Combined chemosensitivity and chromatin profiling prioritizes drug combinations in CLL

Abstract

The Bruton tyrosine kinase (BTK) inhibitor ibrutinib has substantially improved therapeutic options for chronic lymphocytic leukemia (CLL). Although ibrutinib is not curative, it has a profound effect on CLL cells and may create new pharmacologically exploitable vulnerabilities. To identify such vulnerabilities, we developed a systematic approach that combines epigenome profiling (charting the gene-regulatory basis of cell state) with single-cell chemosensitivity profiling (quantifying cell-type-specific drug response) and bioinformatic data integration. By applying our method to a cohort of matched patient samples collected before and during ibrutinib therapy, we identified characteristic ibrutinib-induced changes that provide a starting point for the rational design of ibrutinib combination therapies. Specifically, we observed and validated preferential sensitivity to proteasome, PLK1, and mTOR inhibitors during ibrutinib treatment. More generally, our study establishes a broadly applicable method for investigating treatment-specific vulnerabilities by integrating the complementary perspectives of epigenetic cell states and phenotypic drug responses in primary patient samples.

Figure 1. Integrative analysis of epigenetic cell state and cell-selective chemosensitivity in ibrutinib-treated CLL patients.

Biobanked peripheral blood mononuclear cells (PBMCs) from chronic lymphocytic leukemia (CLL) patients isolated before and during ibrutinib treatment were subjected to chromatin accessibility mapping by ATAC-seq and to chemosensitivity profiling using pharmacoscopy, a single-cell automated imaging method for quantifying cell-selective drug response. To connect ibrutinib-induced changes in cell state to induced drug vulnerabilities, we mapped the ATAC-seq and pharmacoscopy data into the shared space of molecular pathways, which provide a joint basis for integrative analysis and prioritization of ibrutinib-based drug combinations for the treatment of CLL and potentially other hematopoietic malignancies.

Figure 2. Chromatin accessibility mapping for matched CLL patient samples collected before and during ibrutinib treatment.

(a) UCSC Genome Browser snapshots showing chromatin accessibility data obtained by ATAC-seq for matched samples (n=36) collected before ibrutinib treatment (blue) and during ibrutinib treatment (green). ChIP-seq profiles for two promoter/enhancer associated histone marks (H3K27ac shown in red, H3K4me1 shown in yellow) in IGHV unmutated CLL (uCLL) as well as IGHV mutated CLL (mCLL) are included as an additional reference. Regions that significantly lose or gain accessibility are highlighted in yellow. (b) Principal component analysis based on ATAC-seq signal intensities of all open chromatin sites in all CLL samples (n=36). Principal component 1 separated the samples by their IGHV mutation status, while there was no obvious correlate of principal component 2. (c) Principal component 3 separated the samples by their ibrutinib treatment status.

Figure 3. Differential analysis of ibrutinib-induced changes in chromatin accessibility for matched CLL patient samples.

(a) Scatterplot comparing ATAC-seq signal intensities across all open chromatin sites between samples collected before and during ibrutinib treatment. Significant changes correspond to an FDR-adjusted p-value below 0.01 and an absolute log₂ fold change above 1 as calculated by the DESeq2 software. The diagonal is shown as a dashed line, as a reference indicating regions with no change in chromatin accessibility upon ibrutinib treatment. (b) Heatmap of normalized chromatin accessibility (Z-scores) for all regions with significantly differential chromatin accessibility according to panel a. (c) Region set enrichment analysis for genomic regions with reduced chromatin accessibility during ibrutinib treatment based on LOLA analysis, showing the twelve most significantly enriched region sets. (d) De novo motif enrichment analysis for regions with reduced chromatin accessibility during ibrutinib treatment. Reported p-values were calculated by the HOMER software using a binomial test. (e) Gene set analysis for enrichment of NCI-Nature and KEGG pathways among genes located in the vicinity of regions with reduced chromatin accessibility during ibrutinib treatment. Enrichment scores were calculated by the Enrichr software and represent the log p-value of a Fisher’s exact test multiplied by a Z-score of deviation from the expected rank. (f) Pathway-centric assessment of changes in chromatin accessibility induced by ibrutinib treatment. Normalized ATAC-seq signals of all genes in each KEGG pathway were aggregated to rank pathways. Yellow/orange dots denote pathways characterized by higher chromatin accessibility during ibrutinib treatment than before ibrutinib treatment, while blue/purple dots indicate pathways with lower chromatin accessibility.

Figure 4. Single-cell chemosensitivity profiling for matched CLL patient sample pairs collected before and during ibrutinib treatment.

(a) Heatmap of CD19⁺ cell-selective cytotoxicity for a subset of the 131 tested drugs (full results are shown in Supplementary Figure 3) in CLL samples collected before and during clinical ibrutinib treatment, averaged across patients. In the first two rows, red indicates drugs that were selective for the CD19⁺ cell fraction, while green indicates drugs that were anti-selective. The third row depicts the difference in CD19⁺ cell-selective cytotoxicity for samples collected before and during ibrutinib treatment, where blue is more selective, yellow is less selective, and white indicates no change. (b) All 131 drugs plotted according to their difference in CD19⁺ cell-selective cytotoxicity (y-axis) versus the difference in general cytotoxicity toward all PBMC populations (x-axis), before and after ibrutinib. The dot color indicates CD19⁺ cell-selective cytotoxicity during ibrutinib treatment. (c) Ranking of the observed change in CD19⁺ cell-selective cytotoxicity at the level of KEGG pathways, aggregating the data across all drugs annotated with the respective KEGG pathway. The pharmacoscopy results were based on data for 10 CLL patients with matched samples collected before and during ibrutinib (for one patient, the sample collected during ibrutinib treatment was excluded due to low data quality as the result of poor cell viability after thawing, and the patient was removed from the pharmacoscopy data analysis). Drugs were screened over two 384-well plates per sample in two concentrations (10 μm and 1 μm), where each concentration point was performed in triplicate (10 μM) or in duplicate (1 μM). Drug sensitivities were normalized to DMSO, and there were approximately 40 DMSO control wells on each plate.

Figure 5. Prioritization and validation of ibrutinib-based drug combinations based on combined chemosensitivity and chromatin profiling.

(a) Integrative analysis of differential chromatin accessibility (x-axis) and differential cell-selective chemosensitivity (y-axis) at the pathway level. Red dots denote pathways characterized by higher chromatin accessibility and/or higher chemosensitivity during ibrutinib treatment than before ibrutinib treatment, while blue dots indicate lower chromatin accessibility and/or lower chemosensitivity. (b) Heatmap of CD19⁺ cell-selective cytotoxicity for combination matrixes of ibrutinib (y-axis) and six partner drugs (x-axis). Red indicates drug combinations that were selective for the CD19⁺ cell fraction, while green indicates combinations that were anti-selective. Results shown are averages across five patient samples, where each concentration point was measured in triplicate for each patient sample. (c) Drug responses (% viability) of primary CLL cells pretreated with different concentrations of ibrutinib in a co-culture model using primary bone marrow stromal cells. Viability was normalized to the effect of ibrutinib as a single agent. Error bars indicate the standard error of the mean (SEM) calculated across samples (numbers in brackets).