Cancer-associated fibroblast driven paracrine IL-6/STAT3 signaling promotes migration and dissemination in invasive lobular carcinoma
Bibliographic record
Abstract
BACKGROUND: Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer after invasive ductal carcinoma of no special type (NST), accounting for 10-15% of diagnoses. Despite the myriad molecular, histological and clinical differences between ILC and NST tumors, patients are treated in the same way, and although prognosis initially is good, ILC patients have poorer long-term outcomes. Understanding the differences between these two subtypes and identifying ILC-enriched therapeutic targets is necessary to improve patient care. METHODS: Human and mouse cancer-associated fibroblasts (CAFs), ILC cell lines and patient-derived organoids were used for in vitro and in vivo studies, including western blotting, migration, organotypic invasion assays and dissemination in zebrafish embryos. RNASeq was used to identify CAF and interleukin-6 (IL-6)-derived gene signatures. Bioinformatic analysis of public databases and immunohistochemical of human tumor microarrays was carried out. RESULTS: We identified IL-6 as a paracrine CAF-derived factor that activates Signal-Transducer-and-Activator-of-Transcription-3 (STAT3) in human and mouse ILC models. Analysis of human breast tumors showed that the IL-6/JAK/STAT3 pathway is enriched in ER + ILC compared to ER + NST. A 42-gene CAF dependent IL-6 gene signature and 64-gene consensus IL-6 gene signature were generated and were significantly enriched in ER + ILC, with many of the genes overexpressed in ILC tumors. IL-6 treatment suppressed downstream estrogen signaling and also led to the acquisition of a more mesenchymal-like phenotype associated with increased migration and invasion. Finally, IL-6 treatment significantly increased ILC cell dissemination following injection into zebrafish embryos. CONCLUSIONS: CAF-derived IL-6 drives paracrine activation of the IL6/JAK/STAT3 signaling pathway which is enriched in ILC. This leads to the acquisition of pro-tumorigenic phenotypes, highlighting the pathway as a potential therapeutic target in ILC.
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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 | 0.000 | 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 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".