The functional GRHL3-filaggrin axis maintains a tumor differentiation potential and influences drug sensitivity
Bibliographic record
Abstract
Current therapies for treating heterogeneous cancers such as head and neck squamous cell carcinoma (HNSCC) are non-selective and are administered independent of response biomarkers. Therapy resistance subsequently emerges, resulting in increased cellular proliferation that is associated with loss of differentiation. Whether a cancer cell differentiation potential can dictate therapy responsiveness is still currently unknown. A multi-omic approach integrating whole-genome and whole-transcriptome sequencing with drug sensitivity was employed in a HNSCC mouse model, primary patients' data, and human cell lines to assess the potential of functional differentiation in predicting therapy response. Interestingly, a subset of HNSCC with effective GRHL3-dependent differentiation was the most sensitive to inhibitors of PI3K/mTOR, c-Myc, and STAT3 signaling. Furthermore, we identified the GRHL3-differentiation target gene Filaggrin (FLG) as a response biomarker and more importantly, stratified HNSCC subsets as treatment resistant based on their FLG mutational profile. The loss of FLG in sensitive HNSCC resulted in a dramatic resistance to targeted therapies while the GRHL3-FLG signature predicted a favorable patient prognosis. This study provides evidence for a functional GRHL3-FLG tumor-specific differentiation axis that regulates targeted therapy response in HNSCC and establishes a rationale for clinical investigation of differentiation-paired targeted therapy in heterogeneous cancers.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".