PARP14 is a novel target in STAT6 mutant follicular lymphoma
Bibliographic record
Abstract
Abstract The variable clinical course of follicular lymphoma (FL) is determined by the molecular heterogeneity of tumor cells and complex interactions within the tumor microenvironment (TME). IL-4 producing follicular helper T cells (T FH ) are critical components of the FL TME. Binding of IL-4 to IL-4R on FL cells activates JAK/STAT signaling. We identified STAT6 mutations ( STAT6 MUT ) in 13% of FL ( N = 33/258), all clustered within the DNA binding domain. Gene expression data and immunohistochemistry showed upregulation of IL-4/STAT6 target genes in STAT6 MUT FL, including CCL17 , CCL22 , and FCER2 (CD23). Functionally, STAT6 MUT was gain-of-function by serial replating phenotype in pre-B CFU assays. Expression of STAT6 MUT enhanced IL-4 induced FCER2 /CD23, CCL17 and CCL22 expression and was associated with nuclear accumulation of pSTAT6. RNA sequencing identified PARP14 -a transcriptional switch and co-activator of STAT6- among the top differentially upregulated genes in IL-4 stimulated STAT6 MUT lymphoma cells and in STAT6 MUT primary FL cells. Quantitative chromatin immunoprecipitation (qChIP) demonstrated binding of STAT6 MUT but not STAT6 WT to the PARP14 promotor. Reporter assays showed increased IL-4 induced transactivation activity of STAT6 MUT at the PARP14 promotor, suggesting a self-reinforcing regulatory circuit. Knock-down of PARP14 or PARP-inhibition abrogated the STAT6 MUT gain-of-function phenotype. Thus, our results identify PARP14 as a novel therapeutic target in STAT6 MUT FL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.013 | 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; both teacher heads agree on what is shown here.
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".