CD45+CD326+ Cells are Predictive of Poor Prognosis in Non–Small Cell Lung Cancer Patients
Bibliographic record
Abstract
Abstract Purpose: The epithelial-to-mesenchymal transition, the major process by which some cancer cells convert from an epithelial phenotype to a mesenchymal one, has been suggested to drive chemo-resistance and/or metastasis in patients with cancer. However, only a few studies have demonstrated the presence of CD45/CD326 doubly-positive cells (CD45/CD326 DPC) in cancer. We deployed a combination of cell surface markers to elucidate the phenotypic heterogeneity in non–small cell lung cancer (NSCLC) cells and identified a new subpopulation that is doubly-positive for epithelial and non–epithelial cell-surface markers in both NSCLC cells and patients' malignant pleural effusions. Experimental Design: We procured a total of 39 patients' samples, solid fresh lung cancer tissues from 21 patients and malignant pleural effusion samples from 18 others, and used FACS and fluorescence microscopy to check their surface markers. We also examined the EGFR mutations in patients with known acquired EGFR mutations. Results: Our data revealed that 0.4% to 17.9% of the solid tumor tissue cells and a higher percentage of malignant pleural effusion cells harbored CD45/CD326 DPC expressing both epithelial and nonepithelial surface markers. We selected 3 EGFR mutation patients and genetically confirmed that the newly identified cell population really originated from cancer cells. We also found that higher proportions of CD45/CD326 DPC are significantly associated with poor prognosis. Conclusions: In conclusion, varying percentages of CD45/CD326 DPC exist in both solid cancer tissue and malignant pleural effusion in patients with NSCLC. This CD45/CD326 doubly-positive subpopulation can be an important key to clinical management of patients with NSCLC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".