Molecular classification of hormone‐sensitive and castration‐resistant prostate cancer, using nonnegative matrix factorization molecular subtyping of primary and metastatic specimens

BACKGROUND: Despite the rapidly evolving therapeutic landscape, immunotherapy has demonstrated limited activity in prostate cancer. A greater understanding of the molecular landscape, particularly the expression of immune‐related pathways, will inform future immunotherapeutic strategies. Consensus nonnegative matrix factorization (cNMF) is a novel model of molecular classification analyzing gene expression data, focusing on biological interpretation of metagenes and selecting meaningful clusters. OBJECTIVE: We aimed to identify molecular subtypes of prostate cancer using cNMF and correlate these with existing biomarkers to inform future immunotherapeutic strategies. METHODS: A cohort of archival tumor specimens from hormone‐sensitive and castration‐resistant disease was studied. Whole transcriptomic profiles were generated using TruSeq RNA Access technology and subjected to cNMF. Comprehensive genomic profiling was performed with the FoundationOne assay. NMF subtypes were characterized by gene expression pathways, genomic alterations and correlated with clinical data, then applied to The Cancer Genome Atlas data set. RESULTS: We studied 164 specimens, including 52 castration‐resistant and 13 paired primary/metastatic specimens. cNMF identified four distinct subtypes. NMF1 (19%) is enriched for immune‐related and stromal‐related pathways with transforming growth factor β (TGFβ) signature. NMF2 (36%) is associated with FOXO‐mediated transcription signature and AKT signaling, NMF3 (26%) is enriched for ribosomal RNA processing, while NMF4 (19%) is enriched for cell cycle and DNA‐repair pathways. The most common gene alterations included TMPRSS22 (42%), TP53 (23%), and DNA‐repair genes (19%), occurring across all subtypes. NMF4 is significantly enriched for MYC and Wnt‐signaling gene alterations. TMB, CD8 density, and PD‐L1 expression were low overall. NMF1 and NMF4 were NMF2 was associated with superior overall survival. CONCLUSIONS: Using cNMF, we identified four molecularly distinct subtypes which may inform treatment selection. NMF1 demonstrates the most inflammatory signature with asuppressive TGFβ signature, suggesting potential benefit with immunotherapy combination strategies targeting TGFβ and PD‐(L)1. Prospective studies are required to evaluate the use of this novel model to molecularly stratify patients for optimal treatment selection.