MétaCan
Menu
Back to cohort
Record W2047537157 · doi:10.4161/cc.22776

BRCA1 mutations drive oxidative stress and glycolysis in the tumor microenvironment

2012· article· en· W2047537157 on OpenAlexaff
Ubaldo Martinez‐Outschoorn, Renee M. Balliet, Zhao Lin, Diana Whitaker‐Menezes, Ruth Birbe, Alessandro Bombonati, Stephanos Pavlides, Rebecca Lamb, Sharon Sneddon, Anthony Howell, Federica Sotgia, Michael P. Lisanti

Bibliographic record

VenueCell Cycle · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsBiologyCancer researchStromal cellOxidative stressCancer cellTumor microenvironmentCell cultureDownregulation and upregulationCancerFibroblastCell biologyEndocrinologyGeneticsGene

Abstract

fetched live from OpenAlex

Mutations in the BRCA1 tumor suppressor gene are commonly found in hereditary breast cancer. Similarly, downregulation of BRCA1 protein expression is observed in the majority of basal-like breast cancers. Here, we set out to study the effects of BRCA1 mutations on oxidative stress in the tumor microenvironment. To mimic the breast tumor microenvironment, we utilized an in vitro co-culture model of human BRCA1-mutated HCC1937 breast cancer cells and hTERT-immortalized human fibroblasts. Notably, HCC1937 cells induce the generation of hydrogen peroxide in the fibroblast compartment during co-culture, which can be inhibited by genetic complementation with the wild-type BRCA1 gene. Importantly, treatment with powerful antioxidants, such as NAC and Tempol, induces apoptosis in HCC1937 cells, suggesting that microenvironmental oxidative stress supports cancer cell survival. In addition, Tempol treatment increases the apoptotic rates of MDA-MB-231 cells, which have wild-type BRCA1, but share a basal-like breast cancer phenotype with HCC1937 cells. MCT4 is the main exporter of L-lactate out of cells and is a marker for oxidative stress and glycolytic metabolism. Co-culture with HCC1937 cells dramatically induces MCT4 protein expression in fibroblasts, and this can be prevented by either BRCA1 overexpression or by pharmacological treatment with NAC. We next evaluated caveolin-1 (Cav-1) expression in stromal fibroblasts. Loss of Cav-1 is a marker of the cancer-associated fibroblast (CAF) phenotype, which is linked to high stromal glycolysis, and is associated with a poor prognosis in numerous types of human cancers, including breast cancers. Remarkably, HCC1937 cells induce a loss of Cav-1 in adjacent stromal cells during co-culture. Conversely, Cav-1 expression in fibroblasts can be rescued by administration of NAC or by overexpression of BRCA1 in HCC1937 cells. Notably, BRCA1-deficient human breast cancer samples (9 out of 10) also showed a glycolytic stromal phenotype, with intense mitochondrial staining specifically in BRCA1-deficient breast cancer cells. In summary, loss of BRCA1 function leads to hydrogen peroxide generation in both epithelial breast cancer cells and neighboring stromal fibroblasts, and promotes the onset of a reactive glycolytic stroma, with increased MCT4 and decreased Cav-1 expression. Importantly, these metabolic changes can be reversed by antioxidants, which potently induce cancer cell death. Thus, antioxidant therapy appears to be synthetically lethal with a BRCA1-deficiency in breast cancer cells and should be considered for future cancer prevention trials. In this regard, immunostaining with Cav-1 and MCT4 could be used as cost-effective biomarkers to monitor the response to antioxidant therapy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.210
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations85
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCell CycleSame topicCancer Genomics and DiagnosticsFrench-language works237,207