Transcriptomic analysis of human primary breast cancer identifies fatty acid oxidation as a target for metformin
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies suggest that metformin may reduce the incidence of cancer in patients with diabetes and multiple late phase clinical trials assessing the potential of repurposing this drug are underway. Transcriptomic profiling of tumour samples is an excellent tool to understand drug bioactivity, identify candidate biomarkers and assess for mechanisms of resistance to therapy. METHODS: Thirty-six patients with untreated primary breast cancer were recruited to a window study and transcriptomic profiling of tumour samples carried out before and after metformin treatment. RESULTS: Multiple genes that regulate fatty acid oxidation were upregulated at the transcriptomic level and there was a differential change in expression between two previously identified cohorts of patients with distinct metabolic responses. Increase in expression of a mitochondrial fatty oxidation gene composite signature correlated with change in a proliferation gene signature. In vitro assays showed that, in contrast to previous studies in models of normal cells, metformin reduces fatty acid oxidation with a subsequent accumulation of intracellular triglyceride, independent of AMPK activation. CONCLUSIONS: We propose that metformin at clinical doses targets fatty acid oxidation in cancer cells with implications for patient selection and drug combinations. CLINICAL TRIAL REGISTRATION: NCT01266486.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".