Superior therapy response predictions for patients with acute myeloid leukemia (AML) using Cellworks Singula: MyCare-009-01.

Abstract

e19502 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of AML patients remains relatively poor. Therapy selection is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. The Cellworks Singula™ report predicts response for physician prescribed therapies (PPT) using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. We test the hypothesis that Singula is a more accurate predictor of patient-specific therapy response than PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 494 AML patients aged 2 to 85 years (median 54) treated with PPT. Patient omics data was available from PubMed. The accuracy of Singula was compared to that of PPT using McNemar’s test to account for the correlation between Singula and PPT. Multivariate logistic regression modeled complete response (CR) as a function of patient age, PPT, and Singula against any non-response (NR). Likelihood ratio tests were performed to further validate if Singula provides predictive information beyond PPT or patient age. Similar analyses were performed for overall survival (OS) using proportional hazards regression. Results: Singula was a better predictor for CR than PPT (McNemar’s χ2 = 72.0, p-value < 0.0001), with an overall accuracy of 88.5% (95% CI: 85.3%, 91.1%) compared to 70.2% (95% CI: 66.0%, 74.2%) for PPT. Singula exhibited a sensitivity and specificity of 97.1% and 68.0%, respectively. In multivariate regression analysis, Singula (p < 0.0001) remained an independent predictor for CR after adjusting for patient age (p = 0.0329) while PPT became not significant (p = 0.75). Singula was also an independent predictor for OS (p < 0.0001) after adjusting for patient age (p = 0.0018) and PPT (p = 0.0011). For all 100 true negatives, Singula generated alternative standard of care therapy selections with predicted clinical response. Conclusions: Singula is a superior independent predictor for CR and OS compared to PPT in AML patients. The Singula report can also validate therapy selection, correctly identify non-responders to PPT and further provide alternative therapy selections.