Androgen production, uptake, and conversion (APUC) genes define prostate cancer patients with distinct clinical outcomes.

BACKGROUND: Prostate cancer (PC) is driven by aberrant signaling of the androgen receptor (AR) or its ligands, and androgen deprivation therapies (ADT) are a cornerstone of treatment. ADT responsiveness may be associated with germline alterations in genes that regulate androgen production, uptake, and conversion (APUC). METHODS: We analyzed whole-exome sequencing (WES) and whole transcriptome sequencing (WTS) data from prostate tissues (SU2C/PCF, TCGA, GETx). We also interrogated the Caris POA DNA (592-gene/whole exome) and RNA (whole transcriptome) NGS databases. Algorithm for Linking Activity Networks (ALAN) was used to quantify all pairwise gene-to-gene associations. Real-world overall survival (OS) was determined from insurance claims data using Kaplan-Meier estimates. RESULTS: Six APUC genes (HSD3B1, HSD3B2, CYP3A43, CYP11A1, CYP11B1, CYP17A1) exhibited coalescent gene behavior in a cohort of metastatic tumors (n = 208). In the Caris POA dataset, the 6 APUC genes (APUC-6) exhibited robust clustering in primary prostate (n = 4,490) and metastatic (n = 2,593) biopsies. Surprisingly, tumors with elevated APUC-6 expression had statically lower expression of AR, AR-V7, and AR signaling scores suggesting ligand-driven disease biology. APUC-6 genes instead associated with the expression of alternative steroid hormone receptors, ESR1/2 and PGR. We used RNA expression of AR or APUC-6 genes to define two subgroups of tumors with differential association with hallmark pathways and cell surface targets. CONCLUSIONS: The APUC-6 high/AR-low tumors represented a subgroup of patients with good clinical outcomes in contrast to the AR-high or neuroendocrine prostate cancers. Altogether, measuring the aggregate expression of APUC-6 genes in current genomic tests identifies PCs that are ligand- (rather than AR-) driven and require distinct therapeutic strategies. FUNDING: NCI/NIH 1R37CA288972-01, NCI Cancer Center Support P30 CA077598, DOD W81XWH-22-2-0025, R01 CA249279.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts.

Resistance to androgen-deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform into emergent aggressive variant prostate cancer (AVPC), which has neuroendocrine (NE)-like features. In this work, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflect and retain key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. We compared 15 NE versus 33 AdCa samples, which included six different PDX tumors for each group in biological replicates, and identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of concordance from PDX tumor-matched protein and mRNA revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa. IMPLICATIONS: Overall, our study highlights the importance of protein-based identification when compared with RNA and provides a rich resource of new and feasible targets for clinical assay development and in understanding the underlying biology of these tumors.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts.

Resistance to androgen deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform to emergent aggressive variant prostate cancer (AVPC) which has neuroendocrine (NE)-like features. To this end, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflects and retains key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. When we compared 15 NE versus 33 AdCa PDX samples, we identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of protein and RNA concordance from these tumors revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems.

Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.