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

Choi, Ickwon; Chung, Amy W.; Suscovich, Todd J.; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O’Connell, Robert J.; Francis, Donald P.; Robb, Merlin L.; Michael, Nelson L.; Kim, Jerome H.; Alter, Galit; Ackerman, Margaret E.; Bailey‐Kellogg, Chris

doi:10.1371/journal.pcbi.1004185

Cited by 41 publications

(46 citation statements)

References 35 publications

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“…As evidence of the significance of Fc domain-driven effector functions to the in vivo activity of even antibodies with highly potent Fv domains continues to accumulate (3, 45–48), and the relevance that divergent subclass distributions in response to vaccination may play in efficacy in humans has become more apparent (49–51), an improved understanding of the correspondence of antibody biology between NHP and humans is likely to facilitate translational efforts centered on this model organism.…”

Section: Discussionmentioning

confidence: 99%

“…Notably, a series of studies have evaluated the significance of differences in IgG subclass selection on the activity and potential mechanistic link to efficacy in human HIV vaccine studies (49–51). Because rhesus macaques are the most frequently used preclinical model in HIV vaccine evaluation, there has been significant interest in understanding whether differential induction of subclasses is also associated with vaccine efficacy in macaques.…”

Section: Discussionmentioning

confidence: 99%

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Biophysical and Functional Characterization of Rhesus Macaque IgG Subclasses

et al. 2016

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Antibodies raised in Indian rhesus macaques [Macaca mulatta (MM)] in many preclinical vaccine studies are often evaluated in vitro for titer, antigen-recognition breadth, neutralization potency, and/or effector function, and in vivo for potential associations with protection. However, despite reliance on this key animal model in translation of promising candidate vaccines for evaluation in first in man studies, little is known about the properties of MM immunoglobulin G (IgG) subclasses and how they may compare to human IgG subclasses. Here, we evaluate the binding of MM IgG1, IgG2, IgG3, and IgG4 to human Fc gamma receptors (FcγR) and their ability to elicit the effector functions of human FcγR-bearing cells, and unlike in humans, find a notable absence of subclasses with dramatically silent Fc regions. Biophysical, in vitro, and in vivo characterization revealed MM IgG1 exhibited the greatest effector function activity followed by IgG2 and then IgG3/4. These findings in rhesus are in contrast with the canonical understanding that IgG1 and IgG3 dominate effector function in humans, indicating that subclass-switching profiles observed in rhesus studies may not strictly recapitulate those observed in human vaccine studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Biophysical and Functional Characterization of Rhesus Macaque IgG Subclasses

et al. 2016

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“…Perhaps the simplest way to visualize relationships between many different measured parameters is through the construction of correlation networks (Fig. ) . These diagrams allow for the visualization of significant correlative relationships between paired measured features of interest.…”

Section: Data‐driven Tools: Overview and Examplesmentioning

confidence: 99%

Prospects from systems serology research

Arnold

Chung

2017

Immunology

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SummaryAntibodies are highly functional glycoproteins capable of providing immune protection through multiple mechanisms, including direct pathogen neutralization and the engagement of their Fc portions with surrounding effector immune cells that induce anti‐pathogenic responses. Small modifications to multiple antibody biophysical features induced by vaccines can significantly alter functional immune outcomes, though it is difficult to predict which combinations confer protective immunity. In order to give insight into the highly complex and dynamic processes that drive an effective humoral immune response, here we discuss recent applications of ‘Systems Serology’, a new approach that uses data‐driven (also called ‘machine learning’) computational analysis and high‐throughput experimental data to infer networks of important antibody features associated with protective humoral immunity and/or Fc functional activity. This approach offers the ability to understand humoral immunity beyond single correlates of protection, assessing the relative importance of multiple biophysical modifications to antibody features with multivariate computational approaches. Systems Serology has the exciting potential to help identify novel correlates of protection from infection and may generate a more comprehensive understanding of the mechanisms behind protection, including key relationships between specific Fc functions and antibody biophysical features (e.g. antigen recognition, isotype, subclass and/or glycosylation events). Reviewed here are some of the experimental and computational technologies available for Systems Serology research and evidence that the application has broad relevance to multiple different infectious diseases including viruses, bacteria, fungi and parasites.

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“…In our experience, diverse approaches can achieve consistent performance using a consistent set of measurements (32). In contrast, in the context of small studies that compare responses between study arms, t-tests can be very sensitive to inclusion of all subjects—that is, a response measurement difference may meet a given significance threshold across all subjects, but fall short of this cutoff if a subset or even a single subject is excluded.…”

Section: Defining Humoral Signaturesmentioning

confidence: 99%

“…In order to gain insights into the properties of antibodies that support recruitment of effective functional responses, another study developed and applied an ML framework to identify and model associations among properties of antibodies and corresponding functional responses from antibody subclass-specificity data collected from RV144 vaccine recipients (32). This study demonstrated that models trained to encapsulate antibody feature-function relationships were able to robustly predict the quality of the polyclonal functional response using information about the specific antibody subtypes that were present.…”

Section: Systems Serologymentioning

confidence: 99%

Systems serology for evaluation of HIV vaccine trials

Ackerman

Barouch

Alter

2017

Immunological Reviews

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Summary The scale and scope of the global epidemic, coupled to challenges with traditional vaccine development approaches, point towards a need for novel methodologies for HIV vaccine research. While the development of vaccines able to induce broadly neutralizing antibodies remains the ultimate goal, to date vaccines continue to fail to induce these rare humoral immune responses. Conversely, growing evidence across vaccine platforms in both non-human primates and humans, point to a role for polyclonal vaccine-induced antibody responses in protection from infection. These candidate vaccines, despite employing disparate viral vectors and immunization strategies, consistently point to a role for functional or non-traditional antibody activities as correlates of immunity. However, the precise mechanism(s) of action of these “binding” antibodies, their specific characteristics, and ability to be selectively induced and/or potentiated to induce complete protection merits parallel investigation to neutralizing antibody-based vaccine design approaches. Ultimately while neutralizing and functional antibody-based vaccine strategies need not be mutually exclusive, defining the specific characteristics of “protective” functional antibodies may provide a target immune profile to potentially induce more robust immunity against HIV. Specifically, one approach to guide the development of functional antibody based vaccine strategies, termed “systems serology”, offers an unbiased and comprehensive approach to systematically survey humoral immune responses, capturing the array of functions and humoral response characteristics that may be induced following vaccination with high resolution. Coupled to machine learning tools, large datasets that explore the “antibody-ome” offer a means to step back from anticipated correlates and mechanisms of protection and toward a more fundamental understanding of coordinated aspects of humoral immune responses, their ability to more globally differentiate among vaccine candidates, and most critically, to identify the features of humoral immunity that distinguish protective from non-protective responses. Overall, the systematic serological approach described here aimed at broadly capturing the enormous biodiversity in antibody profiles that may emerge following vaccination complements existing cutting edge tools in the cellular immunology space that capture vaccine-induced polyfunctional cellular activity by flow cytometry, transcriptional profiling, epigenetic, and metabolomic analysis to offer a means to develop both a more nuanced and more complete understanding of correlates of protection to support the design of functional vaccine strategies.

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

Cited by 41 publications

References 35 publications

Biophysical and Functional Characterization of Rhesus Macaque IgG Subclasses

Biophysical and Functional Characterization of Rhesus Macaque IgG Subclasses

Prospects from systems serology research

Systems serology for evaluation of HIV vaccine trials

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