Artemis (DCLRE1C) Acts as a Target to Enhance Radiotherapy Response in Triple-Negative Breast Cancer
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: The lack of canonical biomarkers and strategies to target radioresistance contribute to poor patient outcomes in triple-negative breast cancer (TNBC). Identifying and targeting novel radioresistance genes will benefit in enhancing radiotherapy response and treatment outcomes in TNBC patients. METHODS: A genome-wide CRISPR screen was performed to identify radioresistance genes in the TNBC cell line. An in vitro clonogenic assay was used to assess the antiproliferative effects of Artemis knockout or pharmacologic inhibition of Artemis, either alone or in combination with RT. Tumor doubling time and animal survival were assessed using an in vivo xenograft model. RNA-seq analysis was performed to identify genes and pathways deregulated under Artemis knockout conditions, both alone and in combination with RT. Cellular senescence was evaluated using a β-galactosidase assay. RESULTS: Our CRISPR screen identified Artemis as a top hit in RT-treated TNBC cells, whose depletion led to radiosensitization in TNBC. Artemis knockout significantly reduced cell proliferation and enhanced the antiproliferative effects of RT in vitro. Compared to mice-bearing control MDA-MB-231 xenografts, Artemis knockout exhibited prolonged survival that was further enhanced with RT. Bulk RNA-sequencing indicated that the antiproliferative and radiosensitization effects of Artemis depletion were mediated by the activation of cellular senescence which was confirmed with a β-galactosidase assay. CONCLUSIONS: Taken together, our results highlight the critical role of Artemis in TNBC cell proliferation and response to radiation. Our findings identify Artemis as a potential biomarker indicative of sensitivity to radiation and a putative target that could be inhibited to enhance the efficacy of RT in TNBC.
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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 | 0.001 |
| 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".