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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Cancer 1/2018

Identification of fluorescence in situ hybridization assay markers for prediction of disease progression in prostate cancer patients on active surveillance

Zeitschrift:
BMC Cancer > Ausgabe 1/2018
Autoren:
Katerina Pestova, Adam J. Koch, Charles P. Quesenberry, Jun Shan, Ying Zhang, Amethyst D. Leimpeter, Beth Blondin, Svetlana Sitailo, Lela Buckingham, Jing Du, Huixin Fei, Stephen K. Van Den Eeden
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12885-017-3910-4) contains supplementary material, which is available to authorized users.

Abstract

Background

Prostate 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.

Methods

We 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.

Results

Seven 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.

Conclusions

Combinations 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.
Zusatzmaterial
Additional file 1: Summary of Patient Information and FISH. (DOCX 15 kb)
12885_2017_3910_MOESM1_ESM.docx
Additional file 2: Correlation Analysis Between FISH Biomarkers and Clinical Parameters. (DOCX 23 kb)
12885_2017_3910_MOESM2_ESM.docx
Literatur
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