Clinical and genomic features of non‐small cell lung cancer occurring in families
Bibliographic record
Abstract
BACKGROUND: Exposure to environmental carcinogens, such as through smoking, is a major factor in the carcinogenesis of non-small cell lung cancer (NSCLC). However, genetic factors may also contribute. METHODS: To identify candidate tumor suppressor genes for NSCLC, we included 23 patients (10 related pairs and 3 individuals) with NSCLC who had other NSCLC-affected first-degree relatives in a local hospital. Exome analyses for both germline and somatic (NSCLC specimens) DNA were performed for 17 cases. Germline exome data of these 17 cases revealed that most of the short variants were identical to the variants in 14KJPN (a Japanese reference genome panel of more than 14 000 individuals) and only a nonsynonymous variant in the DHODH gene, p.A347T, was shared between a pair of NSCLC patients in the same family. This variant is a known pathogenic variant of the gene for Miller syndrome. RESULTS: Somatic genetic alterations in the exome data of our samples showed frequent mutations in the EGFR and TP53 genes. Principal component analysis of the patterns of 96 types of single nucleotide variants (SNVs) suggested the existence of unique mechanisms inducing somatic SNVs in each family. Delineation of mutational signatures of the somatic SNVs with deconstructSigs for the pair of germline pathogenic DHODH variant-positive cases showed that the mutational signatures of these cases included SBS3 (homologous recombination repair defect), SBS6, 15 (DNA mismatch repair), and SBS7 (ultraviolet exposure), suggesting that disordered pyrimidine production causes increased errors in DNA repair systems in these cases. CONCLUSION: Our results suggest the importance of the detailed collection of data on environmental exposure along with genetic information on NSCLC patients to identify the unique combinations that cause lung tumorigenesis in a particular family.
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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 | 0.000 | 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".