Genomic classifier for guiding treatment of intermediate-risk prostate cancers to dose-escalated image-guided radiotherapy without hormone therapy

The NCCN has recently endorsed the stratification of intermediate-risk prostate cancer (IR-PCa) into favorable and unfavourable subgourps, and recommend the addition of androgen deprivation therapy (ADT) to radiotherapy (RT) for unfavorable IR-PCa. Recently, more accurate prognostication was demonstrated by integrating a 22-feature genomic classifier (GC) to the NCCN stratification system. Here, we test the utility of the GC to better identify IR-PCa patients who are sufficiently treated by RT alone.

We identified a novel cohort comprising 121 IR-PCa patients treated with dose-escalated image-guided RT (DE-IGRT; 78 Gy in 39 fractions) without ADT. GC scores were derived from tumor sampled in diagnostic biopsies. Multivariable analyses including both NCCN subclassification and GC scores were performed for biochemical failure (PSA nadir + 2 ng/ml) and metastasis occurrence.

By NCCN subclassification, 33 (27.2%) and 87 (71.9%) of men were classified as favorable and unfavorable IR-PCa, respectively (1 case unclassifiable). GC scores were high in 3 favorable IR-PCa, and low in 60 unfavorable IR-PCa. Higher GC scores, but not NCCN-risk subgroups, were associated with biochemical relapse (HR 1.36 [95%CI=1.09-1.71] per 10% increase, P=0.007) and metastasis (HR 2.05 [95%CI=1.24-4.24], P=0.004). GC predicted biochemical failure at 5-year (AUC 0.78 [95%CI 0.70-0.83]), and the combinatorial NCCN+GC model significantly outperformed the NCCN alone model for predicting early-onset metastasis (AUC 5-year metastasis = 0.89 vs 0.86 [GC alone] vs 0.54 [NCCN alone]).

We demonstrated the accuracy of the GC for predicting disease recurrence in IR-PCa treated with DE-IGRT alone. Our findings highlight the need to evaluate this GC in a prospective clinical trial investigating the role of ADT-RT in clinicogenomic-defined IR-PCa subgroups.

International journal of radiation oncology, biology, physics. 2018 Aug 28 [Epub ahead of print]

Alejandro Berlin, Jure Murgic, Ali Hosni, Melania Pintilie, Adriana Salcedo, Michael Fraser, Suzanne Kamel-Reid, Jingbin Zhang, Qiqi Wang, Carolyn Ch'ng, Samineh Deheshi, Elai Davicioni, Theodorus van der Kwast, Paul C Boutros, Robert G Bristow, Melvin L K Chua

Princess Margaret Cancer Centre, University Health Network, Ontario, Canada;; Department of Radiation Oncology, University of Toronto, Ontario, Canada;; Techna Institute, University Health Network, Ontario, Canada., Princess Margaret Cancer Centre, University Health Network, Ontario, Canada., Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Ontario, Canada;; Department of Medical Biophysics, University of Toronto, Ontario, Canada., Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Ontario, Canada., Laboratory Medicine Program, University Health Network, Ontario, Canada., GenomeDx Biosciences Inc. Vancouver, British Columbia, Canada., Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Ontario, Canada;; Department of Medical Biophysics, University of Toronto, Ontario, Canada;; Department of Pharmacology and Toxicology, University of Toronto, Ontario, Canada., Princess Margaret Cancer Centre, University Health Network, Ontario, Canada;; Department of Radiation Oncology, University of Toronto, Ontario, Canada;; Manchester Cancer Research Centre, Manchester, UK;. Electronic address: ., Princess Margaret Cancer Centre, University Health Network, Ontario, Canada;; Divisions of Radiation Oncology and Medical Sciences, National Cancer Centre Singapore, Singapore;; Oncology Academic Program, Duke-NUS Medical School, Singapore. Electronic address: .