automated-dissection-protocol-for-tumor-enrichment-low-tumor-content

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly ...

Genentech

Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies.

Nicolas Lounsbury

Bioinformatics & Computational Biology

Genentech

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Nicolas Lounsbury

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Bioinformatics & Computational Biology

Genentech

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Bioinformatics & Computational Biology