BackgroundProstate Cancer (PCa) is the second most prevalent cancer among U.S. males. In recent decades many men with low risk PCa have been over diagnosed and over treated. Given significant co-morbidities associated with definitive treatments, maximizing patient quality of life while recognizing early signs of aggressive disease is essential. There remains a need to better stratify newly diagnosed men according to the risk of disease progression, identifying, with high sensitivity and specificity, candidates for active surveillance versus intervention therapy. The objective of this study was to select fluorescence in situ hybridization (FISH) panels that differentiate non-progressive from progressive disease in patients with low and intermediate risk PCa.MethodsWe performed a retrospective case-control study to evaluate FISH biomarkers on specimens from PCa patients with clinically localised disease (T1c-T2c) enrolled in Watchful waiting (WW)/Active Surveillance (AS). The patients were classified into cases (progressed to clinical intervention within 10 years), and controls (did not progress in 10 years). Receiver Operating Characteristic (ROC) curve analysis was performed to identify the best 3–5 probe combinations. FISH parameters were then combined with the clinical parameters ─ National Comprehensive Cancer Network (NNCN) risk categories ─ in the logistic regression model.ResultsSeven combinations of FISH parameters with the highest sensitivity and specificity for discriminating cases from controls were selected based on the ROC curve analysis. In the logistic regression model, these combinations contributed significantly to the prediction of PCa outcome. The combination of NCCN risk categories and FISH was additive to the clinical parameters or FISH alone in the final model, with odds ratios of 5.1 to 7.0 for the likelihood of the FISH-positive patients in the intended population to develop disease progression, as compared to the FISH-negative group.ConclusionsCombinations of FISH parameters discriminating progressive from non-progressive PCa were selected based on ROC curve analysis. The combination of clinical parameters and FISH outperformed clinical parameters alone, and was complimentary to clinical parameters in the final model, demonstrating potential utility of multi-colour FISH panels as an auxiliary tool for PCa risk stratification. Further studies with larger cohorts are planned to confirm these findings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-017-3910-4) contains supplementary material, which is available to authorized users.

Background: Overdiagnosis and overtreatment of men with lower-risk Prostate Cancer (PCa) is a concern due to co-morbidities and healthcare costs. Maximizing patient quality of life while recognizing early signs of aggressive disease is essential. There is a need to stratify patients according to the risk of disease progression, identifying with high sensitivity and specificity patients to biopsy, re-biopsy and monitor. Within the high risk population, there is a need to identify when to proceed with treatment. Methods: We performed a retrospective study to evaluate FISH biomarkers on PCa specimens with histologically confirmed, clinically localized disease (T1c-T2c) enrolled in Active Surveillance (AS). AS patients were classified into two categories: Progressive (clinical intervention within 10 years), and non-progressive (did not progress to intervention in 10 years). Cohorts were matched by clinical characteristics (Gleason score, disease stage and grade, PSA level, age, race) as possible. The study objective was to establish a FISH biomarker panel to differentiate non-progressive from progressive prostate cancer in the low and intermediate risk groups. Receiver Operating Characteristics (ROC) curve analysis was performed to obtain the best single parameters based upon Area Under the Curve (AUC). For each FISH parameter cutoffs were determined based on Sensitivity, Specificity and Distance from Ideal (DFI). Further ROC analysis was used to identify the best 3-5 probe combinations. FISH parameters were combined with clinical parameters in the final model. Results: Five combinations of FISH parameters with the highest AUC and sensitivity in discriminating case and control groups were selected based on ROC curve analysis. Combinations were superior in performance to single probes. The best individual parameter, MYC gain, had an AUC of 0.6998. The two best combinations of parameters, MYC Gain/NKX3.1 Gain/NMYC Gain/PTEN Homozygous and MYC Gain/ETV1 Break Apart/NKX3.1 Gain/PTEN Homozygous had AUC of 0.7363 and 0.7236, respectively. In the logistic regression analysis, these combinations contributed significantly to the prediction of PCa outcome (progressive vs non-progressive, odds ratios of 5.522 and 6.118, respectively). The combination of clinical parameters and FISH outperformed clinical parameters or FISH alone, with odds ratios of 5.955 and 6.787 in the final model. Conclusions: Combinations of FISH parameters discriminating cases (progressive) from controls (non-progressive) were selected based on ROC curve analysis. The combination of clinical parameters and FISH outperformed clinical parameters alone, and was complimentary to clinical parameters (NCCN Risk Groups) in the final model, demonstrating potential utility of multi-color FISH panels as an auxiliary tool for PCa risk stratification. Further studies with larger cohorts are planned to confirm these findings. Citation Format: Adam J. Koch, Ying Zhang, Beth Blondin, Svetlana Sitailo, Huixin Fei, Stephen Van Den Eeden, Ekaterina Pestova. Fluorescence in situ hybridization panels for prediction of disease progression in prostate cancer patients on active surveillance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4730. doi:10.1158/1538-7445.AM2017-4730