B1976
Title: Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning
Authors: David Benkeser - Emory University (United States) [presenting]
Abstract: Recent studies have indicated that it is possible to protect individuals from HIV infection using a passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of neutralizing many possible strains of the virus. This is particularly challenging in the context of a highly diverse pathogen like HIV. It is, therefore, of great interest to leverage existing observational data sources to discover antibodies that are able to neutralize HIV viruses via residues where existing antibodies show modest protection. These observational data include genetic features of many diverse HIV genetic sequences, as well as in vitro measures of antibody resistance. We propose methods to analyze these data to identify important genetic features using the outcome-adaptive, collaborative targeted minimum loss-based estimation (CTMLE) approach using random forests. We demonstrate via simulation that the approach enjoys statistical benefits over existing approaches and apply the approach to the Compile, Analyze and Tally Nab Panels (CATNAP) database to identify AA positions that are potentially causally related to resistance to neutralization by several different antibodies.
