Identification of stromal genes differentially expressed in lobular breast cancer highlights role for pregnancy-associated-plasma protein-A
Bibliographic record
Abstract
Abstract Background Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer and exhibits a number of clinico-pathological characteristics that are distinct from the more common invasive ductal carcinoma (IDC). Despite these differences, ILC is treated in the same way as IDC. We set out to identify alterations in the tumor microenvironment (TME) of ILC with potential clinical significance. Methods We used laser-capture microdissection (LCM) to separate tumor epithelium from stroma in 23 ER+ ILC samples. Gene expression analysis was used to identify genes that are enriched in the stroma of ILC, but not IDC or normal breast. Results 45 genes involved in regulation of the extracellular matrix (ECM) were enriched in the stroma of ILC, but not stroma from ER+ IDC or normal breast. Of these, 10 were expressed in cancer-associated fibroblasts (CAFs) and were increased in ILC compared to IDC in bulk gene expression datasets. PAPPA was the most enriched in the stroma compared to the tumor epithelial compartment in ILC. PAPPA encodes pregnancy-associated plasma protein-A (PAPP-A), a metalloproteinase that cleaves insulin-like binding protein-4 (IGFBP-4) increasing IGF-1 bioavailability and subsequent downstream signaling. Analysis of PAPPA and IGF1 associated genes identified a paracrine signaling pathway and active PAPP-A was shown to be secreted from primary CAFs. Comprehensive survival analysis across 3000 breast cancers identified PAPPA as a potential ILC-specific prognostic marker. Conclusions This is the first study to demonstrate molecular differences in the TME between ILC and IDC and identifies PAPP-A, a CAF-derived proteinase, as a potential prognostic marker.
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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 | 0.001 | 0.001 |
| 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".