Abstract 2395: The role of exonized mobile elements in chimeric transcript emergence in glioblastoma
Bibliographic record
Abstract
Abstract Background: Glioblastoma (GBM) is the most aggressive type of primary brain tumor in adults, with a median survival of approximately 15 months. Unfortunately, the prognosis for GBM has remained unfavorable for decades. The genetic and transcriptomic heterogeneity of GBM complicates treatment, underscoring the need for a more comprehensive understanding of its molecular basis. Recently, retrotransposons have been shown to influence gene expression and the formation of chimeric transcripts. Although they are associated with various cancers, their precise role in GBM remains unclear. In this study, using paired-end RNA-Seq data from The Cancer Genome Atlas (TCGA) and the GBM (U251) cell line, we examined the exonization of mobile elements and the generation of GBM-specific chimeric transcripts. For retroelement references, we utilized RepeatMasker annotations. To detect, characterize, and quantify the expression of chimeric transcripts, we developed FREDY, a tool designed to identify the exonization of retrotransposable elements in RNA-seq data. Initially, we identified 1, 012 chimeric transcripts linked to the exonization of intragenic (intronic) mobile elements. Specifically, Alu element exonization was the most common (∼45%) and occurred primarily in the 5' regions of transcripts. As expected, the majority (83%) of these exonizations involved Alu elements located in the opposite transcriptional direction of their host genes. In contrast, LINE1 elements and retrocopies of protein-coding genes were predominantly exonized (46% and 56.7%, respectively) in the 3' regions of host genes and in the same transcriptional direction as their host genes. Notably, we identified two candidate chimeric transcripts impacting the tumor suppressor gene ROS1. Furthermore, when comparing potential protein domains, we found that the coding sequences of these chimeric transcripts lacked two ROS1 domains, suggesting a premature cessation of transcription and potentially translation. Additionally, we quantified that these chimeric transcripts represent 84% of the total expression of the ROS1 gene in the identified GBM samples. Conclusions: We have established a framework for detecting chimeric transcript formation in cancer through the exonization of mobile elements. We applied this approach to GBM samples and cell lines, identifying chimeric transcripts of the tumor suppressor gene ROS1 that may play a significant role in GBM carcinogenesis. Sponsor: FAPESP 20/02413-4 Citation Format: Rafael L. Mercuri, Thiago L. Miller, Filipe F. dos Santos, Matheus de Lima, Aline Rangel-Pozzo, Pedro A. Galante. The role of exonized mobile elements in chimeric transcript emergence in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2395.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".