Identification of ephrinB2 positive vessels in non-Hodgkin lymphoma subtypes.
Bibliographic record
Abstract
e19542 Background: Current anti-angiogenic therapies for tumors target signaling pathways such as VEGF. However, the role of alternative vascular pathways in tumor biology in general, and lymphoma biology specifically, are less well understood. Recently, the Eph receptor tyrosine kinase signaling pathway has emerged as a novel target for therapeutic inhibition. The EphB family member EphB4, and its reverse signaling ligand ephrinB2, are indispensible for developmental angiogenesis. Inhibition of ephrinB2 signaling results in impaired tumor growth in pre-clinical models. We therefore sought to determine if ephrinB2 is expressed in the microenvironment of human lymphomas and thereby represents a novel therapeutic target. Methods: We evaluated by immunohistochemistry on paraffin tissue sections expression of ephrinB2, CD34 (endothelial cells) and CD163 (macrophages) in LN biopsies from 12 cases of FL and 11 cases of DLBCL retrieved from the archives of the Department of Pathology at the University of Chicago (2000-2011). Eleven cases of follicular hyperplasia or quiescent LNs were used as controls (Cs). Slides were digitally scanned and examined both semi-quantitatively and by digital image analysis. Stained cells were quantified both in internal areas and at lymphoma-fat interfaces. Results: CD34+ microvessel densities were comparable between DLBCL and C groups but showed a significantly higher trend of interface angiogenic sprouts in FLs. Interface recruited CD163+ cell numbers were significantly higher in DLBCLs than in both FLs and Cs (p<0.0001). EphrinB2-expressing blood vessels were significantly increased in FLs but not in DLBCL. Conclusions: FLs demonstrate an increase in ephrinB2+/CD34+ vessels at the lymphoma-fat interface. In contrast, DLBCLs demonstrate no increase in ephrinB2+ vessels versus controls. However, DLBCLs were notable for increased recruitment of CD163+ macrophages at the tumor/host interface. These data represent the first analysis of ephrinB2 expression in human lymphomas and demonstrate heterogeneity between locations within a lymphoma and between histologic subtypes. Analysis of ephrinB2 in human lymphomas reveals marked heterogeneity in lymphoma microenvironmental domains.
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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 | 0.002 | 0.001 |
| 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) | 0.003 | 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".