Abstract PO-40: FOXO1 mutations mimic positive selection signals to promote germinal center B-cell expansion and lymphomagenesis
Bibliographic record
Abstract
Abstract The transcription factor FOXO1 directs germinal center (GC) polarity and supports affinity maturation by modulating immune activation programs in GC B cells. Recurrent somatic mutations targeting FOXO1 in GC-derived B-cell non-Hodgkin lymphomas (B-NHL) are thought to disrupt its negative regulation by PI3K and function as gain-of-function, constitutively active alleles. Herein, we demonstrate that B-NHL FOXO1 mutants are instead hypomorphic alleles encoding proteins with altered transcriptional activities. Analysis of Foxo1 mutant mouse models, engineered cell lines, and primary samples shows that B-NHL FOXO1 mutations induce simultaneous hyperactivation of Stress Activated Protein Kinase -SAPK/JNK and Phosphoinositide 3-kinase -PI3K/AKT signaling pathways and gene expression programs characteristic of GC B cells undergoing positive selection. These alterations confer mutant B cells with a competitive advantage in response to key immune signals, leading to abnormal amplification of GC responses. Moreover, we find that FOXO1 mutant-driven transcriptional programs are prevalent in human B-NHL and predict poor clinical outcomes. These results imply the frequent co-option of GC positive selection programs in the pathogenesis of GC-derived lymphomas. Citation Format: Mark P. Roberto, Gabriele Varano, Rosa Viñas-Castells, Antony B. Holmes, Rahul Kumar, Pedro Farinha, David W. Scott, David Dominguez-Sola. FOXO1 mutations mimic positive selection signals to promote germinal center B-cell expansion and lymphomagenesis [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-40.
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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) | 0.005 | 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".