Abstract 669: AmoyDx essential NGS panel (LC 10), one solution covering tumor tissue and plasma for detection of EGFR and cMET alterations in NSCLC
Abstract Background: Approximately 50% of Asian non-small cell lung cancer (NSCLC) patients carry EGFR and MET alterations with available targeted therapies. LC10 was designed for detection of genetic alterations in 10 driver genes in NSCLC, using FFPE or plasma samples. Notably, current commercial testing systems often use different NGS platforms for tissue and plasma analysis, limiting direct comparability and sample accessibility. This study validates EGFR and cMET alteration detection in tissue and plasma samples using a unified NGS platform (LC10) and assesses its implications for treatment planning in NSCLC patients. Methods: LC10 is used for detection of 10 lung cancer driver genes by using ddCapture® technology with increased recovery rate and capture specificity. The analytical validation utilized clinical samples with varying dilutions of specific mutations (EGFR ex20ins, common mutations and MET ex14 skipping) and 11 synthesized plasmids in HEK-293T cells with gradient concentrations of 3rd TKI acquired resistance mutations (muts), along with a total of 277 plasma and 163 tissue samples. During clinical validation stage, paired peripheral blood and tumor tissue samples collected from 316 NSCLC patients were analyzed. Both tissue and plasma samples were tested with an average sequencing depth of 10, 000X. The recommended input for tissue DNA and plasma ctDNA is 50ng and 30ng respectively. Results: LC10 showed competent performance in EGFR ex20ins, common muts, 3rd TKI resistance muts and MET ex14 skipping detection with LOD 1% in tissue and 0.3% in plasma analysis. Specifically, a total of 16 different EGFR ex20ins types were validated during analytical validation stage. Among the 316 NSCLC patients (median age: 59 years; 51.6% male), 80.1% were treatment-naïve, while 19.9% had relapsed with drug resistance. Patients from stage IIIB-IV comprised 37.3% of the cohort (n: 118). In these 118 stage IIIB-IV patients, 57 EGFR muts (19del: 21, L858R: 23, T790M: 7, exon20ins: 4, other: 2) and 1 MET ex14 skipping muts were detected in plasma, achieving an overall percent agreement (OPA) of 86.4% with tissue samples. Concordance by muts site was as follows: 19del: 94.9%, L858R: 91.5%, T790M: 94.1%, exon20ins: 100%, and MET exon 14 skipping: 98.3%. Among the 189 stage I-IIIA patients, 12 EGFR muts and 1 MET exon 14 skipping mut were detected in plasma with OPA of 60.31% compared to tissue. Considering the “non-shedding” nature of early-stage lung adenocarcinoma, this concordance rate is comparable to previous studies. Conclusion: In advanced-NSCLC, plasma is a dependable source for guiding targeted therapies, capturing both primary driver muts and acquired resistance. Employing LC10 provides a one-stop platform for both tissue and plasma testing in NSCLC diagnosis and treatment, which enhances the precision of muts detection with broader sample accessibility. Citation Format: Min Qing, Jingxun Wu, Yuping Li, Jianmin Li, Xing Li, Cheng Li, Yin Shi, Xuejun Chen, Zhenjun Tan, Xiaofang Zhuo, Chengjuan Xiong, Longen Zhou, Flora Berisha, Changbin Zhu. AmoyDx essential NGS panel (LC 10), one solution covering tumor tissue and plasma for detection of EGFR and cMET alterations in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 669.
Authors
- Min Qing (ORCID: https://orcid.org/0000-0001-9491-0360)
- Shi Yin (ORCID: https://orcid.org/0000-0003-0841-8391)
- Changbin Zhu
- Zhenjun Tan (ORCID: https://orcid.org/0000-0001-9249-3751)
- Flora Berisha
- Cheng Li (ORCID: https://orcid.org/0000-0002-1766-1409)
- Xing Li (ORCID: https://orcid.org/0000-0001-9145-4769)
- Yuping Li (ORCID: https://orcid.org/0000-0002-5833-931X)
- Xuejun Chen (ORCID: https://orcid.org/0000-0002-7650-5756)
- Jianmin Li (ORCID: https://orcid.org/0009-0001-8966-4203)
- Jingxun Wu
- Xiaofang Zhuo
- Chengjuan Xiong
- Longen Zhou
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- First Affiliated Hospital of Xiamen University (CN)
- Dian Diagnostics (China) (CN)
- Janssen (United States) (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-669
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00