The genomic landscape of young and old lung cancer patients highlights age‐dependent mutation frequencies and clinical actionability in young patients
Bibliographic record
Abstract
The aim of the study was to investigate age-dependent tendency of genomic alterations in lung cancer, and also to examine mutational profiles and its association with clinical treatment outcomes in young adenocarcinoma patients. By studying 7858 lung cancer samples using targeted-gene sequencing, we investigated genomic differences and clinical on-treatment time (OTT) to different therapies between young (≤ 45 years) and old (> 45 years) patients. The age-dependent trend test for genomic alterations in all patients revealed steady increases in tumor mutation burden and alterations in a number of genes with age, including KRAS, MET, CDKN2A, PIK3CA and MDM2, while the frequencies of ALK, ROS1 and RET fusions and ERBB2 mutations were decreasing. The highest rate of EGFR alterations was observed in the 45 ~ 50 years age group. Comparisons of young and old adenocarcinoma patients found that young patients were characterized by a higher prevalence of ALK, ROS1 and RET fusions, and ERBB2 exon-20 insertions and EGFR exon-19 deletions. Actionable mutations were highly prevalent in young adenocarcinoma patients, with 88% of patients harboring at least one actionable genetic alteration. First-line therapies in EGFR-positive patients (n = 979) by EGFR tyrosine kinase inhibitors or chemotherapy resulted in similar OTT between young and old patients. Somatic interaction analyses implied that young EGFR-positive patients were more likely to also have PIK3CA, MET, TP53 and RB1 mutations than old patients. Lung cancer in young patients, and especially those with adenocarcinoma, exhibited different clinical features and genomic attributes compared to old patients, which should be considered for therapeutic decision-making purposes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".