MS3-3: Energy Metabolism in Breast Cancer: Translational Science Insights Relevant to Effects of Diet, Exercise, and Metformin on Risk and Prognosis.
Notice bibliographique
Résumé
Abstract Energy metabolism is relevant to breast cancer at both the cellular and whole organism levels. Whole organism energy balance determines body mass, which has been associated with variations in both breast cancer risk and prognosis. Experimentally, breast carcinogenesis is facilitated by excess caloric intake and inhibited by caloric restriction. The simplistic notion that excess food intake provides additional energy to breast epithelial cells at risk for transformation or to breast cancers, leading to aggressive behavior, is not supported by experimental data. Rather, variations in energy balance have important influences on the hormonal and cytokine environment of the patient, and these influence carcinogenesis and tumor behaviour. Experimental models provide evidence that one such mediating hormone is insulin. Most breast cancers have insulin receptors. When mice with breast cancer are experimentally manipulated to have insulin deficiency (type I) diabetes, tumor growth rate is slowed (despite hyperglycemia). Conversley, when mice are provided with a “junk food” diet, insulin levels rise, tumor insulin receptor activation increases, and tumors grow more quickly. However, when breast cancers evolve to have activating muations in signalling networks downstream of insulin receptors, they become more aggressive and unresponsive to variations in energy intake and insulin no longer influences their behavior. There is retrospective pharmacoepidemiologic evidence for a substantial ( ∼50%) reduction in breast cancer risk in type II (hyperinsulinemic) diabetic patients prescribed metformin. This has contributed to current interest in the hypothesis that metformin has uses in cancer prevention or treatment. Metformin acts to reduce cellular ATP production by inhibiting mitochondrial respiratory complex I. This results in activation of AMPK. In liver, this results in reduced gluconeogenesis, which reduces the hyperglycemia and hyperinsulinemia of type II diabetes. This systemic effect may reduce proliferation of the subset of neoplasms that are growth stimulated by insulin, but does not operate in the absence of baseline hyperinsulinemia. Other mechanisms of metformin action involve direct effects on at-risk or transformed cells. These mechanisms require adequate levels of the drug in the relevant cells, but metformin doses used in diabetes treatment may not achieve optimum concentrations in cancers, particularly those that lack the active transport molecules responsible for cellular metformin uptake. Overall, laboratory studies suggest that any benefits of metformin will not be homogeneous among a population of at-risk women in a prevention context, nor among breast cancer patients in a treatment context. The validation of candidate predictive biomarkers for metformin benefit, together with more detailed pharmokinetic data, may allow for optimized clinical trial design. Further research is also required to clarify if metformin should best be evaluated as a single agent or in combinations. Thus, metformin and derivatives can be regarded as lead compounds for optimization, and this line of research may lead to novel metabolic approaches to breat cancer prevention and treatment. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr MS3-3.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».