Abstract A60: Transcriptional profile of CD56negCD16pos Natural Killer cells within endemic Burkitt lymphoma
Bibliographic record
Abstract
Abstract Endemic Burkitt lymphoma (eBL), the most common pediatric cancer in equatorial Africa, has long been associated with two etiologic agents: Epstein-Barr virus (EBV) and Plasmodium falciparum malaria. On the other hand, Natural Killer (NK) cells are essential in the clearance of infected cells as well as play a critical role in tumor immunosurveillance. Despite chronic viral infections as HIV, HCV, which have been found to promote the expansion of a CD56 (negative) NK cell subset with impaired effector function, previous cancer studies have been limited to investigations of CD56dimCD16pos NK cells. We previously published that eBL children had significantly more CD56negCD16pos NK cells compared to age-matched healthy children from a low and high malaria transmission area. Here, we characterized this CD56negCD16pos NK subset using histology staining, multicolor flow cytometry, and RNA-sequencing. We found CD56negCD16pos NK cells to be morphologically similar to CD56dimCD16pos NK cells but had higher expression of FCGR3B (CD16b) (p=0.000008) and MPEG1 (Perforin 2) (p=0.0000007), and lower expression of KLRF1 (NKp80), IL18RAP and IL18R1 (IL18 receptor) (p=0.000003, p=0.000003 and p=0.00005, respectively). Phenotypically, CD56negCD16pos NK cells share characteristics of adaptive NK cells; however, they differ from “memory-like” NK cells, with consistent expression of NKG2C/CD57, lower expression of NKp46 and CD160, and higher expression of KIR3DL1 compared to CD56dimCD16pos NK cells. Finally, higher plasma levels of IL-12p70 and IL-2 (known to help NK cell activation and proliferation) and IL-6 and MIP1a were found in healthy compared to eBL children. Together, these findings suggest that the expansion of CD56negCD16pos NK cells could help explain diminished tumor immunosurveillance and might be a mechanism by which EBV-infected cells evades NK-cell mediated immunity. Citation Format: Catherine S. Forconi, Cliff Oduor, John M. Ong’echa, Jeff Bailey, Ann M. Moormann. Transcriptional profile of CD56negCD16pos Natural Killer cells within endemic Burkitt lymphoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A60.
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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 | 0.000 | 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) | 0.003 | 0.001 |
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".