Performance analysis of a novel artificial intelligence-based computational method on published ex vivo drug sensitivity data to support targeted treatment decisions in precision oncology.

Abstract

e13618 Background: Comprehensive molecular profiling is readily available for clinical practice. An extensive amount of published evidence provides information about the potential functional relevance of many driver genes and genetic alterations. But, due to the large number of driver genes and alterations, and the long-tail frequency distribution of these alterations and their possible combinations, very few single driver alterations have reached high-enough-level evidence alone to support clinical decisions. We hypothesized that by aggregating evidence following logical principles of reasoning by a computational system, this vast molecular information can be used to improve personalized treatment decisions. In the present study, we used previously published ex vivo drug sensitivity data to analyze the performance of an artificial intelligence-based computational method, the digital drug assignment (DDA), which has shown utility by improving treatment decisions in case of complex molecular profiles acquired in the SHIVA01 trial. Methods: We selected 111 cases with whole-genome sequencing (WGS) and ex vivo drug sensitivity data of a previously published acute myeloid leukemia study (Tyner et al, 2018). WGS variants were filtered for a preselected hematology-related panel of 446 genes and uploaded to a DDA-based software system to calculate the aggregated evidence level (AEL) values of associated molecularly targeted agents. DDA-predicted sensitivity (or resistance) was defined as AEL > 0 (or AEL < 0) in the presence of at least one actionable driver. Area under the curve (AUC) values were used for determining the ex vivo sensitivity or resistance of leukemia cells to 40 approved drugs and 53 developmental compounds. Results: The AUC values were significantly different in the drug-sensitive and -resistant groups forecasted by the DDA (167.1 and 205.5, respectively, p < 0.0001) and differed significantly from the average AUC value (194.6). Overall, sensitivity was correctly predicted in 66% of compound-sample pairs (n = 671). 88 approved drugs had AEL value over 1000; of these 73% were effective according to the ex vivo results. While forecasted resistance was confirmed in 63% of the cases. With only the most sensitive/resistant 20% of cases considered from the ex vivo data, sensitivity was accurately predicted in 75% of approved compound-sample pairs (n = 173). 37 approved drugs had AEL value over 1000; of these 81% were confirmed as sensitive. Conclusions: The DDA-based computational reasoning has a promising performance in forecasting sensitivity and resistance to a broad spectrum of targeted agents based on molecular information. Therefore, it has the potential to automate, standardize and improve complex molecular profile-based targeted treatment decisions.