Abstract B046: Multimodal biomarkers that predict the presence of Gleason pattern 4: Potential impact for active surveillance

Purpose: Latent Grade Group (GG) ≥2 prostate cancer can impact the performance of active surveillance (AS) protocols. To date, molecular biomarkers for AS have relied solely on RNA or protein. We trained and independently validated multimodal (mRNA abundance, DNA methylation, and DNA copy number) biomarkers that more accurately separate GG1 from GG≥2 cancers. Materials and Methods: Low- and intermediate-risk prostate cancer patients were assigned to training (n=333) and validation (n=202) cohorts. We profiled the abundance of 342 mRNAs, 100 DNA copy number aberration (CNA) loci and 14 hypermethylation sites at two locations per tumor. Using the training cohort with cross- validation, we evaluated methods for training classifiers of pathologic GG≥2 in centrally reviewed radical prostectomies (RPs). We trained two distinct classifiers, PRONTO-e and PRONTO-m, and validated them in an independent RP cohort. Results: PRONTO-e comprises 353 mRNA and CNA features. PRONTO-m includes 94 clinical, mRNAs, CNAs and methylation features at 14 and 12 loci, respectively. In independent validation, PRONTO-e and PRONTO-m predicted GG≥2 with respective true positive rates of 0.81 and 0.76, false positive rates of 0.43 and 0.26. Both classifiers were resistant to sampling error and identified more upgraded men than a well-validated pre-surgical risk calculator, CAPRA (p