Research on high-throughput detection of plasma cell-free DNA for targeted therapy-related genes screening and prognosis prediction in non-small cell lung cancer patients

doi:10.19401/j.cnki.1007-3639.2025.04.003

Abstract

Abstract:

Background and purpose: High-throughput detection of plasma cell-free DNA (cfDNA) is widely used for multi-cancer targeted therapy drug screening, and this study investigated the relationship between the type and number of plasma cfDNA class Ⅰ and Ⅱ targeted therapy-related gene variants and cancer survival in patients with non-small cell lung cancer (NSCLC). Methods: The sequencing results and clinical data of NSCLC patients who underwent tumor plasma cfDNA high-throughput sequencing projects in Sun Yat-sen University Cancer Center from 2021 to 2023 were collected. The survival follow-up of enrolled patients was carried out from the day of plasma collection on June 1, 2021 to May 27, 2024, and GraphPad Prism 8.0 and SPSS Statistics 25.0 were used. Univariate and multivariate statistical analyses were conducted on the types and numbers of class Ⅰ and class Ⅱ targeted therapy-related genes in the survival and clinical data of patients and sequencing results (Ethical approval: B2024-359-01). Results: A total of 313 patients included in this study with NSCLC were categorized into stage Ⅰ 25 patients (7.98%), stage Ⅱ 20 patients (6.39%), stage Ⅲ 38patients (12.14%), and stage Ⅳ 230 patients (73.48%). Pathological diagnosis results showed that adenocarcinoma accounted for 90.10%, squamous cell carcinoma accounted for 5.11%, large cell carcinoma accounted for 2.87% and other classifications accounted for 1.92%. The number and the percentage of class Ⅰ and class Ⅱ targeted therapy drug-related genes in the plasma cfDNA NSCLC patients were 0 (25.24%), 1 (17.57%), 2 (19.17%), 3 (14.38%), 4 (8.31%), and 5 or more (15.34%). The results of statistical analysis showed that 3 genes with the highest mutation frequencies were EGFR, TP53 and ERBB2, and the mutation frequency of EGFR gene was 36.04%. The mutation frequency of TP53 gene was 30.63%. The mutation frequency of ERBB2 gene was 4.95%. The survival time of patients is related to not only the expression of hotspot targeted genes, but also the number of class Ⅰ and Ⅱ target-related gene variants detected by plasma cfDNA high-throughput sequencing. The survival time of the patients with no targeted therapy-related locus variants after treatment was longer compares with targeted therapy-related locus variants, which can reduce the risk of death by 63.2%. However, patients with a single gene locus variant had longer survival time and lower risk of death than those with multiple driver locus variants, and the measured class Ⅰ and Ⅱ targeted therapy drugs were within 3 genes. Overall, the smaller the number of genes, the longer the survival. Conclusions: The number of class Ⅰ and class Ⅱ targeted therapy-related gene variants in plasma cfDNA high-throughput sequencing also has an effect on the survival of patients after treatment. Plasma cfDNA level detected by high-throughput sequencing could be a prognostic factor for the NSCLC patients.

Key words: Non-small cell lung cancer, Plasma cell-free DNA, High-throughput sequencing, Gene mutation sites, Targeted therapy drug selection, Survival analysis

DENG Qiling, SONG Di, XI Kexin, XIE Xiaoting, WU Xiaoyan, ZHAO Wei. Research on high-throughput detection of plasma cell-free DNA for targeted therapy-related genes screening and prognosis prediction in non-small cell lung cancer patients[J]. China Oncology, 2025, 35(4): 355-364.

Figures/Tables 11

Tab. 1

Somatic variant classification explained"

Somatic variant classification	Level of evidence	Explained
Class Ⅰ variants (strong clinical significance)	A	FDA/NMPA approved for use as a biomarker that is responsive or resistant to cancer treatment in patients
	A	Professional guidelines (NCCN) identify biomarkers that are responsive to or resistant to cancer treatment in patients
	A	professional guideline (NCCN) clarifies biomarkers that have diagnostic or prognostic significance for the patient's tumor
	B	Expert consensus studies identify biomarkers that are responsive to or resistant to cancer treatment in patients
	B	Expert consensus studies identify biomarkers that have diagnostic or prognostic significance for the patient's tumor
	C	FDA/NMPA approved biomarkers for other tumor treatments that are responsive or resistant
Class Ⅱ variant (potential clinical significance)	C	Professional guidelines (NCCN) recommend biomarkers that are responsive to or resistant to other oncology treatments
	C	Has been used as a biomarker for clinical trial screening and enrolment criteria
	C	The results of several small studies have shown biomarkers of significance for the diagnosis or prognosis of cancer in patients
	D	The results of the study indicate biomarkers of significance for the diagnosis, treatment, or prognosis of other tumors
	D	Biomarkers with potential therapeutic significance in preclinical studies
	D	There are a few studies and case reports or conclusions that do not reach a consensus to assess the diagnostic or prognostic significance of the disease with other biomarkers Class Ⅲ variants
Category Ⅲ variants (variants of unknown clinical significance)	-	Not detected in population databases, specific subpopulation databases, pan-cancer databases, tumor-specific databases
	-	There is no conclusive evidence of a cancer type
Class Ⅳ variants (benign polymorphisms and suspected benign variants)	-	No evidence of cancer associated with the population database, specific subpopulation database.

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References 20

[1]	NIKITA SANDEEP WAGLE MBBS M, et al. Cancer statistics, 2023[J]. CA A Cancer J Clin, 2023, 73(1): 17-48.
[2]	FILHO A M, LAVERSANNE M, FERLAY J, et al. The GLOBOCAN 2022 cancer estimates: data sources, methods, and a snapshot of the cancer burden worldwide[J]. Int J Cancer, 2025, 156(7): 1336-1346.
[3]	张景暄, 付庭吕, 李宁, 等. 《肺癌的全球负担: 当前状态和未来趋势》要点解读[J]. 中国胸心血管外科临床杂志, 2024, 31(1): 17-23.
	ZHANG J X, FU T L, LI N, et al. Interpretation of the global burden of lung cancer: current status and future trends[J]. Chin J Clin Thorac Cardiovasc Surg, 2024, 31(1): 17-23.
[4]	RAHADIANI N, STEPHANIE M, MANATAR A F, et al. The diagnostic utility of cfDNA and ctDNA in liquid biopsies for gastrointestinal cancers over the last decade[J]. Oncol Res Treat, 2025, 48(3): 125-141.
[5]	SON J W, LEE J, JEON J H, et al. Validation of IASLC 9th edition TNM classification for lung cancer: focus on N descriptor[J]. BMC Cancer, 2024, 24(1): 1460. doi: 10.1186/s12885-024-13139-z pmid: 39604857
[6]	应超, 蔡燕宁, 郝淑文, 等. 三种游离核酸提取试剂盒对血浆cfDNA提取性能的比较[J]. 实用医学杂志, 2023, 39(12): 1556-1563.
	YING C, CAI Y N, HAO S W, et al. Performance comparison of three cell-free nucleic acid extraction kits for plasma cfDNA extraction[J]. J Pract Med, 2023, 39(12): 1556-1563.
[7]	李营歌, 董熠, 余舒阳, 等. 《2023 CSCO非小细胞肺癌诊疗指南》罕见靶点诊疗更新[J]. 肿瘤防治研究, 2023, 50(12): 1232-1236.
	LI Y G, DONG Y, YU S Y, et al. Diagnostic and therapeutic strategy updates of rare oncogenic mutations in Chinese Society of Clinical Oncology guidelines on diagnosis and treatment of non-small cell lung cancer (2023 edition)[J]. Cancer Res Prev Treat, 2023, 50(12): 1232-1236.
[8]	CHANG Y S, TU S J, CHEN Y C, et al. Mutation profile of non-small cell lung cancer revealed by next generation sequencing[J]. Respir Res, 2021, 22(1): 3.
[9]	林艺聪, 王悦, 薛倩倩, 等. EGFR T790M突变非小细胞肺癌患者的临床病理学、免疫微环境特征及对预后预测的意义[J]. 中国癌症杂志, 2024, 34(4): 368-379. doi: 10.19401/j.cnki.1007-3639.2024.04.004
	LIN Y C, WANG Y, XUE Q Q, et al. Clinical pathological characteristics and immune microenvironment significance of EGFR T790M mutation in non-small cell lung cancer patients and its prognostic implications[J]. China Oncol, 2024, 34(4): 368-379.
[10]	SINGH V, KATIYAR A, MALIK P, et al. Identification of molecular biomarkers associated with non-small cell lung carcinoma (NSCLC) using whole-exome sequencing[J]. Cancer Biomark, 2024, 41(3/4): CBM-220211.
[11]	AKIRA S, SHINGO M, HIBIKI U, et al. A large-scale prospective concordance study of plasma- and tissue-based next-generation targeted sequencing for advanced non-small cell lung cancer (LC-SCRUM-liquid)[J]. Clin Cancer Res Off J Am Assoc Cancer Res, 2022, 29(8): 1506-1514.
[12]	LEE S B, KIM J W, KIM H G, et al. Longitudinal comparative analysis of circulating tumor DNA and matched tumor tissue DNA in patients with metastatic colorectal cancer receiving palliative first-line systemic anti-cancer therapy[J]. Cancer Res Treat, 2024, 56(4): 1171-1182.
[13]	BAE J H, LIU R L, ROBERTS E, et al. Single duplex DNA sequencing with CODEC detects mutations with high sensitivity[J]. Nat Genet, 2023, 55(5): 871-879. doi: 10.1038/s41588-023-01376-0 pmid: 37106072
[14]	WANG Z J, CHENG Y, AN T T, et al. Detection of EGFR mutations in plasma circulating tumor DNA as a selection criterion for first-line gefitinib treatment in patients with advanced lung adenocarcinoma (BENEFIT): a phase 2, single-arm, multicentre clinical trial[J]. Lancet Respir Med, 2018, 6(9): 681-690.
[15]	WU X N, ZHAO J, YANG L, et al. Next-generation sequencing reveals age-dependent genetic underpinnings in lung adenocarcinoma[J]. J Cancer, 2022, 13(5): 1565-1572. doi: 10.7150/jca.65370 pmid: 35371328
[16]	PINHEIRO P S, CALLAHAN K E, MEDINA H N, et al. Lung cancer in never smokers: distinct population-based patterns by age, sex, and race/ethnicity[J]. Lung Cancer, 2022, 174: 50-56. doi: 10.1016/j.lungcan.2022.10.009 pmid: 36334356
[17]	MALHOTRA J, MUDDASANI R, FRICKE J, et al. Clinical utility of a circulating tumor cell-based cerebrospinal fluid assay in the diagnosis and molecular analysis of leptomeningeal disease in patients with advanced non-small cell lung cancer[J]. JCO Precis Oncol, 2024, 8: e2400373.
[18]	GEORGE J, MAAS L, ABEDPOUR N, et al. Evolutionary trajectories of small cell lung cancer under therapy[J]. Nature, 2024, 627(8005): 880-889.
[19]	AKIN D, KAHRAMAN ÇETI N N, ERDOĞDU İ H, et al. Clinicopathological significance of mutation profile detected by next generation sequencing in different metastatic organs of non-small cell lung cancers[J]. Pathol Res Pract, 2024, 260: 155463.
[20]	MIYAUCHI E, MORITA S, NAKAMURA A, et al. Updated analysis of NEJ009: gefitinib-alone versus gefitinib plus chemotherapy for non-small cell lung cancer with mutated EGFR[J]. J Clin Oncol, 2022, 40(31): 3587-3592.

Item	P value	HR(A/B)	95% CI
The number of loci is 0	0.001	-	-
The number of loci is 1	0.142	2.016	1.984-5.062
The number of loci is 2	0.002	3.848	1.645-8.824
The number of loci is 3	0	5.006	2.334-12.440
The number of loci is 4	0.005	4.530	1.445-11.734
The number of loci is 5	0	4.629	2.047-10.606
History of surgery	0.001	1.752	1.244-2.467
Clinical staging	0.002	0.509	0.336-0.773