Retrospective Assessment of a Serum Proteomic Test in a Phase III Study Comparing Erlotinib plus Placebo with Erlotinib plus Tivantinib (MARQUEE) in Previously Treated Patients with Advanced Non-Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: The VeriStrat test provides accurate predictions of outcomes in all lines of therapy for patients with non-small cell lung cancer (NSCLC). We investigated the predictive and prognostic role of VeriStrat in patients enrolled on the MARQUEE phase III trial of tivantinib plus erlotinib (T+E) versus placebo plus erlotinib (P+E) in previously treated patients with advanced NSCLC. METHODS: Pretreatment plasma samples were available for 996 patients and were analyzed by matrix-assisted laser desorption/ionization-time of flight mass spectrometry to generate VeriStrat labels (good, VS-G, or poor, VS-P). RESULTS: = .0108). Among EGFR mutation-positive patients, those with VS-G status had a median OS more than twice that of any other group (OS: 31.6 mo for T+E and 22.8 mo for P+E), whereas VS-P patients had similar survival rates as VS-G, EGFR-wild type patients (OS: 13.7 mo for T+E and 6.5 mo for P+E). CONCLUSION: In these analyses, VeriStrat showed a prognostic role within EGOC PS categories and regardless of treatment arm and EGFR status, suggesting that VeriStrat could be used to identify EGFR mutation-positive patients who will have a poor response to EGFR tyrosine kinase inhibitors. IMPLICATIONS FOR PRACTICE: This study suggests that VeriStrat testing could enhance the prognostic role of performance status and smoking status and replicates findings from other trials that showed that the VeriStrat test identifies EGFR mutation-positive patients likely to have a poor response to EGFR tyrosine kinase inhibitors (TKIs). Although these findings should be confirmed in other populations, VeriStrat use could be considered in EGFR mutation-positive patients as an additional prognostic tool, and these results suggest that EGFR mutation-positive patients with VeriStrat "poor" classification could benefit from other therapeutic agents given in conjunction with TKI monotherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".