Abstract IA009: Oncogenic transcription factors drive highly tumor-specific LincRNA
Bibliographic record
Abstract
Abstract Many cancers are characterized by gene fusions encoding oncogenic chimeric transcription factors (TFs) such as EWS::FLI1 in Ewing sarcoma (EwS). EWS::FLI1 induces a complete reprogramming of mesenchymal stem cells, the putative cell of origin of Ewing sarcoma, through binding and subsequent generation of thousands of neoenhancers at GGAA microsatellite repeats. We show that EWS::FLI1 induces the robust expression of a specific set of novel spliced and polyadenylated transcripts within otherwise transcriptionally silent regions of the genome. These neo-genes (NGs) are virtually undetectable in large collections of normal tissues or non-EwS tumors and can be silenced by CRISPR interference at regulatory EWS::FLI1-bound microsatellites. Ribosome profiling and proteomics further show that some NGs are translated into highly EwS-specific peptides. More generally, hundreds of NGs can be detected in diverse cancers characterized by chimeric TFs. Altogether, we identify the transcription, processing, and translation of novel, specific, highly expressed multi-exonic transcripts from otherwise silent regions of the genome as a new activity of aberrant TFs in cancer. These neotranscripts, which can be classified as LincRNAs, or neopeptides may have specific functions in the tumor cells. The latter may also constitute ideal neoepitopes as they are tumor-specific, clonally expressed in sarcoma cells, induced by the driver genetic alterations and public for a given sarcoma. Citation Format: Olivier Delattre, Julien Vibert, Olivier Saulnier, Joshua Waterfall. Oncogenic transcription factors drive highly tumor-specific LincRNA [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA009.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".