Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE
Fig 1
Performance portrayal of translational models trained on GDSC cell line data using the R-package FORESEE to predict GSE6434 patient drug response.
Of a total of 3,920 modeling pipelines, the best modeling pipeline had the following settings: drug: Docetaxel, cell response type: ln(IC50), cell response transformation: binarization with k-means, sample selection: all, duplication handling: remove all duplicates, homogenization: remove unwanted variation, feature selection: landmark genes, feature preprocessing: none, black box algorithm: elastic net (while lasso would have yielded the same performance). (A) The receiver operating curve of the best model reveals an AUC of 0.986. (B) The comparison of the true responders and non-responders and their separation obtained from the best FORESEE model shows an almost perfect distinction, with a p-value of a t-test of 4.19e-6. (C) The performance distribution of all 3,920 model pipelines reveals a median AUC of ROC of 0.579.