Defining microenvironment-induced transcription profiles in breast cancer liver metastases
Bibliographic record
Abstract
Breast cancer is the most common type of cancer diagnosed in Canadian women, with metastatic spread contributing to the majority of cancer-related deaths. The liver is the third most frequent site of breast cancer metastasis, but not much is known about the hepatic microenvironment's role in regulating the growth and survival of metastatic cells in the liver. In order to elucidate these interactions, we used laser capture microdissection and microarray analysis to compare gene expression patterns of liver metastases and primary tumors. We employed liver-aggressive 4T1 breast cancer cells derived from an in vivo selection process to generate mammary tumors and liver metastases in female Balb/c mice. Mice with liver metastases were kept for three different time periods post-injection to assess the changes in gene expression during metastatic development. Laser capture microdissection was used to isolate cores and margins of tumors and liver metastases, as well as tumor-adjacent and –distal normal liver. Transcription profiling revealed significant gene expression changes within breast cancer cells growing in the fat pad and the liver microenvironments. We identified a set of immune-related genes overexpressed in the liver metastases that may represent putative myeloid/granulocytic cell markers. Lcn2 and S100a8/S100a9 were found to be exclusively expressed in the immune compartment of liver metastases, particularly within smaller lesions. Since concurrent studies in our laboratory have revealed a similar recruitment of Gr1+/NE+ cells around breast cancer liver metastases, Lcn2 and S100a8/a9 represent interesting avenues with which to investigate the role that these infiltrating cell types play in supporting breast cancer liver metastasis.
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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".