Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are
recognized by autologous T cells in the host. As neoepitopes are not subject to central …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Abstract Treatment with combined immune checkpoint blockade (CICB) targeting CTLA-4
and PD-1 is associated with clinical benefit across tumor types, but also a high rate of …

Abstract Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced
non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features …

Immune checkpoint blockade has provided a paradigm shift in cancer therapy, but the
success of this approach is very variable; therefore, biomarkers predictive of clinical efficacy …

T cell immunity is central for the control of viral infections. To characterize T cell immunity,
but also for the development of vaccines, identification of exact viral T cell epitopes is …

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing
with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However …

Neoantigens, which are derived from tumour-specific protein-coding mutations, are exempt
from central tolerance, can generate robust immune responses, and can function as bona …

Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly
challenging. This challenge is primarily due to three dominant factors: data accuracy, data …

The interplay between an evolving cancer and a dynamic immune microenvironment
remains unclear. Here we analyse 258 regions from 88 early-stage, untreated non-small-cell …