Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees

Abstract

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates. Author Summary: Antibodies are one of the central mechanisms that the human immune system uses to eliminate infection: an antibody can recognize a pathogen or infected cell using its Fab region while recruiting additional immune cells through its Fc that help destroy the offender. This mechanism may have been key to the reduced risk of infection observed among some of the vaccine recipients in the RV144 HIV vaccine trial. In order to gain insights into the properties of antibodies that support recruitment of effective functional responses, we developed and applied a machine learning-based framework to find and model associations among properties of antibodies and corresponding functional responses in a large set of data collected from RV144 vaccine recipients. We characterized specific important relationships between antibody properties and functional responses, and demonstrated that models trained to encapsulate relationships in some subjects were able to robustly predict the quality of the functional responses of other subjects. The ability to understand and build predictive models of these relationships is of general interest to studies of the antibody response to vaccination and infection, and may ultimately lead to the development of vaccines that will better steer the immune system to produce antibodies with beneficial activities.

Publication information

Originally published in PLOS Computational Biology on 2015-04-13. Original DOI: 10.1371/journal.pcbi.1004185.

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