Genomic Landscape of Non-Small Cell Lung Cancer (NSCLC) in East Asia Using Circulating Tumor DNA (ctDNA) in Clinical Practice
Bibliographic record
Abstract
Plasma-based next-generation sequencing (NGS) has demonstrated the potential to guide the personalized treatment of non-small cell lung cancer (NSCLC). Inherent differences in mutational genomic profiles of NSCLC exist between Asian and Western populations. However, the published mutational genomic data of NSCLC has largely focused on Western populations. We retrospectively analyzed results from comprehensive NGS of plasma (Guardant360®) from patients with advanced non-squamous NSCLC, as seen in clinical practice. Tests were ordered between January 2016 and December 2020 in Hong Kong, Korea, Taiwan, Japan and Southeast Asia. The assay identified single-nucleotide variants (SNV), insertions and deletions, and fusions and amplifications in 74 genes. In total, 1608 plasma samples from patients with advanced non-squamous NSCLC were tested. The median turnaround time for test results was 7 days. Of the samples with detectable ctDNA (85.6%), 68.3% had alterations in at least one NCCN-recommended NSCLC biomarker. EGFR driver mutations were most frequent (48.6%), followed by alterations of KRAS (7.9%), ERBB2 (4.1%) and ALK (2.5%). Co-mutations of EGFR and KRAS occurred in 4.7% of samples. KRAS G12C was identified in 18.6% of all samples with KRAS mutations. Common mutations, such as exon 19 deletions and L858R, accounted for 88.4% of EGFR driver mutations. Among the samples with any EGFR driver mutation, T790M was present in 36.9%, including 7.7% with additional alterations associated with osimertinib resistance (MET amplification, C797X). Comprehensive plasma-based NGS provided the timely and clinically informative mutational genomic profiling of advanced non-squamous NSCLC in East Asian patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".