Abstract 6437: Development of therapeutic vaccines against ovarian cancer
Bibliographic record
Abstract
Abstract Epithelial ovarian cancer (EOC) has a devastating impact on the health of women and has not significantly benefited from advances in immunotherapy mainly because of the lack of well-defined actionable antigen targets. It is therefore essential to discover tumor-specific antigens (TSAs) that are specific to ovarian cancer, shared by a significant proportion of patients and capable of being targeted by the immune system. Using a groundbreaking method, we have previously identified 91 aberrantly expressed TSAs (aeTSAs), 18% of which were shared by at least 80% of the TCGA cohort (The Cancer Genome Atlas). These TSAs originate from unmutated non-exonic genomic sequences and their expression results from cancer-specific epigenetic changes. In the present study, our goal was to evaluate the immunogenicity of these aeTSAs. To do so, 49/91 antigenswere selected based on their presentation by high-frequency HLA allotypes (9 alleles included) and their expression in a large proportion of EOCs patients. Using functional in vitro expansion of naive CD8 T cells by co-culture with TSA-pulsed dendritic cells (DC) followed by CDR3 TCR sequencing, we first assessed the ability of aeTSAs in stimulating the immune system. Notably, 98% of our antigens were able to significantly expand CD8 T cell clonotypes, indicating that their repertoires are available for vaccination. In addition, tetramer staining of CD8 T cell populations after culture with TSA-pulsed DCs revealed that 32% of our aeTSAs tested (13/40) could expand specific CD8 T cells at levels that are detectable by flow cytometry. When selecting an optimal vaccination strategy, DC vaccines are particularly attractive. Using mass spectrometry to measure the abundance of TSAs presented at the cell surface, we next compared two modalities for engineering TSA-based DC vaccines: synthetic peptide pulsing vs TSA-encoding RNA minigenes transfection. Our preliminary results show that synthetic peptide pulsing leads to higher amounts of peptides presented at the surface of the dendritic cells compared to RNA minigenes electroporation. Moreover, we show a direct correlation between the abundance of peptides detected by MS immediately after pulsing on DCs and their predicted binding affinity. This correlation is not maintained with time (24h after pulsing), suggesting that the detection of peptides 24h post-pulsing is linked to the stability of peptide-MHC complexes rather than peptide binding affinity. In conclusion, we show that aeTSAs are attractive targets for EOC immunotherapy, as most of them can expand sizeable populations of CD8 T cells. We also reveal that direct pulsing of aeTSAs on DCs leads to better peptide presentation than RNA minigene transfection. These results are of capital importance, as optimal TSA presentation by DCs leads to stronger anti-tumor responses. We believe that our approach could have a significant impact on immunotherapy of EOC, and eventually of other cancer types. Citation Format: Leslie Hesnard, Catherine Thériault, Maxime Cahuzac, Chantal Durette, Krystel Vincent, Marie-Pierre Hardy, Gabriel Ouellet Lavallée, Joël Lanoix, Pierre Thibault, Claude Perreault. Development of therapeutic vaccines against ovarian cancer [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 6437.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".