Abstract 4312: A novel miRNA-based predictive model for biochemical failure following post-prostatectomy salvage radiation therapy
Authors: Erica Hlavin Bell, Simon Kirste, Jessica L. Fleming, Petra Stegmaier, Vaness Drendel, Xiaokui Mo, Stella Ling, Denise Fabian, Isabel Manring, Cordula A. Jilg, Wolfgang Schultze-Seemann, Maureen McNulty, Debra L. Zynger, Douglas Martin, Julia White, Martin Werner, Anca L. Grosu, Arnab Chakravarti
Journal: Cancer Research (2015)

Abstract Prostate cancer (PCa) is the second most common cancer among men worldwide. Radical prostatectomy (RP) is a standard treatment for PCa, yet 30-40% of these men experience biochemical failure (BF) and must undergo additional treatment such as radiation therapy (RT). Unfortunately, a subset of these patients develop resistance to RT and have disease progression. It is important to stratify men based on risk of recurrence as well as identify the most suitable treatment approach to take in PCa to reduce non-necessary patient burden. Currently used methods that include clinical and histopathological factors lack disease specificity and sensitivity. A more informative classification system is needed and molecular biomarkers may serve to bridge the gap of the inadequacies of current prognostic and predictive methods. There were two major objectives for this study. The first was to identify microRNA (miRNA) signatures that predict time to BF post-RP. Secondly, and most novel to the field of RT, we sought to determine miRNAs that could predict BF following post-RP salvage RT as well as develop a new model using both miRNAs and clinical factors. Using the NanoString Human v2 array, we profiled 800 miRNAs in forty-three PCa patients that all experienced BF post-RP and subsequently underwent salvage RT. We identified an 88-miRNA signature that could predict time to BF post-RP using multivariate Cox regression analysis. We observed that these 88 miRNAs could classify patients into two groups (early vs late BF) and that the probabilities to the time to first BF were significantly different between the groups. To identify miRNAs that could independently predict BF post-salvage RT, we performed a multivariate Cox regression analysis with lymph node status and Gleason score which lead to the discovery of nine miRNAs. We wanted to not only identify miRNAs that could independently predict BF post-salvage RT, but also develop a model using these miRNAs in combination with currently used clinical factors to improve upon existing methods. We performed a Cox regression analysis including lymph node status, Gleason score, and the 9 independently identified miRNAs and applied a step-wise model selection strategy to determine the best predictive miRNAs. Two miRNAs with Gleason score and lymph node status had the best predictability. Further specificity and sensitivity analysis indicated that the addition of these miRNAs greatly improved the predictive ability of lymph node status and Gleason score alone (AUC = 0.83 vs 0.66). To the best of our knowledge, this is the first report correlating molecular biomarkers with response to salvage RT. This research has the potential to greatly impact future treatment strategies by using molecular biomarker profiles alone or in combination with other clinical factors to assign the most suitable therapy to individuals the first time and thus avoid over or under treatment. Citation Format: Erica Hlavin Bell, Simon Kirste, Jessica L. Fleming, Petra Stegmaier, Vaness Drendel, Xiaokui Mo, Stella Ling, Denise Fabian, Isabel Manring, Cordula A. Jilg, Wolfgang Schultze-Seemann, Maureen McNulty, Debra L. Zynger, Douglas Martin, Julia White, Martin Werner, Anca L. Grosu, Arnab Chakravarti. A novel miRNA-based predictive model for biochemical failure following post-prostatectomy salvage radiation therapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4312. doi:10.1158/1538-7445.AM2015-4312