ArticleBMC medical genomics2019
A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MESIA: multi-epigenome sample integration approach for precise peak calling.Scientific reports · 2023Article
- Tissue-specific identification of multi-omics features for pan-cancer drug response prediction.iScience · 2022Article
- Canine and murine models of osteosarcoma.Veterinary pathology · 2022Review
- Prospective clinical trial testing COXEN-based gene expression models of chemosensitivity in dogs with spontaneous osteosarcoma.Cancer chemotherapy and pharmacology · 2021Article
- Impact of between-tissue differences on pan-cancer predictions of drug sensitivity.PLoS computational biology · 2021Article
- Predicting chemosensitivity using drug perturbed gene dynamics.BMC bioinformatics · 2021Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundThe 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.
methodsUsing 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.
resultsOur 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.
conclusionThese 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.
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