Aberrations in Cytokine Signaling in Leukemia: Variations in Phosphorylation and O-GlcNAcylation
Bibliographic record
Abstract
Tumor-induced immunosuppression can occur by multiple mechanisms, each posing a significant obstacle to immunotherapy. Evidence presented in this dissertation suggests that aberrant cytokine signaling, as a result of altered metabolism of Chronic Lymphocytic Leukemia (CLL) cells, confers a selective advantage for tumor survival and growth. Cells from CLL patients with aggressive disease (as indicated by high-risk cytogenetics) were found to exhibit prolongation in Interferon (IFN)-induced STAT3 phosphorylation, and increased levels of reactive oxygen species (ROS) in these cells reflected these signaling processes. Changes in the relative balance of phospho-STAT3 and phospho-STAT1 levels, in response to combinations of IL-2 + Toll-like receptor (TLR)-7 agonist + phorbol esters, as well as IFN, were associated with the immunosuppressive and immunogenic states of CLL cells. In addition, immunosuppressive leukemic cells were found to express high levels of proteins with O-linked N-acetylglucosamine (O-GlcNAc) modifications, due to increased metabolic activity through the Hexosamine Biosynthetic Pathway (HBP), which caused impaired intracellular signaling responses and affected disease progression. A conclusion of the studies presented here is that the intrinsic immunosuppressive properties of leukemic cells may be overcome by agents such as Resveratrol that target metabolic pathways of these cells.
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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) | 0.002 | 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".