Wednesday, July 25, 2018
10AM PDT | 1PM EDT | 6PM BST | 7PM CEST
Checkpoint inhibitors have opened new avenues for treating cancer, however patients treated with these drugs often show poor durability or responsiveness to treatment, and immune toxicity. Somatic mutations expressed in tumors generate T cell-specific neoantigens not present in normal tissues making them good candidates for anti-tumor vaccine therapy. Most currently used in-silico cancer vaccine prediction pipelines have low sensitivity and specificity because they rely on features associated with the presentation of the antigen on the surface of cells without considering features required for T cell receptor (TCR) binding.
At MedGenome, we have developed OncoPeptVAC – a machine learning-based approach to identify features in neoantigen-derived peptides favoring TCR binding. Using exome and RNA sequencing data from tumor/normal pair, OncoPeptVAC identifies immunogenic peptides from neoantigens. The predicted immunogenic peptides are tested in an ex vivo dendritic cell – CD8+ T cell activation assay to assess their ability to generate T cells with cytolytic phenotype. In this webinar we will present data to demonstrate the superior performance of the OncoPeptVAC pipeline compared to other neoantigen prediction pipelines that use HLA-binding to predict immunogenic peptides, screening for immunogenicity of predicted peptides using dendritic cell T cell co-culture assays and utility of TCR repertoire profiling to assess functional T cell response.
During this webcast the speaker will address:
- Challenges in accurate prediction of immunogenic neoantigens
- Benefits of the OncoPeptVAC platform
- Assay for immunogenicity of peptides predicted using OncoPeptVAC using OncoPeptSCRN
- Insights into the discoveries made possible using OncoPeptVAC in combination with OncoPeptSCRN & TCR repertoire assays
- Benefits of TCR repertoire profiling to assay for immunogenicity