Speaker
Description
The immune response to vaccination is highly heterogeneous and arises
from a dynamic interplay of immune components. In this talk, I will
discuss how we employed random forests (RFs) to classify differences
in immunogenicity between older people with HIV (PWH) on ART and
age-matched controls who received up to five SARS-CoV-2 vaccinations
[1]. Harnessing machine learning (ML) to learn immune
interdependencies offers the potential to decode immune signatures
linked to a specified comorbidity, and further reveal individualized
patterns laying the groundwork for precision-guided vaccination and
targeted clinical follow-up. Our data set contains an extensive range
of immune features, including serum and saliva IgG and IgA responses,
ELISpot IFNg and IL2 responses to SARS-CoV-2 spike peptides, ACE2
receptor displacement, and SARS-CoV-2 neutralization capacity; all
tracked longitudinally up to 104 weeks in each individual following
SARS-CoV-2 vaccine doses 1 through 5. In this biomarker space, RFs
identify highly important and unimportant combinations of features
that distinguish PWH from controls, and further reveal a subset of PWH
whose immune signatures resemble controls, suggesting near-complete
immunologic restoration from a vaccine perspective in these
individuals. Our results highlight the effectiveness in utilizing RFs
to identify complex immunological interdependencies.
