High-throughput flow cytometry screening of human hepatocellular carcinoma reveals CD146 to be a novel marker of tumor-initiating cells
Why this work is in the frame
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Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) remains a common and lethal cancer. Cancer stem cells, or tumor-initiating cells (TICs), are thought to contribute to the pathogenesis of HCC, but remain to be fully characterized. Unbiased screens of primary human HCC cells for the identification of novel HCC TIC markers have not been reported. We conducted high-throughput flow cytometry (HT-FC) profiling to characterize the expression of 375 CD antigens on tumor cells from 10 different human HCC samples. We selected 91 of these for further analysis based on HT-FC data that showed consistent expression in discrete, rare, sortable populations of HCC cells. Nine of these CD antigens demonstrated significantly increased expression in the EpCAM+ stem/progenitor fraction of a human HCC cell line and were further evaluated in primary human HCC tissues from 30 different patients. Of the nine tested, only CD146 demonstrated significantly increased expression in HCC tumor tissue as compared with matched adjacent non-tumor liver tissue. CD146+CD31−CD45− cells purified from HCC tumors and cell lines demonstrated a unique phenotype distinct from mesenchymal stem cells. As compared with other tumor cell fractions, CD146+CD31−CD45− cells showed significantly increased colony-forming capacity in vitro, consistent with TICs. This study demonstrates that HT-FC screening can be successfully applied to primary human HCC and reveals CD146 to be a novel TIC marker in this disease.
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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) | 0.001 | 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 it