Fc‐gamma receptor polymorphisms, cetuximab therapy, and overall survival in the CCTG CO.20 trial of metastatic colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Two germ line Fc-γ receptor (FCGR) polymorphisms, rs1801274 [FCGR2A; His(H)131Arg(R)] and rs396991 [FCGR3A; Phe(F)158Val(V)], produce altered proteins through amino acid substitutions. We previously reported that the FCGR2A H/H genotype was associated with longer overall survival (OS) in cetuximab-treated chemotherapy-refractory patients with metastatic colorectal cancer. Here, we aimed to replicate and extend this finding in the Canadian Clinical Trials Group CO.20 trial. METHODS: After germ line DNA genotyping, polymorphic relationships with survival were assessed using log-rank tests and hazard ratios (HR) from Cox proportional hazard models, adjusting for known prognostic factors. The dominant genetic inheritance model was used for the main analysis. RESULTS: Of 592 wild-type KRAS patients treated with cetuximab, those with the FCGR2A H/H genotype (n = 165, 28%) had improved OS (HR: 0.66, P < 0.001; median absolute benefit, 1.3 months) compared to those with R/- genotype (n = 427, 72%). Patients with H/R had intermediate results under a codominant genetic inheritance model (HR: 0.72, P = 0.003). No significant associations were found between FCGR3A genotype and OS. In an exploratory analysis, patients with the combination of FCGR2A H/H + FCGR3A F/F genotype had significantly better OS (HR: 0.33, P = 0.003; median absolute benefit, 12.5 months) than patients with the combination of double-variant R/R + V/V genotype. Progression-free survival results were similar to OS. Toxicity rates were not associated with either polymorphism. CONCLUSIONS: The FCGR2A genotype was associated with efficacy but not with toxicity in wild-type KRAS, cetuximab-treated colorectal cancer patients. FCGR3A genotype may modulate the relationship between FCGR2A polymorphism and outcome. FCGR2A is a promising biomarker for clinical management for these patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".