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Development and validation of genomic predictors of radiation sensitivity using preclinical data
BACKGROUND: Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. The fundamental paradigm that has guided radiotherapeutic regimens are ‘one-size-fits-all’, which are not in line with the dogma of precision medicine...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377977/ https://www.ncbi.nlm.nih.gov/pubmed/34416855 http://dx.doi.org/10.1186/s12885-021-08652-4 |
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author | Manem, Venkata S. K. |
author_facet | Manem, Venkata S. K. |
author_sort | Manem, Venkata S. K. |
collection | PubMed |
description | BACKGROUND: Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. The fundamental paradigm that has guided radiotherapeutic regimens are ‘one-size-fits-all’, which are not in line with the dogma of precision medicine. While there were efforts to build radioresponse signatures using OMICS data, their ability to accurately predict in patients is still limited. METHODS: We proposed to integrate two large-scale radiogenomics datasets consisting of 511 with 23 tissues and 60 cancer cell lines with 9 tissues to build and validate radiation response biomarkers. We used intrinsic radiation sensitivity, i.e., surviving fraction of cells (SF2) as the radiation response indicator. Gene set enrichment analysis was used to examine the biological determinants driving SF2. Using SF2 as a continuous variable, we used five different approaches, univariate, rank gene ensemble, rank gene multivariate, mRMR and elasticNet to build genomic predictors of radiation response through a cross-validation framework. RESULTS: Through the pathway analysis, we found 159 pathways to be statistically significant, out of which 54 and 105 were positively and negatively enriched with SF2. More importantly, we found cell cycle and repair pathways to be enriched with SF2, which are inline with the fundamental aspects of radiation biology. With regards to the radiation response gene signature, we found that all multivariate models outperformed the univariate model with a ranking based approach performing well compared to other models, indicating complex biological processes underpinning radiation response. CONCLUSION: To summarize, we found biological processes underpinning SF2 and systematically compared different machine learning approaches to develop and validate predictors of radiation response. With more patient data available in the future, the clinical value of these biomarkers can be assessed that would allow for personalization of radiotherapy. |
format | Online Article Text |
id | pubmed-8377977 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83779772021-08-23 Development and validation of genomic predictors of radiation sensitivity using preclinical data Manem, Venkata S. K. BMC Cancer Research BACKGROUND: Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. The fundamental paradigm that has guided radiotherapeutic regimens are ‘one-size-fits-all’, which are not in line with the dogma of precision medicine. While there were efforts to build radioresponse signatures using OMICS data, their ability to accurately predict in patients is still limited. METHODS: We proposed to integrate two large-scale radiogenomics datasets consisting of 511 with 23 tissues and 60 cancer cell lines with 9 tissues to build and validate radiation response biomarkers. We used intrinsic radiation sensitivity, i.e., surviving fraction of cells (SF2) as the radiation response indicator. Gene set enrichment analysis was used to examine the biological determinants driving SF2. Using SF2 as a continuous variable, we used five different approaches, univariate, rank gene ensemble, rank gene multivariate, mRMR and elasticNet to build genomic predictors of radiation response through a cross-validation framework. RESULTS: Through the pathway analysis, we found 159 pathways to be statistically significant, out of which 54 and 105 were positively and negatively enriched with SF2. More importantly, we found cell cycle and repair pathways to be enriched with SF2, which are inline with the fundamental aspects of radiation biology. With regards to the radiation response gene signature, we found that all multivariate models outperformed the univariate model with a ranking based approach performing well compared to other models, indicating complex biological processes underpinning radiation response. CONCLUSION: To summarize, we found biological processes underpinning SF2 and systematically compared different machine learning approaches to develop and validate predictors of radiation response. With more patient data available in the future, the clinical value of these biomarkers can be assessed that would allow for personalization of radiotherapy. BioMed Central 2021-08-20 /pmc/articles/PMC8377977/ /pubmed/34416855 http://dx.doi.org/10.1186/s12885-021-08652-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Manem, Venkata S. K. Development and validation of genomic predictors of radiation sensitivity using preclinical data |
title | Development and validation of genomic predictors of radiation sensitivity using preclinical data |
title_full | Development and validation of genomic predictors of radiation sensitivity using preclinical data |
title_fullStr | Development and validation of genomic predictors of radiation sensitivity using preclinical data |
title_full_unstemmed | Development and validation of genomic predictors of radiation sensitivity using preclinical data |
title_short | Development and validation of genomic predictors of radiation sensitivity using preclinical data |
title_sort | development and validation of genomic predictors of radiation sensitivity using preclinical data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377977/ https://www.ncbi.nlm.nih.gov/pubmed/34416855 http://dx.doi.org/10.1186/s12885-021-08652-4 |
work_keys_str_mv | AT manemvenkatask developmentandvalidationofgenomicpredictorsofradiationsensitivityusingpreclinicaldata |