Comparison of Next-Generation Sequencing Platforms for Clinical Testing of Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Personalized treatment of lung cancer using therapies that target activating oncogenic mutations such as EGFR and ALK has become the standard of care.Current molecular testing is routinely performed for single genes and increasingly in a multiplex format.However, the scarcity of sufficient biopsy material has necessitated a more high-throughput and comprehensive testing approach.Next Generation Sequencing (NGS) offers great promise as a highly sensitive method of detection for a variety of biopsy sources (tissue, blood, pleural effusions).However, there are multiple NGS platforms and panels with varying advantages and disadvantages.This pilot study compared four different library construction methods (Ion AmpliSeq, Illumina TruSeq, and Raindance Thunderbolts amplicon-based methods and Roche EZSeq sequence capture method) and two different sequencing instruments (Ion Torrent PGM and Illumina MiSeq).A common set of ten tumor/normal pairs from lung adenocarcinoma patients were analysed by all platforms.Additional samples were analysed in subsets of the platforms.To assess the feasibility of sequencing circulating free DNA (cfDNA) from plasma and pleural effusions, two additional samples were analysed on two amplicon-based platforms.A bioinformatic pipeline for automated sequence data analysis was developed using the Galaxy environment.To determine the most cost-effective, technically streamlined library construction and sequencing method, we compared coverage statistics, sensitivity, variant detection, and workflow for all platforms.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".