Abstract PR-09: Meta-Analysis of Neoantigens: Insights from the Cancer Epitope Database and Analysis Resource (CEDAR)
Bibliographic record
Abstract
Abstract Cancer cells accumulate somatic mutations that can give rise to novel amino acid sequences absent from the normal human proteome. These mutation-derived, cancer-specific peptides are referred to as “neo-peptides.” A subset of neo-peptides capable of eliciting an immune response are termed “neo-epitopes.” Neo-epitopes hold significant promise for precision cancer immunotherapy, both as therapeutic targets and as biomarkers for prognosis and treatment response. Given their central role in immuno-oncology, we performed a meta-analysis to systematically assess how experimental evidence shapes current understanding of neo-epitope biology. Our study is the largest reported to date. Using the Cancer Epitope Database and Analysis Resource (CEDAR), we analyzed over 16,000 neo-peptides tested in more than 20,000 T cell assays across 180 studies. We analyzed frequently utilized assay types, the genes neo- epitopes are derived from, and their driver gene status, variations across cancer types, mutation patterns and their physicochemical properties, HLA restrictions, and predicted MHC binding. We found that validated neo-epitope frequencies varied across cancer types, with the highest rates in skin and lung and the lowest in colorectal cancer. Neo-epitopes were enriched in driver genes such as TP53 and KRAS. However, testing frequency correlated with mutation prevalence, revealing a bias toward recurrent mutations. Despite the high sequence similarity among RAS family members, validated neo-epitope overlap was minimal, challenging pan-RAS strategies. Shared neo-epitopes across cancer types are rare, with only 16 validated in more than one cancer type. While most assays involved HLA class I, class II alleles presented a higher proportion of validated neo-epitopes. Specific alleles, including HLA-B*40:01 and HLA- DRB1*11:01, were enriched for presenting neo-epitopes, whereas others, like HLA-A*02:01, were enriched for presenting neo-peptides that are not recognized by T cells. Finally, amino acid substitutions that altered hydrophobicity or charge were more common in neo-epitopes. Citation Format: Zeynep Kosaloglu-Yalcin, Ibel Carri, Bjoern Peters, Alessandro Sette. Meta-Analysis of Neoantigens: Insights from the Cancer Epitope Database and Analysis Resource (CEDAR) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr PR-09.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".