Global Team Identifies Key Factors That Make Tumor Proteins Trigger Immune Responses
A consortium approach identified key parameters of tumor epitope immunogenicity, filtering out 98% of non-immunogenic peptides while maintaining precision above 0.70.
Quick Facts
What This Study Found
An integrated model combining peptide presentation and recognition features filtered out 98% of non-immunogenic peptides with precision above 0.70, and was validated in an independent cohort of 310 epitopes.
Key Numbers
608 epitopes assessed; ~8% confirmed immunogenic; peptide-MHC stability identified as top predictor.
How They Did This
Global consortium where participants predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were assessed for T cell binding in patient-matched samples, with validation in an independent 310-epitope cohort.
Why This Research Matters
Personalized cancer vaccines depend on correctly identifying which tumor mutations will provoke an immune response. This consortium-derived model dramatically improves neoantigen prediction, potentially making immunotherapy more targeted and effective.
The Bigger Picture
As cancer immunotherapy moves toward personalized neoantigen vaccines, the ability to accurately predict which tumor peptides will trigger immune responses is foundational. This work provides both a predictive framework and an open data resource that could accelerate vaccine development across cancer types.
What This Study Doesn't Tell Us
The model was developed and validated using specific tumor types and HLA alleles, which may limit generalizability. T cell binding assays measure recognition potential but don't guarantee clinical anti-tumor responses.
Questions This Raises
- ?How well does this model perform across diverse HLA types and tumor mutational burdens?
- ?Can this predictive framework be integrated into clinical trial design for neoantigen vaccines?
- ?What additional features beyond presentation and recognition could further improve prediction accuracy?
Trust & Context
- Key Stat:
- 98% of non-immunogenic peptides filtered out by the prediction model
- Evidence Grade:
- Strong in vitro evidence with multi-center consortium validation in an independent cohort, though clinical translation remains to be demonstrated.
- Study Age:
- Published in 2020. Neoantigen prediction has continued to advance, but this consortium dataset remains a key reference resource.
- Original Title:
- Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction.
- Published In:
- Cell, 183(3), 818-834.e13 (2020)
- Authors:
- Wells, Daniel K, van Buuren, Marit M, Dang, Kristen K, Hubbard-Lucey, Vanessa M, Sheehan, Kathleen C F, Campbell, Katie M, Lamb, Andrew, Ward, Jeffrey P, Sidney, John, Blazquez, Ana B, Rech, Andrew J, Zaretsky, Jesse M, Comin-Anduix, Begonya, Ng, Alphonsus H C, Chour, William, Yu, Thomas V, Rizvi, Hira, Chen, Jia M, Manning, Patrice, Steiner, Gabriela M, Doan, Xengie C, Merghoub, Taha, Guinney, Justin, Kolom, Adam, Selinsky, Cheryl, Ribas, Antoni, Hellmann, Matthew D, Hacohen, Nir, Sette, Alessandro, Heath, James R, Bhardwaj, Nina, Ramsdell, Fred, Schreiber, Robert D, Schumacher, Ton N, Kvistborg, Pia, Defranoux, Nadine A
- Database ID:
- RPEP-05195
Evidence Hierarchy
Frequently Asked Questions
What is a neoantigen?
A neoantigen is a new protein fragment created by tumor-specific mutations that the immune system can potentially recognize as foreign and attack.
Why is predicting immunogenic epitopes so difficult?
Only a tiny fraction of tumor mutations produce peptides that are actually presented on cell surfaces and recognized by T cells, making it like finding needles in a haystack.
Read More on RethinkPeptides
Cite This Study
https://rethinkpeptides.com/research/RPEP-05195APA
Wells, Daniel K; van Buuren, Marit M; Dang, Kristen K; Hubbard-Lucey, Vanessa M; Sheehan, Kathleen C F; Campbell, Katie M; Lamb, Andrew; Ward, Jeffrey P; Sidney, John; Blazquez, Ana B; Rech, Andrew J; Zaretsky, Jesse M; Comin-Anduix, Begonya; Ng, Alphonsus H C; Chour, William; Yu, Thomas V; Rizvi, Hira; Chen, Jia M; Manning, Patrice; Steiner, Gabriela M; Doan, Xengie C; Merghoub, Taha; Guinney, Justin; Kolom, Adam; Selinsky, Cheryl; Ribas, Antoni; Hellmann, Matthew D; Hacohen, Nir; Sette, Alessandro; Heath, James R; Bhardwaj, Nina; Ramsdell, Fred; Schreiber, Robert D; Schumacher, Ton N; Kvistborg, Pia; Defranoux, Nadine A. (2020). Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction.. Cell, 183(3), 818-834.e13. https://doi.org/10.1016/j.cell.2020.09.015
MLA
Wells, Daniel K, et al. "Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction.." Cell, 2020. https://doi.org/10.1016/j.cell.2020.09.015
RethinkPeptides
RethinkPeptides Research Database. "Key Parameters of Tumor Epitope Immunogenicity Revealed Thro..." RPEP-05195. Retrieved from https://rethinkpeptides.com/research/wells-2020-key-parameters-of-tumor
Access the Original Study
Study data sourced from PubMed, a service of the U.S. National Library of Medicine, National Institutes of Health.
This study breakdown was produced by the RethinkPeptides research team. We analyze and report published research findings without making health recommendations. All interpretations are based solely on the published abstract and study data.