Next-generation sequencing as a valuable tool for mutational spectrum in advanced-stage NSCLC patients
Bibliographic record
Abstract
Background and aim: Lung cancer remains one of the most threatening malignancies, ranking as the second most diagnosed cancer, and it continues to be the leading cause of cancer-related deaths worldwide. Challenges persist with late diagnosis and the high mutational burden characteristic of lung cancer. Methods: Our study focuses on identifying the mutational spectrum of a cohort of advanced-stage non-small cell lung cancer (NSCLC) patients using a minimally invasive method through blood collection. To analyze the mutational landscape of these patients, we employed plasma DNA for the next-generation sequencing (NGS) cancer panel Ion Torrent, which contains 50 of the most mutated genes in lung cancer. All protocols for extraction, quality and quantity control, and library preparation follow the manufacturer's rules. Bioinformatics analysis was performed to select pathogenic mutations versus non-pathogenic-benign ones. Results: This approach is particularly valuable for patients in advanced stages (III and IV, n=10) of lung adenocarcinoma and lung squamous cell carcinoma, who lack surgical options and limited therapeutic avenues. The comprehensive sequencing analysis revealed that nine of the ten lung cancer patients carried a TP53 mutation. Also, several other mutations exist in various cases, showing heterogeneous profiling. Conclusions: Our findings demonstrate the potential of liquid biopsies in providing crucial genetic insights that can guide personalized treatment strategies, improving the management and outcomes for patients with advanced lung cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".