# Design and Delivery of Neoantigen-based Tumor Vaccines

> **NIH NIH F30** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $42,157

## Abstract

ABSTRACT
Design and Delivery of Neoantigen-based Tumor Vaccines
 Neoantigens are a class of cancer antigen derived from mutations occurring within the genome of the tumor,
and they present an attractive target for therapeutic tumor vaccines. Despite advancements in the
development of neoantigen tumor vaccines, extensive challenges impede clinical translation. These challenges
include how to optimally deliver neoantigenic peptides for vaccination and how to select for immunogenic
epitopes from a set of computationally-predicted neoantigens.
 To address the challenges associated with neoantigen delivery (Aim 1), we will develop a poly(lactic-co-
glycolic acid) (PLGA)-based neoantigen-delivering nanoparticle (ndNP) platform as a vehicle for vaccination in
B16F10 melanoma and BBN963 basal bladder cancer models. We will study the interactions of these ndNPs
with dual checkpoint-inhibitor/co-stimulator immunotherapy, measuring efficacy through tumor growth and
survival studies. Immune monitoring through flow cytometry, RNA-sequencing, and IFN-γ ELISpot of tumor and
draining lymph nodes will be used to study how therapeutic combinations differentially affect immune cell
activation and phenotypic distributions.
 To address optimal selection of neoantigens for therapeutic vaccination (Aim 2), we will develop a predictive
algorithm based on correlative analysis of intrinsic neoantigen features with their immunogenicity. Current
neoantigen prediction methods have high false-positive rates, with no robust methods to predict for therapeutic
efficacy of these predicted neoantigens. From the literature and preliminary studies, we believe five intrinsic
neoantigen features correlate with immunogenicity: 1) predicted peptide/MHC binding affinity, 2) amino acid
physicochemical characteristics, 3) mutational position, 4) gene expression, and 5) derivative gene function.
Using B16F10 and BBN963 neoantigens that we previously predicted and validated with IFN-γ ELISpot, we will
correlate these five intrinsic neoantigen features with ability to elicit IFN-γ production. Correlative analysis will
include univariable and multivariable elastic net regression, and the predictive algorithm derived from these
analyses will be validated using neoantigens predicted from the MC-38 colon adenocarcinoma tumor model.
 Pursuit of these aims could provide beneficial knowledge for the translation of neoantigen tumor vaccine
strategies. The aims of this proposal will provide training in a unique skillset of immuno-oncology,
nanotechnology, and computational biology. The training provided under this award will facilitate my
development toward my career goal of leading an independently funded immuno-oncology laboratory focused
on therapy development.

## Key facts

- **NIH application ID:** 9850558
- **Project number:** 5F30CA225136-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Christof Chiu Smith
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $42,157
- **Award type:** 5
- **Project period:** 2018-02-13 → 2023-02-12

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9850558

## Citation

> US National Institutes of Health, RePORTER application 9850558, Design and Delivery of Neoantigen-based Tumor Vaccines (5F30CA225136-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9850558. Licensed CC0.

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