EGFR expression using immunohistochemistry (IHC) testing as a tool for selecting patients (pts) for treatment with cetuximab
Bibliographic record
Abstract
13000 Background: EGFR expression, as determined by IHC, is currently used to select patients for cetuximab therapy. Based on prior studies in colorectal cancer patients, approximately 60 to 75% of patients express EGFR. There is increasing evidence that EGFR expression is not predictive of response to cetuximab therapy, and does not properly select patients who might benefit from such therapy (Chung et al 2005, Saltz et al 2005, Scartozi et al 2004, NCCN 2005). Such selection limits access to a considerable number of patients who might otherwise benefit. Methods: A clinical trial of cetuximab (Erbitux®) monotherapy is being conducted in 60 EGFR-undetectable patients with metastatic colorectal cancer at 14 sites in the US and Canada to explore the relationship between EGFR expression and cetuximab activity. Results: As of January 5, 2006, 112 patients have been screened. Of these patients, 33 (29%) were EGFR-undetectable and continued screening for study enrollment; 52 (46%) tested positive for EGFR expression; and 2 (2%) did not have enough tissue to evaluate EGFR status and were not enrolled onto the trial. The remaining 25 pts (22%), were initially found to be EGFR-undetectable by IHC testing at local labs, but were subsequently identified as EGFR-positive after reevaluation at a highly experienced, centralized laboratory. Conclusion: The majority of patients tested for EGFR expression are tested using the EGFR pharmDx™ IHC assay. Results of the IHC-based assay for EGFR expression are highly dependent upon sample preparation and the methods used in conducting the assay. Variability in methods among labs may result in poor identification of pts expressing EGFR. This finding, together with the growing evidence that EGFR expression is not predictive of response to cetuximab therapy, indicate that the current routine practice of tumor IHC EGFR testing for the purpose of selecting cetuximab therapy may be inappropriate and pts who could potentially benefit from cetuximab therapy are being excluded from a treatment option. [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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".