Unraveling the role of stromal disruption in aggressive breast cancer etiology and outcomes
Bibliographic record
Abstract
BACKGROUND: Aggressive (typically high-grade) breast cancers (BCs) remain major contributors to BC-related mortality globally. The tissue changes underpinning their etiology and outcomes, however, remain poorly characterized. METHODS: Spatially resolved machine-learning algorithms were used to characterize "stromal disruption" as a morphological metric of reduced/altered extracellular matrix and increased immune, inflammatory, and/or wound response-related processes in normal, benign breast disease (BBD), and invasive hematoxylin and eosin (H&E)-stained breast tissues. Associations of stromal disruption with BC etiologic factors were assessed among 4023 healthy breast tissue donors, its impact on BC incidence was assessed among 974 BBD patients in a nested case-control study, while its prognostic associations were assessed in 4 BC patient cohorts (n = 4223). RESULTS: Epidemiologic risk factors for aggressive BC, including younger age, multiparity, Black race, obesity, and family history, demonstrated strong associations with increasing stromal disruption in H&E sections prior to tumor development. Substantial stromal disruption in BBD H&E was associated with ∼4-fold increased risk of aggressive (high-grade) BC and ∼3 years shorter latency from BBD to BC diagnosis, independently of BBD histology. Across BC cohorts, stromal disruption in H&E was associated with aggressive (mostly high-grade) tumor phenotypes and with markedly poor prognosis among ER-positive patients, irrespective of histology. The immunobiology of stromal disruption reflected heightened innate (CD68+), adaptive (CD3+CD4+, CD3+CD8+), immunoregulatory (CD3+CD4+FOXP3+), immune escape (PD1+PDL1+), endothelial (CD31+), and myofibroblast (α-SMA+) marker expression. CONCLUSION: Our findings highlight the active stromal role in aggressive BC etiology and outcomes, opening possibilities for readily identifying high-risk women across the BC continuum that may benefit from stroma-centric preventative or therapeutic strategies.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".