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Updated: Jan 21, 2026
Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
Published on: October 10, 2025
Targeted deep sequencing from multiple sources demonstrates increased NOTCH1 alterations in lung cancer patient
Yuwei Liao1,2, Zhaokui Ma2, Yu Zhang2
1The Second Hospital of Dalian Medical University, Dalian, China.
Introduction:
Targeted therapies are based on specific gene alterations. Various specimen types have been used to determine gene alterations, however, no systemic comparisons have yet been made. Herein, we assessed alterations in selected cancer-associated genes across varying sample sites in lung cancer patients.
Materials And Methods:
Targeted deep sequencing for 48 tumor-related genes was applied to 153 samples from 55 lung cancer patients obtained from six sources: Formalin-fixed paraffin-embedded (FFPE) tumor tissues, pleural effusion supernatant (PES) and pleural effusion cell sediments (PEC), white blood cells (WBCs), oral epithelial cells (OECs), and plasma.
Results:
Mutations were detected in 96% (53/55) of the patients and in 83% (40/48) of the selected genes. Each sample type exhibited a characteristic mutational pattern. As anticipated, TP53 was the most affected sequence (54.5% patients), however this was followed by NOTCH1 (36%, across all sample types). EGFR was altered in patient samples at a frequency of 32.7% and KRAS 10.9%. This high EGFR/ low KRAS frequency is in accordance with other TCGA cohorts of Asian origin but differs from the Caucasian population where KRAS is the more dominant mutation. Additionally, 66% (31/47) of PEC samples had copy number variants (CNVs) in at least one gene. Unlike the concurrent loss and gain in most genes, herein NOTCH1 loss was identified in 21% patients, with no gain observed. Based on the relative prevalence of mutations and CNVs, we divided lung cancer patients into SNV-dominated, CNV-dominated, and codominated groups.
Conclusions:
Our results confirm previous reports that EGFR mutations are more prevalent than KRAS in Chinese lung cancer patients. NOTCH1 gene alterations are more common than previously reported and reveals a role of NOTCH1 modifications in tumor metastasis. Furthermore, genetic material from malignant pleural effusion cell sediments may be a noninvasive manner to identify CNV and participate in treatment decisions.
Insights
This study compared gene alterations across various lung cancer sample types. Pleural effusion cell sediments offer a noninvasive method for detecting genetic changes and guiding treatment decisions.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Targeted therapies rely on identifying specific gene alterations in cancer.
- Systematic comparisons of gene alterations across different sample types in lung cancer are lacking.
Purpose of the Study:
- To assess and compare genetic alterations in key cancer-associated genes across diverse sample sources from lung cancer patients.
- To investigate the utility of different sample types for detecting mutations and copy number variants (CNVs).
Main Methods:
- Targeted deep sequencing of 48 tumor-related genes was performed on 153 samples from 55 lung cancer patients.
- Samples were obtained from six sources: FFPE tissues, pleural effusion supernatant (PES), pleural effusion cell sediments (PEC), white blood cells (WBCs), oral epithelial cells (OECs), and plasma.
Main Results:
- Mutations were detected in 96% of patients and 83% of genes analyzed.
- TP53, NOTCH1, EGFR, and KRAS were frequently altered, with specific patterns observed across sample types.
- Pleural effusion cell sediments (PEC) showed a high prevalence of copy number variants (CNVs) and can be a source for noninvasive genetic analysis.
Conclusions:
- EGFR mutations are more common than KRAS in Chinese lung cancer patients, aligning with previous findings.
- NOTCH1 alterations are more frequent than previously recognized and may play a role in tumor metastasis.
- Malignant pleural effusion cell sediments provide a noninvasive approach for identifying CNVs, aiding in treatment decisions.
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