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A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies
BACKGROUND: The availability and generation of large amounts of genomic data has led to the development of a new paradigm in cancer treatment emphasizing a precision approach at the molecular and genomic level. Statistical modeling techniques aimed at leveraging broad scale in vitro, in vivo, and cl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6580596/ https://www.ncbi.nlm.nih.gov/pubmed/31208429 http://dx.doi.org/10.1186/s12920-019-0519-2 |
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author | Mannheimer, Joshua D. Duval, Dawn L. Prasad, Ashok Gustafson, Daniel L. |
author_facet | Mannheimer, Joshua D. Duval, Dawn L. Prasad, Ashok Gustafson, Daniel L. |
author_sort | Mannheimer, Joshua D. |
collection | PubMed |
description | BACKGROUND: The availability and generation of large amounts of genomic data has led to the development of a new paradigm in cancer treatment emphasizing a precision approach at the molecular and genomic level. Statistical modeling techniques aimed at leveraging broad scale in vitro, in vivo, and clinical data for precision drug treatment has become an active area of research. As a rapidly developing discipline at the crossroads of medicine, computer science, and mathematics, techniques ranging from accepted to those on the cutting edge of artificial intelligence have been utilized. Given the diversity and complexity of these techniques a systematic understanding of fundamental modeling principles is essential to contextualize influential factors to better understand results and develop new approaches. METHODS: Using data available from the Genomics of Drug Sensitivity in Cancer (GDSC) and the NCI60 we explore principle components regression, linear and non-linear support vector regression, and artificial neural networks in combination with different implementations of correlation based feature selection (CBF) on the prediction of drug response for several cytotoxic chemotherapeutic agents. RESULTS: Our results indicate that the regression method and features used have marginal effects on Spearman correlation between the predicted and measured values as well as prediction error. Detailed analysis of these results reveal that the bulk relationship between tissue of origin and drug response is a major driving factor in model performance. CONCLUSION: These results display one of the challenges in building predictive models for drug response in pan-cancer models. Mainly, that bulk genotypic traits where the signal to noise ratio is high is the dominant behavior captured in these models. This suggests that improved techniques of feature selection that can discriminate individual cell response from histotype response will yield more successful pan-cancer models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0519-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6580596 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65805962019-06-24 A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies Mannheimer, Joshua D. Duval, Dawn L. Prasad, Ashok Gustafson, Daniel L. BMC Med Genomics Research Article BACKGROUND: The availability and generation of large amounts of genomic data has led to the development of a new paradigm in cancer treatment emphasizing a precision approach at the molecular and genomic level. Statistical modeling techniques aimed at leveraging broad scale in vitro, in vivo, and clinical data for precision drug treatment has become an active area of research. As a rapidly developing discipline at the crossroads of medicine, computer science, and mathematics, techniques ranging from accepted to those on the cutting edge of artificial intelligence have been utilized. Given the diversity and complexity of these techniques a systematic understanding of fundamental modeling principles is essential to contextualize influential factors to better understand results and develop new approaches. METHODS: Using data available from the Genomics of Drug Sensitivity in Cancer (GDSC) and the NCI60 we explore principle components regression, linear and non-linear support vector regression, and artificial neural networks in combination with different implementations of correlation based feature selection (CBF) on the prediction of drug response for several cytotoxic chemotherapeutic agents. RESULTS: Our results indicate that the regression method and features used have marginal effects on Spearman correlation between the predicted and measured values as well as prediction error. Detailed analysis of these results reveal that the bulk relationship between tissue of origin and drug response is a major driving factor in model performance. CONCLUSION: These results display one of the challenges in building predictive models for drug response in pan-cancer models. Mainly, that bulk genotypic traits where the signal to noise ratio is high is the dominant behavior captured in these models. This suggests that improved techniques of feature selection that can discriminate individual cell response from histotype response will yield more successful pan-cancer models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0519-2) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-17 /pmc/articles/PMC6580596/ /pubmed/31208429 http://dx.doi.org/10.1186/s12920-019-0519-2 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Mannheimer, Joshua D. Duval, Dawn L. Prasad, Ashok Gustafson, Daniel L. A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies |
title | A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies |
title_full | A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies |
title_fullStr | A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies |
title_full_unstemmed | A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies |
title_short | A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies |
title_sort | systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6580596/ https://www.ncbi.nlm.nih.gov/pubmed/31208429 http://dx.doi.org/10.1186/s12920-019-0519-2 |
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