Abstract
T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy and new therapeutics are much needed. Profiling patient leukemia drug sensitivities ex vivo, we discovered that 44.4% of childhood and 16.7% of adult T-ALL cases exquisitely respond to dasatinib. Applying network-based systems pharmacology analyses to examine signal circuitry, we identified preTCR–LCK activation as the driver of dasatinib sensitivity and T-ALL-specific LCK dependency was confirmed in genome-wide CRISPR-Cas9 screens. Dasatinib-sensitive T-ALL exhibited high BCL-XL activity, low BCL2 activity and venetoclax resistance. Discordant sensitivity of T-ALL to dasatinib and venetoclax is strongly correlated with T-cell differentiation, particularly with the dynamic shift in LCK versus BCL2 activation. Finally, single-cell analysis identified leukemia heterogeneity in LCK and BCL2 signaling and T-cell maturation stage, consistent with dasatinib response. In conclusion, our results indicate that developmental arrest in T-ALL drives differential activation of preTCR–LCK and BCL2 signaling in this leukemia, providing unique opportunities for targeted therapy.
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Data availability
Details of data access are provided on the permalink page on St. Jude Cloud (https://pecan.stjude.cloud/permalink/PGx-TALL). Briefly, genomic data are available at St. Jude Cloud (SJC-PB-1022), NCBI GEO (GSE158457) and EGA (EGAS00001004700). Genome-scale CRISPR-Cas9 screen result can be obtained at the DepMap Portal (https://depmap.org/portal/achilles) with raw data available at FigShare (https://figshare.com/articles/dataset/DepMap_19Q4_Public/11384241/3).
The TARGET T-ALL dataset is available in dbGAP (phs000218 and phs000464) and the microarray-based T-ALL expression profile is at NCBI GEO (GSE32215). Drug Bank database is at go.drugbank.com and KEGG pathway database is at genome.jp/kegg/pathway.html. The data that support the findings of this study are available from the corresponding authors upon request. Source data are provided with this paper.
Code availability
Codes for NetBID analysis and dasatinib biomarker score calculation are available at GitHub (https://github.com/jyyulab/dasatinib-TALL). Source data are provided with this paper.
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Acknowledgements
We thank the patients and families who participated in the clinical trials included in this study for donating specimens for research and the clinicians and research staff for assistance in sample collection, processing and curation. We also thank H. Williams and S. Zhou for their assistance in the drug-sensitivity profiling and phospho-proteomics profiling assays, respectively. We appreciate the thoughtful discussions with T. Sanda at the National University of Singapore. This work was in part supported by the National Institutes of Health (R35CA210030, R37CA36401, P50GM115279, R01GM118578, R01GM134382 and P30CA21765), the St. Baldrick’s Foundation and the Pan-Mass Challenge Team Crank and by the American Lebanese Syrian Associated Charities. Y. Gocho was supported by Tokyo Children’s Cancer Study Group overseas scholarship and Nippon Medical School, Japan.
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Authors and Affiliations
Contributions
J.J.Y., J.Y., Y.G. and J. Hu designed the study and interpreted the results; Y.G., J. Hu, G.D., A.J., T.-N.L., J. Hunt, B.J., L.R., D.M. and B.S. performed the experiments; J.L., Wentao Yang, Wenjian Yang, H.S., X.H., K.H., S.P., L.S. and S.N. performed data analyses; N.V.D., Jingliao Zhang, L.S., K.R.C., Y.Z., K.R., H.W., E.J., W.S., B.E., E.P., G.R., M.L., J.E., Jinghui Zhang, J.P., H.C., S.P., M.V.R., H.I., X.Z., S.K., C.H.P., M.K., D.T., C.G.M., K.S. and W.E.E. contributed reagents, materials and analyses tools; J.J.Y., J.Y., Y.G., J. Hu and J.L. wrote the manuscript.
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Competing interests
N.V.D. is a current employee of Genentech, Inc., a member of the Roche Group. S.N. is a current employee of Sema4 Inc. K.S. currently has funding from Novartis International AG and has previously consulted for Rigel Pharmaceuticals on topics unrelated to this manuscript. M.V.R. J.J.Y. and St. Jude receive investigator-initiated research funding from Servier for work unrelated to this manuscript. J.J.Y. and J.Y. are also named as co-inventors on a pending provisional US patent application filed by St. Jude based on this research.
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Peer review information Nature Cancer thanks Anna Bigas, Oliver Hantschel, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 ALL sensitivity to dasatinib and cytotoxic drugs.
a, Dasatinib LC50 distribution of all 352 ALL patient samples (including 307 B-ALL and 45 T-ALL samples). b, Of 86 pediatric B-ALL samples, 17.4% were sensitive to dasatinib, whereas 29.4% of 221 adult B-ALL samples were dasatinib-sensitive. c, Dasatinib LC50 reliably identified BCR-ABL1 B-ALL based on the receiving operating characteristic analysis. Dasatinib LC50 from 307 B-ALL (including 71 BCR-ABL B-ALL) was analyzed, and a LC50 cutoff at 80 nM achieved the optimal balance in sensitivity and specificity that distinguishes BCR-ABL samples from other B-ALLs. d, Examples of dose−response curve of dasatinib-sensitive or –resistant B-ALL and T-ALL. Sensitive and resistant cases are shown in red and blue, respectively. The sensitive B-ALL sample is BCR-ABL1-positive. For each patient at each drug concentration, cells were tested in duplicates. e, Of drugs tested for at least 30 cases in the T-ALL cohorts, LC50 of prednisolone and asparaginase exhibited a bimodal distribution whereas 6-MP and daunorubicin did not. N = 43, 38, 40, and 41 patients for prednisone, daunorubicin, 6-MP, and L-Asparaginase, respectively.
Extended Data Fig. 2 Comparison of T-ALL sensitivity to four ABL inhibitors.
a,b, PDX-derived leukemia cells were tested for sensitivity to each of the four inhibitors. A total of 11 cases were selected to represent dasatinib-sensitive vs resistant T-ALL and were xenografted in NSG mice to develop PDX. Mice were sacrificed once leukemia burden reached predefined endpoint, and human leukemia cells were harvested and subjected to drug sensitivity profiling ex vivo. Cells were incubated with increasing concentrations of each ABL inhibitor for 96 hours and cell viability was determined by flow cytometry as described in Methods. c, Dose-dependent cell death was determined for four ABL inhibitors in 8 T-ALL cell lines. HSB-2 (harbors TCR-LCK fusion), KOPT-K1 (harbors TCR-LMO2 fusion) and ALL-SIL (harbors ABL class fusion) were sensitive (LC50s are less than 0.05 nM) to both dasatinib and ponatinib but resistant to imatinib and nilotinib. For each leukemia case at each drug concentration, cells were tested in duplicates.
Extended Data Fig. 3 NetBID identified LCK and related genes for association with dasatinib sensitivity in T-ALL.
a, Activity of each gene was inferred using NetBID as described in Method and then compared between dasatinib-sensitive vs –resistant T-ALL with P value listed in the far right column. Top panel describes the expression ranking of all genes from the most highly expressed in dasatinib-sensitive cases on the left to the most highly expressed in resistant T-ALL on the right. Each gene in this pathway regulates a multitude of targets and their expression is indicated in two rows with positive-regulated target genes on the top and negatively regulated target genes at the bottom. In the case of LCK, it has 188 positive and 147 negative targets, each represented by a vertical line. Red lines indicate high expression in dasatinib sensitive T-ALL and blue lines indicate high expression in dasatinib-resistant cases. P-value was estimated using two-tailed t test. b, NetBID results for dasatinib target genes for association with dasatinib sensitivity in T-ALL. Dasatinib target gene list is derived from three databases Drug Bank, DGIdb, and chemical proteomic-based TKI target profiling, as shown in the Venn diagram in the left panel. Thirteen targets are commonly identified across three sources. NetBID analysis identified four of these 13 targets with a significantly higher activity (LCK, SRC, FYN and FGR) in dasatinib-sensitive samples compared to resistant cases, with P value for the differential gene activity listed in the right panel. P-value was estimated using two-tailed t test. c, PTCRA and LCK activity were compared between dasatinib-sensitive vs -resistant T-ALL samples with RNA-seq data in the pharmacotyping cohort (N = 15 and 30, respectively), P-value was estimated using two-tailed t test. Boxplots show summary of data in terms of the minimum, maximum, sample median, and the first and third quartiles. d,e, Gene networks used to infer PTCRA (d) and LCK (e) activity in NetBID. Each spoke represents a target gene (positively-regulated as red and negatively regulated as blue), with gene name indicated at the edge of each arrow. P-value was estimated using two-tailed t test. f, Running NetBID analysis using only pediatric cases in the discovery cohort (N = 12 and 15 patients for dasatinib-sensitive and –resistant, respectively), we re-estimated Z score for each gene which were then correlated with those from NetBID analysis using all T-ALL cases. Genes in the 30 biomarker panel are labeled and P value was estimated using Pearson correlation test.
Extended Data Fig. 4 LCK signaling is essential for dasatinib sensitivity in T-ALL.
a, Phospho-flow of LCK, ZAP70 and CD247 in dasatinib-sensitive vs –resistant T-ALL cell lines. Cells were treated with increasing concentrations of dasatinib for 1 h and then subjected to intracellular staining for phosphorylated LCK, ZAP70, and CD247, as described in Method. Phospho-protein was quantified by flow cytometry and normalized with samples not exposed to dasatinib as 100%. HSB-2 and KOPT-K1 (sensitive to dasatinib) is plotted in darker colors while CEM (resistant) is plotted in lighter colors. P-values were derived by ANOVA. b-d, LCK T316M mutation confers dasatinib resistance in KOPT-K1 cells. LCK T316M mutation was ectopically expressed in dasatinib-sensitive T-ALL KOPT-K1 cells. KOPT-K1 cells with wildtype LCK overexpression or empty vector control remained sensitive to dasatinib and ponatinib while overexpression of T316M LCK resulted in resistance to dasatinib (b) and ponatinib (c). Meanwhile, all three lines were resistant to ABL-specific inhibitor imatinib (d). For each leukemia sample at each drug concentration, cells were tested in duplicates. e. In KOPT-K1 cells with expressing wildtype LCK or empty vector control, LCK phosphorylation was inhibited by dasatinib in a dose-dependent manner, whereas LCK phosphorylation was unablated by dasatinib in cells expressing the T136M mutant LCK. Standard deviation is derived from biologically independent samples (N = 3) and is plotted as error bar. P value was estimated using Wilcoxon test. f,g, Genome-wide CRISPR screen identifies preTCR pathway genes as dependencies in T-ALL. F. LCK, ZAP70 and CD247 dependency score (x-axis) versus gene dependency probability (y-axis) demonstrates that a subset of T-ALL lines (blue, N = 3) show dependency on these preTCR pathway genes compared to all other cell lines screened (hematologic cancer cell lines in black, N = 73 and other cancer cell lines in gray, N = 613). Gene dependency of greater than 0.5 indicates a high probability that a cell line is dependent and corresponds to an approximate dependency score of −0.5. More negative gene dependency scores indicate greater effect on cell line survival. g, Gene dependency score (x-axis) versus gene dependency probability (y-axis) demonstrates that none of the T-ALL lines (blue, N = 3) show dependency on SRC kinase family genes (other than LCK shown in Fig. 2J) compared to all other cell lines screened (hematologic cancer cell lines in black, N = 73, and other cancer cell lines in gray, N = 613). Gene dependency of greater than 0.5 indicates a high probability that a cell line is dependent and corresponds to an approximate dependency score of -0.5. More negative gene dependency scores indicate greater effect on cell line survival.
Extended Data Fig. 5 Comparison of T-ALL sensitivity to three LCK inhibitors.
a,b, A total of 11 cases were selected to represent dasatinib-sensitive vs resistant T-ALL (N = 7 and 4 in Panels a and b, respectively) and were xenografted in NSG mice to develop PDX. Mice were sacrificed once leukemia burden reached predefined endpoint, and human leukemia cells were harvested and subjected to drug sensitivity profiling ex vivo. Cells were incubated with increasing concentrations of each LCK inhibitor for 96 hours and cell viability was determined by flow cytometry as described in Methods. c, Dose-dependent cell death was determined for two drugs in 8 T-ALL cell lines. For both drugs, HSB-2, (harbors TCR-LCK fusion) and KOPT-K1 (harbors TCR-LMO2 fusion) showed the highest sensitivity compared to ALL-SIL (harbors ABL class fusion) and other T-ALL cell lines. For each leukemia sample at each drug concentration, cells were tested in duplicates.
Extended Data Fig. 6 Biomarker model of dasatinib sensitivity in T-ALL.
a,b, A panel of 30 genes were selected as biomarkers from the top 461 driver genes in the NetBID analysis, as described in Methods. Dasatinib biomarker score was plotted for T-ALL cases, in the discovery cohort (a, N = 45) and in the validation cohort (b, N = 13). P value was estimated using two-tailed t test. c, Similarly, activity of each biomarker gene was estimated for the TARGET T-ALL cohort, from which an unsupervised clustering analysis was performed, as shown in the heatmap. Each row is a biomarker gene and each column represents a T-ALL case, with color discriminating the level of inferred gene activity. T-ALL subtype is indicated in the top row by color. d,e, Predicted LCK and PTCRA activity in the TARGET T-ALL cohort (N = 261). Activity of LCK and PTCRA was estimated for each T-ALL case from its RNA-seq data by NetBID algorithm. T-ALL subtypes were defined as previously described (Liu et al., 2017). f, In TARGET cohort, dasatinib biomarker score of cases in LMO2/LYL1 subtype showed a bimodal distribution. Cases with high biomarker score (dasatinib-sensitive, blue curve) exhibited a worse event-free survival compared to those with low biomarker score. P-value was estimated using Cox regression. g,h, Differential gene expression analyses of dasatinib sensitivity in T-ALL. In the discovery cohort, differences in gene expression between dasatinib-sensitive vs –resistant T-ALL was examined using the Limma method based on a linear model and results are presented as the volcano plot compared (g). Pathway analysis was performed with 254 genes that met the significance and effect size threshold (adjusted P < 1e-3 and log2 fold change >2), using the KEGG pathway database. P-value was inferred by Fisher exact test. I,j, Comparison of predicted dasatinib sensitivity in pediatric and adult T-ALL. i, Dasatinib biomarker score was significantly higher in pediatric cases than adults in the discovery cohort. This was also validated in an independent microarray-based T-ALL gene expression dataset (NCBI GSE32215) with 37 adult and 191 pediatric patient samples. j,T-ALL subtype was inferred from gene expression profile for cases in the GSE32215 dataset. Pediatric cases have a higher prevalence of the TAL1 and TAL2 subtypes whereas adults have a higher frequency of HOXA and LMO2/LYL1 subtypes. P value was estimated using two-tailed t test. Boxplots show summary of data in terms of the minimum, maximum, sample median, and the first and third quartiles.
Extended Data Fig. 7 SPI1-rearrangement is associated with developmental arrest and related to dasatinib sensitivity in T-ALL.
a, Dose-dependent cell death was determined for dasatinib using the ex vivo drug sensitivity assay as described in Methods. For each patient at each drug concentration, cells were tested in duplicates. b, Mouse Lin-Sca+Ckit + (LSK) cells were isolated from bone marrow and transduced lentivirally with SPI1 fusion gene or empty vector. After in vitro differentiation in the presence of OP9-DL1 cells and Il7 and Flt3 ligand, LSK cells were subjected to flow-cytometry analysis. TCF7-SPI1 expressing cells exhibited differentiation blockade at DN stage while the empty control cells were able to extensively differentiate to double positive and single positive stages.
Extended Data Fig. 8 Activity of dasatinib sensitivity-related genes across normal T cell developmental stages in mouse and human.
a,b, The activity of dasatinib sensitivity-related genes vary by T cell differentiation stage in mouse. RNA-based gene expression profile was obtained from the previously published dataset (Mingueneau et al. 2013), and NetBID was used to infer gene activity. a, NetBID-inferred activity of the 30 biomarker genes. Each mouse T cell developmental stage is represented as a column and each row indicates different genes in the biomarker panel. Gene activity is represented by color (low to high as blue to red). b, NetBID-inferred activity of PTCRA and LCK. Horizontal bars indicate the mean of gene activity for each T cell population. DN3-4 stages are highlighted in red. c–e, NetBID-inferred dasatinib biomarker score and activity of PTCRA and LCK in 10 normal human T cell developmental stages. RNA-based gene expression profile of human T cells was obtained from the previously published dataset (Casero et al. 2015). NetBID was used to infer LCK (d) and PTCRA (e) activity and biomarker score (c). Thy3 and Thy4 (approximately equivalent to DN3-4 and DN4 stages in mouse) are highlighted in red.
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.
Extended Data Fig. 10 Schematic summary of main analyses, experiments, and major findings of this study.
Text is highlighted in red to indicate those unique to this report and advances compared to previous findings.
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Gocho, Y., Liu, J., Hu, J. et al. Network-based systems pharmacology reveals heterogeneity in LCK and BCL2 signaling and therapeutic sensitivity of T-cell acute lymphoblastic leukemia. Nat Cancer 2, 284–299 (2021). https://doi.org/10.1038/s43018-020-00167-4
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DOI: https://doi.org/10.1038/s43018-020-00167-4
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