Effect of FCGR2A and FCGR3A variants on CLL outcome
Bibliographic record
Abstract
Polymorphisms of activating Fc-γ receptors (FCGRs) on natural killer cells and macrophages result in variable affinity for immunoglobulin G1 monoclonal antibodies and subsequently modulate antibody-dependent cellular cytotoxicity (ADCC) activity. Whether single-nucleotide polymorphisms of FCGRs correlate with survival of chronic lymphocytic leukemia (CLL) patients treated with a monoclonal antibody containing regimen is unclear. We assessed the FCGR3A and FCGR2A genotype of patients enrolled in the REACH trial, where patients received fludarabine and cyclophosphamide (FC) or rituximab plus FC (R-FC). FCGR3A and FCGR2A polymorphisms did not demonstrate prognostic significance in the FC arm (P = .42 and P = .64, respectively) or R-FC arm (P = .41 and P = .88, respectively) with respect to progression free survival. Patients with intermediate affinity genotypes (FV and HR) benefited significantly from addition of rituximab (hazard ratio = 0.55 [0.37-0.8 CI]; P = .0017 and hazard ratio = 0.63 [0.44-0.9 CI]; P = .011, respectively). Similar benefit was suggested for patients with high- affinity VV and HH (hazard ratio = 0.86 [0.4-1.84 CI]; P = .7 and hazard ratio = 0.7 [0.41-1.18 CI]; P = .18, respectively) and low-affinity FF and RR (hazard ratio = 0.85 [0.56-1.29 CI]; P = .44 and hazard ratio = 0.82 [0.47-1.42 CI]; P = .48, respectively). Overall, our results suggest that FCGR2A and FCGR3A polymorphisms do not significantly influence the outcomes of relapsed or refractory CLL patients treated with FC or the monoclonal antibody regimen R-FC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".