The use of a multi-metric readout screen to identify EHMT2/G9a-inhibition as a modulator of cancer-associated fibroblast activation state
Bibliographic record
Abstract
Cancer-associated fibroblasts (CAFs) play a pivotal role in cancer progression, including mediating tumour cell invasion via their pro-invasive secretory profile and ability to remodel the extracellular matrix (ECM). Given that reduced CAF abundance in tumours correlates with improved outcomes in various cancers, we set out to identify epigenetic targets involved in CAF activation in regions of tumour-stromal mixing with the goal of reducing tumour aggressiveness. Using the GLAnCE (Gels for Live Analysis of Compartmentalized Environments) platform, we performed an image-based, phenotypic screen that enabled us to identify modulators of CAF abundance and the capacity of CAFs to induce tumour cell invasion. We identified EHMT2 (also known as G9a), an enzyme that targets the methylation of histone 3 lysine 9 (H3K9), as a potent modulator of CAF abundance and CAF-mediated tumour cell invasion. Transcriptomic and functional analysis of EHMT2-inhibited CAFs revealed EHMT2 participated in driving CAFs towards a pro-invasive phenotype and mediated CAF hyperproliferation, a feature typically associated with activated fibroblasts in tumours. Our study suggests that EHMT2 regulates CAF state within the tumour microenvironment by impacting CAF activation, as well as by magnifying the pro-invasive effects of these activated CAFs on tumour cell invasion through promoting CAF hyperproliferation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".