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
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Front Artif Intell. 2022 Aug 11;5:876100. doi: 10.3389/frai.2022.876100. eCollection 2022.
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.
需要确定三阴性乳腺癌（TNBC）中对新辅助化疗（NACT）有反应的预测生物标志物。我们之前获得的证据表明，血液中的多胺特征与TNBC的发生和进展有关。在本研究中，我们评估了血浆多胺和其他代谢物是否可以识别出对NACT反应较小的TNBC患者。与那些实现完全病理缓解（pCR/RCB-0）或残留疾病极少（RCB-I）的患者相比，接受NACT后有中度至广泛肿瘤负荷（RCB-II/III）的TNBC患者，其治疗前血浆乙酰化多胺水平升高。我们进一步将人工智能应用于综合代谢谱，以识别与治疗反应相关的其他代谢物。使用深度学习模型（DLM），开发了一个由两种多胺以及另外九种代谢物组成的代谢物panel，以改进对RCB-II/III的预测。DLM对于识别不太可能对NACT有反应且可能从其他治疗方式中获益的TNBC患者具有潜在的临床价值。
