BATS

Date/Time: 

Nov 3, 2017 - 12:00 PM EDT

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.

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.