Tumor–Immune Cell Crosstalk Drives Immune Cell Reprogramming Towards a Pro-Tumor Proliferative State Involving STAT3 Activation
Bibliographic record
Abstract
Background/Objectives: Tumor-induced immune reprogramming is increasingly recognized as a key mechanism by which cancers evade surveillance and promote disease progression. The interaction between cancer and immune cells within the tumor microenvironment (TME) can drive phenotypic and functional changes in immune populations, facilitating metastasis and immune evasion. Methods: In this study, we used co-culture models to expose THP1 monocytes to triple-negative breast cancer (TNBC) cells, MDA-MB-231 and BT-549, either directly or indirectly via tumor-conditioned media, to mimic tumor–immune cell communication. Transcriptomic and pathway analyses revealed that cancer-exposed monocytes adopt a reprogrammed phenotype marked by activation of pro-tumorigenic signaling pathways, enhanced proliferative capacity, and elevated expression of pro-inflammatory cytokines such as IL6. Results: Functional assays confirmed a significant increase in monocyte proliferation under both direct and indirect tumor exposure. Importantly, we demonstrated that this tumor-driven proliferation of THP1 cells could be suppressed by the STAT3 inhibitor STAT3-IN-12. This highlights the critical role of STAT3 signaling in mediating immune cell transformation and supporting a novel immunomodulatory approach for therapeutic intervention. Conclusions: These findings support the potential for targeting tumor-educated transcriptional programs as a novel immunomodulatory strategy in cancer treatment. Restoring immune cell homeostasis and suppressing pro-tumor phenotypes through pharmacological inhibition of the key signaling nodes such as STAT3 may complement existing cancer therapies. This study provides new insights into immune cell plasticity in cancer and identifies actionable strategies to counteract tumor-driven immune dysregulation.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".