Abstract
Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.
Highlights
Cancers are complex, dynamic diseases characterized by aberrant cellular processes such as excessive proliferation, resistance to apoptosis, and genomic instability [1]
We focus on DNA methylation profiles, using cell-line data from the Genomics of Drug Sensitivity in Cancer (GDSC) database [7] in combination with tumor data from The Cancer Genome Atlas (TCGA) [46]
For each combination of algorithm and data-subsampling scenario, we evaluated the performance of all hyperparameter combinations (Table 1) using the inner folds; we used Mean misclassification error (MMCE) (Mean Misclassification Error) [68] for classification and Mean squared error (MSE) (Mean Squared Error) [69] for regression as evaluation metrics in the inner folds
Summary
Dynamic diseases characterized by aberrant cellular processes such as excessive proliferation, resistance to apoptosis, and genomic instability [1]. One goal of cancer research is to advance precision medicine through identifying genomic and epigenomic features that influence treatment outcomes in individuals [4]. In this context, therapeutic decisions have the potential to be guided by molecular signatures. After a candidate drug has been identified, researchers may seek to identify molecular markers associated with those responses, comparing cell lines that respond to the drug against those that do not. Such markers might be useful for elucidating drug mechanisms or eventually predicting clinical responses in patients [7]
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