<i>FCGR3A</i>/<i>2A</i> polymorphisms and diffuse large B‐cell lymphoma outcome treated with immunochemotherapy: a meta‐analysis on 1134 patients from two prospective cohorts
Bibliographic record
Abstract
Single nucleotide polymorphisms (SNPs) in FCγ-receptor genes FCGR3A (rs396991) and FCGR2A (rs1801274) influence the affinity of the Fc portion of anti-CD20 immunoglobulin G1 monoclonal antibody. Their roles in diffuse large B-cell lymphoma (DLBCL) treated with rituximab in combination with anthracycline-based chemotherapy remain controversial. To address this question, we genotyped FCGR2A and FCGR3A SNPs in two prospective DLBCL cohorts from Lymphoma Study Association trials (N = 554) and Iowa/Mayo Specialized Program Of Research Excellence (N = 580). Correlations with treatment response and hematological toxicity were assessed in Lymphoma Study Association. Correlation with event-free survival (EFS) and overall survival (OS) was performed in both cohorts, followed by a meta-analysis to increase power. Our study shows the absence of correlation between these SNPs and treatment response. Grades 3 and 4 febrile neutropenia during treatment was more frequently observed in FCGR3A VV (39%) than VF (29%) and FF (32%) carriers (p = 0.04). Our analysis for EFS and OS shows that FCGR3A was not associated with outcome. In a meta-analysis using an ordinal model, FCGR2A (per R allele) was associated with a better EFS (hazard ratio = 0.87; 95%CI, 0.76-0.99; p = 0.04) and OS (hazard ratio = 0.86; 95%CI, 0.73-1.00; p = 0.05) which was not altered after adjustment for the International Prognostic Index. Overall, our data demonstrate that patients with DLBCL with the low-affinity FCγRIIA RR had an unexpectedly better outcome than FCγRIIA H carriers. Whether rituximab efficacy is improved in FCγRIIA RR patients due a clearance reduction or other functions of FCγRIIA in DLBCL should be investigated (clinicaltrials.gov identifiers: NCT00135499, NTC00135499 NCT00140595, NCT00144807, NCT00144755, NCT01087424, and NCT00301821). Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".