Clinical Outcomes for Plasma-Based Comprehensive Genomic Profiling Versus Standard-of-Care Tissue Testing in Advanced Non–Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Somatic genomic testing is recommended by numerous expert guidelines to inform targeted therapy treatment for patients with advanced nonsquamous non-small cell lung cancer (aNSCLC). The NILE study was a prospective observational study that demonstrated noninferiority of cell-free circulating tumor DNA (cfDNA)-based tumor genotyping compared to tissue-based genotyping to find targetable genomic alterations in patients with newly diagnosed nonsquamous aNSCLC. As the cohort has matured, clinical outcomes data can now be analyzed. METHODS: This prospective, multicenter North American study enrolled patients with previously untreated nonsquamous aNSCLC who had standard of care (SOC) tissue genotyping performed and concurrent comprehensive cfDNA analysis (Guardant360). Patients with targetable genomic alterations, as defined by NCCN guidelines, who were treated with physician's choice of therapy had objective response rates, disease control rate, and time to treatment collected and compared to published outcomes. RESULTS: Among 282 patients, 89 (31.6%) had an actionable biomarker, as defined by NCCN, detected by tissue (21.3%) and/or cfDNA (27.3%) analysis. Sixty-one (68.5%) of these were treated with an FDA-approved targeted therapy guided by somatic genotyping results (EGFR, ALK, ROS1). Thirty-three patients were eligible for clinical response evaluation and demonstrated an objective response rate of 58% and disease control rate of 94%. Twenty-five (76%) and 17 (52%) achieved a durable response > 6 months and 12 months, respectively. The time to treatment (TtT) was significantly faster for cfDNA-informed biomarker detection as compared to tissue genotyping (18 vs. 31 days, respectively; P = .0008). CONCLUSIONS: cfDNA detects guideline-recommended biomarkers at a rate similar to tissue genotyping, and therapeutic outcomes based on plasma-based comprehensive genomic profiling are comparable to published targeted therapy outcomes with tissue profiling, even in community-based centers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".