Abstract
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. In this study, we used the phosphoinositide 3-kinase (PI3K) inhibitor umbralisib as a model to (i) understand the impact of targeted treatment on cell signaling and immunophenotypes in responders and nonresponders, (ii) identify molecular features that predict individual treatment responses, and (iii) suggest alternative treatment options for the nonresponders.
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 Bruton tyrosine kinase (BTK) inhibitor acalabrutinib (NCT04624633, n = 12).
We found that umbralisib monotherapy led to significant changes in (phospho)protein levels, including AKT (pS473), in responders but not in nonresponders. Furthermore, the proportion of cytotoxic natural killer (NK) cells increased at the end of the study but only in responders, suggesting a role in the antitumor 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 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 nonresponders.
Functional phenotyping reveals differential cellular responses to umbralisib treatment in responders and nonresponders; predicts treatment response of individual patients with CLL; and suggests alternative treatment options for the nonresponders.
RSS Feeds