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Patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) are generally older and have more comorbidities. Therefore, identifying personalized treatment options for each patient early and accurately is essential. To address this, we developed a computational biology modeling (CBM) and digital drug simulation platform that relies on somatic gene mutations and gene CNVs found in malignant cells of individual patients. Drug treatment simulations based on unique patient-specific disease networks were used to generate treatment predictions. To evaluate the accuracy of the genomics-informed computational platform, we conducted a pilot prospective clinical study (NCT02435550) enrolling confirmed MDS and AML patients. Blinded to the empirically prescribed treatment regimen for each patient, genomic data from 50 evaluable patients were analyzed by CBM to predict patient-specific treatment responses. CBM accurately predicted treatment responses in 55 of 61 (90%) simulations, with 33 of 61 true positives, 22 of 61 true negatives, 3 of 61 false positives, and 3 of 61 false negatives, resulting in a sensitivity of 94%, a specificity of 88%, and an accuracy of 90%. Laboratory validation further confirmed the accuracy of CBM-predicted activated protein networks in 17 of 19 (89%) samples from 11 patients. Somatic mutations in the TET2, IDH1/2, ASXL1, and EZH2 genes were discovered to be highly informative of MDS response to hypomethylating agents. In sum, analyses of patient cancer genomics using the CBM platform can be used to predict precision treatment responses in MDS and AML patients.

Background: Monosomy of chromosome 7/Del 7 (-7) or its long arm (del(7q)) is one of the most common cytogenetic abnormalities in pediatric and adult myeloid malignancies, particularly in adverse-risk acute myeloid leukemias (AMLs). Monosomy 7 with complex karyotype further worsens the prognosis. Therefore, predicting response of therapies in this segment of patients is urgently needed to improve disease management by customizing therapy to the profile genomics instead of the conventional method of trial and error or one-size-fits all treatments. Aim: Predictive analysis of disease characteristics of Monosomy 7 and Sub-clustering of this segment along with identification of therapy options for treatment of this cohort using computational biology modeling (CBM). Methods: We selected a cohort of 1168 AML patients whose genomics and clinical data was collated from public domain datasets and literature. Using CBM, patients with (-7) were identified from this larger cohort and were sub-clustered using a machine learning approach. The genomic data of the representative patients per sub-cluster of (-7) cohort were used to generate disease-specific protein network maps. Digital drug simulations were done by quantitatively measuring drug effect and calculating a disease inhibition score (DIS) that is a composite of proliferation, viability and apoptosis along with impact on disease specific biomarker score. Each patient-specific map was digitally screened for the extent by which standard of care (SOC) and combinations with Non-SOC inhibited the disease. Results: CBM identified 187 (-7) patients (16%) that grouped into 8 sub-clusters (Table 1). CBM analysis of genomics of each sub-cluster representative identified combination therapy options for this cohort with poor response to SOC, supported by genomics driven disease characteristics. Monosomy 7 aberrations would lead to decreased expression of EZH2, CARD11, EIF3, PMS2, HUS1, KMT2C (MLL3), CDK5 and IKZF1. Azacitidine (AZA) is predicted to be a non-responder in this cohort due to decreased expression of EZH2, that would impact DNA methylation via reduced recruitment of DNMTs. Lenalidomide (LEN) is also predicted to be a non-responder due to decreased expression of IKZF1, CARD11, and EIF3 despite presence of Del5q, a strong inclusion for LEN response. This segment has shown high microsatellite instability (MSI) characteristics and this could be linked to reduced mismatch repair (MMR) pathway due to reduced expression of PMS2, HUS1 and KMT2C. This characteristic could explain certain patient cases with (-7) who have responded to Cytarabine. (Figure 1) However, the response rate of chemotherapy in (-7) cohort is With (-7), CBM analysis determined an increased expression of BCL2 due to reduced methylation and HOXA9 expression. (PMID: 9562974) Proteasomal subunit PSMB5 gets down regulated due to CDK5 loss which would make the cohort more sensitive to proteasomal inhibition. This can also help with controlling EZH2 levels in the disease via regulating EZH2 proteasomal degradation. (PMID: 21289309, 27941792) CBM predicted that a combination of Cytarabine + Bortezomib and Cytarabine + Venetoclax could be effective in the (-7) AML cohort. Conclusions: CBM analysis of AML patient genomics identified abnormal protein networks and drug resistance pathways in patients with monosomy 7, and combination therapy options in the (-7) sub-clusters. EZH2 was identified as a key reason for resistance and combinations of Cytarabine with proteasome or BCL2 inhibitor was predicted to be effective in this cohort. Disclosures Vali: Cell Works Group Inc.: Employment. Cogle:Celgene: Other: Steering Committee Member of Connect MDS/AML Registry. Singh:Cellworks Research India Private Limited: Employment. Usmani:Cellworks Research India Private Limited: Employment. Pampana:Cellworks Research India Private Limited: Employment. Shyamasundar:Cellworks Research India Private Limited: Employment. Sikora:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment.