Fig. 6: Clonal abundance predicts drug sensitivity in primary AML samples.

a Schematic depicting how linear regression of drug response (AUC) against VAF can identify correlations between drug sensitivity and the clonal prevalence of mutations. Red points represent sensitivity trends while blue points represent resistance trends. b Volcano plot of drug response between mutated and wild-type samples for de novo samples from the Beat AML study. Points are sized based on the number of samples analyzed and colored by significance (Bonferroni FDR < 0.1; red = sensitive, blue = resistant). c Copy number-corrected VAF distribution for mutations with paired drug data in the de novo cohort of the Beat AML study (n^{mut + drug} ≥ 5; n = 202 biologically independent patient samples). For each distribution, the boxplot represents the boundaries for the first and third quartiles with a line at each median; whiskers delimit the highest data point below the third quartile +1.5× the interquartile distance and the lowest data point above the first quartile −1.5× the interquartile distance. d Dotplot of the most significant (p < 0.05) drug-gene correlations identified through linear regression of drug AUC against mutation VAF in de novo AML samples. Points are sized based on the range of VAFs for each mutation and are colored based on the type of drug sensitivity trend (red—sensitive; blue—resistant). Asterisks represent drug-gene associations with a Bonferroni FDR < 0.1. e, f Representative binary distributions (left) and AUC-VAF scatterplots (right) for clinically relevant sensitivity and resistance VAF correlations for IDH1 (e; n^WT = 195 samples; n^Mut = 10 samples) and NRAS (f; n^WT = 177 samples; n^Mut = 14 samples)^, respectively. For each distribution (left), the boxplot represents the boundaries for the first and third quartiles with a line at each median; whiskers delimit the highest data point below the third quartile +1.5× the interquartile distance and the lowest data point above the first quartile −1.5× the interquartile distance; p-values are calculated using a two-sided Wilcoxon rank-sum test. For each scatterplot (right), shaded bands represent 95% confidence intervals for each linear regression. For each error band, the measure of center is the line of best fit as derived from linear regression between the drug AUC and VAF for each mutation-drug pair. g Schematic (top) depicting the potential correlation between the subclonal prevalence of a secondary mutation (e.g. FLT3-ITD) and sensitivity to inhibitors. AUC-VAF scatterplots (bottom) for pairwise genotypes with enough samples (n ≥ 5; DNMT3A:FLT3) where linear regression of drug AUC against VAF revealed strong resistance trends. Shaded bands represent 95% confidence intervals for each linear regression. For each error band, the measure of center is the line of best fit as derived from linear regression between the drug AUC and VAF for each mutation-drug pair. h Schematics representing possible relationships between VAF and drug response. Source data are provided as a Source Data file.