Faculty, Staff and Student Publications
Publication Date
1-1-2022
Journal
Frontiers in Artificial Intelligence
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.
Keywords
triple-negative breast cancer, biomarkers, artificial intelligence, deep-learning model, neoadjuvant chemotherapy, prediction
DOI
10.3389/frai.2022.876100
PMID
36034598
PMCID
PMC9403735
PubMedCentral® Posted Date
8-11-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons
