Gene expression based inference of cancer drug sensitivity

Smriti Chawla, Anja Rockstroh, Melanie Lehman, Ellca Ratther, Atishay Jain, Anuneet Anand, Apoorva Gupta, Namrata Bhattacharya, Sarita Poonia, Priyadarshini Rai, Nirjhar Das, Angshul Majumdar, Jayadeva, Gaurav Ahuja, Brett G. Hollier, Colleen C. Nelson () and Debarka Sengupta ()
Additional contact information
Smriti Chawla: Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
Anja Rockstroh: Queensland University of Technology, Translational Research Institute
Melanie Lehman: Queensland University of Technology, Translational Research Institute
Ellca Ratther: Queensland University of Technology, Translational Research Institute
Atishay Jain: Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
Anuneet Anand: Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
Apoorva Gupta: Delhi Technological University, Shahbad Daulatpur
Namrata Bhattacharya: Queensland University of Technology, Translational Research Institute
Sarita Poonia: Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
Priyadarshini Rai: Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
Nirjhar Das: Indian Institute of Technology Delhi
Angshul Majumdar: Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
Jayadeva: Indian Institute of Technology Delhi
Gaurav Ahuja: Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
Brett G. Hollier: Queensland University of Technology, Translational Research Institute
Colleen C. Nelson: Queensland University of Technology, Translational Research Institute
Debarka Sengupta: Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.

Date: 2022
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DOI: 10.1038/s41467-022-33291-z

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