Abstract
BACKGROUND: Established genetic biomarkers in chronic lymphocytic leukemia (CLL) have been useful in predicting response to chemoimmunotherapy but are less predictive of response to targeted therapies. With several such targeted therapies now approved for CLL, identifying novel, non-genetic predictive biomarkers of response may help to select the optimal therapy for individual patients. METHODS: We coupled data from a functional precision medicine technique called BH3-profiling, which assesses cellular cytochrome c loss levels as indicators for survival dependence on anti-apoptotic proteins, with multi-omics data consisting of targeted and whole-exome sequencing, genome-wide DNA methylation profiles, RNA-sequencing, protein and functional analyses, to identify biomarkers for treatment response in CLL patients. RESULTS: We initially studied 73 CLL patients from a discovery cohort. We found that greater dependence on the anti-apoptotic BCL-2 protein was associated with prognostically favorable genetic biomarkers. Furthermore, BCL-2 dependence was strongly associated with gene expression patterns and signaling pathways that suggest a more targeted drug-sensitive milieu and was predictive of drug responses. We subsequently demonstrated that these associations were causal in cell lines and additional CLL patient samples. To validate the findings from our discovery cohort and in vitro studies, we utilized primary CLL cells from 54 additional patients treated on a prospective, phase-2 clinical trial of the BTK inhibitor ibrutinib given in combination with chemoimmunotherapy (fludarabine, cyclophosphamide, rituximab) and confirmed in this independent dataset that higher BCL-2 dependence predicted favorable clinical response, independent of the genetic background of the CLL cells. CONCLUSION: We comprehensively defined BCL-2 dependence as a potential functional and predictive biomarker of treatment response in CLL, underscoring the importance of characterizing apoptotic signaling in CLL to stratify patients beyond genetic markers and identifying novel combinations to exploit BCL-2 dependence therapeutically. Our approach has the potential to help optimize targeted therapy combinations for CLL patients.