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Gene expression based inference of cancer drug sensitivity
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 the...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9515171/ https://www.ncbi.nlm.nih.gov/pubmed/36167836 http://dx.doi.org/10.1038/s41467-022-33291-z |
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author | Chawla, Smriti Rockstroh, Anja Lehman, Melanie Ratther, Ellca Jain, Atishay Anand, Anuneet Gupta, Apoorva Bhattacharya, Namrata Poonia, Sarita Rai, Priyadarshini Das, Nirjhar Majumdar, Angshul Jayadeva Ahuja, Gaurav Hollier, Brett G. Nelson, Colleen C. Sengupta, Debarka |
author_facet | Chawla, Smriti Rockstroh, Anja Lehman, Melanie Ratther, Ellca Jain, Atishay Anand, Anuneet Gupta, Apoorva Bhattacharya, Namrata Poonia, Sarita Rai, Priyadarshini Das, Nirjhar Majumdar, Angshul Jayadeva Ahuja, Gaurav Hollier, Brett G. Nelson, Colleen C. Sengupta, Debarka |
author_sort | Chawla, Smriti |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9515171 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95151712022-09-29 Gene expression based inference of cancer drug sensitivity Chawla, Smriti Rockstroh, Anja Lehman, Melanie Ratther, Ellca Jain, Atishay Anand, Anuneet Gupta, Apoorva Bhattacharya, Namrata Poonia, Sarita Rai, Priyadarshini Das, Nirjhar Majumdar, Angshul Jayadeva Ahuja, Gaurav Hollier, Brett G. Nelson, Colleen C. Sengupta, Debarka Nat Commun Article 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. Nature Publishing Group UK 2022-09-27 /pmc/articles/PMC9515171/ /pubmed/36167836 http://dx.doi.org/10.1038/s41467-022-33291-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chawla, Smriti Rockstroh, Anja Lehman, Melanie Ratther, Ellca Jain, Atishay Anand, Anuneet Gupta, Apoorva Bhattacharya, Namrata Poonia, Sarita Rai, Priyadarshini Das, Nirjhar Majumdar, Angshul Jayadeva Ahuja, Gaurav Hollier, Brett G. Nelson, Colleen C. Sengupta, Debarka Gene expression based inference of cancer drug sensitivity |
title | Gene expression based inference of cancer drug sensitivity |
title_full | Gene expression based inference of cancer drug sensitivity |
title_fullStr | Gene expression based inference of cancer drug sensitivity |
title_full_unstemmed | Gene expression based inference of cancer drug sensitivity |
title_short | Gene expression based inference of cancer drug sensitivity |
title_sort | gene expression based inference of cancer drug sensitivity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9515171/ https://www.ncbi.nlm.nih.gov/pubmed/36167836 http://dx.doi.org/10.1038/s41467-022-33291-z |
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