APOE+ Tumor-Associated Macrophages and CD4-DOCK4 T Cells Reveal Distinct Microenvironmental Features in HER2-Low and HER2-0 Hormone Receptor-Positive Breast Cancer
Bibliographic record
Abstract
Novel anti-HER2 antibody-drug conjugates (ADCs), such as trastuzumab deruxtecan (T-DXd), have shown efficacy in tumors with varying HER2 expression, including HER2-low and even tumors with minimal HER2 presence. This has sparked interest in the biology underlying the HER2 expression spectrum. Using molecular and multiplexed imaging, we revealed distinct immune and stromal features in treatment-naive, hormone receptor-positive (HR+) HER2-low versus HER2-0 tumors. HER2-0 tumors exhibit inflammatory and tissue remodeling gene signatures, with enrichment of APOE⁺ tumor-associated macrophages (TAMs) and DOCK4⁺ CD4 T cells. In contrast, HER2-low tumors are more immunosuppressed, with elevated cell cycle, metabolic, and estrogen signaling pathways, suggesting increased proliferative activity. These findings underscore key biological differences between HR+ HER2-low and HER2-0 breast cancers, and may inform more tailored therapeutic strategies. Statement of significance: This study revealed the distinct biological profiles of HR+ HER2-low and HER2-0 breast tumors. HER2-0 tumors exhibit inflammatory and tissue remodeling signatures, whereas HER2-low tumors have elevated cell cycle, metabolic, and estrogen signaling. These insights may help refine therapeutic approaches to improve outcomes for breast cancer patients.
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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".