Lymphatic Metastasis of Breast Cancer Cells Is Associated with Differential Gene Expression Profiles that Predict Cancer Stem Cell-like Properties and the Ability to Survive, Establish and Grow in a Foreign Environment
Bibliographic record
Abstract
Although lymphatic dissemination is a major route for breast cancer metastasis, there has been little work to determine what factors control the ability of tumor cells to survive, establish and show progressive growth in a lymph node environment. This information is of particular relevance now, in the era of sentinel lymph node biopsy, where smaller intranodal tumor deposits are being detected earlier in the course of disease, the clinical relevance of which is uncertain. In this study, we compared differentially expressed genes in cell lines of high (468LN) vs. low (468GFP) lymphatic metastatic ability, and related these to clinical literature on genes associated with lymphatic metastatic ability and prognosis, to identify genes of potential clinical relevance. This approach revealed differential expression of a set of genes associated with 'cancer stem cell-like' properties, as well as networks of genes potentially associated with survival and autonomous growth. We explored these differences functionally and found that 468LN cells have a higher proportion of cells with a cancer stem cell-like (CD44+/CD24-) phenotype, have a higher clonogenic potential and a greater ability to survive, establish and grow in a foreign (lymph node and 3D Matrigel) microenvironment, relative to 468GFP cells. Differentially expressed genes which reflect these functions provide candidates for investigation as potential targets for therapy directed against early lymphatic 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 | 0.000 | 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".