Abstract B063: Towards the use of Raman spectroscopy to guide prostate biopsy procedure

Abstract

Abstract For the first time, molecular information deduced from Raman spectroscopy measurements was spatially correlated with histopathologic analysis to detect and characterize prostate cancer (PCa) using Raman spectroscopy (RS). Ex vivo post-prostatectomy specimen were interrogated using a handheld RS probe. Up to 50 areas (of 500µm in diameter each) per prostate slice were correlated spatially with hematoxylin, phloxine, and saffron (HPS) stained images for detailed histopathologic characteristics, including percentage of benign glands and cancer grade group (International Society of Urological Pathology, ISUP): (1) each measured area was identified with a spot of Indian ink; (2) a photograph was taken at the end of the measurements to build a mask including the prostate slice contour and circles corresponding to the measured areas; (3) the whole prostate slice was reconstructed from the HPS scans guided by anatomic landmarks to insure spatial correspondence with the fresh prostate slice photograph. The Raman spectra were analyzed in terms of univariate statistics of specific Raman peaks corresponding to biomolecules such as DNA/RNA, amino acids, or lipids. In addition, the spectra were submitted to multivariate statistical classification using machine learning algorithms (artificial neural network). In order to test the validity of ex vivo conditions regarding intraoperative (i.e., in vivo) applications, we measured and compared different rat organs (including brain, prostate, ovarian, spleen, kidney, and liver) in in vivo and ex vivo conditions. Previous studies about the use of RS for PCa detection were done exclusively on cell lines or snap-frozen human tissue by considering the overall patient diagnosis. PCa tissues are highly heterogeneous morphologically: cancer glands are intermingled with benign glands and tumors are often multiclonal. Therefore, local histopathologic criteria must be considered. We designed a method that allows us to correlate spatially each Raman spectrum with precise diagnostic criteria and we were able to distinguish extraprostatic from prostatic tissue with an accuracy of 82% (sensibility of 82%, specificity of 83%), malignant from benign prostatic tissue with an accuracy over 86% (sensibility of 87%, specificity of 86%), and different cancer grade groups, for instance, with an accuracy of 84% for distinguishing ISUP grade group 1 from 5. When comparing in vivo with ex vivo rat tissues, preliminary results showed that the difference between average spectra is of the same magnitude than the variability within the same tissue type (same organ from different rats), therefore meaning that ex vivo RS data could be used to train classifier for in vivo diagnosis. Thanks to our robust methodology spatially correlating the optical measurements with local histopathologic characteristics, our work on ex vivo human prostates has proved that we have the capability to characterize the heterogeneity of prostate tissue in the context of PCa. In addition, we established that the use of ex vivo specimen to mimic intraoperative condition is valid. These are promising results, especially in the context of optical guided biopsy procedure to help collect targeted tissue to decrease the false-negative rate nowadays associated with the procedure (~30%). Citation Format: Kelly Aubertin, Mirela Birlea, Michael Pinto, Karl St-Arnaud, Vincent Quoc Trinh, Andrée-Anne Grosset, Frédéric Leblond, Dominique Trudel. Towards the use of Raman spectroscopy to guide prostate biopsy procedure [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 B063.