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Data from Combinatorial BCL2 Family Expression in Acute Myeloid Leukemia Stem Cells Predicts Clinical Response to Azacitidine/Venetoclax

Authors :
Andreas Trumpp
Tim Sauer
Carsten Müller-Tidow
Simon Raffel
Michael Heuser
Caroline Pabst
Michael Hundemer
Christoph Röllig
Richard F. Schlenk
Lisa Vierbaum
Stefanie Gryzik
Susanna Grabowski
Julia M. Unglaub
Vera Thiel
Darja Karpova
Elisa Donato
Rabia Shahswar
Maike Janssen
Barbara Betz
Cecilia Reyneri
Karolin Stumpf
Simon Renders
Aino-Maija Leppä
Alexander Waclawiczek
Publication Year :
2023
Publisher :
American Association for Cancer Research (AACR), 2023.

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.Significance: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.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....3e60368bef9c8c1d25cda8992603243d