Breast Cancer Metastasis: Do Variations in Inherited Genes Make a Difference?
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».