Extended Data Fig. 9: Relationship of LCK/BCL-XL/BCL2 activities with dasatinib and venetoclax sensitivity.
a, LCK activity was inversely correlated with venetoclax sensitivity in vitro. LCK activity was inferred from RNA-seq data using NetBID in the discovery cohort (P value was estimated using ANOVA). b-d, ETP T-ALL is associated with high BCL2 and low LCK/BCL-XL activity. BCL2/LCK/BCL-XL activity was estimated for cases in the TARGET cohort. Each case was annotated with ETP status. ETP cases exhibit low activity of LCK (b) and BCL-XL (d) while have high BCL2 activity (c). P-value was estimated using ANOVA. Boxplots show summary of data in terms of the minimum, maximum, sample median, and the first and third quartiles. e, NetBID analysis of venetoclax sensitivity in a subset of T-ALL case in the discovery cohort (N = 34 patients) identified 656 driver genes for drug response. Genes in the pre-TCR signaling pathway were most enriched in pathway analysis (downregulation linked to venetoclax sensitivity). f-m, scRNA-seq analysis identified intra-leukemia heterogeneity in LCK activity. T-ALL cells from SJ53 were incubated with dasatinib or vehicle for 4 days in vitro. scRNA-seq was then performed using viable cells from each group separately but transcription profiling data was pooled for subsequent analyses. Vehicle-treated cells mimicked naïve and sensitive to dasatnib whereas cells survived dasatinib exposure (dasatinib-treated) represented drug resistant cell population. f, tSNE visualization shows the distribution of dasatinib-treated (brick red) and naïve (green) cells in SJ65 and SJ53. Single cell RNA-seq and data analyses were described in Methods. g, LCK and BCL-XL activity was inferred by NetBID from single-cell RNA-seq of SJ66 and SJ53. P value were calculated using Pearson correlation, and color indicates cell populations (C1, C2, and C3 represented dasatinib resistant [red], responsive [green] and sensitive [blue] groups). h, Left panel, unsupervised clustering analysis of scRNA-seq of vehicle and dasatinib-treated T-ALL cells from SJ53. Each dot represent a single cell visualized in a two-dimensional projection by t-SNE. Three clusters (C1, C2, and C3, in red, green, and blue, respectively) were identified using k-means clustering. Right panel, cell composition of each cluster is visualized by stack plot with red and green indicating the % of cells from vehicle or dasatinib-treated samples. C1, C2, and C3 consisted of increasing proportion of naïve dasatinib-sensitive cells, representing populations with low, intermediate, and high sensitivity to dasatinib, respectively. i, Distribution of dasatinib biomarker score across three clusters. j, LCK activity was highest in cluster C3, intermediate in C2, and lowest in C1, paralleling the proportion of dasatinib-sensitive population. LCK activity is color-coded (from low to high, blue–red) on t-SNE plot. k, BCL2 activity was lowest in cluster C3, intermediate in C2, and highest in C1, paralleling the proportion of dasatinib-sensitive population. BCL2 activity is color-coded (from low to high, blue–red) on t-SNE plot. l, Inverse correlation of LCK and BCL2 activity at the single cell level in SJ53. Each dot represents a cell and color discriminate clusters C1, C2, and C3 (red, green, and blue, respectively). Correlation coefficient and P value were estimated using Pearson correlation. m, Differentiation stage of each population was projected by examining the gene expression signature characteristic of ETP or DN3/DN4 T cells. Signature was derived from differential expression analysis of mouse T cell expression dataset (Mingueneau et al., 2013). Heat map indicates the average of each gene (rows) for cells within each cluster (columns), after Z-normalization.
