Association of extensive RNA disruption with natural killer cell-mediated death of K562 chronic myelogenous leukemia cells
Bibliographic record
Abstract
Abstract Extensive degradation of tumour 28S and 18S ribosomal RNAs, coupled with the accumulation of ribosomal RNA degradation products, is associated with pathologic complete response and improved disease-free-survival in breast cancer patients. Various chemotherapy agents and cellular stressors are known to trigger this process, termed ‘RNA disruption’, in tumour cells. However, it’s unclear whether immunotherapies, with or without chemotherapy administration, also trigger RNA disruption. To address this question, we assessed the ability of natural killer (NK) cells to induce RNA disruption and cell death in K562 chronic myeloid leukemia cells in vitro. We found that NK cells strongly stimulated RNA disruption, cytotoxicity (loss of plasma membrane integrity) and cell death (generation of cells with a subG1 DNA content) in K562 cells. Pre-activation of NK cells with interleukin-2 or pre-treatment of K562 cells with the chemotherapy drug doxorubicin augmented RNA disruption in K562 cells. RNA degradation patterns looked very similar between NK cell-treated and doxorubicin-treated K562 cells. Our observations suggest that RNA disruption is strongly associated with cell death irrespective of the death-inducing stimulus and raise the prospect that tumour RNA disruption may be a useful biomarker for quantifying cancer patients’ response to immunotherapies, with or without co-administration of chemotherapy drugs.
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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) | 0.002 | 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".