Cancer Research and Treatment

Original Articles

Lung and Thoracic cancer

Revolutionizing Non–Small Cell Lung Cancer Diagnosis: Ultra-High-Sensitive ctDNA Analysis for Detecting Hotspot Mutations with Long-term Stored Plasma: Ji-Young Lee, Seyeon Jeon, Ha Ra Jun, Chang Ohk Sung, Se Jin Jang, Chang-Min Choi, Sung-Min Chun; Cancer Res Treat. 2024;56(2):484-501. Published online October 23, 2023; DOI: https://doi.org/10.4143/crt.2023.712

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Purpose
Circulating cell-free DNA (cfDNA) has great potential in clinical oncology. The prognostic and predictive values of cfDNA in non–small cell lung cancer (NSCLC) have been reported, with epidermal growth factor receptor (EGFR), KRAS, and BRAF mutations in tumor-derived cfDNAs acting as biomarkers during the early stages of tumor progression and recurrence. However, extremely low tumor-derived DNA rates hinder cfDNA application. We developed an ultra-high-sensitivity lung version 1 (ULV1) panel targeting BRAF, KRAS, and EGFR hotspot mutations using small amounts of cfDNA, allowing for semi-quantitative analysis with excellent limit-of-detection (0.05%).
Materials and Methods
Mutation analysis was performed on cfDNAs extracted from the plasma of 104 patients with NSCLC by using the ULV1 panel and targeted next-generation sequencing (CT-ULTRA), followed by comparison analysis of mutation patterns previously screened using matched tumor tissue DNA.
Results
The ULV1 panel demonstrated robust selective amplification of mutant alleles, enabling the detection of mutations with a high degree of analytical sensitivity (limit-of-detection, 0.025%-0.1%) and specificity (87.9%-100%). Applying ULV1 to NSCLC cfDNA revealed 51.1% (23/45) samples with EGFR mutations, increasing with tumor stage: 8.33% (stage I) to 78.26% (stage IV). Semi-quantitative analysis proved effective for low-mutation-fraction clinical samples. Comparative analysis with PANAMutyper EGFR exhibited substantial concordance (κ=0.84).
Conclusion
Good detection sensitivity (~80%) was observed despite the limited volume (1 mL) and long-term storage (12-50 months) of plasma used and is expected to increase with high cfDNA inputs. Thus, the ULV1 panel is a fast and cost-effective method for early diagnosis, treatment selection, and clinical follow-up of patients with NSCLC.

Citations

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The Role of ctDNA for Diagnosis and Histological Prediction in Early Stage Non-Small-Cell Lung Cancer: A Narrative Review
Carolina Sassorossi, Jessica Evangelista, Alessio Stefani, Marco Chiappetta, Antonella Martino, Annalisa Campanella, Elisa De Paolis, Dania Nachira, Marzia Del Re, Francesco Guerrera, Luca Boldrini, Andrea Urbani, Stefano Margaritora, Angelo Minucci, Emil
Diagnostics.2025; 15(7): 904. CrossRef
Longitudinal dynamics of circulating tumor DNA for treatment monitoring in patients with breast cancer recurrence
Tae-Kyung Robyn Yoo, Ji-Young Lee, Hwan Park, Whi-Kyung Cho, Seyeon Jeon, Ha Ra Jun, Sae Byul Lee, Il Yong Chung, Hee Jeong Kim, Beom Seok Ko, Jong Won Lee, Byung Ho Son, Sei-Hyun Ahn, Jae Ho Jeong, Jeong Eun Kim, Jin-Hee Ahn, Kyung Hae Jung, Sung-Bae Kim
Scientific Reports.2024;[Epub] CrossRef

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Gastrointestinal cancer

Histopathologic and Molecular Biomarkers of PD-1/PD-L1 Inhibitor Treatment Response among Patients with Microsatellite Instability‒High Colon Cancer: Jaewon Hyung, Eun Jeong Cho, Jihun Kim, Jwa Hoon Kim, Jeong Eun Kim, Yong Sang Hong, Tae Won Kim, Chang Ohk Sung, Sun Young Kim; Cancer Res Treat. 2022;54(4):1175-1190. Published online January 12, 2022; DOI: https://doi.org/10.4143/crt.2021.1133

Abstract PDF Supplementary Material PubReader ePub

Purpose
Recent clinical trials have reported response rates < 50% among patients treated with programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors for microsatellite instability‒high (MSI-H) colorectal cancer (CRC), and factors predicting treatment response have not been fully identified. This study aimed to identify potential biomarkers of PD-1/PD-L1 inhibitor treatment response among patients with MSI-H CRC.
Materials and Methods
MSI-H CRC patients enrolled in three clinical trials of PD-1/PD-L1 blockade at Asan Medical Center (Seoul, Republic of Korea) were screened and classified into two groups according to treatment response. Their histopathologic features and expression of 730 immune-related genes from the NanoString platform were evaluated, and a machine learning–based classification model was built to predict treatment response among MSI-H CRCs patients.
Results
A total of 27 patients (15 responders, 12 non-responders) were included. A high degree of lymphocytic/neutrophilic infiltration and an expansile tumor border were associated with treatment response and prolonged progression-free survival (PFS), while mucinous/signet-ring cell carcinoma was associated with a lack of treatment response and short PFS. Gene expression profiles revealed that the interferon-γ response pathway was enriched in the responder group. Of the top eight differentially expressed immune-related genes, PRAME had the highest fold change in the responder group. Higher expression of PRAME was independently associated with better PFS along with histologic subtypes in the multivariate analysis. The classification model using these genes showed good performance for predicting treatment response.
Conclusion
We identified histologic and immune-related gene expression characteristics associated with treatment response in MSI-H CRC, which may contribute to optimal patient stratification.

Citations

Citations to this article as recorded by

The Relationship of PRAME Expression with Clinicopathologic Parameters and Immunologic Markers in Melanomas: In Silico Analysis
Yasemin Cakir, Banu Lebe
Applied Immunohistochemistry & Molecular Morphology.2025; 33(2): 117. CrossRef
Exploration of the regulatory mechanism of norcantharidin on sine oculis homeobox homolog 4 in colon cancer using transcriptome sequencing and bioinformatic
Fanqin Zhang, Chao Wu, Jingyuan Zhang, Zhihong Huang, Antony Stalin, Yiyan Zhai, Shuqi Liu, Jiarui Wu
Journal of Traditional Chinese Medical Sciences.2025;[Epub] CrossRef
Biomarkers to predict efficacy of immune checkpoint inhibitors in colorectal cancer patients: a systematic review and meta-analysis
Hang Yu, Qingquan Liu, Keting Wu, Shuang Tang
Clinical and Experimental Medicine.2024;[Epub] CrossRef
An Insight into the Peculiarities of Signet-Ring Cell Carcinoma of the Colon – a Narrative Review
Loredana Farcaș, Diana Voskuil-Galoș
Journal of Medical and Radiation Oncology.2024; 4(7): 1. CrossRef
High serum IL-6 correlates with reduced clinical benefit of atezolizumab and bevacizumab in unresectable hepatocellular carcinoma
Hannah Yang, Beodeul Kang, Yeonjung Ha, Sung Hwan Lee, Ilhwan Kim, Hyeyeong Kim, Won Suk Lee, Gwangil Kim, Sanghoon Jung, Sun Young Rha, Vincent E. Gaillard, Jaekyung Cheon, Chan Kim, Hong Jae Chon
JHEP Reports.2023; 5(4): 100672. CrossRef
Identification of ZBTB4 as an immunological biomarker that can inhibit the proliferation and invasion of pancreatic cancer
Zhe Yang, Feiran Chen, Feng Wang, Xiubing Chen, Biaolin Zheng, Xiaomin Liao, Zhejun Deng, Xianxian Ruan, Jing Ning, Qing Li, Haixing Jiang, Shanyu Qin
BMC Cancer.2023;[Epub] CrossRef
PD-L1 Expression in Colorectal Carcinoma: A Comparison of 3 Scoring Methods in a Cohort of Jordanian Patients
Heyam A. Awad, Maher A. Sughayer, Jumana M. Obeid, Yaqoot N. Heilat, Ahmad S. Alhesa, Reda M. Yousef, Nabil M. Hasasna, Shafiq A. Masoud, Tareq Saleh
Applied Immunohistochemistry & Molecular Morphology.2023; 31(6): 379. CrossRef
Systemic Delivery of a STING Agonist‐Loaded Positively Charged Liposome Selectively Targets Tumor Immune Microenvironment and Suppresses Tumor Angiogenesis
Eun‐Jin Go, Hannah Yang, Wooram Park, Seung Joon Lee, Jun‐Hyeok Han, So Jung Kong, Won Suk Lee, Dong Keun Han, Hong Jae Chon, Chan Kim
Small.2023;[Epub] CrossRef
Review of the Immune Checkpoint Inhibitors in the Context of Cancer Treatment
Norah A. Alturki
Journal of Clinical Medicine.2023; 12(13): 4301. CrossRef
Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives
Nian-Nian Zhong, Han-Qi Wang, Xin-Yue Huang, Zi-Zhan Li, Lei-Ming Cao, Fang-Yi Huo, Bing Liu, Lin-Lin Bu
Seminars in Cancer Biology.2023; 95: 52. CrossRef
Artificial intelligence for prediction of response to cancer immunotherapy
Yuhan Yang, Yunuo Zhao, Xici Liu, Juan Huang
Seminars in Cancer Biology.2022; 87: 137. CrossRef

6,797 View
263 Download
8 Web of Science
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