Identification of recurrent regulated alternative splicing events across human solid tumors
Bibliographic record
Abstract
Cancer is a complex disease that involves aberrant gene expression regulation. Discriminating the modified expression patterns driving tumor biology from the many that have no or little contribution is important for understanding cancer molecular basis. Recurrent deregulation patterns observed in multiple cancer types are enriched for such driver events. Here, we studied splicing alterations in hundreds of matched tumor and normal RNA-seq samples of eight solid cancer types. We found hundreds of cassette exons for which splicing was altered in multiple cancer types and identified a set of highly frequent altered splicing events. Specific splicing regulators, including RBFOX2, MBNL1/2 and QKI, appear to account for many splicing alteration events in multiple cancer types. Together, our results provide a first global analysis of regulated splicing alterations in cancer and identify common events with a potential causative role in solid tumor development.
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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 | 0.003 | 0.001 |
| 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 | 0.001 | 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".