<i><scp>R</scp>ead<scp>M</scp>ax</i>—a novel reading and scoring approach for <i><scp>EGFR</scp></i> gene copy number to predict therapeutic benefit of erlotinib treatment in <i><scp>EGFR</scp></i> wild‐type non‐small‐cell lung cancer
Bibliographic record
Abstract
EGFR mutation testing is now well established as a means of selecting the optimal first-line therapy for patients with advanced non-small-cell lung cancer (NSCLC). However, deciding on the correct treatment for EGFR wild-type NSCLC remains a challenge. EGFR fluorescence in-situ hybridization (FISH) testing of gene copy number has been a promising marker, but has provided mixed results despite attempts to standardize the reading and scoring process. The novel ReadMax reading and scoring system focuses on the most aberrant cells, to identify oncogene addiction, rather than taking a representative reading as in the Colorado method. The methodology was developed using historical samples from the TRUST and MERIT studies, followed by re-reading of the samples from the SATURN trial. Analysis of samples using the ReadMax methodology revealed that progression-free survival (PFS) and overall survival (OS) were improved in patients with ReadMax FISH-positive (RM FISH+) tumours, compared with those whose tumours were not RM FISH+: PFS hazard ratios (HRs) were 0.52 for RM FISH+ versus 0.93 for not RM FISH+; OS HRs were 0.69 and 0.92, respectively. For PFS, HR for RM FISH+ versus not RM FISH+ in the SATURN erlotinib group was 0.53 (p = 0.003). The PFS and OS results were also similar in the EGFR wild-type population (PFS HRs were 0.63 and 0.96; OS HRs were 0.61 and 0.84, respectively), although amplification of the EGFR gene in patients with EGFR wild-type disease was not found to be predictive of treatment outcomes, which was unexpected but not unprecedented. KRAS status was not found to affect outcomes. Further experience is required to refine the ReadMax methodology and fully establish its validity and robustness. In conclusion, the ReadMax scoring system to identify patients with EGFR FISH-positive NSCLC is a promising technique, which could improve treatment options and outcomes for patients with advanced NSCLC, in particular for EGFR wild-type tumours.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".