Abstract 2987: BamQuery: a new proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens
Bibliographic record
Abstract
Abstract MHC class I-associated peptides (MAPs), collectively referred to as the immunopeptidome, have a pivotal role in cancer immunosurveillance. While MAPs were long thought to be solely generated by the degradation of canonical proteins, recent advances in the field of proteogenomics (genomically-informed proteomics) evidenced that ∼10% of them originate from allegedly noncoding genomic sequences. Among these sequences, endogenous retroelements (EREs) are under intense scrutiny as a possible source of actionable tumor antigens (TAs). With the increasing number of cancer-oriented immunopeptidomic and proteogenomic studies comes the need to accurately attribute an RNA expression level to each MAP identified by mass-spectrometry. Here, we introduce BamQuery (BQ), a computational tool to attribute an exhaustive RNA expression to MAPs of any genomic origin (exon, intron, UTR, intergenic) from bulk and single-cell RNA-sequencing data. By using BQ on large datasets of published MAPs identified by mass spectrometry, we show that many of them can arise from more than one genomic region. Indeed, 27% of MAPs reported as deriving from protein-coding exons (canonical MAPs) could also arise from non-canonical genomic regions, sometimes with greater probability, and 61% of non-canonical MAPs could arise from more than a single genomic origin (334 possible regions on average per non-canonical MAP; up to 35,343 for EREs). The consideration of all these origins evidenced an unsuspected high RNA expression in normal human tissues of (i) published neoantigens/TAs (mutated or not); (ii) MAPs derived from proteasomal splicing, supposedly not genomically templated, and (iii) MAPs derived from viruses. In particular, the high expression of candidate immunotherapeutic targets such as TAs highlights the relevance of BamQuery and the necessity of using it to validate such antigens before translating their usage in clinical trials. We also demonstrate that BamQuery can be used to directly identify safe and actionable TAs as well as to predict their immunogenicity through our freely accessible web portal (https://bamquery.iric.ca/search). Therefore, BQ could become an essential tool in any TA prioritization pipeline in the near future. Citation Format: Maria-Virginia Ruiz Cuevas, Marie-Pierre Hardy, Jean-David Larouche, Anca Apavaloaei, Eralda Kina, Krystel Vincent, Patrick Gendron, Jean-Philippe Laverdure, Chantal Durette, Pierre Thibault, Sebastien Lemieux, Claude Perreault, Gregory Ehx. BamQuery: a new proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2987.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".