# Evaluation of a therapeutic vaccination strategy with motif neoepitope peptide-pulsed autologous dendritic cells for non-small cell lung cancer patients harboring a charged HLA-B binding pocket.

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $183,494

## Abstract

PROJECT SUMMARY
Lung cancer is the leading cause of cancer related deaths in the United States and the World. Inefficient
prediction of functional tumor neoantigens and insufficient understanding of host anti-tumor immune responses
limit optimization of immunotherapeutic approaches. We recently demonstrated that programmed cell death 1
(PD-1) inhibitors, which lead to durable responses in a minority of non-small cell lung cancer (NSCLC) patients,
have greater efficacy in patients with charged human leukocyte antigen (HLA)-B binding pockets whose tumors
harbor mutation(s) leading to what we have designated as “motif neoepitopes”. Motif neoepitopes lead to an
amino acid substitution in the second position of a nonamer (anchor for HLA-binding), generating a change in
charge from the wild type peptide in which the resultant amino acid has a charge opposite to the HLA-B binding
pocket. This substitution leads to enhanced binding affinity to the corresponding HLA-B supertype demonstrated
by in vitro competition assays. These data suggest that optimal presentation of motif neoepitopes by
corresponding charged HLA supertypes results in effective host anti-tumor immune responses in vivo.
Dendritic cell (DC)-based vaccination has emerged as a potential component for immunotherapy due to both its
favorable toxicity profile and its essential role in antigen-specific T cell priming and activation. We have expertise
in clinical studies evaluating a DC in situ vaccination strategy in NSCLC. In this proposal, we intend to combine
DCs as functional antigen presenting cells (APCs) with putative motif neoepitopes as an innovative vaccination
approach to enhance host systemic tumor-specific T cell responses and potentiate clinical benefits of current
immunotherapies.
We hypothesize that 1) peptides derived from motif neoepitopes are functional neoantigens in vivo that are
capable of inducing host tumor-specific immune responses, and 2) autologous DCs pulsed with motif
neoepitope-derived peptides, particularly at optimal conditions, will induce systemic activation of motif
neoepitope-specific T cells. As part of this proposal, we are analyzing multiple biospecimens collected from our
ongoing phase I trial of intratumoral injection of autologous DCs combined with PD-1 inhibition in advanced
NSCLC. Collected specimens include serial blood and tumor biopsies as well as autologous DCs. We will
evaluate whether exposing DCs to peptides derived from motif neoepitopes can induce tumor-specific T cell
activation in co-culture experiments. We will assess the binding affinity of these peptides and the corresponding
wild type peptides to their respective HLA-B supertype. We will further optimize conditions, including addition of
PD-1 blockade, to achieve optimal T cell activation by autologous DCs pulsed with motif neoepitope-derived
peptides. These studies will greatly enhance our understanding of the potential function of motif neoepitopes in
inducing host anti-tumor immune r...

## Key facts

- **NIH application ID:** 10721983
- **Project number:** 1R21CA277172-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** EDWARD B GARON
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $183,494
- **Award type:** 1
- **Project period:** 2023-07-25 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10721983

## Citation

> US National Institutes of Health, RePORTER application 10721983, Evaluation of a therapeutic vaccination strategy with motif neoepitope peptide-pulsed autologous dendritic cells for non-small cell lung cancer patients harboring a charged HLA-B binding pocket. (1R21CA277172-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10721983. Licensed CC0.

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