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Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
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A novel NGS-based diagnostic algorithm for classifying multifocal lung adenocarcinomas in pN0M0 patients
Xin Zhang1, Xiaoxi Fan1, Changbo Sun1,2
1Department of Thoracic Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, PR China.
The Journal of Pathology. Clinical Research
|December 29, 2022
Summary
Next-generation sequencing (NGS) combined with histology accurately distinguishes multiple primary lung adenocarcinomas (MPLAs) from intrapulmonary metastases (IPMs). This refined algorithm improves lung cancer diagnosis, staging, and treatment decisions for patients with multifocal lung adenocarcinomas (MLAs).
Area of Science:
- Oncology
- Molecular Diagnostics
- Thoracic Surgery
Background:
- Accurate classification of multifocal lung adenocarcinomas (MLAs), encompassing multiple primary lung adenocarcinomas (MPLAs) and intrapulmonary metastases (IPMs), is crucial for effective staging and treatment planning.
- The diagnostic utility of molecular approaches, specifically next-generation sequencing (NGS), in differentiating MPLAs from IPMs in pN0M0 MLA patients remains underexplored.
Purpose of the Study:
- To evaluate the efficacy of NGS in classifying pN0M0 MLAs.
- To develop and validate a refined algorithm integrating NGS and comprehensive histologic assessment (CHA) for accurate MLA diagnosis.
Main Methods:
- Next-generation sequencing (NGS) analysis was performed on 101 lesion pairs from 45 pN0M0 MLA patients initially diagnosed with MPLA by CHA.
- Positive controls included 5 patients with intrathoracic metastases, and negative controls comprised 197 patients with unifocal lung adenocarcinomas.
- A refined algorithm combining NGS and histologic findings was developed and validated.
Main Results:
- A predefined NGS criterion accurately classified IPMs in positive controls but misdiagnosed 3.1% of negative control lesion pairs as IPMs.
- In the study group, 14 IPM lesion pairs were identified, with at least 7 potential misdiagnoses using conventional NGS criteria.
- The refined algorithm correctly diagnosed all known MPLAs and IPMs, with identified IPMs corroborated by CHA reassessment. Patients diagnosed with MPLA using the refined algorithm showed significantly better progression-free survival than those diagnosed with IPMs (p < 0.0001).
Conclusions:
- An NGS-based algorithm effectively distinguishes IPMs from MPLAs in MLA patients.
- The refined algorithm offers superior diagnostic accuracy and prognostic value compared to conventional NGS or CHA alone.
- This NGS-integrated approach holds significant clinical utility for complementing traditional diagnostic methods and guiding patient management in MLA cases.
