Protein profiles predict treatment responses to the PI3K inhibitor umbralisib in patients with chronic lymphocytic leukemia.

Purpose: The management of chronic lymphocytic leukemia (CLL) has significantly improved with targeted therapies. However, many patients experience a suboptimal response. To optimally select the best therapy, predictive biomarkers are necessary. Here, we used the PI3K inhibitor umbralisib as a model to (i) understand how targeted treatment affects cell signaling and immunophenotypes in responders and non-responders; (ii) identify molecular features that predict individual treatment responses; and (iii) suggest alternative treatment options for the non-responders.

Experimental design: We performed functional phenotyping of CLL cells from patients enrolled in two clinical trials with umbralisib, administered either as a monotherapy (NCT02742090, n=55) or in combination with the BTK inhibitor acalabrutinib (NCT04624633, n=12).

Results: We found that umbralisib monotherapy led to significant changes in (phospho)protein levels, including AKT (pS473), in responders but not in non-responders. Furthermore, the proportion of cytotoxic natural killer cells increased at the end of study, but only in responders, suggesting a role in the anti-tumor response. To identify molecular predictors of response, we used the baseline levels of 30 (phospho)proteins in the monotherapy cohort as input features for a machine learning model, which achieved a significant prediction accuracy in cross-validation and maintained its predictive power in the combination cohort. Drug sensitivity profiling of the CLL cells at baseline suggested that PI3K + Bcl-2 inhibitors are effective in umbralisib non-responders.

Conclusions: Functional phenotyping reveals differential cellular responses to umbralisib treatment in responders and non-responders; predicts treatment response of individual CLL patients; and suggests alternative treatment options for the non-responders.