Regional perturbation of gene transcription is associated with intrachromosomal rearrangements and gene fusion transcripts in high grade ovarian cancer
Bibliographic record
Abstract
Genomic rearrangements are a hallmark of cancer biology and progression, allowing cells to rapidly transform through alterations in regulatory structures, changes in expression patterns, reprogramming of signaling pathways, and creation of novel transcripts via gene fusion events. Though functional gene fusions encoding oncogenic proteins are the most dramatic outcomes of genomic rearrangements, we investigated the relationship between rearrangements evidenced by fusion transcripts and local expression changes in cancer using transcriptome data alone. 9,953 gene fusion predictions from 418 primary serious ovarian cancer tumors were analyzed, identifying depletions of gene fusion breakpoints within coding regions of fused genes as well as an N-terminal enrichment of breakpoints within fused genes. We identified 48 genes with significant fusion-associated upregulation and furthermore demonstrate that significant regional overexpression of intact genes in patient transcriptomes occurs within 1 megabase of 78 novel gene fusions that function as central markers of these regions. We reveal that cancer transcriptomes select for gene fusions that preserve protein and protein domain coding potential. The association of gene fusion transcripts with neighboring gene overexpression supports rearrangements as mechanism through which cancer cells remodel their transcriptomes and identifies a new way to utilize gene fusions as indicators of regional expression changes in diseased cells with only transcriptomic data.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".