Identifying Novel Drug Vulnerabilities in Specified Molecular Subsets of Chronic Lymphocytic Leukemia
Authors: Parvin Salma , Knisbacher Binyamin , Aryal Aditi , Fernandes Stacey M. , Medalion Shlomi , Leib Guy , Gustafsson Johan , Amit Shir , Meidan Eden C , Klein Nadav Y , Nafussy Savion , Neuberg Donna S. , Wu Catherine J. , Brown Jennifer R. , Getz Gad , Letai Anthony G. 
Published: December 2024

Despite many advances in the treatment of CLL in the past decade, chronic lymphocytic leukemia (CLL) remains incurable for most patients. Identifying novel active drugs in CLL and building a precision medicine framework that can match CLL patients to the appropriate drugs are therefore of high priority.

To address this challenge, we optimized high-throughput dynamic BH3 profiling (HT-DBP), a functional assay that rapidly measures the initiation of apoptotic signaling after ex vivo exposure to drugs, for interrogation of CLL samples. The main advantages of this assay over other ex vivo assays are: (i) rapidity - less than 24 hours ex vivo study is required, especially important in CLL where cell viability substantially decreases after 24 hours; (ii) miniaturization - a limited number of primary cells are required; and (iii) scalability - hundreds of drug response tests are possible in parallel on one 384-well plate. These features maximize the information gained from each patient sample.

We performed HT-DBP on 82 primary CLL samples previously characterized by exome, transcriptome and methylome profiling, using 42 drugs (60% FDA approved) selected for potential relevance to CLL biology. We isolated peripheral blood mononuclear cells (PBMCs) and cultured them in conditioned media derived from stromal cells to reduce spontaneous apoptosis (Parvin et al., Blood Advances 2023). Target cells were treated with a drug for 20 hours followed by mitochondrial BH3 peptide exposure. Mitochondrial outer membrane permeabilization (MOMP) was then measured on digitonin-permeabilized cells in response to BH3-only synthetic peptides that mimic pro-apoptotic BCL-2 family proteins. Mitochondrial cytochrome c release was quantified as a measure of MOMP by flow cytometry, gating on CD19+ and CD5+ cells. Our main measure of apoptotic priming used the BIM BH3 peptide, while the BAD BH3 and MS1 BH3 peptides informed on BCL-2 and MCL-1 dependence, respectively. The main measure of drug effect was a drug-induced increase in mitochondrial sensitivity to the BIM BH3 peptide, which we call an increase in apoptotic priming.

Our screen revealed differential drug-induced apoptotic priming across the range of drugs. Supporting the clinical and biological relevance of our results, venetoclax and ibrutinib were highly effective across CLL, nutlin-3 was ineffective in p53 mutant CLL. Novel drugs with the greatest pan-CLL effects include abexinostat, navitoclax, cerdulatinib, gandotinib and nutlin-3. The assay was robust, as indicated by an 0.92 median Pearson correlation across replicates. Additionally, the majority of drugs had greater effect on CLL samples than on healthy PBMCs (p&lt;0.001, paired t-test), supporting their specificity.

We asked whether we observed differential drug effects associated with specific molecular features, including exomes, methylomes, and gene expression clusters (ECs) of prognostic significance we identified in the CLL-map project (Knisbacher et al., Nat Genet 2022). We found many such associations including high sensitivity of IGHV-mutated CLL (M-CLL) to nutlin-3, IGHV-unmutated CLL (U-CLL) to Onalespib (MWU test, q&lt;0.1), the intermediate epigenetic subtype (i-CLL) to Rapamycin (ANOVA, q&lt;0.1). RNA subtype EC-m4 (TNF- and IFN- high M-CLLs) was specifically sensitive to nutlin-3 and onalespib; and EC-m2 (trisomy 12 enriched M-CLLs) demonstrated resistance to venetoclax and sensitivity to abexinostat (MWU test, p&lt;0.05). Response to lenalidomide was associated with trisomy 12 (MWU, p=0.002). We have validated select associations and predictions of primary interest on an independent validation set of CLL patient samples. To systematize our findings, we used our data to build multivariable machine-learning models that predict drug response from molecular data and applied it to the CLL-map cohort.

In summary, we present an experimental strategy to rapidly prioritize novel treatments for CLL patients and a computational framework to inform precision medicine based on molecular markers for future patients. Due to its scalability and modest requirements for ex vivo culture, this approach is potentially applicable to other hematologic malignancies as well.