Patient‐ and Cell Type‐Specific Heterogeneity of Metformin Response
Bibliographic record
Abstract
Most FDA-approved drugs are not equally effective in all patients, suggesting that identification of biomarkers to predict responders to a chemoprevention agent will be needed to stratify patients and achieve maximum benefit. The goal of this study was to investigate both patient-specific and cell context-specific heterogeneity of metformin response, using fibroblast cell lines and induced pluripotent stem cells differentiated into lung epithelial lineages. We performed cell survival analysis, transcriptome and whole exome sequencing analysis on both patient-derived cell lines and cancer cell lines to assess differential metformin response and identify response genes. We found differences in response to metformin treatment across a variety of cell lines and cellular contexts, suggesting that heterogeneity may be patient- and cell type-specific. Gene expression profiling and analysis of metformin-sensitive and metformin-resistant cells identified differentially expressed genes that may be able to stratify patients into metformin responders and non-responders. Sequencing analysis found genomic alterations that correlated with metformin response. These results suggest that the identification of genomic biomarkers for patients who may respond to metformin treatment can provide an opportunity for individualizing metformin chemoprevention in the clinical setting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".