HER2Δ16 Engages ENPP1 to Promote an Immune-Cold Microenvironment in Breast Cancer
Bibliographic record
Abstract
The tumor-immune microenvironment (TIME) is a critical determinant of therapeutic response. However, the mechanisms regulating its modulation are not fully understood. HER2Δ16, an oncogenic splice variant of the HER2, has been implicated in breast cancer and other tumor types as a driver of tumorigenesis and metastasis. Nevertheless, the underlying mechanisms of HER2Δ16-mediated oncogenicity remain poorly understood. Here, we show that HER2∆16 expression is not exclusive to the clinically HER2+ subtype and associates with a poor clinical outcome in breast cancer. To understand how HER2 variants modulated the tumor microenvironment, we generated transgenic mouse models expressing either proto-oncogenic HER2 or HER2Δ16 in the mammary epithelium. We found that HER2∆16 tumors were immune cold, characterized by low immune infiltrate and an altered cytokine profile. Using an epithelial cell surface proteomic approach, we identified ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) as a functional regulator of the immune cold microenvironment. We generated a knock-in model of HER2Δ16 under the endogenous promoter to understand the role of Enpp1 in aggressive HER2+ breast cancer. Knockdown of Enpp1 in HER2Δ16-derived tumor cells resulted in decreased tumor growth, which correlated with increased T-cell infiltration. These findings suggest that HER2Δ16-dependent Enpp1 activation associates with aggressive HER2+ breast cancer through its immune modulatory function. Our study provides a better understanding of the mechanisms underlying HER2Δ16-mediated oncogenicity and highlights ENPP1 as a potential therapeutic target in aggressive HER2+ 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) | 0.000 | 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".