Identification of the Genomics and Transcriptomics Regulators of the DNA Damage Response in Breast Cancer
Bibliographic record
Abstract
Breast cancer is one of the most prevalent malignancies and the most common cancer in female patients. In recent years, the clinical utilization of a class of drugs called poly (ADP-ribose) polymerase inhibitors has been observed to be detrimental to cells that harbor defective DNA damage repair mechanisms. Implementation of these drugs entails a series of unprecedented challenges, including the development of drug resistance to this treatment strategy. Thus, it is essential to gain a better understanding of the mechanisms that regulate the DNA damage response to maximize the treatment efficacy in breast cancer patients and minimize unwanted side effects. In this study, through the utilization of single-cell- and bulk-level transcriptional data, we set out to identify molecules and molecular circuits associated with DNA damage response in breast cancer patients. By identifying differentially expressed genes in single-cell cancer cell populations inherently different in DNA damage response, further clustering bulk RNA-sequencing samples based on the expression of these genes, and performing network and enrichment analysis at the bulk level, we have characterized breast cancer samples based on their DNA damage response. Moreover, we have been able to identify a central network module whose members can serve as treatment targets and yield further insights into the mechanisms of drug resistance and DNA damage response in breast cancer. Overall, this study contributes to the characterization of the transcriptional circuits involved in the heterogeneity of DDR in breast cancer and provides candidate avenues for the investigation of potential therapeutic interventions.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".