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SYM-A2 : Artificial Intelligence for Proteomics and Biomarker Discovery

오거나이저: 한도현(서울대학교병원)

김상욱(포항공과대학교) / 김한상(연세대학교) / 최연호(고려대학교)



Tiannan Guo, MD, PhD
Speaker
Tiannan Guo, MD, PhD   CV
Affiliation
Westlake University
Title
AI-empowered proteinbiomarker for thyroid nodules

 

김상욱
Speaker
김상욱   CV
Affiliation
포항공과대학교(POSTECH)
Title
Network-based machine-learning to predict drug efficacy in cancer patients
Abstract

Cancer patient classification using predictive biomarkers for anti-cancer drug and immunotherapy responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Furthermore, immune checkpoint inhibitors (ICIs) have improved the survival of cancer patients over the past several years. However, the minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. We have developed a network-based machine-learning framework to predict drug efficacy and to identify ICI treatment biomarkers in cancer patients. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. Moreover, network-based predictions accurately predict ICI treatment responses in three different cancer types—melanoma, gastric cancer, and bladder cancer. Compared with other conventional biomarkers, our network-based predictions showed improved performance in response predictions for cancer therapies including chemo and immunotherapy.