Treatment effects (TEs) of EGFR monoclonal antibodies (mAbs) in metastatic colorectal cancer (mCRC) patients (pts) with KRAS, NRAS, and BRAF mutation (MT) status: Individual patient data (IPD) meta-analysis of randomized trials from the ARCAD database.
Bibliographic record
Abstract
4090 Background: EGFR mAbs have become incorporated into clinical practice for the management of mCRC over the last decade. KRAS and NRAS mutations are used as predictive biomarkers and BRAF V600E mutations are associated with an adverse prognosis. The observed TE within biomarker subpopulations has varied between studies. Methods: IPD from randomized trials with head-to-head comparison between EGFR mAb versus no EGFR mAb (chemotherapy alone or BSC) in mCRC, across all lines of therapy (first, second and later), were pooled. Biomarker subpopulations are defined in the table. Overall survival (OS) and progression-free survival (PFS) were compared between groups by Cox model, stratified by studies and adjusted by age, gender, and performance status. TEs were estimated by adjusted hazard ratio (HRadj) and 95% confidence interval (CI). Within each biomarker subgroup, EGFR mAb efficacy was explored according to multiple exploratory factors, including line of therapy, type of backbone chemo, gender, sidedness and site of metastasis. Interaction tests were performed. P-values < 0.01 were considered statistically significant to account for multiple comparisons. Results: 5729 pts from 8 studies with data available for ≥ 1 biomarker were analysed. PFS benefits (median 9.2 mos in EGFR mAbs, 8.0 mos in no EGFR mAbs) were confirmed in triple-WT pts, but not for OS (refer to table). No OS/PFS benefits were observed for pts with any of the MT tumors. Exploratory analyses showed a potential detrimental TE of EGFR mAbs in KRAS MT mCRC with liver metastasis (OS: HRadj 1.22, p = .003, pinteraction .0056; PFS: HRadj 1.24, p = .0009, pinteraction .0008). These results were confirmed within the subgroup of pts with all 3 biomarkers available. Conclusions: This is the largest IPD analysis to explore the predictive value of RAS/BRAF biomarkers in mCRC. Our findings demonstrate that there is no evidence of efficacy of EGFR mAbs in KRAS, BRAF and/or NRAS MT mCRC. EGFR mAbs might have a detrimental effect in KRAS MT mCRC with liver metastases. [Table: see text]
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.027 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".