Integrative molecular profiling of routine clinical prostate cancer specimens.
BACKGROUND: Comprehensive molecular profiling led to the recognition of multiple prostate cancer (PCa) molecular subtypes and driving alterations, but translating these findings to clinical practice is challenging. PATIENTS AND METHODS: We developed a formalin-fixed paraffin-embedded (FFPE) tissue compatible integrative assay for PCa molecular subtyping and interrogation of relevant genetic/transcriptomic alterations (MiPC). We applied MiPC, which combines capture-based next generation sequencing and quantitative reverse transcription PCR (qRT-PCR), to 53 FFPE PCa specimens representing cases not well represented in frozen tissue cohorts, including 8 paired primary tumor and lymph node metastases. Results were validated using multiplexed PCR based NGS and Sanger sequencing. RESULTS: We identified known and novel potential driving, somatic mutations and copy number alterations, including a novel BRAF T599_V600insHT mutation and CYP11B2 amplification in a patient treated with ketoconazole (a potent CYP11B2 inhibitor). qRT-PCR integration enabled comprehensive molecular subtyping and provided complementary information, such as androgen receptor (AR) target gene module assessment in advanced cases and SPINK1 over-expression. MiPC identified highly concordant profiles for all 8 tumor/lymph node metastasis pairs, consistent with limited heterogeneity amongst driving events. MiPC and exome sequencing were performed on separately isolated conventional acinar PCa and prostatic small cell carcinoma (SCC) components from the same FFPE resection specimen to enable direct comparison of histologically distinct components. While both components showed TMPRSS2:ERG fusions, the SCC component exclusively harbored complete TP53 inactivation (frameshift variant and copy loss) and two CREBBP mutations. CONCLUSIONS: Our results demonstrate the feasibility of integrative profiling of routine PCa specimens, which may have utility for understanding disease biology and enabling personalized medicine applications.
Grasso, CS; Cani, AK; Hovelson, DH; Quist, MJ; Douville, NJ; Yadati, V; Amin, AM; Nelson, PS; Betz, BL; Liu, C-J; Knudsen, KE; Cooney, KA; Feng, FY; McDaniel, AS; Tomlins, SA
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