Repeat Next-Generation Sequencing (15-Gene Panel) in Unifocal, Synchronous, and Metachronous Non-Small-Cell Lung Cancer—A Single-Center Experience
Bibliographic record
Abstract
In advanced non-squamous non-small-cell lung cancer (NSCLC), routine testing with next-generation sequencing (NGS) is recommended to identify actionable genomic alterations (AGAs). The therapeutic implications of repeated NGS testing on synchronous and metachronous tumors are unclear. Between February 2017 and October 2020, NSCLC samples from a single institution were reflex-tested using a targeted 15-gene NGS panel (TruSight Tumor 15, Illumina). Thirty-eight patients were identified with multiple NGS results from 82 samples: 11% were from single unifocal, 51% were from synchronous, and 38% were from metachronous tumors. Changes in EGFR, KRAS, PI3KCA, and TP53 variants were found in 22 patients’ samples (58%). No changes were seen with longitudinal testing of multiple samples from single unifocal tumors, while changes were observed in 60% of synchronous and 71% of metachronous tumors. Of these, 26% of patients had AGA differences between samples. Acknowledging the limited sample size, a significant difference in overall survival was observed between synchronous separate primaries and metastasis. Repeat NGS testing of synchronous and metachronous NSCLC tumors may identify differing variants in >50% of patients. These changes may reflect separate primary lung carcinomas, tumor heterogeneity among intrapulmonary metastases, and clonal evolution. NGS testing of multiple tumors may enhance the identification of therapeutic targets for treatment decisions.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".