Predicting Resistance to the Combination of ATO and ATRA in APL Patients with PML-Rara Fusions, Using a Computational Biology Modeling Approach: Mycare-021-01

Background: Acute promyelocytic leukemia (APL) is a biologically and clinically distinct subtype of acute myeloid leukemia (AML) with unique molecular pathogenesis, clinical manifestations, and treatment. APL is cytogenetically characterized by a balanced translocation t(15;17) (q24;q21), which involves the retinoic acid receptor alpha (RARA) gene on chromosome 17 and the promyelocytic leukemia (PML) gene on chromosome 15 that results in a PML-RARA fusion gene (PMID: 30575821). The PML-RARA fusion gene is the most critical event involved in the pathogenesis of APL, reported in 99% of APL patients (PMID: 32182684). The fusion confers a selective sensitivity to the targeted drugs, arsenic trioxide (ATO) and all-trans-retinoic acid (ATRA), with response rates over 90% (PMID: 31635329). However, the mechanism of resistance in the minority of non-responders is not well understood. This study used the Cellworks Omics Biology Model (CBM) to predict response to the combination of ATO-ATRA in patients harboring the PML-RARA fusion and identify mechanisms of resistance. Methods: Outcomes of 30 APL patients treated with ATRA or ATRA plus ATO were compared with outcomes predicted by CBM (Table 1). Genomic data from 6 publications (Table 2) derived from whole exome sequencing (WES), targeted next-generation sequencing (NGS), copy number variation (CNV) and/or karyotype data were used. All data was anonymized, de-identified and exempt from IRB review. The available genomic data for each profile was entered into the CBM which generates a patient-specific disease protein network model using PubMed and other online resources. The CBM predicts the patient-specific biomarker and phenotype response of a personalized diseased cell to drug agents, radiation and cell signaling. Disease biomarkers that are unique to each patient were identified within the protein network models. ATO and ATRA were simulated on all 30 patient cases. The treatment impact was assessed by quantitatively measuring the drug's effect on a cell growth score which is a composite of the quantified values for cell proliferation, survival, and apoptosis, along with the simulated impact on each patient-specific disease biomarker score. Each patient-specific model was also digitally screened to identify response to ATO and ATRA. Results: The CBM correctly predicted the response to ATO and ATRA in 28 of 30 cases. The overall prediction accuracy was 93% with a PPV of 100%, NPV of 60%, sensitivity of 93%, and spec