Using a Xenograft Model of Human Breast Cancer Metastasis to Find Genes Associated with Clinically Aggressive Disease
Bibliographic record
Abstract
Metastasis is the primary cause of death from breast cancer. A xenograft model was used to identify genes potentially involved with metastasis, comparing expression in the poorly metastatic GI101A human breast cancer cell line and a highly metastatic variant, GILM2. cDNA microarray analyses of these isogenic variants were done using 16K Operon 70-mer oligonucleotide microarray slides. Differentially expressed genes were identified by ANOVA, and differences of > or =2.5-fold were found for 106 genes. Changes in protein or RNA expression were confirmed for 10 of 12 genes. Three markers, heat shock protein 70 (HSP-70), chemokine (C-X-C motif) ligand 1 (CXCL-1), and secreted leukocyte protease inhibitor (SLPI), were studied further with breast cancer tissue microarrays using a novel method of automated quantitative analysis. This uses cytokeratin to define pixels as breast cancer (tumor mask) within the tissue array spot and then measures intensity of marker expression using a cyanine 5-conjugated antibody within the mask. Scores were correlated with clinicopathologic variables. High HSP-70 expression and high nuclear CXCL-1 expression in primary tumors were both associated with decreased survival (P = 0.05 and 0.027, respectively). Expression of each marker was strongly associated with lymph node involvement (P = 0.0002, 0.008, 0.0012, and 0.012 for HSP-70, nuclear CXCL-1, cytoplasmic CXCL-1, and SLPI, respectively). Identification of genes associated with metastasis in experimental models may have clinical implications for the management of breast cancer, because some of these are associated with lymph node metastasis and survival and might be useful as prognostic markers or molecular targets for novel therapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".