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Record W2119962878 · doi:10.1093/jnci/djn185

Breast Cancer Metastasis: Do Variations in Inherited Genes Make a Difference?

2008· article· en· W2119962878 on OpenAlexaboutno aff
Gail McBride

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsnot available
Fundersnot available
KeywordsMetastasisBreast cancerBiologyGeneCancerCancer researchMetastasis Suppressor GeneCarcinogenesisGenetics

Abstract

fetched live from OpenAlex

A network of inherited gene polymorphisms — slightly different forms of the same gene — may predict whether a breast cancer will metastasize, according to new studies in mice by geneticist Kent Hunter, Ph.D. , and colleagues at the National Cancer Institute. The most recent study, published in April in the Proceedings of the National Academy of Sciences, focused on one gene called Brd4, which the scientists believe may be one of the main drivers in a network of breast can cer metastasis – related genes. Found in mice and humans, the gene is normally involved in cell proliferation, cell cycle progression, and DNA replication, all of which can go awry in metastasis. It also interacts with another important gene called Sipa1, which is already known to infl uence breast tumor invasiveness in mice. The researchers concluded that, when expressed, the genes in this network alter other genes in the breast cancer cells’ extracellular matrix — the scaffolding of proteins and other factors on which cells rest — encouraging a tendency toward metastasis. Although most metastasis research in recent years has revolved around mutations in tumor cells or the nearby tissue environment, Hunter’s laboratory has taken a different approach, searching for germline polymorphisms that could help explain the wide variability in breast cancer metastasis rates. “Initially people focused on the nature of the tumor cell itself and what oncogenes and tumor suppressor genes had been expressed, and then they began to realize that the interaction is between the tumor cell and the host environment,” said Ann Chambers, Ph.D., an oncologist who studies metastasis at the London Health Sciences Centre in Ontario. “Dr. Hunter has now added inherited susceptibility to this, perhaps suggesting new therapeutic approaches.” In the PNAS study, the researchers implanted the Brd4 gene into breast cancer cells in some mice and a “control gene” into breast cancer cells of other mice. They found that mice with the Brd4 gene had fewer metastases than mice with the control gene. The researchers believe that activation of Brd4 (resulting in either an increased amount or function of the protein) reduces tumor growth and metastasis by infl uencing the response of tumor cells to signals from the extracellular matrix. Using human gene information from microarray data from the National Center for Biotechnology Information, Hunter’s team found 379 human genes that are similar to genes affected by Brd4 expression in mice. Differences in the human Brd4 pathway, the researchers say, seemed to drive pathways involving these other genes, ultimately affecting relapse and survival. In fact, starting with the Brd4 gene, the researchers were able to predict survival and relapse in fi ve different groups of breast cancer patients. They could also predict the survival of patients whose breast cancers had not spread to their lymph nodes and/or whose breast cancers were estrogen receptor positive. (About 70% of breast cancers are estrogen receptor positive, which is associated with a lower risk of metastasis.) Another of the group’s reports, published in March in Clinical and Experimental Metastasis, discussed seven candidate metastasis susceptibility genes, including Brd4, Sipa1, and another gene called Rrp1b. All are components of what Hunter and his colleagues call the diasporin pathway, a tumor progression – related transcriptional pathway that predicts breast cancer survival.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.035
GPT teacher head0.302
Teacher spread0.267 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2008
Admission routes1
Has abstractyes

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