Drug response prediction for lung cancer patients using biophysical simulation and machine learning

Rizwan Qureshi*, Tanvir Alam, Jia Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Lung cancer is one of the most prevalent contributors to cancer deaths worldwide. The over-expression of Epidermal growth factor receptor (EGFR) is found in about 60% of non-small cell lung cancer (NSCLC) patients. Food and Drug Administration (FDA) has approved small molecule inhibitors, targeting the kinase domain of EGFR and to stop the abnormal growth of the cancer cells. These inhibitors produce encouraging results, but the long term efficacy remains limited due to secondary point mutations. In this work, we have developed a framework, using molecular dynamics (MD) simulation and machine learning to predict the drug response in lung cancer patients and to understand the mechanism of drug resistance. The experiments on an independent cohort of 61 patients shows the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
EditorsDonald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3867-3869
Number of pages3
ISBN (Electronic)9781665468190
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, United States
Duration: 6 Dec 20228 Dec 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Conference

Conference2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Country/TerritoryUnited States
CityLas Vegas
Period6/12/228/12/22

Keywords

  • Cancer
  • Drug Resistance
  • Machine Learning
  • Molecular Dynamics Simulation
  • Oncology
  • Precision Medicine

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