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

Abstract

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.

Ex vivo drug screening defines novel drug sensitivity patterns for informing personalized therapy in myeloid neoplasms

“…Ex vivo DSS will likely synergize with genomic data and emerging precision medicine approaches such as in silico computational biology modeling. 47,48 Combining both genomics-based and ex vivo functional data may further refine precision therapy, enhance the selection of rational drug combinations, and ultimately improve outcomes for patients with myeloid neoplasms.…”

The effect of decitabine-combined minimally myelosuppressive regimen bridged allo-HSCT on the outcomes of pediatric MDS from 10 years’ experience of a single center

“…However, the cytogenetic data is far more limited with great heterogeneity which should be carefully interpreted. Recently, with the increased access to gene mutation landscape, genetic counseling for both patients and their families would affect pediatric MDS's clinical diagnosis and therapeutic decision s [36][37][38]. The gene mutation assay was performed among the 16 patients of this cohort and 14 of them were verified to carry different gene mutations (Supplementary Table 1).…”

The Mutational Landscape of Acute Myeloid Leukaemia Predicts Responses and Outcomes in Elderly Patients from the PETHEMA-FLUGAZA Phase 3 Clinical Trial

“…In this context, mutations in TET2 have previously been associated with responses to HMAs in MDS, or in AML with 20–30% blasts [ 7 , 21 ]. Likewise, via a genomics-informed computation biology platform recently developed to predict responses to HMAs in 18 patients with MDS/AML, the authors found that gain-of-function mutations in EZH2 and IDH1 / 2 were predictors of the response to the CpG-methylating effects of azacytidine via DNMT1 inhibition [ 22 ]. In contrast to our data, Jung and colleagues found that U2AF1 mutation was significantly associated with non-response to azacytidine in patients with MDS [ 23 ].…”