Abstract B16: Identification of tumor-specific antigens shared by induced pluripotent stem cells
Bibliographic record
Abstract
Abstract The development of cancer immunotherapeutics is limited by the tumor-specific antigen (TSA) discovery. Cancer cells and pluripotent stem cells, i.e., embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), show a significant transcriptomic overlap. Indeed, when used as prophylactic vaccines in another study, iPSCs prevented tumor growth in mouse models of lung, breast, and skin cancer, suggesting that we could mine iPSCs for the discovery of TSAs. We use a recently developed proteogenomic approach combining RNA-sequencing and mass spectrometry to characterize the immunopeptidome (MHC class I-associated peptides) of iPSCs. Analyses of hitherto one human iPS cell line allowed us to identify 9 MHC class I-associated antigens that were not expressed by normal somatic tissues and thus are likely immunogenic. 50% of these antigens were highly expressed in different cancer types from The Cancer Genome Atlas, highlighting their potential for cancer immunotherapy. Expression of these TSAs resulted from aberrant expression of noncoding genomic regions and not from mutations, making them ideal candidates for antigen-based cancer vaccines. Our study indicates which stem cell features present in cancer cells might be harnessed for cancer immunotherapy. Moreover, we show for the first time that the immunopeptidome of iPSCs reflects the state of pluripotency. Cancer stem cells are known to drive disease relapse after treatment, while cancer circulating cells possessing pluripotent stem cell features are more efficient at producing metastases. These factors indicate that treatments targeted at stemness could improve clinical outcome, and that iPSC-informed TSAs hold promise for prophylactic and therapeutic cancer vaccines. Citation Format: Anca Apavaloaei, Marie-Pierre Hardy, Jean-David Larouche, Qingchuan Zhao, Krystel Vincent, Jean-Philippe Laverdure, Chantal Durette, Joel Lanoix, Pierre Thibault, Claude Perreault. Identification of tumor-specific antigens shared by induced pluripotent stem cells [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr B16.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".