Impact of primary tumor sidedness on erlotinib efficacy in patients with metastatic colorectal cancer treated with bevacizumab maintenance: Results from the DREAM phase III trial.
Bibliographic record
Abstract
737 Background: Primary tumor sidedness (PTS) could be a predictive maker for treatment efficacy of EGFR inhibitors monoclonal antibodies in patients with wild-type (WT) RAS metastatic colorectal cancer (MCRC), cetuximab having limited efficacy in patients with WT-RAS right-sided tumors. DREAM study demonstrated that adding erlotinib, an oral EGFR tyrosine kinase inhibitor (TKI) to bevacizumab during maintenance therapy improved clinical outcomes (RR, PFS, OS) in patients with MCRC, whatever KRAS status. The aim of this post-hoc analysis is to evaluate the clinical outcomes according to KRAS mutational status and PTS when adding erlotinib to bevacizumab maintenance therapy. Methods: PTS was retrospectively collected in patients from the DREAM phase III trial treated with bevacizumab with or without erlotinib as maintenance therapy for MCRC who have been controlled by induction therapy. The limit for the definition of PTS was splenic flexure, and rectal tumors were considered as left-sided tumors. The primary endpoint was overall survival (OS). Results: Among 452 patients who received maintenance therapy, PTS ascertainment was 84.7% (n = 383) with 265 (71.0%) patients having left-sided primary tumor and 108 (28.9%) having right-sided primary tumors (3 patients had both and tumor location was unknown in 7 patients). Median OS and treatment effect are presented in table 1. Conclusions: The greatest OS benefit of adding erlotinib to bevacizumab maintenance therapy was observed in patients with WT-KRAS and right-sided MCRC, suggesting a clinical impact of the different mechanism of action between EGFR TKI and monoclonal antibodies. Clinical trial information: NCT00265824. [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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".