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Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy
Immunotherapy has shown significant promise as a treatment for cancer, such as lung cancer and melanoma. However, only 10%–30% of the patients respond to treatment with immune checkpoint blockers (ICBs), underscoring the need for biomarkers to predict response to immunotherapy. Here, we developed De...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044175/ https://www.ncbi.nlm.nih.gov/pubmed/35494249 http://dx.doi.org/10.1016/j.isci.2022.104228 |
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author | Kang, Yuqi Vijay, Siddharth Gujral, Taranjit S. |
author_facet | Kang, Yuqi Vijay, Siddharth Gujral, Taranjit S. |
author_sort | Kang, Yuqi |
collection | PubMed |
description | Immunotherapy has shown significant promise as a treatment for cancer, such as lung cancer and melanoma. However, only 10%–30% of the patients respond to treatment with immune checkpoint blockers (ICBs), underscoring the need for biomarkers to predict response to immunotherapy. Here, we developed DeepGeneX, a computational framework that uses advanced deep neural network modeling and feature elimination to reduce single-cell RNA-seq data on ∼26,000 genes to six of the most important genes (CCR7, SELL, GZMB, WARS, GZMH, and LGALS1), that accurately predict response to immunotherapy. We also discovered that the high LGALS1 and WARS-expressing macrophage population represent a biomarker for ICB therapy nonresponders, suggesting that these macrophages may be a target for improving ICB response. Taken together, DeepGeneX enables biomarker discovery and provides an understanding of the molecular basis for the model’s predictions. |
format | Online Article Text |
id | pubmed-9044175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-90441752022-04-28 Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy Kang, Yuqi Vijay, Siddharth Gujral, Taranjit S. iScience Article Immunotherapy has shown significant promise as a treatment for cancer, such as lung cancer and melanoma. However, only 10%–30% of the patients respond to treatment with immune checkpoint blockers (ICBs), underscoring the need for biomarkers to predict response to immunotherapy. Here, we developed DeepGeneX, a computational framework that uses advanced deep neural network modeling and feature elimination to reduce single-cell RNA-seq data on ∼26,000 genes to six of the most important genes (CCR7, SELL, GZMB, WARS, GZMH, and LGALS1), that accurately predict response to immunotherapy. We also discovered that the high LGALS1 and WARS-expressing macrophage population represent a biomarker for ICB therapy nonresponders, suggesting that these macrophages may be a target for improving ICB response. Taken together, DeepGeneX enables biomarker discovery and provides an understanding of the molecular basis for the model’s predictions. Elsevier 2022-04-09 /pmc/articles/PMC9044175/ /pubmed/35494249 http://dx.doi.org/10.1016/j.isci.2022.104228 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Kang, Yuqi Vijay, Siddharth Gujral, Taranjit S. Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy |
title | Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy |
title_full | Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy |
title_fullStr | Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy |
title_full_unstemmed | Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy |
title_short | Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy |
title_sort | deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044175/ https://www.ncbi.nlm.nih.gov/pubmed/35494249 http://dx.doi.org/10.1016/j.isci.2022.104228 |
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