A Genomic Signature Predicting Venetoclax Treatment Response in AML Identified By Protein Network Mapping and Validated By Ex Vivo Drug Sensitivity Testing: A Beat AML Project Study

Background: Data from a small clinical trial of venetoclax in acute myeloid leukemia (AML) recently supported FDA breakthrough therapy designation for use in combination with hypomethylating agents in treatment-naïve patients who are ineligible for high-dose induction chemotherapy. Approval of this BCL-2 inhibitor raises the question of mechanism of action in AML and patient selection for treatment. Unfortunately, no biomarkers or methods exist to predict venetoclax response in AML, making treatment selection challenging.

Aim: To define a novel genomic signature rule to predict AML response to venetoclax therapy and to validate the rule with ex vivo drug sensitivity testing.

Methods: The Beat AML project (supported by the Leukemia & Lymphoma Society) collects clinical data and bone marrow specimens from AML patients. Bone marrow samples are analyzed by conventional cytogenetics, whole-exome sequencing, RNA-seq, and an ex vivo drug sensitivity assay. For 19 of these randomly chosen patients, every available genomic abnormality was inputted into a computational biology program (Cellworks Group) that uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated protein pathways. Digital drug simulations with venetoclax were conducted by quantitatively measuring drug effect on a composite AML disease inhibition score (i.e., cell proliferation, viability, and apoptosis). Computational predictions of drug response were compared to venetoclax IC₅₀ values from the Beat AML ex vivo testing.

Results: Ten of the 19 AML patients were predicted by computer simulation to respond to venetoclax, and 9 of those 10 patients had the lowest IC₅₀ values to venetoclax. Nine of the 19 patients were predicted to not respond to venetoclax, and 8 of those 9 patients had the highest IC₅₀ values to venetoclax. Ex vivo venetoclax responses were correctly matched to their computer simulation prediction in 17 of 19 cases, and incorrectly matched in 2 cases. The positive predictive value of the computational method was 90%, negative predictive value was 89%, sensitivity was 90%, specificity was 89%, and accuracy was 89%. Amplification of the genes RB1CC1 and/or RB1was predicted by computational modeling to increase MCL1, which made venetoclax less responsive in the digital drug simulation. This genomic rule was validated with 3 AML patients: one who received venetoclax as treatment and showed refractory disease, and 2 patients who achieved complete remission after venetoclax treatment.

Conclusion: We identified a new genomic signature, confirmed by functional testing, that predicts AML response to venetoclax treatment. This unique computer-based approach is intended to inform the design of phase 2 and 3 clinical trials of venetoclax in AML patients for a forthcoming precision enrollment clinical trial.