. Methods: We developed and validated a robust objective biomarker of prostate cancer grade using pathologic grading of prostatectomy tissues as the gold standard. We created training (333 patients) and validation (202 patients) cohorts of Cancer of the Prostate Risk Assessment (CAPRA) low- and intermediate-risk prostate cancer patients. To address intra-tumoral heterogeneity, each tumor was sampled at two locations. We profiled the abundance of 342 mRNAs complemented by 100 canonical DNA copy number aberration loci (CNAs) and 14 hypermethylation events. Using the training cohort with cross-validation, we evaluated models for training classifiers of pathologic Grade Group ≥2, Restricting to strategies resulting in true negative rates ≥0.5, true positive (TP) rates ≥0.8, we selected two strategies to train classifiers, PRONTO-e and PRONTO-m. Results: The PRONTO-e classifier comprises 353 mRNA and CNA features, while the PRONTO-m classifier comprises 94 mRNA, CNA, methylation and clinical features. Both classifiers (PRONTO-e, PRONTO-m) validated in the independent cohort, with respective TP rates of 0.809 and 0.760, false positive rates of 0.429 and 0.262, F1 scores of 0.709 and 0.724, and AUCs of 0.792 and 0.818. Conclusions: Two classifiers were developed and validated in separate cohorts, each achieved excellent performance by integrating different types of molecular data. Implementation of classifiers with these performance characteristics could markedly improve current active surveillance approaches without increasing patient morbidity and may help better inform patients on their individual need for definitive therapy versus active surveillance.