Abstract B079: Prospective study on the use of Raman molecular imaging to determine postoperative oncological outcomes in patients with prostate cancer: Analysis of a single center
Authors: Arash Samiei, Ralph Miller, Jeffrey Cohen, Heather Gomer, Shona Stewart, Patrick Treado
Journal: Cancer Research (2018)

Abstract Background: Prostate cancer (PCa) is the most common cancer in males and the second leading cause of cancer deaths. One of the most confounding problems faced by urologists is predicting the progression of PCa. Current prognostic tools for PCa rely solely on clinical and pathologic variables that cannot always accurately predict the progression of the disease. More accurate diagnostic tools to aid clinicians and patients to decide the course of treatment, even in the early stage of cancer, have long been sought. In a prospective study, we utilized Raman molecular imaging (RMI) to identify patients at risk for biochemical recurrence following a prostatectomy. RMI is a technology that combines the molecular chemical analysis capability of Raman spectroscopy with high-definition digital image visualization, enabling analysis of the molecular environment of prostate tissue by providing highly specific molecular imaging data. Objective: The aim of this study is to evaluate RMI as a methodology for predicting the outcome of PCa patients following radical prostatectomy. Methods: PCa patients who underwent prostatectomy were prospectively enrolled with informed consent. Surgeries were performed by two practiced surgeons in a single clinical center from 2009–2011, and 60 months’ follow-up time was recorded. Pathology data were reviewed by one genitourinary pathologist. Preoperative and postoperative data (preoperation prostate-specific antigen level [PSA], Nadir PSA, Gleason scores, pathologic stage, biochemical recurrence time) were recorded, and biochemical failure was defined as 2 consecutive increases in postoperative PSA (≥0.2 ng/mL). Patient identifiers were removed. Raman molecular images were collected from unstained prostatectomy tissue sections. Raman spectral data were extracted from epithelial and stromal regions, and multivariate statistical methodologies were applied to these spectra. Partial least squares discriminant analysis (PLS-DA) was employed in conjunction with leave-one-patient-out cross-validation to generate models based on the pathology data and RMI. Results: Initial data analysis was performed on a representative population of 38 PCa patients. In this population the mean (median) age at the time of diagnosis was 60 (61) years; the pathologic stage for 57.9% of the patients was T3 and for 42.1% of the patients T2. Nine patients (23.7%) progressed to biochemical failure in 60 months. Analysis of RMI performed on prostatectomy tissues from these patients indicates that the RMI data are distinguishable between those with biochemical failure (progressors) and those with no evidence of disease (NED). A PLS-DA model comprising 9 progressors and 29 NED patients differentiated between the two tissue classes with 89% sensitivity and 90% specificity, and with an area under the ROC curve of 0.87. In this model, classification accuracy was 90% and the p–value &amp;lt; 0.01. Analysis revealed most notable differentiation between progressors and NEDs when evaluating the epithelium and stroma as separate histologic elements. A PLS-DA model based on epithelial cells from 9 progressors and 10 NEDs discriminated between the two populations with 89% sensitivity and 100% specificity, with AUROC of 0.92 and a p-value &amp;lt; 0.01. A similar model differentiated between the stroma of progressors and NEDs with 100% specificity and 100% sensitivity, and a p-value well below 0.01. Conclusion: RMI is a novel technique that shows promise for identifying patients at risk of progression by visualizing molecular information not seen using other current methods. In Gleason 7 disease, RMI indicates distinctive chemical differences in patients who had biochemical failure postoperatively. This preliminary work lays the foundation for the further study of RMI for evaluating prostate tissue and developing an assay that may impact clinicians and patients with PCa. Citation Format: Arash Samiei, Ralph Miller, Jeffrey Cohen, Heather Gomer, Shona Stewart, Patrick Treado. Prospective study on the use of Raman molecular imaging to determine postoperative oncological outcomes in patients with prostate cancer: Analysis of a single center [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B079.