AUTHOR=Irajizad Ehsan , Wu Ranran , Vykoukal Jody , Murage Eunice , Spencer Rachelle , Dennison Jennifer B. , Moulder Stacy , Ravenberg Elizabeth , Lim Bora , Litton Jennifer , Tripathym Debu , Valero Vicente , Damodaran Senthil , Rauch Gaiane M. , Adrada Beatriz , Candelaria Rosalind , White Jason B. , Brewster Abenaa , Arun Banu , Long James P. , Do Kim Anh , Hanash Sam , Fahrmann Johannes F. TITLE=Application of Artificial Intelligence to Plasma Metabolomics Profiles to Predict Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer JOURNAL=Frontiers in Artificial Intelligence VOLUME=5 YEAR=2022 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.876100 DOI=10.3389/frai.2022.876100 ISSN=2624-8212 ABSTRACT=

There is a need to identify biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are less likely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied artificial intelligence to comprehensive metabolic profiles to identify additional metabolites associated with treatment response. Using a deep learning model (DLM), a metabolite panel consisting of two polyamines as well as nine additional metabolites was developed for improved prediction of RCB-II/III. The DLM has potential clinical value for identifying TNBC patients who are unlikely to respond to NACT and who may benefit from other treatment modalities.