1. The impact of intratumoral heterogeneity on prognostic biomarkers in localized prostate cancer
- Author
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Adriana Salcedo, Robert G. Bristow, Alice Meng, Jure Murgic, Alejandro Berlin, Michael Brundage, Neil Fleshner, Melvin L.K. Chua, Michael Fraser, Paul C. Boutros, Harry C. Brastianos, and Theodorus van der Kwast
- Subjects
Oncology ,Cancer Research ,medicine.medical_specialty ,Prostate biopsy ,medicine.diagnostic_test ,business.industry ,Aggressive disease ,medicine.disease ,Genomic biomarkers ,03 medical and health sciences ,Prostate cancer ,0302 clinical medicine ,030220 oncology & carcinogenesis ,Internal medicine ,medicine ,business ,030215 immunology - Abstract
46 Background: Genomic biomarkers can identify patients that harbour aggressive disease. The utility of these biomarkers is uncertain due to genomic variation between prostate biopsy specimens. To quantify the robustness of genomic biomarkers, we performed spatio-genomic characterization of distinct tumor foci. We scored three validated DNA-based biomarkers of early biochemical recurrence: percentage of genome with a copy number aberration (PGA), a 100-loci biomarker, and an optimized 31- loci biomarker derived from the previous. For each biomarker, we determined their robustness to intratumoral heterogeneity in association with predicting early biochemical recurrence (eBCR; ≤18 months) and long term control (LTC; ≥48 months). Methods: We queried a registry of 1054 patients with high-risk prostate cancer who underwent a radical prostatectomy (RP). We developed a cohort (n = 42) risk matched by clinicopathologic prognostic indices. Half of the patients had eBCR, while the other half had LTC. We profiled multiple tumor foci per patient, analyzing 119 tumor foci. For each focus, CNA profiles were generated, and three biomarker scores were calculated. For each patient and biomarker, we calculated the score of the lowest-score region, the highest-score region, or sampling of all foci and use the mean score. Results: All three biomarkers distinguished LTC from eBCR. PGA scores separated the two groups with an area under the receiver operator curves (AUC) ranging from 0.75-0.80. The 100- and 31-loci signatures, had AUCs ranging from 0.76-0.85 and 0.76-0.80 respectively. Using Cox proportional hazards modeling, we found that PGA was associated with LTC (Hazard ratio (HR) range: 2.56-6.22; p < 0.05. This was replicated for the 100-loci signature (HR range: 3.55-5.23; p < 0.05). The 31-loci detected associations with eBCR independent of how different foci were summarized (log-rank p-value range: 5.1 x 10-4- 5.9 x 10-3). Conclusions: Despite divergence in biomarker scores, all three predicted eBCR. Our study suggests that genomic biomarkers can overcome intratumoral heterogeneity, making discrete samples potentially adequate in patients with high-risk disease to determine the risk of eBCR after radical treatment.
- Published
- 2019
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