Hereditary ovarian cancer and two-compartment tumor metabolism
Bibliographic record
Abstract
Mutations in the BRCA1 tumor suppressor gene are commonly found in hereditary ovarian cancers. Here, we used a co-culture approach to study the metabolic effects of BRCA1-null ovarian cancer cells on adjacent tumor-associated stromal fibroblasts. Our results directly show that BRCA1-null ovarian cancer cells produce large amounts of hydrogen peroxide, which can be abolished either by administration of simple antioxidants (N-acetyl-cysteine; NAC) or by replacement of the BRCA1 gene. Thus, the BRCA1 gene normally suppresses tumor growth by functioning as an antioxidant. Importantly, hydrogen peroxide produced by BRCA1-null ovarian cancer cells induces oxidative stress and catabolic processes in adjacent stromal fibroblasts, such as autophagy, mitophagy and glycolysis, via stromal NFκB activation. Catabolism in stromal fibroblasts was also accompanied by the upregulation of MCT4 and a loss of Cav-1 expression, which are established markers of a lethal tumor microenvironment. In summary, loss of the BRCA1 tumor suppressor gene induces hydrogen peroxide production, which then leads to metabolic reprogramming of the tumor stroma, driving stromal-epithelial metabolic coupling. Our results suggest that new cancer prevention trials with antioxidants are clearly warranted in patients that harbor hereditary/familial BRCA1 mutations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".