RIPK2: New Elements in Modulating Inflammatory Breast Cancer Pathogenesis
Bibliographic record
Abstract
Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer that is associated with significantly high mortality. In spite of advances in IBC diagnoses, the prognosis is still poor compared to non-IBC. Due to the aggressive nature of the disease, we hypothesize that elevated levels of inflammatory mediators may drive tumorigenesis and metastasis in IBC patients. Utilizing IBC cell models and patient tumor samples, we can detect elevated NF-κB activity and hyperactivation of non-canonical drivers of NF-κB (nuclear factor kappaB)-directed inflammation such as tyrosine phosphorylated receptor-interacting protein kinase 2 (pY RIPK2), when compared to non-IBC cells or patients. Interestingly, elevated RIPK2 activity levels were present in a majority of pre-chemotherapy samples from IBC patients at the time of diagnosis to suggest that patients at diagnosis had molecular activation of NF-κB via RIPK2, a phenomenon we define as "molecular inflammation". Surprisingly, chemotherapy did cause a significant increase in RIPK2 activity and thus molecular inflammation suggesting that chemotherapy does not resolve the molecular activation of NF-κB via RIPK2. This would impact on the metastatic potential of IBC cells. Indeed, we can demonstrate that RIPK2 activity correlated with advanced tumor, metastasis, and group stage as well as body mass index (BMI) to indicate that RIPK2 might be a useful prognostic marker for IBC and advanced stage breast cancer.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".