Increased Pathway Complexity Is a Prognostic Biomarker in Metastatic Castration Resistant Prostate Cancer
Simple Summary Circulating tumour DNA profiling can cost efficiently accelerate biomarker discovery within oncology trials. However, biomarker identification in metastatic castration resistant prostate cancer is confounded by a heterogeneous genomic landscape with few commonly perturbed genes and a large number of infrequently mutated, yet potentially biologically relevant, cancer drivers. Hence, large sample sizes are required for the stratified evaluation of these infrequent perturbations. To circumvent this issue, we investigated whether grouping genomic alterations with other events within the same cellular pathways would offer increased precision for biomarker discovery. We undertook an individual patient level pooled analysis of 342 patients with metastatic castration resistant prostate cancer initiating abiraterone acetate or enzalutamide. We found that the total number of altered pathways, which we termed the pathway complexity index (PCI) was associated with a poor prognosis. Since genomic profiling is now standard practice in interventional oncology trials, our findings highlight the importance of comprehensive genomic profiling for biomarker discovery and utilization. Abstract Metastatic castration resistant prostate cancer (mCRPC) is a heterogeneous disease, characterized by common and rare driver gene alterations that provide a selective growth advantage for progressing tumour cells. We hypothesized that the number of distinct gene driver alteration affected pathways or gene classes was associated with poor prognosis in patients initiating androgen receptor signalling inhibitors (ARSi) We performed a post hoc analysis of an amalgamated baseline circulating tumour DNA (ctDNA) mutational landscape dataset of ARSi treated men with mCRPC (n 342) We associated the detected hotspot, pathogenic, and/or high impact protein function affecting perturbations in 39 genes into 13 pathways. Progression free (PFS) and overall survival (OS) were analysed using Kaplan Meier curves and multivariate Cox regression models. Driver gene alterations were detected in 192/342 (56.1% evaluable patients. An increased number of affected pathways, coined pathway complexity index (PCI) resulted in a decremental PFS and OS, and was independently associated with prognosis once =3 pathway or gene classes were affected (PFS HR (95%CI) 1.7 (1.02 2.84) p 0.04, and OS HR (95%CI) 2.5 (1.06 5.71) p 0.04) Additionally, visceral disease and baseline PSA and plasma ctDNA levels were independently associated with poor prognosis. Elevated PCI is associated with poor ARSi outcome and supports comprehensive genomic profiling to better infer mCRPC prognosis.
Cancers