Endothelial cells do not arise from tumor-initiating cells in human hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Conventional models of carcinogenesis suggest that tumors recruit blood vessel formation from normal host tissues. This concept has recently been challenged by prominent studies of glioblastoma, which suggest that intratumoral endothelial cells (ECs) may arise from cancer stem cells/tumor-initiating cells (TICs). Hepatocellular carcinoma (HCC) is a common, highly vascularized tumor with few effective therapies, against which anti-angiogenic strategies are being actively explored. TICs are felt to play a role in HCC pathobiology, but their contributions to tumor vasculature have not been studied. METHODS: We examined human HCCs in settings that selected for tumor formation from functionally defined TICs, and in which the origin of intratumoral ECs from TICs as opposed to host tissues could be clearly distinguished. We generated HCC nodules in the livers of immunodeficient mice by intrasplenic injection of HCC cells from cell lines and patient specimens and studied the tumor ECs by immunohistochemistry for mouse and human markers. We then used immunohistochemistry for EC markers in combination with fluorescence in situ hybridization (FISH) for X and Y chromosomes to study the endothelium of recurrent HCC specimens resected from sex-mismatched liver allografts of patients who had undergone liver transplantation for HCC. RESULTS: We observed that all ECs in intrahepatic human HCC xenografts expressed mouse rather than human CD31. FISH analysis of recurrent HCCs resected from patients with sex-mismatched liver allografts revealed that all CD31+ and CD34+ intratumoral ECs originated from the donor allograft rather than the tumor. CONCLUSIONS: These observations suggest that the vasculature of human HCC arises from normal host tissues rather than from TICs, supporting ongoing efforts to target angiogenesis in HCC as it is currently understood, and suggesting that the contribution of TICs to the vasculature of other cancers is disease-specific.
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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) | 0.001 | 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 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".