Breast Carcinoma–Associated Fibroblasts Rarely Contain p53 Mutations or Chromosomal Aberrations
Bibliographic record
Abstract
It has become increasingly clear that the cells within the tumor microenvironment play a critical role in cancer growth and metastasis. Studies in experimental models suggest that carcinoma-associated fibroblasts (CAF) differ from normal fibroblasts and are capable of promoting cancer progression through a variety of mechanisms. At present, a definitive view is lacking on whether genomic abnormalities are present and whether they might underlie the observed phenotypic differences. This study reports the molecular analysis of the largest series of breast CAFs reported to date, with an array comparative genomic hybridization-based DNA copy number analysis of cultured CAFs derived from 25 freshly resected human breast cancers. We found DNA copy number changes consisting of the whole arm of chromosomes 6p and 9p plus interstitial 4q loss in only one sample. No abnormalities were observed in non-tumor-associated fibroblast counterparts. Karyotyping of the same CAF revealed further chromosomal abnormalities, which included clonal loss of chromosomes, chromosomal duplications, and less frequent chromosomal rearrangements. These abnormalities were not associated with alterations in the global gene expression profile of this particular CAF, relative to its non-tumor-associated fibroblast counterpart. Moreover, this particular patient's CAF also displayed the only p53 mutation in the cohort, the first time such a mutation has been reported in freshly cultured human CAFs. These findings argue that the procancerous effects of CAFs are unlikely to be due to DNA copy number-type genomic abnormalities in the CAFs themselves. As such, breast CAFs should be mainly regarded as genomically stable cellular constituents that exist within complex cancer microenvironments.
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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 | 0.001 |
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".