MS3-3: Energy Metabolism in Breast Cancer: Translational Science Insights Relevant to Effects of Diet, Exercise, and Metformin on Risk and Prognosis.
Bibliographic record
Abstract
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".