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Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines
Recent progress in clinical development of KRAS inhibitors has raised interest in predicting the tumor dependency on frequently mutated RAS-pathway oncogenes. However, even without such activating mutations, RAS proteins represent core components in signal integration of several membrane-bound kinas...
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/PMC8770632/ https://www.ncbi.nlm.nih.gov/pubmed/35046405 http://dx.doi.org/10.1038/s41540-021-00211-8 |
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author | Ulmer, Bastian Odenthal, Margarete Buettner, Reinhard Roth, Wilfried Kloth, Michael |
author_facet | Ulmer, Bastian Odenthal, Margarete Buettner, Reinhard Roth, Wilfried Kloth, Michael |
author_sort | Ulmer, Bastian |
collection | PubMed |
description | Recent progress in clinical development of KRAS inhibitors has raised interest in predicting the tumor dependency on frequently mutated RAS-pathway oncogenes. However, even without such activating mutations, RAS proteins represent core components in signal integration of several membrane-bound kinases. This raises the question of applications of specific inhibitors independent from the mutational status. Here, we examined CRISPR/RNAi data from over 700 cancer cell lines and identified a subset of cell lines without KRAS gain-of-function mutations (KRAS(wt)) which are dependent on KRAS expression. Combining machine learning-based modeling and whole transcriptome data with prior variable selection through protein-protein interaction network analysis by a diffusion kernel successfully predicted KRAS dependency in the KRAS(wt) subgroup and in all investigated cancer cell lines. In contrast, modeling by RAS activating events (RAE) or previously published RAS RNA-signatures did not provide reliable results, highlighting the heterogeneous distribution of RAE in KRAS(wt) cell lines and the importance of methodological references for expression signature modeling. Furthermore, we show that predictors of KRAS(wt) models contain non-substitutable information signals, indicating a KRAS dependency phenotype in the KRAS(wt) subgroup. Our data suggest that KRAS dependent cancers harboring KRAS wild type status could be targeted by directed therapeutic approaches. RNA-based machine learning models could help in identifying responsive and non-responsive tumors. |
format | Online Article Text |
id | pubmed-8770632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87706322022-02-04 Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines Ulmer, Bastian Odenthal, Margarete Buettner, Reinhard Roth, Wilfried Kloth, Michael NPJ Syst Biol Appl Article Recent progress in clinical development of KRAS inhibitors has raised interest in predicting the tumor dependency on frequently mutated RAS-pathway oncogenes. However, even without such activating mutations, RAS proteins represent core components in signal integration of several membrane-bound kinases. This raises the question of applications of specific inhibitors independent from the mutational status. Here, we examined CRISPR/RNAi data from over 700 cancer cell lines and identified a subset of cell lines without KRAS gain-of-function mutations (KRAS(wt)) which are dependent on KRAS expression. Combining machine learning-based modeling and whole transcriptome data with prior variable selection through protein-protein interaction network analysis by a diffusion kernel successfully predicted KRAS dependency in the KRAS(wt) subgroup and in all investigated cancer cell lines. In contrast, modeling by RAS activating events (RAE) or previously published RAS RNA-signatures did not provide reliable results, highlighting the heterogeneous distribution of RAE in KRAS(wt) cell lines and the importance of methodological references for expression signature modeling. Furthermore, we show that predictors of KRAS(wt) models contain non-substitutable information signals, indicating a KRAS dependency phenotype in the KRAS(wt) subgroup. Our data suggest that KRAS dependent cancers harboring KRAS wild type status could be targeted by directed therapeutic approaches. RNA-based machine learning models could help in identifying responsive and non-responsive tumors. Nature Publishing Group UK 2022-01-19 /pmc/articles/PMC8770632/ /pubmed/35046405 http://dx.doi.org/10.1038/s41540-021-00211-8 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 Ulmer, Bastian Odenthal, Margarete Buettner, Reinhard Roth, Wilfried Kloth, Michael Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines |
title | Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines |
title_full | Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines |
title_fullStr | Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines |
title_full_unstemmed | Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines |
title_short | Diffusion kernel-based predictive modeling of KRAS dependency in KRAS wild type cancer cell lines |
title_sort | diffusion kernel-based predictive modeling of kras dependency in kras wild type cancer cell lines |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770632/ https://www.ncbi.nlm.nih.gov/pubmed/35046405 http://dx.doi.org/10.1038/s41540-021-00211-8 |
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