Development of metastatic and non‐metastatic tumor lines from a patient's prostate cancer specimen—identification of a small subpopulation with metastatic potential in the primary tumor
Bibliographic record
Abstract
BACKGROUND: Metastasis is the major cause of prostate cancer deaths. Tumor heterogeneity in both primary and metastatic prostate cancers is a major hurdle in elucidating the mechanisms of metastasis in this disease. To circumvent this obstacle and improve our understanding of prostate cancer metastasis, we developed multiple tumor tissue lines from one patient's primary prostate cancer specimen to examine differences in metastatic ability. METHODS: Pieces of tissue from different foci of a patient's primary prostate tumor were grafted into subrenal capsules of NOD-SCID mice and transplantable tumor sublines were established by serial passage. The metastatic ability of each subline was tested via orthotopic grafting into mice. Chromosomal alterations exclusively presented in a metastatic subline were examined by SKY and investigated for their presence in parental tissues by fluorescence in situ hybridization. RESULTS: Three transplantable sublines were developed, resembling the primary tumor histologically, exhibiting poor differentiation, and different growth rates. Importantly, the LTL-220N and LTL-221N sublines were non-metastatic, whereas the LTL-220M subline was spontaneously metastatic in vivo. SKY analysis showed limited but unique chromosomal alterations in each subline. Some chromosomal alterations, exclusively present in the metastatic LTL-220M subline, were also observed in a small portion of the parental cancer tissues. CONCLUSION: The results indicate that in primary prostate tumors metastatic potential can be confined to a minority of cancer cells. Subrenal capsule xenograft methodology can be used to dissect heterogeneous cancer cells in a patient's primary tumor and sublines derived from such cells provide valuable tools for investigating mechanisms underlying prostate cancer 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".