Dichotomous effects of cellular expression of STAT3 on tumor growth of HNSCC
Bibliographic record
Abstract
STAT3 signaling has been shown to regulate cellular function and cytokine production in the tumor microenvironment (TME). Within the head and neck squamous cell carcinoma (HNSCC) TME, we previously showed that therapeutic targeting of STAT3 in combination with radiation resulted in improved tumor growth delay. However, given the independent regulatory effects STAT3 has on anti-tumor immunity, we aimed to decipher the effects of individually targeting STAT3 in the cancer cell, regulatory T cells (Tregs), and natural killer (NK) cell compartments in driving tumor growth and resistance to therapy in HNSCCs. We utilized a CRISPR knockout system for genetic deletion of STAT3 within the cancer cell as well as two genetic knockout mouse models, FoxP3-Cre/STAT3 fl and NKp46-Cre/STAT3 fl, for Tregs and NK cell targeting, respectively. Our data revealed differences in development of resistance to treatment with STAT3 CRISPR knockout in the cancer cell, driven by differential recruitment of immune cells. Knockout of STAT3 in Tregs overcomes this resistance and results in Treg reprogramming and recruitment and activation of antigen-presenting cells. In contrast, knockout of STAT3 in the NK cell compartment results in NK cell inactivation and acceleration of tumor growth. These data underscore the complex interplay between the cancer cell and the immune TME and carry significant implications for drug targeting and design of combination approaches in HNSCCs. STAT3 signaling has been shown to regulate cellular function and cytokine production in the tumor microenvironment (TME). Within the head and neck squamous cell carcinoma (HNSCC) TME, we previously showed that therapeutic targeting of STAT3 in combination with radiation resulted in improved tumor growth delay. However, given the independent regulatory effects STAT3 has on anti-tumor immunity, we aimed to decipher the effects of individually targeting STAT3 in the cancer cell, regulatory T cells (Tregs), and natural killer (NK) cell compartments in driving tumor growth and resistance to therapy in HNSCCs. We utilized a CRISPR knockout system for genetic deletion of STAT3 within the cancer cell as well as two genetic knockout mouse models, FoxP3-Cre/STAT3 fl and NKp46-Cre/STAT3 fl, for Tregs and NK cell targeting, respectively. Our data revealed differences in development of resistance to treatment with STAT3 CRISPR knockout in the cancer cell, driven by differential recruitment of immune cells. Knockout of STAT3 in Tregs overcomes this resistance and results in Treg reprogramming and recruitment and activation of antigen-presenting cells. In contrast, knockout of STAT3 in the NK cell compartment results in NK cell inactivation and acceleration of tumor growth. These data underscore the complex interplay between the cancer cell and the immune TME and carry significant implications for drug targeting and design of combination approaches in HNSCCs.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 teacher head, 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".