IMMU-54. Leveraging deep immunopeptidomics to identify tumor antigens from glioblastoma tissue samples
Bibliographic record
Abstract
Abstract Cancer immunotherapy is a promising avenue for treating patients who exhaust their current standard lines of care. These therapies work by promoting anti-cancer adaptive immunity. The process of adaptive immunity relies largely upon T cell-mediated recognition of tumor specific or associated peptides presented upon the Major Histocompatibility Complex (MHC). Given longstanding limitations in profiling MHC presented peptides, several computational tools have been developed to instead predict peptide MHC binding. Here, we leverage recent technical advances in proteomics to generate deep immunopeptidomics data from 20 cancer, including 14 glioblastoma, tissue samples and utilize this data to identify tumor antigens. Immunopeptidomics profiling was performed on a Bruker timsTOF Ultra mass spectrometer platform, yielding an average of 19,283 peptides/sample, and a total of 106,079 unique peptides from 12,209 genes across the cohort. Peptides from multiple sources can drive immunogenic recognition, including tumor associated antigens (TAAs), mutated tumor specific antigens (mTSAs), and aberrantly expressed tumor specific antigens (aeTSAs). Here, we built a workflow for personalized immunopeptidomics that incorporates germline variation, variant phasing, and clonality to perform searches for mTSAs, TAAs, and aeTSAs. Initial analyses applied to samples from glioblastoma patients revealed several TAAs abundantly present on patient specific MHC alleles. Next, this work will be applied to aeTSAs and mTSAs to identify a candidate set of high-quality targets for immunotherapy. In addition, this data will be used to comprehensively assess the accuracy of peptide MHC binding prediction tools run on the matched transcriptomes and genomes in this cohort.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".