Single-nucleotide Fcγ receptor polymorphisms do not impact obinutuzumab/rituximab outcome in patients with lymphoma
Bibliographic record
Abstract
Single-nucleotide polymorphisms (SNPs) have been shown to influence Fcγ receptor (FcγR) affinity and activity, but their effect on treatment response is unclear. We assessed their importance in the efficacy of obinutuzumab or rituximab combined with chemotherapy in untreated advanced follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL) in the GALLIUM (www.clinicaltrials.gov #NCT01332968) and GOYA (#NCT01287741) trials, respectively. Genomic DNA was extracted from patients enrolled in GALLIUM (n = 1202) and GOYA (n = 1418). Key germline SNPs, FCGR2A R131H (rs1801274), FCGR3A F158V (rs396991), and FCGR2B I232T (rs1050501), were genotyped and assessed for their impact on investigator-assessed progression-free survival (PFS). In both cohorts there was no prognostic effect of FCGR2A or FCGR3A. In FL, FCGR2B was associated with favorable PFS in univariate and multivariate analyses comparing I232T with I232I, with a more modest association for rituximab-treated (univariate: hazard ratio [HR], 0.78; 95% confidence interval [CI], 0.54-1.14; P = .21) vs obinutuzumab-treated patients (HR, 0.56; 95% CI, 0.34-0.91; P = .02). Comparing T232T with I232I, an association was found for obinutuzumab (univariate: HR, 2.76; 95% CI, 1.02-7.5; P = .0459). Neither observation retained significance after multiple-test adjustment. FCGR2B was associated with poorer PFS in multivariate analyses comparing T232T with I232I in rituximab- but not obinutuzumab-treated patients with DLBCL (HR, 4.40; 95% CI, 1.71-11.32; P = .002; multiple-test-adjusted P = .03); however, this genotype was rare (n = 13). This study shows that FcγR genotype is not associated with response to rituximab/obinutuzumab plus chemotherapy in treatment-naive patients with advanced FL or DLBCL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".