Patient‐ and Cell Type‐Specific Heterogeneity of Metformin Response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it