Abstract 672: Establishment of a drug-tumor interaction database using Lantern Pharma’s Response Algorithm for Drug Positioning and Rescue (RADRTM)
Authors: Umesh Kathad, Yuvanesh Vedaraju, Aditya Kulkarni, Barry Henderson, Gregory Tobin, Panna Sharma, Arun Asaithambi
Journal: Cancer Research (2019)

Abstract The Response Algorithm for Drug positioning and Rescue (RADRTM) technology is Lantern Pharma's proprietary Artificial Intelligence (Al)-based machine learning approach for biomarker panel 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. RADRTM uses transcriptomic, drug sensitivity datasets and systems biology inputs and generates gene expression-based responder/non-responder profiles for specific tumor indications with high accuracy. RADRTM uses a unique process flow and a combination of machine learning algorithms to extract drug-specific biomarkers from whole transcriptome level input (~18000 genes). RADRTM comprises three main modules: data pre-processing, feature selection, and response prediction. Data pre-processing includes data cleaning, transformation and normalization. For dimensionality reduction and feature selection, RADRTM performs gene filtering via biological and statistical feature selection methods. In the final component, an AI-driven program reduces the intermediate number (approximately 500) of genes to a more manageable number (10 - 50) of candidate biomarkers capable of predicting drug sensitivity or insensitivity. Lantern’s RADRTM AI application incorporates automated supervised machine learning strategies along with big data analytics, statistics and systems biology to enable identification of new correlations of genetic biomarkers with drug activity. Using RADRTM we have created a database of drug response prediction models for more than 120 drug-tumor type combinations in a preclinical setting that is expected to keep growing. These drug- and cancer-specific RADRTMmodels have further enabled the classification of clinical records into distinct response groups, as well as generated gene expression signatures as features predictive of response. The average response prediction accuracy lies above 80%. We also demonstrate the utility of such a database in clinical translation through various performance metrics including but not limited to true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC). This database links the majority of FDA approved and selected investigational drugs with appropriate cancer indications and the associated RADRTM-derived responder/non-responder profiles in terms of gene expression signatures. This database could directly inform the drug-companion diagnostic co-developmental pathways for new drugs and cancer indications. Citation Format: Umesh Kathad, Yuvanesh Vedaraju, Aditya Kulkarni, Barry Henderson, Gregory Tobin, Panna Sharma, Arun Asaithambi. Establishment of a drug-tumor interaction database using 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 672.