Predicting T cell recognition of MHC class I restricted neoepitopes

Zeynep Koşaloğlu-Yalçın1, Manasa Lanka1, Angela Frentzen1

  • 1Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.

Oncoimmunology
|November 1, 2018
PubMed

Insights

Identifying immunogenic neoepitopes is crucial for cancer immunotherapy. Our study reveals that binding predictions and length-rescaling best identify these cancer-specific targets, improving patient treatment strategies.

Area of Science:

  • Cancer immunology and immunotherapy
  • T cell recognition of cancer mutations

Background:

  • Somatic mutations create neoepitopes, key targets in cancer immunology.
  • High-throughput sequencing identifies numerous neoepitope candidates, but most lack immunogenicity.
  • Prioritizing immunogenic neoepitopes is essential for effective cancer immunotherapy.

Purpose of the Study:

  • To dissect the properties of naturally processed, immunogenic neoepitopes.
  • To establish appropriate tools and thresholds for neoepitope prioritization in cancer.
  • To refine neoepitope filtering approaches for improved immunotherapy.

Main Methods:

  • Assembled a dataset of known, immunogenic neoepitopes.
  • Evaluated various predictors for neoepitope immunogenicity parameters.
  • Analyzed binding affinities of mutated and non-mutated peptides.

Main Results:

  • Binding predictions combined with length-rescaling effectively discriminate immunogenic neoepitopes.
  • Most immunogenic neoepitopes exhibited strong predicted binding affinities.
  • Non-mutated peptides showed nearly comparable binding affinities to neoepitopes.

Conclusions:

  • Binding affinity and length-rescaling are critical parameters for filtering neoepitopes.
  • The findings provide a rational basis for current neoepitope filtering strategies.
  • Improved neoepitope selection can enhance the efficacy of cancer immunotherapy.

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