Principles governing A-to-I RNA editing in the breast cancer transcriptome
Bibliographic record
Abstract
ABSTRACT A-to-I editing substitutes inosines for adenosines at specific positions in mRNAs and can substantially alter a cell’s transcriptome. Currently, little is known about how RNA editing operates in cancer. Transcriptome analysis of 68 normal and cancerous breast tissues revealed that the editing enzyme ADAR acts uniformly, on the same loci, across tissues. Controlled ADAR expression experiments demonstrated that the editing frequency at all loci is proportional to both ADAR expression levels and the individual locus’ editability—a propensity to be edited determined by the surrounding nucleotide sequence. Comparison of tumor transcriptomes to those of normal breast and breast organoids, i.e. pure normal breast epithelial cells, demonstrated that the editing frequency is increased in tumor cells. This was consistent with ADAR immunohistochemistry. We also demonstrated that type I interferon response and ADAR DNA copy number explain together 53% of ADAR expression in breast cancers, an observation also valid in nearly all of 20 other cancer types in The Cancer Genome Atlas. Interferon exposure increased ADAR mRNA, protein expression and editing in four breast cell lines. Finally, ADAR silencing using shRNA lentivirus transduction in breast cancer cell lines led to more cell proliferation and less apoptosis. Our results reveal that A-to-I editing is a pervasive, yet reproducible, source of variation that is controlled by two factors, 1q amplification and inflammation, both highly prevalent among human cancers. This suggests the potential for a new class of therapeutic targets and an unexpected role for inflammation in cancers.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".