Abstract 4014: Clinical validation of Lantern Pharma’s Response Algorithm for Drug Positioning and Rescue (RADRTM)
Authors: Vedaraju Yuvanesh , Kathad Umesh , Kulkarni Aditya , Henderson Barry , Tobin Gregory , Sharma Panna , Asaithambi Arun 
Published: August 2019

Abstract
Lantern Pharma has developed a technology platform termed RADRTM that can be used to predict true responders before conducting a clinical trial in order to achieve higher success rates. RADRTM is an Artificial Intelligence (Al)-based machine learning approach for complex biomarker identification and patient stratification. RADRTM is a combination of three automated modules working sequentially to generate drug- and tumor-specific gene signatures predictive of response. RADRTM integrates biological knowledge, data-driven feature selection, and robust Al algorithms to facilitate hypothesis-free drug- and cancer-specific biomarker development. We present retrospective analyses performed as part of RADRTM validation using at least 9 independent datasets of patients from selected cancer types treated with approved drugs including chemotherapy, targeted therapy and immune-oncology agents. Pre-treatment patient gene expression profiles along with corresponding treatment outcomes were used as algorithm inputs. Model training was typically performed using an initial set of genes derived from cancer cell line data when available, and further applied to a subset of patient data for model tuning and final gene signature development. Model testing and performance computation were carried out on patient records held out as blinded datasets. The response prediction accuracy, true positive rate (TPR), true negative rate (TNR) false discovery rate, positive predictive value and Matthew’s Correlation Coefficient were among the model performance metrics calculated. On average, RADRTM achieved a response prediction accuracy of 80% during clinical validation. For instance, in an analysis of 92 breast cancer patients, RADRTM generated a signature of 18 genes whose expression level was predictive of Paclitaxel treatment response at an overall accuracy of 78% and 81% TPR/ 76% TNR. The above results imply that the application of the RADRTM program to this Paclitaxel trial in breast cancer patients could have potentially reduced the number of patients in the treatment arm from 92 unselected patients to 24 biomarker-selected patients to produce the same number of responders. Moreover, we cite published evidence correlating genes from this 18-gene signature with increased Paclitaxel sensitivity in breast cancer. The value of the platform architecture is derived from its validation through the analysis of about 6 million oncology-specific clinical data points, more than 120 drug-cancer interactions, and over 600 patient records. Thus, by implementing unique biological, statistical and machine learning workflows, Lantern Pharma's RADRTM technology is capable of deriving robust biomarker panels for pre-selecting true responders for recruitment into clinical trials which may improve the success rate of oncology drug approvals.
Citation Format: Yuvanesh Vedaraju, Umesh Kathad, Aditya Kulkarni, Barry Henderson, Gregory Tobin, Panna Sharma, Arun Asaithambi. Clinical validation of Lantern Pharma’s Response Algorithm for Drug Positioning and Rescue (RADRTM) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4014.