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Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning

Author

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  • Jin Yutong

    (Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA)

  • Benkeser David

    (Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA)

Abstract

Recent studies have indicated that it is possible to protect individuals from HIV infection using 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. Such information feeds directly into the clinical trial pipeline for monoclonal antibody therapies by providing information on (i) whether and to what extent combinations of antibodies can generate superior protection and (ii) strategies for analyzing past clinical trials to identify in vivo evidence of antibody resistance. These observational data include genetic features of many diverse HIV genetic sequences, as well as in vitro measures of antibody resistance. The statistical learning problem we are interested in is developing statistical methodology that can be used to analyze these data to identify important genetic features that are significantly associated with antibody resistance. This is a challenging problem owing to the high-dimensional and strongly correlated nature of the genetic sequence data. To overcome these challenges, we propose an outcome-adaptive, collaborative targeted minimum loss-based estimation approach using random forests. We demonstrate via simulation that the approach enjoys important statistical benefits over existing approaches in terms of bias, mean squared error, and type I error. We apply the approach to the Compile, Analyze, and Tally Nab Panels database to identify AA positions that are potentially causally related to resistance to neutralization by several different antibodies.

Suggested Citation

  • Jin Yutong & Benkeser David, 2022. "Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 280-295, January.
    • Handle: RePEc:bpj:causin:v:10:y:2022:i:1:p:280-295:n:1
    DOI: 10.1515/jci-2021-0053
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    References listed on IDEAS

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    1. Silke Janitza & Ender Celik & Anne-Laure Boulesteix, 2018. "A computationally fast variable importance test for random forests for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 885-915, December.
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