Speaker:
Title:
Methods to Isolate Tumor-Specific Antibodies Induced by Immunotherapy
Abstract:
Combination immunotherapy has been shown to potently eliminate
tumors and induce immunological memory in mouse models. Mice that are cured
by this therapy develop endogenous antibodies that are protective against the
tumor. Interestingly, endogenous antibodies found in a subset of these mice
show specificity toward other tumor cells, suggesting the possible existence
of shared antigens. To better understand the specificity of these antibodies,
this work explores different approaches to isolating tumor-specific
antibodies from mouse organs.
Speaker:
Title:
An Unbiased Determination of pMHC Repertoires for Improved Antigen Prediction
Abstract:
Cancer immunotherapy is often all-or-none: inducing tumor
eradication in some, but providing no benefit to others. One method to
improve response rates is neoantigen vaccination, boosting a patient’s
immunity against their unique tumor-specific antigens. However, this method
depends upon programs that are trained on small and nondiverse data sets to
predict which tumor antigens are presented to the immune system. Here we
present a yeast-displayed peptide-MHC library method to generate large and
unbiased data sets to improve these predictions.