Pathogenic Genomic Alterations in Circulating Tumor DNA Predict Overall Survival in Men with Metastatic Castrate-resistant Prostate Cancer.

Abstract

Although validated prognostic models exist for men with metastatic castration-resistant prostate cancer (mCRPC), current tools do not incorporate genomic biomarkers such as circulating tumor DNA (ctDNA) aneuploidy or pathogenic genetic alterations (PGAs). This study aimed to estimate the prevalence of PGAs in ctDNA, assess their correlation with ctDNA aneuploidy fraction, and evaluate their association with overall survival (OS). Additionally, we developed and validated a clinical-genetic (CG) model to predict OS. We analyzed ctDNA from 776 patients enrolled in the Alliance phase 3 trial (A031201). PGAs were derived using the AR-ctDETECT assay. The net reclassification improvement (NRI) evaluated the added value of the CG model, and the time-dependent area under the receiver operating characteristic curve (tAUC) assessed the accuracy of the OS model. Feature selection using random survival forest identified gains in androgen receptor (AR), AR enhancer, MYC, RSPO2, CCND1, BRAF, and MET; losses or pathogenic variants in ZBTB16, PTEN, MSH6, PPP2R2A, NKX3-1, ZFHX3, TP53, ZNFR3, RB1, FANCA, CHECK1, APC, and CHD1; and a pathogenic AR variant. The CG model significantly outperformed the clinical model, with an average tAUC of 0.77 (95% confidence interval [CI]: 0.73-0.79) for the CG model compared with the tAUC of 0.72 (95% CI: 0.69-0.75, p = 0.01) for the clinical model, and NRI of 0.29 between the models. Patients were categorized by the predicted risk scores into poor-, intermediate-, and low-risk groups with median OS of 19.6, 33.6, and 60.8 mo, respectively. Incorporation of PGAs from ctDNA into a CG model improved OS prediction by nearly 30% over a clinical model. This model can classify patients into risk groups and is useful for selecting patients in future mCRPC trials.