Identification of Alternative Splicing Markers for Breast Cancer
Bibliographic record
Abstract
Breast cancer is the most common cause of cancer death among women under age 50 years, so it is imperative to identify molecular markers to improve diagnosis and prognosis of this disease. Here, we present a new approach for the identification of breast cancer markers that does not measure gene expression but instead uses the ratio of alternatively spliced mRNAs as its indicator. Using a high-throughput reverse transcription-PCR-based system for splicing annotation, we monitored the alternative splicing profiles of 600 cancer-associated genes in a panel of 21 normal and 26 cancerous breast tissues. We validated 41 alternative splicing events that significantly differed in breast tumors relative to normal breast tissues. Most cancer-specific changes in splicing that disrupt known protein domains support an increase in cell proliferation or survival consistent with a functional role for alternative splicing in cancer. In a blind screen, a classifier based on the 12 best cancer-associated splicing events correctly identified cancer tissues with 96% accuracy. Moreover, a subset of these alternative splicing events could order tissues according to histopathologic grade, and 5 markers were validated in a further blind set of 19 grade 1 and 19 grade 3 tumor samples. These results provide a simple alternative for the classification of normal and cancerous breast tumor tissues and underscore the putative role of alternative splicing in the biology of cancer.
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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 | 0.001 | 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) | 0.000 | 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".