Genomic Characterization of Non–Small-Cell Lung Cancer in African Americans by Targeted Massively Parallel Sequencing
Bibliographic record
Abstract
PURPOSE: Technologic advances have enabled the comprehensive analysis of genetic perturbations in non-small-cell lung cancer (NSCLC); however, African Americans have often been underrepresented in these studies. This ethnic group has higher lung cancer incidence and mortality rates, and some studies have suggested a lower incidence of epidermal growth factor receptor mutations. Herein, we report the most in-depth molecular profile of NSCLC in African Americans to date. METHODS: A custom panel was designed to cover the coding regions of 81 NSCLC-related genes and 40 ancestry-informative markers. Clinical samples were sequenced on a massively parallel sequencing instrument, and anaplastic lymphoma kinase translocation was evaluated by fluorescent in situ hybridization. RESULTS: The study cohort included 99 patients (61% males, 94% smokers) comprising 31 squamous and 68 nonsquamous cell carcinomas. We detected 227 nonsilent variants in the coding sequence, including 24 samples with nonoverlapping, classic driver alterations. The frequency of driver mutations was not significantly different from that of whites, and no association was found between genetic ancestry and the presence of somatic mutations. Copy number alteration analysis disclosed distinguishable amplifications in the 3q chromosome arm in squamous cell carcinomas and pointed toward a handful of targetable alterations. We also found frequent SMARCA4 mutations and protein loss, mostly in driver-negative tumors. CONCLUSION: Our data suggest that African American ancestry may not be significantly different from European/white background for the presence of somatic driver mutations in NSCLC. Furthermore, we demonstrated that using a comprehensive genotyping approach could identify numerous targetable alterations, with potential impact on therapeutic decisions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".