Fig 1.
Response to single and venetoclax containing combination drugs.
(A) The number of sensitive and resistant samples for each single drug in Beat AML Waves 1 + 2 and Waves 3 + 4 cohorts for single drugs (B) and for venetoclax containing combination drugs (for combination drug response prediction, drug response from Eide et al [20] and gene expression from Beat AML dataset were used). Area Under Drug Response Curve (AUC)=100 was used as a cut off for sensitivity calling for both single drugs and drug combinations. Note: Only drugs with at least 20 sensitive and 20 resistant samples in the training set (Waves 1 + 2) and at least 10 sensitive and 10 resistant samples in the testing set (Waves 3 + 4) were included in the analysis.
Fig 2.
Comparative accuracy of kTSP, random forest, linear and RBF kernel SVM and elastic net regression classifiers in terms of sensitivity, specificity, balanced accuracy and Area Under the ROC Curve (AUROC) for single and venetoclax containing combination drug response prediction when the training data is balanced (with SMOTE method) and not balanced (A) For individual drug (B stands for Balanced and NB stands for Not Balanced in y-axis).
(B) All drugs together.
Fig 3.
Visualization of the three cohorts, Beat AML waves 1 + 2, Beat AML waves 3 + 4 and Functional Precision Medicine Tumor Board (FPMTB), based on the first two principal components.
Beat AML dataset, Waves 1 + 2 (371 samples), Waves 3 + 4 (184 samples) has been processed with the same library, SureSelect Strand-Specific RNA Library (Agilent), while the FPMTB (163 samples) dataset which have been processed with different libraries, i.e., NextEra and Scriptseq.
Fig 4.
Selective drug sensitivity scores (sDSSs) of the FPMTB validation samples that are predicted to be resistant and sensitive by the kTSP classifier.
P values were computed with one sided Wilcoxon test followed by Benjamini-Hochberg multiple test correction. The analysis includes only those drugs for which the kTSP classifier was trained on at least 20 sensitive and 20 resistant samples in the Beat AML dataset.
Fig 5.
The comparative accuracy of kTSP and other classifiers on the independent FPMTB validation cohort.
The drugs that are common to BeatAML and FPMTB, had at least 20 sensitive and 20 resistant samples in Beat AML 1-4 and at least 10 sensitive and 10 resistant samples in the FPMTB cohort.