Inferring tumor-specific cancer dependencies through integrating ex vivo drug response assays and drug-protein profiling

Abstract

The development of cancer therapies may be improved by the discovery of tumor-specific molecular dependencies. The requisite tools include genetic and chemical perturbations, each with its strengths and limitations. Chemical perturbations can be readily applied to primary cancer samples at large scale, but mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a chemical compound has affinities to multiple proteins. To computationally infer specific molecular dependencies of individual cancers from their ex vivo drug sensitivity profiles, we developed a mathematical model that deconvolutes these data using measurements of protein-drug affinity profiles. Through integrating a drug-kinase profiling dataset and several drug response datasets, our method, DepInfeR, correctly identified known protein kinase dependencies, including the EGFR dependence of HER2+ breast cancer cell lines, the FLT3 dependence of acute myeloid leukemia (AML) with FLT3-ITD mutations and the differential dependencies on the B-cell receptor pathway in the two major subtypes of chronic lymphocytic leukemia (CLL). Furthermore, our method uncovered new subgroup-specific dependencies, including a previously unreported dependence of high-risk CLL on Checkpoint kinase 1 (CHEK1). The method also produced a detailed map of the kinase dependencies in a heterogeneous set of 117 CLL samples. The ability to deconvolute polypharmacological phenotypes into underlying causal molecular dependencies should increase the utility of high-throughput drug response assays for functional precision oncology.

Fig 1. Principle of the protein dependence inference framework.

(A) Summary of tumor samples and drugs in the three datasets used in this study, and the number of proteins with non-zero coefficients in the inferred protein dependence matrix. (B) Distribution of the number of kinases bound by each drug (kd < 1000nM) and (C) distribution of the number of drugs binding each kinase from the kinobeads profiling data used in our analysis. (D) Inference of the protein dependence matrix using a multivariate multi-response regression model with L1 regularization. A known drug-protein affinity matrix (independent variables) and a known drug-effect matrix (response variables) are used as input for the model to infer the unknown protein dependence matrix.

Fig 2. Results of protein dependence inference on the GDSC dataset.

(A) Shown are the data for nine kinases for which we found significant differences in the protein dependence coefficients between cancer types (one-way ANOVA, BH adjusted p-value < 0.05, Fold Change > 0.1). Within each panel, each point corresponds to a cell line. The coefficients were centered and scaled to obtain a per-protein z-score, and the points are grouped and colored by cancer type (ALL, red; AML, orange; DLBCL, green, BRCAHer-, blue; BRCAHer+, purple). (B) Radar plots of protein dependence coefficients across the different cancer types. Dashed line represents a protein dependence coefficient of zero. (C) Association between NRAS mutation status and MAP2K2 dependence. Association testing was performed using Student’s t-test (two-sided, with equal variance). (D) A heatmap showing the -log10(P-values) with signs determined by direction of fold changes of the associations between mutational background of the cell lines and protein dependence coefficients (Student’s t-test). Blue: associations with higher dependence coefficients in mutated cases; red: lower dependence coefficients in mutated cases; stars indicate the associated pass 10% FDR control.

Fig 3. Results of protein dependence inference on primary CLL samples.

(A) PCA visualization of the CLL samples according to protein dependence matrix (right) and drug sensitivity matrix (left). The points are colored by IGHV mutational status (mutated: blue, unmutated: red), and the results of k-means clustering (k = 2) is indicated by the shading. The cross-tabulation and Rand indices show that the protein dependence matrix-based clustering is more consistent with IGHV mutational status, a known strong stratifying factor in CLL biology (Rand index = 0.623), than the raw drug sensitivity matrix (Rand index = 0.146). (B) A heatmap showing the -log₁₀(P-values) with signs determined by direction of fold changes of the associations between mutational background of the cell lines and protein dependence coefficients (Student’s t-test). Blue: associations with higher dependence coefficients in trisomy 12 positive / U-CLL; red: higher dependence coefficients in Trisomy12 negative / M-CLL; stars indicate the associated pass 10% FDR control. (C) Examples of associations, visualized in beeswarm plots: associations between IGHV mutational status and SIK2 / INPPL1 / BTK dependence and association between trisomy 12 and MAP2K2 dependence. Association testing was performed using Student’s t-test (two-sided, with equal variance) and Benjamini-Hochberg correction was applied.

Fig 4. Association of CHEK1 dependence with IGHV status in CLL.

(A) Beeswarm plot of the CHEK1 protein dependence values in all samples, visualizing the increased dependence on CHEK1 in U-CLL tumors. Association testing was performed using Student’s t-test test (two-sided, with equal variance) and Benjamini-Hochberg correction was applied. (B) A network illustration of the off-target effect of CHEK1 inhibitors that involves BCR components (BTK, SYK, YES1 and LYN). Only the high confidence pairs in the kinobeads dataset are considered. The drugs that only target CHEK1 are colored in red. (C) Beeswarm plots showing the effect of three CHEK1-specific inhibitors in U-CLL and M-CLL samples. Association testing was performed using Student’s t-test test (two-sided, with equal variance) and Benjamini-Hochberg correction was applied. (D) Differentially expressed hallmark gene sets in CLL cells after BCR pathway stimulation through anti-IgM treatment (ArrayExpress ID: E-GEOD-39411). Highlighted upregulated hallmark gene sets are associated with DNA-damage response and cell cycle checkpoint.