Flow cytometric analysis of circulating endothelial cells and endothelial progenitors for clinical purposes in oncology: A critical evaluation
Bibliographic record
Abstract
Malignant tumors are characterized by uncontrolled cell growth and metastatic spread, with a pivotal importance of the phenomenon of angiogenesis. For this reason, research has focused on the development of agents targeting the vascular component of the tumor microenvironment and regulating the angiogenic switch. As a result, the therapeutic inhibition of angiogenesis has become an important component of anticancer treatment, however, its utility is partly limited by the lack of an established methodology to assess its efficacy in vivo. Circulating endothelial cells (CECs), which are rare in healthy subjects and significantly increased in different tumor types, represent a promising tool for monitoring the tumor clinical outcome and the treatment response. A cell population circulating into the blood also able to form endothelial colonies in vitro and to promote vasculogenesis is represented by endothelial progenitor cells (EPCs). The number of both of these cell types is extremely low and they cannot be identified using a single marker, therefore, in absence of a definite consensus on their phenotype, require discrimination using combinations of antigens. Multiparameter flow cytometry (FCM) is ideal for rapid processing of high numbers of cells per second and is commonly utilized to quantify CECs and EPCs, however, remains technically challenging since there is as yet no standardized protocol for the identification and enumeration of these rare events. Methodology in studies on CECs and/or EPCs as clinical biomarkers in oncology is heterogeneous and data have been obtained from different studies leading to conflicting conclusions. The present review presented a critical review of the issues that limit the comparability of results of the most significant studies employing FCM for CEC and/or EPC detection in patients with cancer.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".