Abstract IA021: Neogenes induced by oncogenic chimeric transcription factors as potential targets for therapy
Bibliographic record
Abstract
Abstract We recently showed that oncogenic chiemeric transcription factors (OCTF) of various pediatric cancers induce the expression of highly tumor-specific long intergenic non coding RNAs which are not expressed in normal tissues and which we called neogenes. Since recent reports indicate that such LincRNA may not be as non-coding as initially thought we sought for potential peptides encoded by these neogenes. Using the EWSR1::FLI1 chimera of Ewing sarcoma as a paradigm of such chimeras and through Riboseq profiling and whole cell proteomics we indeed show that some of these neogenes encode peptides. Immunopeptidomic experiments were performed on a series of Ewing sarcoma cell lines and PDX and further showed that peptides encoded by Ewing-specific neogenes are associated with MHC-Class I molecules at the surface of Ewing cells. Ewing peptide-specific CD8 T-cells were isolated from healthy donors. We show that these cytotoxic T-cells can be activated by Ewing cells to secrete cytokins, They can also kill Ewing cells. This killing is specific for the MHC-Class I types, specific for Ewing sarcoma cells as compared to other tumor types and is strictly deendent upon the expression of EWSR1::FLI1 that regulates the neogenes and of the expression of the neogene encoding the peptide. These data support the idea that OCTF-specific neogenes may constitute an interesting resource for various immunotherapy approaches including vaccination, BiTE or TCR-T-cell therapy. Citation Format: Floriane Petit, Ana Lalanne, Céline Collin, Joshua Waterfall, Olivier Lantz, Olivier Delattre. Neogenes induced by oncogenic chimeric transcription factors as potential targets for therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr IA021.
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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) | 0.002 | 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".