Enhanced IFN Sensing by Aggressive Chronic Lymphocytic Leukemia Cells
Bibliographic record
Abstract
Abstract Type I IFN is made by cells in response to stress. Cancer cells exist in a state of stress, but their IFN response is complex and not completely understood. This study investigated the role of autocrine IFN in human chronic lymphocytic leukemia (CLL) cells. CLL cells were found to make low amounts of IFN via TANK-binding kinase 1 pathways, but p-STAT1 and -STAT2 proteins along with IFN-stimulated genes that reflect IFN activation were variably downregulated in cultured CLL cells by the neutralizing IFNAR1 Ab anifrolumab. Patients with CLL were segregated into two groups based on the response of their leukemia cells to anifrolumab. Samples associated with more aggressive clinical behavior indicated by unmutated IGHV genes along with high CD38 and p-Bruton’s tyrosine kinase expression exhibited responses to low amounts of IFN that were blocked by anifrolumab. Samples with more indolent behavior were unaffected by anifrolumab. Hypersensitivity to IFN was associated with higher expression of IFNAR1, MX1, STAT1, and STAT2 proteins and lower activity of negative regulatory tyrosine phosphatases. Autocrine IFN protected responsive CLL cells from stressful tissue culture environments and therapeutic drugs such as ibrutinib and venetoclax in vitro, in part by upregulating Mcl-1 expression. These findings suggest hypersensitivity to IFN may promote aggressive clinical behavior. Specific blockade of IFN signaling may improve outcomes for patients with CLL with higher-risk disease.
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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".