Fc Gamma Receptor 3A and 2A Polymorphisms Do Not Predict Response to Rituximab in Follicular Lymphoma
Bibliographic record
Abstract
PURPOSE: Preclinical studies suggest that SNPs in the Fc gamma receptor (FCGR) genes influence response to rituximab, but the clinical relevance of this is uncertain. EXPERIMENTAL DESIGN: We prospectively obtained specimens for genotyping in the rituximab extended schedule or re-treatment trial (RESORT) study, in which 408 previously untreated, low tumor burden follicular lymphoma (FL) patients were treated with single agent rituximab. Patients received rituximab in 4 weekly doses and responders were randomized to rituximab re-treatment (RR) upon progression versus maintenance rituximab (MR). SNP genotyping was performed in 321 consenting patients. RESULTS: Response rates to initial therapy and response duration were correlated with the FCGR3A SNP at position 158 (rs396991) and the FCGR2A SNP at position 131 (rs1801274). The response rate to initial rituximab was 71%. No FCGR genotypes or grouping of genotypes were predictive of initial response. A total of 289 patients were randomized to RR (n = 143) or to MR (n = 146). With a median follow-up of 5.5 years, the 3-year response duration in the RR arm and the MR arm was 50% and 78%, respectively. Genotyping was available in 235 of 289 randomized patients. In patients receiving RR (n = 115) or MR (n = 120), response duration was not associated with any FCGR genotypes or genotype combinations. CONCLUSIONS: Based on this analysis of treatment-naïve, low tumor burden FL, we conclude that the FCGR3A and FCGR2A SNPs do not confer differential responsiveness to rituximab.
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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 | 0.004 |
| 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 | 0.001 | 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".