Cooperative signaling through the signal transducer and activator of transcription 3 and nuclear factor-κB pathways in subtypes of diffuse large B-cell lymphoma
Bibliographic record
Abstract
The activated B cell-like (ABC) subgroup of diffuse large B-cell lymphoma (DLBCL) is characterized by constitutive activation of the nuclear factor-kappaB (NF-kappaB) pathway. In this study, we showed that the NF-kappaB pathway induced the expression of the cytokines interleukin (IL)-6 and IL-10 in ABC DLBCL cell lines, which also have high levels of total and phosphorylated signal transducer and activator of transcription (STAT) 3 protein, suggesting autocrine signaling. Using RNA interference for STAT3, we defined a gene expression signature of IL-6 and IL-10 signaling through STAT3. Based on this signature, we constructed a molecular predictor of STAT3 signaling that defined a subset of ABC DLBCL tumors with high expression of STAT3, IL-6, and/or IL-10 and their downstream targets. Although the STAT3-high and STAT3-low subsets had equivalent expression of genes that distinguish ABC DLBCL from germinal center B cell-like DLBCL, STAT3-high ABC DLBCLs had higher expression of signatures that reflected NF-kappaB activity, proliferation, and glycolysis. A small-molecule inhibitor of Janus kinase signaling, which blocked STAT3 signature expression, was toxic only for ABC DLBCL lines and synergized with an inhibitor of NF-kappaB signaling. These findings suggest that the biological interplay between the STAT3 and NF-kappaB pathways may be exploited for the treatments of a subset of ABC DLBCLs.
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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) | 0.000 | 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 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".