Supplementary Figure 7 from Combinatorial BCL2 Family Expression in Acute Myeloid Leukemia Stem Cells Predicts Clinical Response to Azacitidine/Venetoclax
posted on 2023-06-02, 08:21authored byAlexander Waclawiczek, Aino-Maija Leppä, Simon Renders, Karolin Stumpf, Cecilia Reyneri, Barbara Betz, Maike Janssen, Rabia Shahswar, Elisa Donato, Darja Karpova, Vera Thiel, Julia M. Unglaub, Susanna Grabowski, Stefanie Gryzik, Lisa Vierbaum, Richard F. Schlenk, Christoph Röllig, Michael Hundemer, Caroline Pabst, Michael Heuser, Simon Raffel, Carsten Müller-Tidow, Tim Sauer, Andreas Trumpp
MAC-Score predict response within mutational subgroups
Funding
HORIZON EUROPE European Research Council (ERC)
Deutsche Forschungsgemeinschaft (DFG)
History
ARTICLE ABSTRACT
The BCL2 inhibitor venetoclax (VEN) in combination with azacitidine (5-AZA) is currently transforming acute myeloid leukemia (AML) therapy. However, there is a lack of clinically relevant biomarkers that predict response to 5-AZA/VEN. Here, we integrated transcriptomic, proteomic, functional, and clinical data to identify predictors of 5-AZA/VEN response. Although cultured monocytic AML cells displayed upfront resistance, monocytic differentiation was not clinically predictive in our patient cohort. We identified leukemic stem cells (LSC) as primary targets of 5-AZA/VEN whose elimination determined the therapy outcome. LSCs of 5-AZA/VEN-refractory patients displayed perturbed apoptotic dependencies. We developed and validated a flow cytometry-based “Mediators of apoptosis combinatorial score” (MAC-Score) linking the ratio of protein expression of BCL2, BCL-xL, and MCL1 in LSCs. MAC scoring predicts initial response with a positive predictive value of more than 97% associated with increased event-free survival. In summary, combinatorial levels of BCL2 family members in AML-LSCs are a key denominator of response, and MAC scoring reliably predicts patient response to 5-AZA/VEN.
Venetoclax/azacitidine treatment has become an alternative to standard chemotherapy for patients with AML. However, prediction of response to treatment is hampered by the lack of clinically useful biomarkers. Here, we present easy-to-implement MAC scoring in LSCs as a novel strategy to predict treatment response and facilitate clinical decision-making.This article is highlighted in the In This Issue feature, p. 1275