# Informatics tools for identification, prioritization and clinical application of neoantigens

> **NIH NIH U01** · WASHINGTON UNIVERSITY · 2022 · $386,237

## Abstract

Project Summary/Abstract
Somatic mutations in cancer cells lead to the production of neoantigens: patient- and tumor-specific peptides
that are capable of inducing T cell recognition. Recent clinical trials have established that, when introduced in a
vaccine, these neoantigens can stimulate anti-tumor immune responses. The path to producing such a
personalized vaccine begins with sequencing a patient’s tumor, identifying candidate somatic mutations and
then computationally predicting which neoepitopes will be most effective at stimulating a T-cell response. This
prediction step should ideally assess a complex interplay of factors, including the type of somatic mutation, the
patient’s class I and II HLA alleles, peptide processing, peptide transport, peptide-MHC binding and many co-
factors of immune recognition and signaling. The best current approaches focus almost entirely on a single
factor (peptide-MHC binding) and have only a 16-43% success rate in predicting immunogenic peptides. To
address this challenge we will develop pVACtools, an informatics toolkit for comprehensive identification,
characterization, and clinical application of neoantigens. This tool will be the first to support all major
neoepitope sources including insertions, deletions, transcript isoforms, gene fusions, peptides from normally
non-coding regions, and B cell or T cell rearrangements (BCRs/TCRs). We will also integrate analysis of Class
I and II peptide-MHC binding. All tools will be developed to support foundational pre-clinical work in animal
models of immunotherapy. Furthermore, we will test several specific hypotheses relating to new predictors of
immunogenicity. To elucidate these factors and enhance prioritization of neoantigens we will create the first
open access database of experimentally and clinically validated neoantigens. Using these data we will address
the question of what peptide-intrinsic and patient-specific features determine the therapeutic potential of a
neoantigen. To validate their translational potential, we will apply our neoantigen tools to clinical trials involving
checkpoint blockade drugs and personalized cancer vaccines. We will develop a visualization interface that
facilitates clinical review and selection of neoantigen candidates for several vaccine delivery platforms. These
tools will be used to perform analysis of >200 cases from ongoing vaccine trials to evaluate their performance
and address key outstanding immunobiology questions including: (a) the importance of particular neoantigen
sources in specific cancer types, (b) the importance of accurately determining HLA mutation/expression, (c) the
significance of having both MHC class I and II restricted peptides in a vaccine, (d) how to identify specific
neoepitope/TCR pairings, and (e) how neoantigens contribute to mechanisms of resistance to
immunotherapies. These tools will thus enable fundamental studies of T cell biology, lead to more effective
personalized cancer vaccine design...

## Key facts

- **NIH application ID:** 10473522
- **Project number:** 5U01CA248235-03
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Malachi Griffith
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $386,237
- **Award type:** 5
- **Project period:** 2020-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10473522

## Citation

> US National Institutes of Health, RePORTER application 10473522, Informatics tools for identification, prioritization and clinical application of neoantigens (5U01CA248235-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10473522. Licensed CC0.

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