A genomics-informed computational biology platform prospectively predicts treatment responses in AML and MDS patients

Patients with myelodysplastic syndromes myelodysplastic syndromes: (my-eh-lo-diss-PLASS-tik SIN-dromez) A group of disorders where the bone marrow does not work well, and the bone marrow cells fail to make enough healthy blood cells. Myelo refers to the bone marrow. Dysplastic means abnormal growth or development. People with MDS have low blood cell count for at… (MDS) or acute myeloid leukemia acute myeloid leukemia: (uh-KYOOT my-uh-LOYD loo-KEE-mee-uh) A cancer of the blood cells. It happens when very young white blood cells (blasts) in the bone marrow fail to mature. The blast cells stay in the bone marrow and become to numerous. This slows production of red blood cells and platelets. Some cases of MDS become… (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.