Peripheral lymph node stromal cells can promote growth and tumorigenicity of breast carcinoma cells through the release of IGF‐I and EGF
Bibliographic record
Abstract
The regional lymph nodes draining primary breast carcinomas are generally the first site to be invaded by disseminating tumor cells. The extent of lymph node involvement remains the most reliable indicator for staging and prognosis of breast cancer. We have investigated host-tumor interactions between breast carcinoma cells and the lymph node stroma, which may control the outcome of lymph node infiltration. In a previous study, we identified integrin-mediated cell adhesion as a correlate of the metastatic potential of human and rat carcinoma cells. Our present objective was to determine whether lymphatic stromal cells can affect cancer cell growth through the elaboration of growth-modulating factors. Two lymphatic stromal cell lines, ST-A4 and ST-B12, were established from normal rat lymph node stromal cell cultures. SFM conditioned by these cells increased the proliferation of human (Hs578T and MCF-7) and rat (TMT-081) breast carcinoma cells by up to 7-fold and augmented their ability to form colonies in semisolid agar by up to 41-fold. This effect was specific as normal, diploid human breast epithelial cells (Hs578Bst), a nontumorigenic, immortalized human breast epithelial cell line (MCF-10A) and a nonmetastatic rat mammary carcinoma cell line (MT-W9B) had either no or reduced responses. RT-PCR analysis revealed that both lymph node stromal cell lines expressed mRNA transcripts for multiple growth factors, including IGF-I, EGF, HGF and PDGF-alpha, and produced detectable levels of IGF-I, EGF and PDGF-alpha, as assessed by Western blotting. Antibody-mediated depletion assays identified IGF-I and EGF as the major mitogenic factors in the CM. The identification of these cells raises the possibility that the lymph node microenvironment may contribute actively to the process of cancer cell dissemination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".