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Updated: Nov 28, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
A New Pipeline to Predict and Confirm Tumor Neoantigens Predict Better Response to Immune Checkpoint Blockade
Yelena Lazdun1, Han Si2, Todd Creasy2
1Translational Functional Genomics, AstraZeneca, Gaithersburg, Maryland.
Abstract:
Mutations that drive oncogenesis in cancer can generate neoantigens that may be recognized by the immune system. Identification of these neoantigens remains challenging due to the complexity of the MHC antigen and T-cell receptor interaction. Here, we describe the development of a systematic approach to efficiently identify and validate immunogenic neoantigens. Whole-exome sequencing of tissue from a patient with melanoma was used to identify nonsynonymous mutations, followed by MHC binding prediction and identification of tumor clonal architecture. The top 18 putative class I neoantigens were selected for immunogenicity testing via a novel in vitro pipeline in HLA-A201 healthy donor blood. Naïve CD8 T cells from donors were stimulated with allogeneic dendritic cells pulsed with peptide pools and then with individual peptides. The presence of antigen-specific T cells was determined via functional assays. We identified one putative neoantigen that expanded T cells specific to the mutant form of the peptide and validated this pipeline in a subset of patients with bladder tumors treated with durvalumab (n = 5). Within this cohort, the top predicted neoantigens from all patients were immunogenic in vitro. Finally, we looked at overall survival in the whole durvalumab-treated bladder cohort (N = 37) by stratifying patients by tertile measure of tumor mutation burden (TMB) or neoantigen load. Patients with higher neoantigen and TMB load tended to show better overall survival. IMPLICATIONS: This pipeline can enable accurate and rapid identification of personalized neoantigens that may help to identify patients who will survive longer on durvalumab.
Insights
Researchers developed a novel pipeline to identify and validate immunogenic neoantigens, crucial for cancer immunotherapy. This method successfully identified neoantigens that elicit T-cell responses, potentially predicting patient survival in cancer treatment.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Cancer mutations can create neoantigens, targets for the immune system.
- Identifying immunogenic neoantigens is complex due to MHC-TCR interactions.
- Efficient methods are needed to discover and validate neoantigens for cancer immunotherapy.
Purpose of the Study:
- To develop and validate a systematic pipeline for identifying immunogenic neoantigens.
- To assess the correlation between neoantigen load, tumor mutation burden (TMB), and patient survival.
- To evaluate the pipeline's utility in patients treated with durvalumab.
Main Methods:
- Whole-exome sequencing to identify nonsynonymous mutations in melanoma.
- MHC binding prediction and tumor clonal architecture analysis.
- In vitro immunogenicity testing using healthy donor CD8 T cells and dendritic cells; validation in bladder cancer patients treated with durvalumab.
Main Results:
- Identified one neoantigen that expanded specific T cells in initial testing.
- Validated the pipeline in bladder cancer patients, confirming immunogenicity of predicted neoantigens in vitro.
- Higher neoantigen load and TMB correlated with improved overall survival in durvalumab-treated patients.
Conclusions:
- The developed pipeline efficiently identifies and validates immunogenic neoantigens.
- Neoantigen load is a potential biomarker for predicting survival in durvalumab-treated patients.
- This approach facilitates personalized neoantigen discovery for enhanced cancer immunotherapy.
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