Bibliographic record
Abstract
English AbstractBreast cancer is a heterogenous set of diseases that arise in the mammary gland, affecting 1 in 8 Canadian women over their lifetimes. Breast cancer classification is based on expression of the hormone receptors and human epidermal growth factor receptor 2 (HER2). The HER2+ subtype represents an aggressive subtype with multiple targeted therapy options. One major advance in the study of breast cancer stems from using genetically engineered mouse models (GEMMs) that represent the various subtypes of human breast cancer. The HER2+ subtype GEMMs are probably the best characterized and much of the understanding of this disease subtype stems from these mouse models. Modelling HER2 allowed for the discovery of a spliced isoform, known here as HER2Δ16, which is highly oncogenic in multiple models of cancers including breast and lung. HER2Δ16 has been demonstrated to promote multiple hallmarks of cancer including upregulating of stem cell capacity, increased metastasis and promoting therapeutic resistance. However, the immune microenvironment of HER2Δ16 expressing tumors has not been studied. Our work focused on understanding the different immune microenvironments driven by either proto-oncogenic HER2 or HER2Δ16. Our interest in the tumor immune microenvironment is due to clinical and pre-clinical observations that the immune microenvironment is essential for therapeutic response or resistance. Our studies demonstrated that HER2Δ16 tumors are characteristically immune cold (low immune cell infiltration) as well as exhibited an anti-inflammatory cytokine profile. Using cell surface proteome profiling we identified that HER2Δ16 induced upregulation of multiple cell surface proteins that regulate immune suppression, cancer stem cell function and metastasis. Our third most upregulated cell surface protein was Ectonucleotide pyrophosphatase/phosphodiesterase 1 (Enpp1) which has been tightly correlated with immune suppression and cancer stem cell properties. Knockdown and inhibition of Enpp1 lead to increased expression of inflammatory cytokines and increased T-cell infiltration in the tumors.We further studied the role of Enpp1 in breast cancer stem cells using our polyoma virus middle T antigen (PyMT)-driven mouse model. We identified that Enpp1 expression is tightly correlated with expression of CD44 a prominent stem cell marker. Additionally, we identified that the highest expression of Enpp1 was in a putative mammary stem cell population. In vitro modeling demonstrated that Enpp1+ cancer cells exhibited an increased in tumor initiating capacity. Additionally, we observe in models of breast cancer recurrence following de-induction an enrichment for Enpp1 expression in recurrent mammary tumors, supporting its role as an immunosuppressive molecule as well as its enrichment on cancer stem cells. We finally demonstrate that Enpp1 is potentially a hypoxia regulated protein, with protein, but not mRNA expression restricted to areas of hypoxia
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".