Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing

Abstract

Human tumours typically harbour a remarkable number of somatic mutations. If presented on major histocompatibility complex class I molecules (MHCI), peptides containing these mutations could potentially be immunogenic as they should be recognized as 'non-self' neo-antigens by the adaptive immune system. Recent work has confirmed that mutant peptides can serve as T-cell epitopes. However, few mutant epitopes have been described because their discovery required the laborious screening of patient tumour-infiltrating lymphocytes for their ability to recognize antigen libraries constructed following tumour exome sequencing. We sought to simplify the discovery of immunogenic mutant peptides by characterizing their general properties. We developed an approach that combines whole-exome and transcriptome sequencing analysis with mass spectrometry to identify neo-epitopes in two widely used murine tumour models. Of the >1,300 amino acid changes identified, ∼13% were predicted to bind MHCI, a small fraction of which were confirmed by mass spectrometry. The peptides were then structurally modelled bound to MHCI. Mutations that were solvent-exposed and therefore accessible to T-cell antigen receptors were predicted to be immunogenic. Vaccination of mice confirmed the approach, with each predicted immunogenic peptide yielding therapeutically active T-cell responses. The predictions also enabled the generation of peptide-MHCI dextramers that could be used to monitor the kinetics and distribution of the anti-tumour T-cell response before and after vaccination. These findings indicate that a suitable prediction algorithm may provide an approach for the pharmacodynamic monitoring of T-cell responses as well as for the development of personalized vaccines in cancer patients.

Predicting T cell recognition of MHC class I restricted neoepitopes

“…most are not immunogenic. 24,28,46 Especially in patients with high mutational load, hundreds or even thousands of mutated peptides are commonly found based on sequencing approaches, highlighting the need to further narrow down this candidate list and increase the specificity in detecting immunogenic neoepitopes.…”

“…24,49 In this context, it is often mentioned that the tumor microenvironment plays a critical role for antitumor T cell responses, not only by inhibiting existing T cells, but also by preventing T cell priming against neoepitopes. 50,51 At the same time, it needs to be stressed that even in the context of e.g.…”

A Targeted LC‐MS Strategy for Low‐Abundant HLA Class‐I‐Presented Peptide Detection Identifies Novel Human Papillomavirus T‐Cell Epitopes

“…Most studies to date used untargeted unbiased LC‐MS 2 detection, which detects large numbers of epitopes presented at higher abundance. Untargeted detection was used for identification of HLA binding motives,35 viral epitopes,36 and tumor mutation‐derived epitopes (neoepitopes) from cancer cell lines37, 38 or primary human tumor material 39, 40. However, this common workflow fails to identify low‐abundant peptides that might still be important for immunotherapy development.…”

A STING-activating nanovaccine for cancer immunotherapy

“…7c). In the colon cancer MC38 model, we selected three tumor neoantigens (Reps1 P45A, Adpgk R304M, Dpagt1 V213L ) 27 . Data also show significantly improved tumor growth inhibition (Fig.…”