Abstract 1483: Examining the role of genome editing enzymes in anti tumor immunity
Bibliographic record
Abstract
Abstract Background: Tumor antigens (TA) on cancer cells can trigger immune response TA are peptides that are either not present, or are present to a much lesser extent, on normal cells compared to tumor cells. CD8+ Cytotoxic T-cells (CTL) recognize TAs bound to class I major histocompatibility complex (MHC) molecules on the surface of tumor cells. We and others have shown that viruses subvert the mutational activity of the endogenous DNA-editing APOBEC enzymes in human host cells towards immune escape. Viruses do so by adapting their genomic DNA code to position APOBEC's favored mutational “hotspot” DNA sequences such that upon being mutated by the host APOBEC enzymes, the mutated viral protein is less immunogenic for CTL recognition. Like viruses, cancer cells have genomes that are highly plastic and adaptable through mutation; therefore, we hypothesize that the APOBEC DNA-editing enzymes are exploited by cancer cells for modulating TA immunogenicity. Aims of the Study: Aim1: To elucidate the impact of A3-mediated mutations on modulating TA immunogenicity for CTL recognition. Aim 2: To examine whether genomic sequences encoding TAs in the human genome have co-evolved to either attract or avoid APOBEC-driven mutations. Methods: Aim 1: Using the human reference proteome from EMBL/EBI, we located APOBEC-mutable hotspots in genomic DNA encoding the immunopeptidome. We simulated APOBEC-mediated mutations and translated to peptide TAs. We used NetMHCpan 4 to measure the impact of TA mutations on MHC class I binding affinity. In parallel, we are searching for already-identified APOBEC-mediated mutations in sequenced tumor genomes in cancer genome databases such as The Cancer Genome Atlas (TCGA), Catalogue of Observed Somatic Mutations in Cancer (COSMIC) and International Cancer Genome Consortium (ICGC). Aim 2: To investigate the enrichment of APOBEC-hotspots in genomic sequences encoding TAs restricted to each HLA, we are tabulating APOBEC hotspots in genomic DNA sequencing encoding TAs vs. non-TA-encoding sequences. At the sequence level, the ratio of hotspot frequency, in combination with scores of predicted impacts on immunogenicity as obtained in aim 1, will be used to glean whether and how the human genomic sequence has co-evolved with APOBECs from the standpoint of TAs. Results and Significance: Based on the preliminary data we observe that APOBEC-mediated mutations can diminish or enhance immunogenicity. Understanding the balance between these two effects is key in informing how APOBEC expression and activity ought to be regarded in the context of cancer immunotherapy. Citation Format: Faezeh Borzooee. Examining the role of genome editing enzymes in anti tumor immunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1483.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".