Quantification of immune cell-mediated destruction of tumor cells <i>in vitro</i> using the RNA disruption assay.
Bibliographic record
Abstract
e15017 Background: Immune checkpoint inhibitors (ICIs) are increasingly being used in the treatment of human cancers, often in combination with cytotoxic chemotherapy drugs. However, current ICIs are costly and are only effective against a fraction of patient tumours. Thus, a tool that could reliably quantify tumour response to ICIs and predict outcome post-treatment would be highly valuable. Patients with non-responding tumours could be spared the costs and toxic side-effects of the ineffective drugs and moved promptly to alternate treatments. We have observed that a variety of structurally and mechanistically distinct chemotherapy agents induce the degradation of ribosomal RNA (rRNA) into a large number of high molecular weight fragments (between the 28S and 18S rRNAs)---a phenomenon we have termed “RNA disruption”. High tumour RNA disruption during treatment, as quantified using the RNA disruption assay (RDA), has been shown to predict pathologic complete response and improved disease-free survival in breast cancer patients. Methods: To assess whether RDA could be used to monitor immune cell killing of tumour cells, we collected peripheral blood mononuclear cells (PBMCs) from consenting healthy human donors and enriched the cell population for natural killer (NK) cells to 90% using a negative selection approach. The cells, with or without IL-2 pre-treatment, were incubated with K562 cells, after which K562 cell killing was quantified over time using a standard immunocytotoxicity assay and K562 cell RNA disruption was measured using RDA. Results: Freshly isolated human NK cells when incubated with K562 chronic myeloid leukemia cells induced cell destruction and RNA disruption in K562 cells in a dose-dependent manner. Pre-incubation with IL-2 augmented both NK cell-mediated RNA disruption and NK-mediated cytotoxicity in K562 cells. Interestingly, the pattern of K562 RNA disruption fragments generated by NK cells was similar to that generated by chemotherapy drugs. Conclusions: RDA was successfully used to quantify immune cell-mediated destruction of tumour cells, raising the prospect of its possible use to monitor tumour destruction by immune cells in vivo and to predict response to ICIs (alone or in concert with cytotoxic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".