Somatic copy number mutations contribute to fitness in transplantation models of spontaneous human breast cancer metastasis
Bibliographic record
Abstract
ABSTRACT The contribution of somatic gene dosage mutations (CNA) to breast cancer metastasis remains poorly defined. Using 9 transplantable human neoadjuvant-naive triple-negative breast cancer xenografts, we studied the fitness of copy number clones in spontaneous metastasis from orthotopic transplant sites. Metastatic site preference was strongly patient-dependent, and the emergence of metastases exhibited a general trend toward slower growth at the orthotopic site. In our models, single-cell whole-genome sequencing of primary and metastatic sites showed that distant metastases were most often the result of minor prevalence clones at the orthotopic site, suggesting that some metastatic phenotypes may be weakly negatively fit at the primary site. We validated the existence of a fitness hierarchy of copy number clones using a previously established paradigm of remixing and retransplanting clones. Single-cell clone analysis of competitive repopulation and re-emergence of metastases showed that CNAs arising in cancer evolution can mediate metastatic fitness. Moreover, some clones displaying strong metastatic tendency exhibited weaker survival at the primary site, consistent with the notion that metastatic phenotypes could have a fitness cost at the primary site. Finally, we conducted RNA-seq analysis combined with DriverNet analysis to dissect the contribution of CNA-mediated versus genome-independent transcriptional states. CNA mutations appeared to contribute strongly to transcriptional differences between clones. Among clones of high metastatic potential, we observed CNA-mediated and CNA-independent convergence on pathways such as epithelial-mesenchymal transition (EMT), established as mediators of metastatic cell survival at distant sites. Taken together, our data point to a contribution of CNA-mediated cancer evolution to metastatic states and identify distant-site context as a key determinant of CNA-mediated fitness.
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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 | 0.000 |
| 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) | 0.001 | 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".