Peptidome Surveillance Across Evolving SARS-CoV-2 Lineages Reveals HLA Binding Conservation in Nucleocapsid Among Variants With Most Potential for T-Cell Epitope Loss in Spike.

To provide a unique global view of the relative potential for evasion of CD8+ and CD4+ T cells by SARS-CoV-2 lineages as they evolve over time, we performed a comprehensive analysis of predicted HLA-I and HLA-II binding peptides in Spike (S) and Nucleocapsid (N) protein sequences of all available SARS-CoV-2 genomes as provided by NIH NCBI at a bi-monthly interval between March and December of 2021. A data supplement of all B.1.1.529 (Omicron) genomes from GISAID in early December was also used to capture the rapidly spreading variant. A key finding is that throughout continued viral evolution and increasing rates of mutations occurring at T-cell epitope hotspots, protein instances with worst-case binding loss did not become the most frequent for any Variant of Concern (VOC) or Variant of Interest (VOI) lineage; suggesting T-cell evasion is not likely to be a dominant evolutionary pressure on SARS-CoV-2. We also determined that throughout the course of the pandemic in 2021, there remained a relatively steady ratio of viral variants that exhibit conservation of epitopes in the N protein, despite significant potential for epitope loss in S relative to other lineages. We further localized conserved regions in N with high epitope yield potential, and illustrated heterogeneity in HLA-I binding across the S protein consistent with empirical observations. Although Omicron's high volume of mutations caused it to exhibit more epitope loss potential than most frequently observed versions of proteins in almost all other VOCs, epitope candidates across its most frequent N proteins were still largely conserved. This analysis adds to the body of evidence suggesting that N may have merit as an additional antigen to elicit immune responses to vaccination with increased potential to provide sustained protection against COVID-19 disease in the face of emerging variants.

Deep Learning Implicitly Handles Tissue Specific Phenomena to Predict Tumor DNA Accessibility and Immune Activity.

DNA accessibility is a key dynamic feature of chromatin regulation that can potentiate transcriptional events and tumor progression. To gain insight into chromatin state across existing tumor data, we improved neural network models for predicting accessibility from DNA sequence and extended them to incorporate a global set of RNA sequencing gene expression inputs. Our expression-informed model expanded the application domain beyond specific tissue types to tissues not present in training and achieved consistently high accuracy in predicting DNA accessibility at promoter and promoter flank regions. We then leveraged our new tool by analyzing the DNA accessibility landscape of promoters across The Cancer Genome Atlas. We show that in lung adenocarcinoma the accessibility perspective uniquely highlights immune pathways inversely correlated with a more open chromatin state and that accessibility patterns learned from even a single tumor type can discriminate immune inflammation across many cancers, often with direct relation to patient prognosis.