Maintenance Therapy Comes of Age for Non–Small-Cell Lung Cancer, but at What Cost?
Bibliographic record
Abstract
PURPOSE: The phase III, randomized, placebo-controlled Sequential Tarceva in Unresectable NSCLC (SATURN; BO18192) study found that erlotinib maintenance therapy extended progression-free survival (PFS) and overall survival in patients with advanced non-small-cell lung cancer (NSCLC) who had nonprogressive disease following first-line platinum-doublet chemotherapy. This study included prospective analysis of the prognostic and predictive value of several biomarkers. PATIENTS AND METHODS: Mandatory diagnostic tumor specimens were collected before initiating first-line chemotherapy and were tested for epidermal growth factor receptor (EGFR) protein expression by using immunohistochemistry (IHC), EGFR gene copy number by using fluorescent in situ hybridization (FISH), and EGFR and KRAS mutations by using DNA sequencing. An EGFR CA simple sequence repeat in intron 1 (CA-SSR1) polymorphism was evaluated in blood. RESULTS: All 889 randomly assigned patients provided tumor samples. EGFR IHC, EGFR FISH, KRAS mutation, and EGFR CA-SSR1 repeat length status were not predictive for erlotinib efficacy. A profound predictive effect on PFS of erlotinib relative to placebo was observed in the EGFR mutation-positive subgroup (hazard ratio [HR], 0.10; P < .001). Significant PFS benefits were also observed with erlotinib in the wild-type EGFR subgroup (HR, 0.78; P = .0185). KRAS mutation status was a significant negative prognostic factor for PFS. CONCLUSION: This large prospective biomarker study found that patients with activating EGFR mutations derive the greatest PFS benefit from erlotinib maintenance therapy. No other biomarkers were predictive for outcomes with erlotinib, although the study was not powered for clinical outcomes in biomarker subgroups other than EGFR IHC-positive [corrected]. KRAS mutations were prognostic for reduced PFS. The study demonstrated the feasibility of prospective tissue collection for biomarker analyses in NSCLC.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".