Nature Communications (Sep 2022)
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,
- Debarka Sengupta
Affiliations
- Smriti Chawla
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- Anja Rockstroh
- Australian Prostate Cancer Research Centre-Queensland, Faculty of Health, School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Translational Research Institute
- Melanie Lehman
- Australian Prostate Cancer Research Centre-Queensland, Faculty of Health, School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Translational Research Institute
- Ellca Ratther
- Australian Prostate Cancer Research Centre-Queensland, Faculty of Health, School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Translational Research Institute
- Atishay Jain
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- Anuneet Anand
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- Apoorva Gupta
- Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur
- Namrata Bhattacharya
- Australian Prostate Cancer Research Centre-Queensland, Faculty of Health, School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Translational Research Institute
- Sarita Poonia
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- Priyadarshini Rai
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- Nirjhar Das
- Department of Electrical Engineering, Indian Institute of Technology Delhi
- Angshul Majumdar
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- Jayadeva
- Department of Electrical Engineering, Indian Institute of Technology Delhi
- Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- Brett G. Hollier
- Australian Prostate Cancer Research Centre-Queensland, Faculty of Health, School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Translational Research Institute
- Colleen C. Nelson
- Australian Prostate Cancer Research Centre-Queensland, Faculty of Health, School of Biomedical Sciences, Centre for Genomics and Personalised Health, Queensland University of Technology, Translational Research Institute
- Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi)
- DOI
- https://doi.org/10.1038/s41467-022-33291-z
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 15
Abstract
Predicting treatment response in cancer remains a highly complex task. Here, the authors develop Precily, a deep neural network framework to predict treatment response in cancer by considering gene expression, pathway activity estimates and drug features, and test this method in multiple datasets and preclinical models.
