Exploring the prognostic significance of arm-level copy number alterations in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Somatic copy number alterations (SCNAs) are prevalent in cancer and play a significant role in both tumorigenesis and therapeutic resistance. While focal SCNAs have been extensively studied, the impact of larger arm-level SCNAs remains poorly understood. Here, we investigated the association between arm-level SCNAs and overall survival in triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer lacking targeted therapies. We identified frequent arm-level SCNAs, including 21q gain and 7p gain, which correlated with poor overall survival in TNBC patients. Further, we identified the expression of specific genes within these SCNAs associated with survival. Notably, we found that the expression of RIPK4, a gene located on 21q, exhibited a strong correlation with poor overall survival. In functional assays, we demonstrated that targeting Ripk4 in a murine lung metastatic TNBC model significantly reduced tumor burden, improved survival, and increased CD4+ and CD8+ T cell infiltration. RIPK4 enhanced the survival of triple-negative breast cancer cells at secondary sites, thereby facilitating the formation of metastatic lesions. Our findings highlight the significance of arm-level SCNAs in breast cancer progression and identify RIPK4 as a putative driver of TNBC metastasis and immunosuppression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".