Genomic analysis of driver-negative lung adenocarcinoma (LA) in lifetime never smokers.
Bibliographic record
Abstract
3571 Background: Genomic events giving rise to driver negative LA in never smokers remain elusive. Here we report results of whole exome sequencing (WES) and targeted RNA sequencing in NS who had no mutation drivers found on routine clinical testing by targeted next generation sequencing (NGS). Methods: The cohort of never smokers with EGFR/ALK negative LA by clinical biomarker testing at Princess Margaret Cancer Centre, were first subjected to various clinical NGS profiling platforms (table). Where tissue was available, those negative for potential drivers in the clinical NGS then underwent WES (mean coverage > 200x) and Oncomine comprehensive v.3 RNA sequencing. We analyzed mutational signatures (MS) of the driver negative cohort based on the COSMIC catalog and assessed the median tumor mutation burden (mTMB mut/Mb -Megabase) in cases without a smoking MS, to avoid confounders. Results: Of 159 never smokers profiled with clinical NGS, potential drivers were found in 86 (54%): 75 (87%) with mutations in known LA driver genes and 11 (13%) with fusions. Among the remaining never smokers that tested negative by clinical NGS, 35 (48%) had available tissue for further testing. The Oncomine panel identified 9 cases (25%) with fusions or MET exon14 mutation (n = 7). Within the driver negative group, 24 (92%) underwent WES. Three tumors had WES base substitution patterns that were consistent with a smoking-related MS (MS4). Twenty-one patients exhibited signatures found common across all cancer types (MS 5), associated with DNA mismatch repair (MS 6, MS 20) or APOBEC over-activation (MS 2, MS13). In the driver-negative group, we identified 7 pts with somatic mutations in the KMT2 family (4 KMT2C, 4 KMT2A, 1 KMT2D), known for putative tumor suppressors and histone methyltransferases. mTMB on the driver negative group was 1.92, while one outlier with APOBEC MS and KMT2C/A mutations had a TMB of 16.8. Conclusions: Never smokers with driver negative LA are a heterogeneous group, with different MS and a wide TMB range. Mutations on KMT2 family are frequently found in driver negative LA in never smokers and warrant further investigations. [Table: see text]
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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".