The advent of prostate specific antigen (PSA) testing led to increased early prostate cancer (PCa) detection and has decreased PCa-related death. However, PSA is not cancer-specific, and the challenge persists of differentiating those PCa patients with indolent tumors from those requiring definitive therapy. Metabolomic profiles have the potential to capture molecular dynamics of disease and to reflect disease status before cellular manifestations become observable by histopathology. With clinical, multiparametric magnetic resonance imaging (mpMRI)-positive, fusion biopsy-targeted tissue cores and mpMRI-negative controls in a training-testing cohort design, we studied the potential of magnetic resonance spectroscopy (MRS) to yield cancer metabolomic profiles that could help discriminate likely indolent from clinically significant disease. Using MRS-based PCa metabolomic analyses, performed prior to histology, our approach is able to: determine metabolomic relevations identified in fusion biopsy targets, estimate the scale of PCa metabolomic fields, and detect clinically significant disease in tissues deemed benign or low-risk PCa by pathology and imaging. Our intact tissue MRS metabolomics evaluations indicated significant differences in individual prostate tissue metabolites based on Target-Contralateral (Contral) paired comparisons for both Training and Testing cohorts. We identified metabolomic differences among Target prostate biopsy cores obtained from mpMRI lesions of different PI-RADS scores, and between Target and non-target Contral cores. As a retrospective study, we also analyzed data collected at the time of the initial prostate biopsy alongside patient status across follow up. By introducing metabolomics, as compared with using PSAd or PI-RADS alone, the sensitivity predictions increased by 80.0% and 25.0%, respectively; NPV increased by 18.1% and 8.0%; and accuracy for PSAd increased by 13.0%. PI-RADS accuracy stayed the same Our results show that tissue metabolomic profiles could augment current MR-based imaging findings and histopathological evaluations of fusion biopsies for certain patient populations by more accurately characterizing them into clinically significant or insignificant subgroups. In our analyses, tissue metabolomics alone, or its combination with other clinical parameters, improved sensitivity and negative predictive values, as well as overall accuracy, for our testing cohort. This method, which relies on performing tissue MRS of needle biopsy cores prior to histopathologic analysis, causes no interruption to patient care. Findings from our study demonstrate the utility and translational potential of cancer metabolomics in personalized treatment for PCa and encourages the development of in vivo PCa metabolomic imaging to enhance the diagnostic utility of mpMRI.
Citation Format: Leo L. Cheng, Adam S. Feldman, Lindsey A. Vandergrift, Isabella H. Muti, Florian Rumpf, Andrew Gusev, Yannick Berker, Marcella R. Cardoso, Taylor L. Fuss, Emily D. Negroponte, Shulin Wu, Felix Ehret, Christopher A. Dietz, Sarah S. Dinges, Thitinan Chulroek, Edouard Nicaise, Piet Habbel, Martin Ayree, Johannes Nowak, Douglas M. Dahl, Chin-Lee Wu, Mukesh Harisinghani. Detecting clinically significant prostate cancers: Tissue metabolomics refines multiparametric MRI-ultrasound fusion prostate biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2222.
