Non-driver somatic alteration burden confers good prognosis in non-small cell lung cancer
Bibliographic record
Abstract
ABSTRACT Background Genomic profiling of patient tumors has linked somatic driver mutations to survival outcomes of non-small cell lung cancer (NSCLC) patients, especially for those receiving targeted therapies. However, it remains unclear whether specific non-driver mutations have any prognostic utility. Methods Whole exomes and transcriptomes were measured from NSCLC xenograft models of patients with diverse clinical outcomes. Penalised regression analysis was performed to identify a set of 865 genes associated with patient survival. The number of somatic copy number aberrations, point mutations and associated expression changes within the 865 genes were used to stratify independent NSCLC patient populations, filtered for chemotherapy naive and early-stage. In-depth genomic analysis and functional testing was conducted on the genomic alterations to understand their effect on improving survival. Results High burden of somatic alterations are associated with longer disease-free survival (HR=0.153, P=1.48×10-4) in NSCLC patients. When somatic alterations burden was integrated with gene expression, we were able to predict prognosis in three independent patient datasets. Patients with high alteration burden could be further stratified based on the presence of immunogenic mutations, revealing another subgroup of patients with even better prognosis (85% with >5 years survival), and associated with cytotoxic T-cell expression. In addition, 95% of these 865 genes lack documented activity relevant to cancer, but are in pathways regulating cell proliferation, motility and immune response were implicated. Conclusion Our results demonstrate that non-driver somatic alterations may influence the outcome of cancer patients by increasing beneficial immune response and inhibiting processes associated to tumorigenesis.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".