Cellworks Omics Biology Model (CBM) to predict therapy response and identify biomarkers for all-trans retinoic acid (ATRA) benefit as addition to induction chemotherapy in adults with acute myeloid leukemia (AML).

Abstract

7027 Background: ATRA combined with arsenic trioxide revolutionized the treatment of APL. Based on promising in vitro data, several clinical trials evaluated ATRA combinations in non-APL AML, in which some patients seemed to benefit from the addition. Thus, predicting response a priori is imperative to determine the optimal treatment for each patient. The CBM was used to evaluate the impact of initial therapy with ATRA combined with cytarabine, etoposide, idarubicin (ATRA-CEI) to assess the biomarkers responsible for response in adults with AML. Methods: AML patients participating in clinical trial NCT00151242 had their leukemia sequenced as part of the trial, and genomic profiles were used for computational modeling by the CBM, which uses curated data about genomic aberrations from PubMed as input to generate disease-specific protein network maps and predict drug responses. Disease biomarkers unique to each patient were identified using biosimulation. Digital drug simulations were conducted by measuring the effect of ATRA-CEI on a composite cell growth score of cell proliferation, apoptosis and other hallmarks of cancer. ATRA-CEI was mapped to the patient genome along with a mechanism of action and validated based on the genomic profile and its biological consequences. Results: Of 171 patients treated with ATRA-CEI, 107 (63%) responded (R) and 64 did not (NR). A subset of 18 patients with favorable genomic features were found to be NR and their non-response was correctly predicted by CBM in all 18 cases. Mutations of DNMT3A, EZH2, ASXL, FLT-3, and GART amplification emerged as novel biomarkers of ATRA-CEI failure (only 37 of 107 responders (35%) with these findings, compared to 70 of 107 responders (65%) without these findings (p = 0.0027)). DNMT3A, EZH2, ASXL1 loss of function mutations activate FABP5, a key mechanism of ATRA resistance, and also activate ABCC1 (PgP), which reduces the efficacy of etoposide and idarubicin by upregulating MDR1. In general, monosomy 7 is expected to confer ATRA resistance due to the presence of EZH2 and KMT2E gene deletions. Indeed, 18 of 32 patients with monosomy 7 did not respond. However, the 14 who responded had co-occurrence of deletions involving IGFBP3, PMS2, HUS1, CDK5, XRCC2/4, AKR1B10, and others that overcame ATRA resistance associated with monosomy 7 and were identified by CBM. Use of CBM helps avoid unnecessary use of ATRA in patients unlikely to respond (19% of cases) thus reducing toxicity and cost without changing efficacy, and also identifies those likely to respond, even when they have monosomy 7, where non-response is the norm. Conclusions: ATRA benefits a subset of patients with non-APL AML. CBM predicted response using computational modeling of all genetic alternations, which explains its success versus traditional one-gene-one-drug approaches.