Ribosomal RNA Degradation (RNA Disruption) in Tumour Cells: Mechanistic Insights and Potential Clinical Utility
Bibliographic record
Abstract
The ribosome in eukaryotic cells is a macromolecular complex composed of four ribonucleic acids and over 80 proteins. This organelle facilitates protein synthesis in cells, and its activity is strongly upregulated in human cancers. Immune cells, a variety of cellular stressors and numerous structurally and mechanistically distinct anti-cancer agents have been shown to induce ribosomal RNA degradation in tumour cells in vitro and in vivo-a phenomenon we termed "RNA disruption". RNA disruption can be quantified in cultured cell lines and patient samples using the RNA disruption assay (RDA). Unlike well-known high-throughput anti-cancer drug sensitivity assays, RDA can distinguish between dying and arrested tumour cells, making it an attractive assay for anti-cancer drug discovery and development. Low tumour RNA disruption during neoadjuvant chemotherapy (as measured using RDA) is strongly associated with residual disease and reduced disease-free survival, making it a potentially valuable chemo-resistance assessment tool. High RNA disruption may also indicate chemo-responsiveness. RDA holds the prospect of being a useful tool to escalate or de-escalate neoadjuvant chemotherapy in cancer patients. Moreover, the assay's ability to predict treatment outcomes during neoadjuvant chemotherapy may permit its use in adaptive clinical trials and in drug approval by regulatory agencies. This review provides insight into the cellular processes involved in chemotherapy-induced RNA disruption. It also describes the results of clinical studies on tumour RNA disruption in cancer patients and suggests possible approaches that could be considered for the utilization of RDAs in the clinical management of breast cancer patients undergoing current neoadjuvant chemotherapy regimens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".