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Li K, Guo Z, Li F, Lu S, Zhang M, Gong Y, Tan J, Sheng C, Hao W, Yang X. Non-invasive determination of gene expression in placental tissue using maternal plasma cell-free DNA fragmentation characters. Gene 2024; 928:148789. [PMID: 39047956 DOI: 10.1016/j.gene.2024.148789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/04/2024] [Accepted: 07/19/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND The expression profiles of placental genes are crucial for understanding the pathogenesis of fetal development and placental-origin pregnancy syndromes. However, owing to ethical limitations and the risks of puncture sampling, it is difficult to obtain placental tissue samples repeatedly, continuously, multiple times, or in real time. Establishing a non-invasive method for predicting placental gene expression profiles through maternal plasma cell-free DNA (cfDNA) sequencing, which carries information about the source tissue and gene expression, can potentially solve this problem. METHODS Peripheral blood and placental samples were collected simultaneously from pregnant women who underwent cesarean section. Deep sequencing was performed on the separated plasma cfDNA and single-cell sequencing was performed on peripheral blood mononuclear cells (PBMC), chorioamniotic membranes (CAM), placental villi (PV), and decidua basalis (DB). The aggregation of corresponding information for each gene was combined with the transcriptome of PBMCs and a differential resolution transcriptome of the placenta. This combined information was then utilized for the construction of gene expression prediction models. After training, all models evaluated the correlation between the predicted and actual gene expression levels using external test set data. RESULTS From five women, more than 20 million reads were obtained using deep sequencing for plasma cfDNA; PBMCs obtained 32,401 single-cell expression profiles; and placental tissue obtained 156,546 single-cell expression profiles (59,069, 44,921, and 52,556 for CAM, PV, and DB, respectively). The cells in the PBMC and placenta were clustered and annotated into five and eight cell types, respectively. A "DEPICT" gene expression prediction model was successfully constructed using deep neural networks. The predicted correlation coefficients were 0.75 in PBMCs, 0.84 in the placenta, and 0.78, 0.80, and 0.77 in CAM, BP, and PV respectively, and greater than 0.68 in different cell lines in the placenta. CONCLUSION The DEPICT model, which can noninvasively predict placental gene expression profiles based on maternal plasma cfDNA fragmentation characteristics, was constructed to overcome the limitation of the inability to obtain real-time placental gene expression profiles and to improve research on noninvasive prediction of placental origin pregnancy syndrome.
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Affiliation(s)
- Kun Li
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Zhiwei Guo
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Fenxia Li
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Shijing Lu
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Min Zhang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Yuyan Gong
- Beijing SeekGene BioSciences Co., Ltd, Beijing, China
| | - Jiayu Tan
- ICU of Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan 528403, China
| | - Chao Sheng
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, China.
| | - Wenbo Hao
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China.
| | - Xuexi Yang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China.
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Kindt CK, Alves CL, Ehmsen S, Kragh A, Reinert T, Vogsen M, Kodahl AR, Rønlev JD, Ardik D, Sørensen AL, Evald K, Clemmensen ML, Staaf J, Ditzel HJ. Genomic alterations associated with resistance and circulating tumor DNA dynamics for early detection of progression on CDK4/6 inhibitor in advanced breast cancer. Int J Cancer 2024. [PMID: 39128978 DOI: 10.1002/ijc.35126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 08/13/2024]
Abstract
Combined CDK4/6 inhibitor (CDK4/6i) and endocrine therapy significantly improves outcome for patients with estrogen receptor-positive (ER+) metastatic breast cancer, but drug resistance and thus disease progression inevitably occur. Herein, we aimed to identify genomic alterations associated with combined CDK4/6i and endocrine therapy resistance, and follow the levels of specific mutations in longitudinal circulating tumor DNA (ctDNA) for early detection of progression. From a cohort of 86 patients with ER+ metastatic breast cancer we performed whole exome sequencing or targeted sequencing of paired tumor (N = 8) or blood samples (N = 5) obtained before initiation of combined CDK4/6i and endocrine therapy and at disease progression. Mutations in oncogenic genes at progression were rare, while amplifications of growth-regulating genes were more frequent. The most frequently acquired alterations observed were PIK3CA and TP53 mutations and PDK1 amplification. Longitudinal ctDNA dynamics of mutant PIK3CA or private mutations revealed increased mutation levels at progression in 8 of 10 patients (80%). Impressively, rising levels of PIK3CA-mutated ctDNA were detected 4-17 months before imaging. Our data add to the growing evidence supporting longitudinal ctDNA analysis for real-time monitoring of CDK4/6i response and early detection of progression in advanced breast cancer. Further, our analysis suggests that amplification of growth-related genes may contribute to combined CDK4/6i and endocrine therapy resistance.
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Affiliation(s)
- Charlotte K Kindt
- Department of Cancer Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
| | - Carla L Alves
- Department of Cancer Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
| | - Sidse Ehmsen
- Department of Cancer Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
- Department of Oncology, Odense University Hospital; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Amalie Kragh
- Department of Oncology, Odense University Hospital; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Thomas Reinert
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Marianne Vogsen
- Department of Oncology, Odense University Hospital; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Annette R Kodahl
- Department of Oncology, Odense University Hospital; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jeanette D Rønlev
- Department of Oncology, Odense University Hospital; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | | | | | | | - Johan Staaf
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Medicon Village, Lund, Sweden
| | - Henrik J Ditzel
- Department of Cancer Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
- Department of Oncology, Odense University Hospital; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
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3
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Noë M, Mathios D, Annapragada AV, Koul S, Foda ZH, Medina JE, Cristiano S, Cherry C, Bruhm DC, Niknafs N, Adleff V, Ferreira L, Easwaran H, Baylin S, Phallen J, Scharpf RB, Velculescu VE. DNA methylation and gene expression as determinants of genome-wide cell-free DNA fragmentation. Nat Commun 2024; 15:6690. [PMID: 39107309 PMCID: PMC11303779 DOI: 10.1038/s41467-024-50850-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
Circulating cell-free DNA (cfDNA) is emerging as an avenue for cancer detection, but the characteristics of cfDNA fragmentation in the blood are poorly understood. We evaluate the effect of DNA methylation and gene expression on genome-wide cfDNA fragmentation through analysis of 969 individuals. cfDNA fragment ends more frequently contained CCs or CGs, and fragments ending with CGs or CCGs are enriched or depleted, respectively, at methylated CpG positions. Higher levels and larger sizes of cfDNA fragments are associated with CpG methylation and reduced gene expression. These effects are validated in mice with isogenic tumors with or without the mutant IDH1, and are associated with genome-wide changes in cfDNA fragmentation in patients with cancer. Tumor-related hypomethylation and increased gene expression are associated with decrease in cfDNA fragment size that may explain smaller cfDNA fragments in human cancers. These results provide a connection between epigenetic changes and cfDNA fragmentation with implications for disease detection.
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Grants
- T32 GM136577 NIGMS NIH HHS
- U01 CA271896 NCI NIH HHS
- R01 CA121113 NCI NIH HHS
- UG1 CA233259 NCI NIH HHS
- P50 CA062924 NCI NIH HHS
- P30 CA006973 NCI NIH HHS
- Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (Dr. Miriam & Sheldon G. Adelson Medical Research Foundation)
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
- EIF | Stand Up To Cancer (SU2C)
- This work was supported in part by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, SU2C in-Time Lung Cancer Interception Dream Team Grant, Stand Up to Cancer-Dutch Cancer Society International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415), the Gray Foundation, the Commonwealth Foundation, the Mark Foundation for Cancer Research, the Cole Foundation, a research grant from Delfi Diagnostics, and US National Institutes of Health grants CA121113, CA006973, CA233259, CA062924, and 1T32GM136577. Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research.
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Affiliation(s)
- Michaël Noë
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Dimitrios Mathios
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Akshaya V Annapragada
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Shashikant Koul
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zacharia H Foda
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jamie E Medina
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephen Cristiano
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christopher Cherry
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel C Bruhm
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Noushin Niknafs
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Vilmos Adleff
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Leonardo Ferreira
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hari Easwaran
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephen Baylin
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jillian Phallen
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert B Scharpf
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Victor E Velculescu
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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4
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Penny L, Main SC, De Michino SD, Bratman SV. Chromatin- and nucleosome-associated features in liquid biopsy: implications for cancer biomarker discovery. Biochem Cell Biol 2024; 102:291-298. [PMID: 38478957 DOI: 10.1139/bcb-2024-0004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
Cell-free DNA (cfDNA) from the bloodstream has been studied for cancer biomarker discovery, and chromatin-derived epigenetic features have come into the spotlight for their potential to expand clinical applications. Methylation, fragmentation, and nucleosome positioning patterns of cfDNA have previously been shown to reveal epigenomic and inferred transcriptomic information. More recently, histone modifications have emerged as a tool to further identify tumor-specific chromatin variants in plasma. A number of sequencing methods have been developed to analyze these epigenetic markers, offering new insights into tumor biology. Features within cfDNA allow for cancer detection, subtype and tissue of origin classification, and inference of gene expression. These methods provide a window into the complexity of cancer and the dynamic nature of its progression. In this review, we highlight the array of epigenetic features in cfDNA that can be extracted from chromatin- and nucleosome-associated organization and outline potential use cases in cancer management.
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Affiliation(s)
- Lucas Penny
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Sasha C Main
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Steven D De Michino
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Scott V Bratman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
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5
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Hou Y, Meng X, Zhou X. Systematically Evaluating Cell-Free DNA Fragmentation Patterns for Cancer Diagnosis and Enhanced Cancer Detection via Integrating Multiple Fragmentation Patterns. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308243. [PMID: 38881520 PMCID: PMC11321639 DOI: 10.1002/advs.202308243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/12/2024] [Indexed: 06/18/2024]
Abstract
Cell-free DNA (cfDNA) fragmentation patterns have immense potential for early cancer detection. However, the definition of fragmentation varies, ranging from the entire genome to specific genomic regions. These patterns have not been systematically compared, impeding broader research and practical implementation. Here, 1382 plasma cfDNA sequencing samples from 8 cancer types are collected. Considering that cfDNA within open chromatin regions is more susceptible to fragmentation, 10 fragmentation patterns within open chromatin regions as features and employed machine learning techniques to evaluate their performance are examined. All fragmentation patterns demonstrated discernible classification capabilities, with the end motif showing the highest diagnostic value for cross-validation. Combining cross and independent validation results revealed that fragmentation patterns that incorporated both fragment length and coverage information exhibited robust predictive capacities. Despite their diagnostic potential, the predictive power of these fragmentation patterns is unstable. To address this limitation, an ensemble classifier via integrating all fragmentation patterns is developed, which demonstrated notable improvements in cancer detection and tissue-of-origin determination. Further functional bioinformatics investigations on significant feature intervals in the model revealed its impressive ability to identify critical regulatory regions involved in cancer pathogenesis.
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Affiliation(s)
- Yuying Hou
- Hubei Key Laboratory of Agricultural BioinformaticsCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China
| | - Xiang‐Yu Meng
- Hubei Key Laboratory of Agricultural BioinformaticsCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China
- Health Science CenterHubei Minzu UniversityEnshi445000China
- Hubei Provincial Clinical Medical Research Center for NephropathyHubei Minzu UniversityEnshi445000China
| | - Xionghui Zhou
- Hubei Key Laboratory of Agricultural BioinformaticsCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China
- Key Laboratory of Smart Farming for Agricultural AnimalsMinistry of Agriculture and Rural AffairsWuhan430070China
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6
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Tang Z, Wang S, Li X, Hu C, Zhai Q, Wang J, Ye Q, Liu J, Zhang G, Guo Y, Su F, Liu H, Guan L, Jiang C, Chen J, Li M, Ren F, Zhang Y, Huang M, Li L, Zhang H, Hou G, Jin X, Chen F, Zhu H, Li L, Zeng J, Xiao H, Zhou A, Feng L, Gao Y, Liu G. Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus. Cell Rep Med 2024:101660. [PMID: 39059385 DOI: 10.1016/j.xcrm.2024.101660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 05/13/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024]
Abstract
Gestational diabetes mellitus (GDM) presents varied manifestations throughout pregnancy and poses a complex clinical challenge. High-depth cell-free DNA (cfDNA) sequencing analysis holds promise in advancing our understanding of GDM pathogenesis and prediction. In 299 women with GDM and 299 matched healthy pregnant women, distinct cfDNA fragment characteristics associated with GDM are identified throughout pregnancy. Integrating cfDNA profiles with lipidomic and single-cell transcriptomic data elucidates functional changes linked to altered lipid metabolism processes in GDM. Transcription start site (TSS) scores in 50 feature genes are used as the cfDNA signature to distinguish GDM cases from controls effectively. Notably, differential coverage of the islet acinar marker gene PRSS1 emerges as a valuable biomarker for GDM. A specialized neural network model is developed, predicting GDM occurrence and validated across two independent cohorts. This research underscores the high-depth cfDNA early prediction and characterization of GDM, offering insights into its molecular underpinnings and potential clinical applications.
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Affiliation(s)
- Zhuangyuan Tang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen 518083, China
| | - Shuo Wang
- Tianjin Women and Children's Health Center, Tianjin 300070, China
| | - Xi Li
- BGI Research, Shenzhen 518083, China; BGI Research, Wuhan 430074, China
| | | | | | - Jing Wang
- Tianjin Women and Children's Health Center, Tianjin 300070, China
| | - Qingshi Ye
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen 518083, China
| | - Jinnan Liu
- Tianjin Women and Children's Health Center, Tianjin 300070, China
| | | | - Yuanyuan Guo
- Tianjin Women and Children's Health Center, Tianjin 300070, China
| | | | - Huikun Liu
- Tianjin Women and Children's Health Center, Tianjin 300070, China
| | - Lingyao Guan
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Chang Jiang
- Tianjin Women and Children's Health Center, Tianjin 300070, China
| | - Jiayu Chen
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Min Li
- Tianjin Women and Children's Health Center, Tianjin 300070, China
| | - Fangyi Ren
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Yu Zhang
- Tianjin Women and Children's Health Center, Tianjin 300070, China
| | - Minjuan Huang
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Lingguo Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen 518083, China
| | | | | | - Xin Jin
- Tianjin Women and Children's Health Center, Tianjin 300070, China; The Innovation Centre of Ministry of Education for Development and Diseases, School of Medicine, South China University of Technology, Guangzhou 510006, China
| | | | | | - Linxuan Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Shenzhen 518083, China
| | - Jingyu Zeng
- BGI Research, Shenzhen 518083, China; College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Han Xiao
- Institute of Maternal and Child Health, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Aifen Zhou
- Institute of Maternal and Child Health, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Obstetrics, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lingyan Feng
- Tianjin Women and Children's Health Center, Tianjin 300070, China.
| | - Ya Gao
- BGI Research, Shenzhen 518083, China; Shenzhen Engineering Laboratory for Birth Defects Screening, Shenzhen, China.
| | - Gongshu Liu
- Tianjin Women and Children's Health Center, Tianjin 300070, China.
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Yang X, Liu Q, Guo Z, Yang X, Li K, Han B, Zhang M, Sun M, Huang L, Cai G, Wu Y. Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients. Breast Cancer Res 2024; 26:112. [PMID: 38965610 PMCID: PMC11225256 DOI: 10.1186/s13058-024-01860-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 06/19/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer. METHOD First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy. RESULTS The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response. CONCLUSION These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.
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Affiliation(s)
- Xu Yang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Qing Liu
- Department of Pathology, The First People's Hospital of Foshan, Foshan, China
| | - Zhiwei Guo
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Xuexi Yang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Kun Li
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Bowei Han
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Min Zhang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Minying Sun
- Department of Primary Public Health, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- Institute of Public Health, Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Limin Huang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Gengxi Cai
- Department of Pathology, The First People's Hospital of Foshan, Foshan, China.
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Yingsong Wu
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.
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Abstract
This review delves into the rapidly evolving landscape of liquid biopsy technologies based on cell-free DNA (cfDNA) and cell-free RNA (cfRNA) and their increasingly prominent role in precision medicine. With the advent of high-throughput DNA sequencing, the use of cfDNA and cfRNA has revolutionized noninvasive clinical testing. Here, we explore the physical characteristics of cfDNA and cfRNA, present an overview of the essential engineering tools used by the field, and highlight clinical applications, including noninvasive prenatal testing, cancer testing, organ transplantation surveillance, and infectious disease testing. Finally, we discuss emerging technologies and the broadening scope of liquid biopsies to new areas of diagnostic medicine.
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Affiliation(s)
- Conor Loy
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA;
| | - Lauren Ahmann
- Department of Pathology, Stanford University, Stanford, California, USA;
| | - Iwijn De Vlaminck
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA;
| | - Wei Gu
- Department of Pathology, Stanford University, Stanford, California, USA;
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9
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Cheng JC, Swarup N, Morselli M, Huang WL, Aziz M, Caggiano C, Kordi M, Patel A, Chia D, Kim Y, Li F, Wei F, Zaitlen N, Krysan K, Dubinett S, Pellegrini M, Wong DW. Single-stranded pre-methylated 5mC adapters uncover the methylation profile of plasma ultrashort Single-stranded cell-free DNA. Nucleic Acids Res 2024; 52:e50. [PMID: 38797520 PMCID: PMC11194076 DOI: 10.1093/nar/gkae276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 03/21/2024] [Accepted: 04/15/2024] [Indexed: 05/29/2024] Open
Abstract
Whole-genome bisulfite sequencing (BS-Seq) measures cytosine methylation changes at single-base resolution and can be used to profile cell-free DNA (cfDNA). In plasma, ultrashort single-stranded cfDNA (uscfDNA, ∼50 nt) has been identified together with 167 bp double-stranded mononucleosomal cell-free DNA (mncfDNA). However, the methylation profile of uscfDNA has not been described. Conventional BS-Seq workflows may not be helpful because bisulfite conversion degrades larger DNA into smaller fragments, leading to erroneous categorization as uscfDNA. We describe the '5mCAdpBS-Seq' workflow in which pre-methylated 5mC (5-methylcytosine) single-stranded adapters are ligated to heat-denatured cfDNA before bisulfite conversion. This method retains only DNA fragments that are unaltered by bisulfite treatment, resulting in less biased uscfDNA methylation analysis. Using 5mCAdpBS-Seq, uscfDNA had lower levels of DNA methylation (∼15%) compared to mncfDNA and was enriched in promoters and CpG islands. Hypomethylated uscfDNA fragments were enriched in upstream transcription start sites (TSSs), and the intensity of enrichment was correlated with expressed genes of hemopoietic cells. Using tissue-of-origin deconvolution, we inferred that uscfDNA is derived primarily from eosinophils, neutrophils, and monocytes. As proof-of-principle, we show that characteristics of the methylation profile of uscfDNA can distinguish non-small cell lung carcinoma from non-cancer samples. The 5mCAdpBS-Seq workflow is recommended for any cfDNA methylation-based investigations.
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Affiliation(s)
- Jordan C Cheng
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Neeti Swarup
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Marco Morselli
- Department of Molecular, Cell, and Developmental Biology, Life Sciences Division, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
| | - Wei-Lun Huang
- Center of Applied Nanomedicine, National Cheng Kung University, Tainan, Taiwan
| | - Mohammad Aziz
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Christa Caggiano
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Misagh Kordi
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Abhijit A Patel
- Department of Therapeutic Radiology, Yale University, New Haven, CT, USA
| | - David Chia
- Department of Pathology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yong Kim
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Feng Li
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Fang Wei
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Noah Zaitlen
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kostyantyn Krysan
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Steve Dubinett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Matteo Pellegrini
- Department of Molecular, Cell, and Developmental Biology, Life Sciences Division, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - David T W Wong
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
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10
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Liu X, Yang M, Hu D, An Y, Wang W, Lin H, Pan Y, Ju J, Sun K. Systematic biases in reference-based plasma cell-free DNA fragmentomic profiling. CELL REPORTS METHODS 2024; 4:100793. [PMID: 38866008 PMCID: PMC11228372 DOI: 10.1016/j.crmeth.2024.100793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/23/2024] [Accepted: 05/20/2024] [Indexed: 06/14/2024]
Abstract
Plasma cell-free DNA (cfDNA) fragmentation patterns are emerging directions in cancer liquid biopsy with high translational significance. Conventionally, the cfDNA sequencing reads are aligned to a reference genome to extract their fragmentomic features. In this study, through cfDNA fragmentomics profiling using different reference genomes on the same datasets in parallel, we report systematic biases in such conventional reference-based approaches. The biases in cfDNA fragmentomic features vary among races in a sample-dependent manner and therefore might adversely affect the performances of cancer diagnosis assays across multiple clinical centers. In addition, to circumvent the analytical biases, we develop Freefly, a reference-free approach for cfDNA fragmentomics profiling. Freefly runs ∼60-fold faster than the conventional reference-based approach while generating highly consistent results. Moreover, cfDNA fragmentomic features reported by Freefly can be directly used for cancer diagnosis. Hence, Freefly possesses translational merit toward the rapid and unbiased measurement of cfDNA fragmentomics.
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Affiliation(s)
- Xiaoyi Liu
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Mengqi Yang
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China; Department of Chemical and Biological Engineering, Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR 999077, China
| | - Dingxue Hu
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China; Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Yunyun An
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Wanqiu Wang
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China; Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Huizhen Lin
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yuqi Pan
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China; Department of Biology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jia Ju
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China; Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China.
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11
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Bie Z, Ping Y, Li X, Lan X, Wang L. Accurate Early Detection and EGFR Mutation Status Prediction of Lung Cancer Using Plasma cfDNA Coverage Patterns: A Proof-of-Concept Study. Biomolecules 2024; 14:716. [PMID: 38927119 PMCID: PMC11202186 DOI: 10.3390/biom14060716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 06/02/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Lung cancer is a major global health concern with a low survival rate, often due to late-stage diagnosis. Liquid biopsy offers a non-invasive approach to cancer detection and monitoring, utilizing various features of circulating cell-free DNA (cfDNA). In this study, we established two models based on cfDNA coverage patterns at the transcription start sites (TSSs) from 6X whole-genome sequencing: an Early Cancer Screening Model and an EGFR mutation status prediction model. The Early Cancer Screening Model showed encouraging prediction ability, especially for early-stage lung cancer. The EGFR mutation status prediction model exhibited high accuracy in distinguishing between EGFR-positive and wild-type cases. Additionally, cfDNA coverage patterns at TSSs also reflect gene expression patterns at the pathway level in lung cancer patients. These findings demonstrate the potential applications of cfDNA coverage patterns at TSSs in early cancer screening and in cancer subtyping.
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Affiliation(s)
- Zhixin Bie
- Department of Minimally Invasive Tumor Therapies Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 Dongdan Dahua Street, Beijing 100730, China; (Z.B.); (X.L.)
| | - Yi Ping
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China;
| | - Xiaoguang Li
- Department of Minimally Invasive Tumor Therapies Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 Dongdan Dahua Street, Beijing 100730, China; (Z.B.); (X.L.)
| | - Xun Lan
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China;
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
| | - Lihui Wang
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China;
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12
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Hu X, Zhang H, Wang Y, Lin Y, Li Q, Li L, Zeng G, Ou R, Cheng X, Zhang Y, Jin X. Effects of blood-processing protocols on cell-free DNA fragmentomics in plasma: Comparisons of one- and two-step centrifugations. Clin Chim Acta 2024; 560:119729. [PMID: 38754575 DOI: 10.1016/j.cca.2024.119729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/11/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Cell-free DNA (cfDNA) fragmentomic characteristics are promising analytes with abundant physiological signals for non-invasive disease diagnosis and monitoring. Previous studies on plasma cfDNA fragmentomics commonly employed a two-step centrifugation process for removing cell debris, involving a low-speed centrifugation followed by a high-speed centrifugation. However, the effects of centrifugation conditions on the analysis of cfDNA fragmentome remain uncertain. METHODS We collected blood samples from 10 healthy individuals and divided each sample into two aliquots for plasma preparation with one- and two-step centrifugation processes. We performed whole genome sequencing (WGS) of the plasma cfDNA in the two groups and comprehensively compared the cfDNA fragmentomic features. Additionally, we reanalyzed the fragmentomic features of cfDNA from 16 healthy individuals and 16 COVID-19 patients, processed through one- and two-step centrifugation in our previous study, to investigate the impact of centrifugation on disease signals. RESULTS Our results showed that there were no significant differences observed in the characteristics of nuclear cfDNA, including size, motif diversity score (MDS) of end motifs, and genome distribution, between plasma samples treated with one- and two-step centrifugation. The cfDNA size shortening in COVID-19 patients was observed in plasma samples with one- and two-step centrifugation methods. However, we observed a significantly higher relative abundance and longer size of cell-free mitochondrial DNA (mtDNA) in the one-step samples compared to the two-step samples. This difference in mtDNA caused by the one- and two-step centrifugation methods surpasses the pathological difference between COVID-19 patients and healthy individuals. CONCLUSIONS Our findings indicate that one-step low-speed centrifugation is a simple and potentially suitable method for analyzing nuclear cfDNA fragmentation characteristics. These results offer valuable guidance for cfDNA research in various clinical scenarios.
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Affiliation(s)
- Xintao Hu
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; BGI Research, Shenzhen 518083, China
| | | | | | - Yu Lin
- BGI Research, Shenzhen 518083, China
| | - Qiuyan Li
- BGI Research, Shenzhen 518083, China
| | | | | | - Rijing Ou
- BGI Research, Shenzhen 518083, China
| | - Xinyu Cheng
- Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Yan Zhang
- BGI Research, Shenzhen 518083, China.
| | - Xin Jin
- BGI Research, Shenzhen 518083, China; School of Medicine, South China University of Technology, Guangzhou 510006, China.
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13
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Takahashi N, Pongor L, Agrawal SP, Shtumpf M, Rajapakse VN, Shafiei A, Schultz CW, Kim S, Roame D, Carter P, Vilimas R, Nichols S, Desai P, Figg WD, Bagheri M, Teif VB, Thomas A. Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.02.597054. [PMID: 38895436 PMCID: PMC11185519 DOI: 10.1101/2024.06.02.597054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Background Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), a cancer whose aggressive clinical course making it exceedingly challenging to obtain tumor biopsies. Methods Here, a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC, we study cfDNA low pass whole genome (0.1X coverage) and exome (130X) sequencing in comparison with time-point matched tumor, characterized using exome and transcriptome sequencing. Results Direct comparison of cfDNA versus tumor biopsy reveals that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not found in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Genomic sequencing coverage of plasma DNA fragments around transcription start sites shows distinct treatment-related changes and captures the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors, allowing prediction of SCLC neuroendocrine phenotypes and treatment responses. Conclusions These findings have important implications for non-invasive stratification and subtype-specific therapies for patients with SCLC, now treated as a single disease.
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Affiliation(s)
- Nobuyuki Takahashi
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
- Medical Oncology Branch, Center Hospital, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Medical Oncology, National Cancer Center East Hospital, Kashiwa, Japan
| | - Lorinc Pongor
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | | | - Mariya Shtumpf
- School of Life Sciences, University of Essex, Colchester, UK
| | - Vinodh N Rajapakse
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Ahmad Shafiei
- Department of Radiology and Imaging Sciences, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Christopher W Schultz
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Sehyun Kim
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Diana Roame
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Paula Carter
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Rasa Vilimas
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Samantha Nichols
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Parth Desai
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - William Douglas Figg
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Mohammad Bagheri
- Department of Radiology and Imaging Sciences, Center for Cancer Research, National Cancer Institute, Bethesda, USA
| | - Vladimir B Teif
- School of Life Sciences, University of Essex, Colchester, UK
| | - Anish Thomas
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, USA
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14
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Stutheit-Zhao EY, Sanz-Garcia E, Liu Z(A, Wong D, Marsh K, Abdul Razak AR, Spreafico A, Bedard PL, Hansen AR, Lheureux S, Torti D, Lam B, Yang SYC, Burgener J, Luo P, Zeng Y, Cheng N, Awadalla P, Bratman SV, Ohashi PS, Pugh TJ, Siu LL. Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors. Cancer Discov 2024; 14:1048-1063. [PMID: 38393391 PMCID: PMC11145176 DOI: 10.1158/2159-8290.cd-23-1060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/18/2024] [Accepted: 02/21/2024] [Indexed: 02/25/2024]
Abstract
Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles using cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) in 204 plasma samples from 87 patients before and during treatment with pembrolizumab from a pan-cancer phase II investigator-initiated trial (INSPIRE). We trained a pan-cancer methylation signature using independent methylation array data from The Cancer Genome Atlas to quantify cancer-specific methylation (CSM) and fragment-length score (FLS) for each sample. CSM and FLS are strongly correlated with tumor-informed ctDNA levels. Early kinetics of CSM predict overall survival and progression-free survival, independently of tumor type, PD-L1, and tumor mutation burden. Early kinetics of FLS are associated with overall survival independently of CSM. Our tumor-naïve mutation-agnostic ctDNA approach integrating methylomics and fragmentomics could predict outcomes in patients treated with pembrolizumab. SIGNIFICANCE Analysis of methylation and fragment length in plasma using cfMeDIP-seq provides a tumor-naive approach to measure ctDNA with results comparable with a tumor-informed bespoke ctDNA. Early kinetics within the first weeks of treatment in methylation and fragment quantity can predict outcomes with pembrolizumab in patients with various advanced solid tumors. This article is featured in Selected Articles from This Issue, p. 897.
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Affiliation(s)
- Eric Y. Stutheit-Zhao
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Enrique Sanz-Garcia
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Zhihui (Amy) Liu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Derek Wong
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Kayla Marsh
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Anna Spreafico
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Philippe L. Bedard
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Aaron R. Hansen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Stephanie Lheureux
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Dax Torti
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Bernard Lam
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Shih Yu Cindy Yang
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Justin Burgener
- Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ping Luo
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Yong Zeng
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Nicholas Cheng
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Scott V. Bratman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Pamela S. Ohashi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Trevor J. Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lillian L. Siu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
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15
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Li JW, Bandaru R, Liu Y. FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596414. [PMID: 38854007 PMCID: PMC11160763 DOI: 10.1101/2024.05.29.596414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Cell-free DNA (cfDNA) fragmentation pattern represents a promising non-invasive biomarker for disease diagnosis and prognosis. Numerous fragmentation features, such as end motif and window protection score (WPS), have been characterized in cfDNA genomic sequencing. However, the analytical tools developed in these studies are often not released to the liquid biopsy community or are inefficient for genome-wide analysis in large datasets. To address this gap, we have developed FinaleToolkit, a fast and memory efficient Python package designed to generate comprehensive fragmentation features from large cfDNA genomic sequencing data. For instance, FinaleToolkit can generate genome-wide WPS features from a ~100X cfDNA whole-genome sequencing (WGS) dataset in 1.2 hours using 16 CPU cores, offering up to a ~50-fold increase in processing speed compared to original implementations in the same dataset. We have benchmarked FinaleToolkit against original studies or implementations where possible, confirming its efficacy. Furthermore, FinaleToolkit enabled the genome-wide analysis of fragmentation patterns over arbitrary genomic intervals, significantly boosting the performance for cancer early detection. FinaleToolkit is open source and thoroughly documented with both command line interface and Python application programming interface (API) to facilitate its widespread adoption and use within the research community: https://github.com/epifluidlab/FinaleToolkit.
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Affiliation(s)
- James W. Li
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL 60611
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109
| | - Ravi Bandaru
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL 60611
| | - Yaping Liu
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL 60611
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16
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Shi X, Guo S, Duan Q, Zhang W, Gao S, Jing W, Jiang G, Kong X, Li P, Li Y, Teng C, Xu X, Chen S, Nian B, Li Z, Zhong C, Yang X, Zhu G, Du Y, Zhang D, Jin G. Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics. J Exp Clin Cancer Res 2024; 43:145. [PMID: 38750539 PMCID: PMC11094938 DOI: 10.1186/s13046-024-03067-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/08/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection. METHODS One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance. RESULTS The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved. CONCLUSIONS Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.
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Affiliation(s)
- Xiaohan Shi
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Shiwei Guo
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Qiaonan Duan
- Department of Clinical and Translational Medicine, 3D Medicines Inc, 158 Xin Junhuan Road, Pujiang Hi-Tech Park, Shanghai, 201114, China
| | - Wei Zhang
- Department of Clinical and Translational Medicine, 3D Medicines Inc, 158 Xin Junhuan Road, Pujiang Hi-Tech Park, Shanghai, 201114, China
| | - Suizhi Gao
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Wei Jing
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Guojuan Jiang
- Department of Clinical and Translational Medicine, 3D Medicines Inc, 158 Xin Junhuan Road, Pujiang Hi-Tech Park, Shanghai, 201114, China
| | - Xiangyu Kong
- Department of Gastroenterology, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Penghao Li
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Yikai Li
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Chuanqi Teng
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Xiaoya Xu
- Department of Clinical and Translational Medicine, 3D Medicines Inc, 158 Xin Junhuan Road, Pujiang Hi-Tech Park, Shanghai, 201114, China
| | - Sheng Chen
- Department of Clinical and Translational Medicine, 3D Medicines Inc, 158 Xin Junhuan Road, Pujiang Hi-Tech Park, Shanghai, 201114, China
| | - Baoning Nian
- Department of Clinical and Translational Medicine, 3D Medicines Inc, 158 Xin Junhuan Road, Pujiang Hi-Tech Park, Shanghai, 201114, China
| | - Zhikuan Li
- Department of Clinical and Translational Medicine, 3D Medicines Inc, 158 Xin Junhuan Road, Pujiang Hi-Tech Park, Shanghai, 201114, China
| | - Chaoliang Zhong
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Xiaolu Yang
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Guangyu Zhu
- Department of Interventional Radiology and Vascular Surgery, Zhongda Hospital, Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu Province, 210009, China.
| | - Yiqi Du
- Department of Gastroenterology, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China.
| | - Dadong Zhang
- Department of Clinical and Translational Medicine, 3D Medicines Inc, 158 Xin Junhuan Road, Pujiang Hi-Tech Park, Shanghai, 201114, China.
| | - Gang Jin
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China.
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17
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Hiatt JB, Doebley AL, Arnold HU, Adil M, Sandborg H, Persse TW, Ko M, Wu F, Quintanal Villalonga A, Santana-Davila R, Eaton K, Dive C, Rudin CM, Thomas A, Houghton AM, Ha G, MacPherson D. Molecular phenotyping of small cell lung cancer using targeted cfDNA profiling of transcriptional regulatory regions. SCIENCE ADVANCES 2024; 10:eadk2082. [PMID: 38598634 PMCID: PMC11006233 DOI: 10.1126/sciadv.adk2082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 02/27/2024] [Indexed: 04/12/2024]
Abstract
We report an approach for cancer phenotyping based on targeted sequencing of cell-free DNA (cfDNA) for small cell lung cancer (SCLC). In SCLC, differential activation of transcription factors (TFs), such as ASCL1, NEUROD1, POU2F3, and REST defines molecular subtypes. We designed a targeted capture panel that identifies chromatin organization signatures at 1535 TF binding sites and 13,240 gene transcription start sites and detects exonic mutations in 842 genes. Sequencing of cfDNA from SCLC patient-derived xenograft models captured TF activity and gene expression and revealed individual highly informative loci. Prediction models of ASCL1 and NEUROD1 activity using informative loci achieved areas under the receiver operating characteristic curve (AUCs) from 0.84 to 0.88 in patients with SCLC. As non-SCLC (NSCLC) often transforms to SCLC following targeted therapy, we applied our framework to distinguish NSCLC from SCLC and achieved an AUC of 0.99. Our approach shows promising utility for SCLC subtyping and transformation monitoring, with potential applicability to diverse tumor types.
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Affiliation(s)
- Joseph B. Hiatt
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Veterans Affairs Puget Sound Healthcare System - Seattle Branch, Seattle, WA, USA
- Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Anna-Lisa Doebley
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Henry U. Arnold
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Mohamed Adil
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Holly Sandborg
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Thomas W. Persse
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Minjeong Ko
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Feinan Wu
- Genomics and Bioinformatics Shared Resource, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Alvaro Quintanal Villalonga
- Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rafael Santana-Davila
- Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Keith Eaton
- Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Caroline Dive
- Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK
| | - Charles M. Rudin
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Graduate Program in Pharmacology, Weill Cornell Medical College; New York, NY, USA
| | - Anish Thomas
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - A. McGarry Houghton
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Gavin Ha
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - David MacPherson
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
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18
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Scaini MC, Catoni C, Poggiana C, Pigozzo J, Piccin L, Leone K, Scarabello I, Facchinetti A, Menin C, Elefanti L, Pellegrini S, Aleotti V, Vidotto R, Schiavi F, Fabozzi A, Chiarion-Sileni V, Rosato A. A multiparameter liquid biopsy approach allows to track melanoma dynamics and identify early treatment resistance. NPJ Precis Oncol 2024; 8:78. [PMID: 38548846 PMCID: PMC10978909 DOI: 10.1038/s41698-024-00567-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 03/14/2024] [Indexed: 04/01/2024] Open
Abstract
Melanoma heterogeneity is a hurdle in metastatic disease management. Although the advent of targeted therapy has significantly improved patient outcomes, the occurrence of resistance makes monitoring of the tumor genetic landscape mandatory. Liquid biopsy could represent an important biomarker for the real-time tracing of disease evolution. Thus, we aimed to correlate liquid biopsy dynamics with treatment response and progression by devising a multiplatform approach applied to longitudinal melanoma patient monitoring. We conceived an approach that exploits Next Generation Sequencing (NGS) and droplet digital PCR, as well as the FDA-cleared platform CellSearch, to analyze circulating tumor DNA (ctDNA) trend and circulating melanoma cell (CMC) count, together with their customized genetic and copy number variation analysis. The approach was applied to 17 stage IV melanoma patients treated with BRAF/MEK inhibitors, followed for up to 28 months. BRAF mutations were detected in the plasma of 82% of patients. Single nucleotide variants known or suspected to confer resistance were identified in 70% of patients. Moreover, the amount of ctDNA, both at baseline and during response, correlated with the type and duration of the response itself, and the CMC count was confirmed to be a prognostic biomarker. This work provides proof of principle of the power of this approach and paves the way for a validation study aimed at evaluating early ctDNA-guided treatment decisions in stage IV melanoma. The NGS-based molecular profile complemented the analysis of ctDNA trend and, together with CMC analysis, revealed to be useful in capturing tumor evolution.
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Affiliation(s)
- Maria Chiara Scaini
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy.
| | - Cristina Catoni
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Cristina Poggiana
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy.
| | - Jacopo Pigozzo
- Medical Oncology 2, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Luisa Piccin
- Medical Oncology 2, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Kevin Leone
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Ilaria Scarabello
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Antonella Facchinetti
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
- Department of Surgery, Oncology and Gastroenterology (DiSCOG), Oncology Section, University of Padua, Padua, Italy
| | - Chiara Menin
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Lisa Elefanti
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Stefania Pellegrini
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Valentina Aleotti
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Riccardo Vidotto
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Francesca Schiavi
- Familial Cancer Clinic, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Alessio Fabozzi
- Oncology Unit 3, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | | | - Antonio Rosato
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
- Department of Surgery, Oncology and Gastroenterology (DiSCOG), Oncology Section, University of Padua, Padua, Italy
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19
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Che H, Jiang P, Choy LYL, Cheng SH, Peng W, Chan RWY, Liu J, Zhou Q, Lam WKJ, Yu SCY, Lau SL, Leung TY, Wong J, Wong VWS, Wong GLH, Chan SL, Chan KCA, Lo YMD. Genomic origin, fragmentomics, and transcriptional properties of long cell-free DNA molecules in human plasma. Genome Res 2024; 34:189-200. [PMID: 38408788 PMCID: PMC10984381 DOI: 10.1101/gr.278556.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024]
Abstract
Recent studies have revealed an unexplored population of long cell-free DNA (cfDNA) molecules in human plasma using long-read sequencing technologies. However, the biological properties of long cfDNA molecules (>500 bp) remain largely unknown. To this end, we have investigated the origins of long cfDNA molecules from different genomic elements. Analysis of plasma cfDNA using long-read sequencing reveals an uneven distribution of long molecules from across the genome. Long cfDNA molecules show overrepresentation in euchromatic regions of the genome, in sharp contrast to short DNA molecules. We observe a stronger relationship between the abundance of long molecules and mRNA gene expression levels, compared with short molecules (Pearson's r = 0.71 vs. -0.14). Moreover, long and short molecules show distinct fragmentation patterns surrounding CpG sites. Leveraging the cleavage preferences surrounding CpG sites, the combined cleavage ratios of long and short molecules can differentiate patients with hepatocellular carcinoma (HCC) from non-HCC subjects (AUC = 0.87). We also investigated knockout mice in which selected nuclease genes had been inactivated in comparison with wild-type mice. The proportion of long molecules originating from transcription start sites are lower in Dffb-deficient mice but higher in Dnase1l3-deficient mice compared with that of wild-type mice. This work thus provides new insights into the biological properties and potential clinical applications of long cfDNA molecules.
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Affiliation(s)
- Huiwen Che
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Peiyong Jiang
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - L Y Lois Choy
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Suk Hang Cheng
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Wenlei Peng
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Rebecca W Y Chan
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Jing Liu
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Qing Zhou
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - W K Jacky Lam
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Stephanie C Y Yu
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - So Ling Lau
- Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Tak Y Leung
- Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - John Wong
- Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Vincent Wai-Sun Wong
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Grace L H Wong
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Stephen L Chan
- State Key Laboratory of Translational Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Clinical Oncology, Sir Y.K. Pao Centre for Cancer, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - K C Allen Chan
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Y M Dennis Lo
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China;
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- State Key Laboratory of Translational Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
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20
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Annapragada AV, Niknafs N, White JR, Bruhm DC, Cherry C, Medina JE, Adleff V, Hruban C, Mathios D, Foda ZH, Phallen J, Scharpf RB, Velculescu VE. Genome-wide repeat landscapes in cancer and cell-free DNA. Sci Transl Med 2024; 16:eadj9283. [PMID: 38478628 PMCID: PMC11323656 DOI: 10.1126/scitranslmed.adj9283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 02/16/2024] [Indexed: 03/22/2024]
Abstract
Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.
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Affiliation(s)
- Akshaya V. Annapragada
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Noushin Niknafs
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - James R. White
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Daniel C. Bruhm
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Christopher Cherry
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jamie E. Medina
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Vilmos Adleff
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Carolyn Hruban
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Dimitrios Mathios
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Zachariah H. Foda
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jillian Phallen
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Robert B. Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Victor E. Velculescu
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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21
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Wever BMM, Steenbergen RDM. Unlocking the potential of tumor-derived DNA in urine for cancer detection: methodological challenges and opportunities. Mol Oncol 2024. [PMID: 38462745 DOI: 10.1002/1878-0261.13628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/20/2023] [Accepted: 01/27/2024] [Indexed: 03/12/2024] Open
Abstract
High cancer mortality rates and the rising cancer burden worldwide drive the development of innovative methods in order to advance cancer diagnostics. Urine contains a viable source of tumor material and allows for self-collection from home. Biomarker testing in this liquid biopsy represents a novel approach that is convenient for patients and can be effective in detecting cancer at a curable stage. Here, we set out to provide a detailed overview of the rationale behind urine-based cancer detection, with a focus on non-urological cancers, and its potential for cancer diagnostics. Moreover, evolving methodological challenges and untapped opportunities for urine biomarker testing are discussed, particularly emphasizing DNA methylation of tumor-derived cell-free DNA. We also provide future recommendations for technical advancements in urine-based cancer detection and elaborate on potential mechanisms involved in the transrenal transport of cell-free DNA.
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Affiliation(s)
- Birgit M M Wever
- Department of Pathology, Amsterdam UMC, location Vrije Universiteit Amsterdam, The Netherlands
- Imaging and Biomarkers, Cancer Center Amsterdam, The Netherlands
| | - Renske D M Steenbergen
- Department of Pathology, Amsterdam UMC, location Vrije Universiteit Amsterdam, The Netherlands
- Imaging and Biomarkers, Cancer Center Amsterdam, The Netherlands
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22
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Garcia-Medina JS, Sienkiewicz K, Narayanan SA, Overbey EG, Grigorev K, Ryon KA, Burke M, Proszynski J, Tierney B, Schmidt CM, Mencia-Trinchant N, Klotz R, Ortiz V, Foox J, Chin C, Najjar D, Matei I, Chan I, Cruchaga C, Kleinman A, Kim J, Lucaci A, Loy C, Mzava O, De Vlaminck I, Singaraju A, Taylor LE, Schmidt JC, Schmidt MA, Blease K, Moreno J, Boddicker A, Zhao J, Lajoie B, Altomare A, Kruglyak S, Levy S, Yu M, Hassane DC, Bailey SM, Bolton K, Mateus J, Mason CE. Genome and clonal hematopoiesis stability contrasts with immune, cfDNA, mitochondrial, and telomere length changes during short duration spaceflight. PRECISION CLINICAL MEDICINE 2024; 7:pbae007. [PMID: 38634106 PMCID: PMC11022651 DOI: 10.1093/pcmedi/pbae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 03/24/2024] [Indexed: 04/19/2024] Open
Abstract
Background The Inspiration4 (I4) mission, the first all-civilian orbital flight mission, investigated the physiological effects of short-duration spaceflight through a multi-omic approach. Despite advances, there remains much to learn about human adaptation to spaceflight's unique challenges, including microgravity, immune system perturbations, and radiation exposure. Methods To provide a detailed genetics analysis of the mission, we collected dried blood spots pre-, during, and post-flight for DNA extraction. Telomere length was measured by quantitative PCR, while whole genome and cfDNA sequencing provided insight into genomic stability and immune adaptations. A robust bioinformatic pipeline was used for data analysis, including variant calling to assess mutational burden. Result Telomere elongation occurred during spaceflight and shortened after return to Earth. Cell-free DNA analysis revealed increased immune cell signatures post-flight. No significant clonal hematopoiesis of indeterminate potential (CHIP) or whole-genome instability was observed. The long-term gene expression changes across immune cells suggested cellular adaptations to the space environment persisting months post-flight. Conclusion Our findings provide valuable insights into the physiological consequences of short-duration spaceflight, with telomere dynamics and immune cell gene expression adapting to spaceflight and persisting after return to Earth. CHIP sequencing data will serve as a reference point for studying the early development of CHIP in astronauts, an understudied phenomenon as previous studies have focused on career astronauts. This study will serve as a reference point for future commercial and non-commercial spaceflight, low Earth orbit (LEO) missions, and deep-space exploration.
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Affiliation(s)
- J Sebastian Garcia-Medina
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Karolina Sienkiewicz
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - S Anand Narayanan
- Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, FL 32306, USA
| | - Eliah G Overbey
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
- BioAstra Inc, New York, NY, USA
| | - Kirill Grigorev
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Krista A Ryon
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Marissa Burke
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Jacqueline Proszynski
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Braden Tierney
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Caleb M Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis & Human Performance Group, Boulder, CO 80302, USA
- Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA
| | - Nuria Mencia-Trinchant
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Remi Klotz
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Veronica Ortiz
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Christopher Chin
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
- BioAstra Inc, New York, NY, USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, NY 10021, USA
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10021, USA
| | - Deena Najjar
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Irina Matei
- Children's Cancer and Blood Foundation Laboratories, Departments of Pediatrics and Cell and Developmental Biology, Drukier Institute for Children's Health, Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA
| | - Irenaeus Chan
- Washington University St. Louis Oncology Division, St. Louis, MO 63100, USA
| | - Carlos Cruchaga
- Washington University St. Louis Oncology Division, St. Louis, MO 63100, USA
| | - Ashley Kleinman
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - JangKeun Kim
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Alexander Lucaci
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Conor Loy
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Omary Mzava
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Iwijn De Vlaminck
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Anvita Singaraju
- Department of Immunology, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Lynn E Taylor
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA
| | - Julian C Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis & Human Performance Group, Boulder, CO 80302, USA
| | - Michael A Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis & Human Performance Group, Boulder, CO 80302, USA
| | | | - Juan Moreno
- Element Biosciences, San Diego, CA 10055, USA
| | | | - Junhua Zhao
- Element Biosciences, San Diego, CA 10055, USA
| | | | | | | | - Shawn Levy
- Element Biosciences, San Diego, CA 10055, USA
| | - Min Yu
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Duane C Hassane
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
| | - Susan M Bailey
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA
- Cell and Molecular Biology Program, Colorado State University, Fort Collins, CO 80523, USA
| | - Kelly Bolton
- Washington University St. Louis Oncology Division, St. Louis, MO 63100, USA
| | - Jaime Mateus
- Space Exploration Technologies Corporation, Hawthorne, CA 90250, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
- BioAstra Inc, New York, NY, USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, NY 10021, USA
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10021, USA
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23
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Chen L, Wu T, Fan R, Qian YS, Liu JF, Bai J, Zheng B, Liu XL, Zheng D, Du LT, Jiang GQ, Wang YC, Fan XT, Deng GH, Wang CY, Shen F, Hu HP, Zhang QZ, Ye YN, Zhang J, Gao YH, Xia J, Yan HD, Liang MF, Yu YL, Sun FM, Gao YJ, Sun J, Zhong CX, Wang Y, Wang H, Kong F, Chen JM, Wen H, Wu BM, Wang CX, Wu L, Hou JL, Wang HY. Cell-free DNA testing for early hepatocellular carcinoma surveillance. EBioMedicine 2024; 100:104962. [PMID: 38184937 PMCID: PMC10808903 DOI: 10.1016/j.ebiom.2023.104962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/17/2023] [Accepted: 12/24/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Liver cirrhosis (LC) is the highest risk factor for hepatocellular carcinoma (HCC) development worldwide. The efficacy of the guideline-recommended surveillance methods for patients with LC remains unpromising. METHODS A total of 4367 LCs not previously known to have HCC and 510 HCCs from 16 hospitals across 11 provinces of China were recruited in this multi-center, large-scale, cross-sectional study. Participants were divided into Stage Ⅰ cohort (510 HCCs and 2074 LCs) and Stage Ⅱ cohort (2293 LCs) according to their enrollment time and underwent Tri-phasic CT/enhanced MRI, US, AFP, and cell-free DNA (cfDNA). A screening model called PreCar Score was established based on five features of cfDNA using Stage Ⅰ cohort. Surveillance performance of PreCar Score alone or in combination with US/AFP was evaluated in Stage Ⅱ cohort. FINDINGS PreCar Score showed a significantly higher sensitivity for the detection of early/very early HCC (Barcelona stage A/0) in contrast to US (sensitivity of 51.32% [95% CI: 39.66%-62.84%] at 95.53% [95% CI: 94.62%-96.38%] specificity for PreCar Score; sensitivity of 23.68% [95% CI: 14.99%-35.07%] at 99.37% [95% CI: 98.91%-99.64%] specificity for US) (P < 0.01, Fisher's exact test). PreCar Score plus US further achieved a higher sensitivity of 60.53% at 95.08% specificity for early/very early HCC screening. INTERPRETATION Our study developed and validated a cfDNA-based screening tool (PreCar Score) for HCC in cohorts at high risk. The combination of PreCar Score and US can serve as a promising and practical strategy for routine HCC care. FUNDING A full list of funding bodies that contributed to this study can be found in Acknowledgments section.
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Affiliation(s)
- Lei Chen
- National Center of Liver Cancer, Navel Medical University, Shanghai, 210822, PR China; International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute/Hospital, Shanghai, 200438, PR China.
| | - Tong Wu
- National Center of Liver Cancer, Navel Medical University, Shanghai, 210822, PR China; Department of Radiation Oncology, General Hospital of Northern Theater Command, Shenyang, l10016, PR China
| | - Rong Fan
- Department of Infectious Diseases, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, PR China; Hepatology Unit, Shenzhen Hospital, Southern Medical University, Shenzhen, PR China
| | - Yun-Song Qian
- Hepatology Department, Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, Ningbo, 315010, PR China
| | - Jing-Feng Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, PR China
| | - Jian Bai
- Berry Oncology Corporation, Beijing, 100102, PR China
| | - Bo Zheng
- National Center of Liver Cancer, Navel Medical University, Shanghai, 210822, PR China; International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute/Hospital, Shanghai, 200438, PR China
| | - Xiao-Long Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, PR China
| | - Dan Zheng
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, PR China
| | - Lu-Tao Du
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, 247 Beiyuan Street, Jinan 250033, Shandong, PR China; Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, 250033, PR China
| | - Guo-Qing Jiang
- Department of Hepatobiliary Surgery, Clinical Medical College, Yangzhou University, Yangzhou, 225001, PR China
| | - Ying-Chao Wang
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, PR China
| | - Xiao-Tang Fan
- Department of Hepatology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, PR China
| | - Guo-Hong Deng
- Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, PR China
| | - Chun-Ying Wang
- Xuzhou Infectious Diseases Hospital, Xuzhou, 221004, PR China
| | - Feng Shen
- Department of Hepatic Surgery IV, Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai, 200438, PR China
| | - He-Ping Hu
- Department of Hepatobiliary Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, 210822, PR China
| | | | - Yi-Nong Ye
- The Department of Infectious Disease, The First People's Hospital of Foshan, Foshan City, 528000, PR China
| | - Jing Zhang
- Berry Oncology Corporation, Beijing, 100102, PR China
| | - Yan-Hang Gao
- The First Hospital of Jilin University, Jilin, 130021, PR China
| | - Jie Xia
- Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, PR China
| | - Hua-Dong Yan
- Hepatology Department, Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, Ningbo, 315010, PR China
| | - Min-Feng Liang
- The Department of Infectious Disease, The First People's Hospital of Foshan, Foshan City, 528000, PR China
| | - Yan-Long Yu
- Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, 024000, PR China
| | - Fu-Ming Sun
- Berry Oncology Corporation, Beijing, 100102, PR China
| | - Yu-Jing Gao
- Xuzhou Infectious Diseases Hospital, Xuzhou, 221004, PR China
| | - Jian Sun
- Department of Infectious Diseases, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, PR China
| | - Chun-Xiu Zhong
- Department of Infectious Diseases, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, PR China
| | - Yin Wang
- Berry Oncology Corporation, Beijing, 100102, PR China
| | - Hui Wang
- Department of Hepatobiliary Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, 210822, PR China
| | - Fei Kong
- The First Hospital of Jilin University, Jilin, 130021, PR China
| | - Jin-Ming Chen
- Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, 024000, PR China
| | - Hao Wen
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, PR China
| | - Bo-Ming Wu
- Hepatology Department, Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, Ningbo, 315010, PR China
| | - Chuan-Xin Wang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, 247 Beiyuan Street, Jinan 250033, Shandong, PR China; Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, 250033, PR China.
| | - Lin Wu
- Berry Oncology Corporation, Beijing, 100102, PR China.
| | - Jin-Lin Hou
- Department of Infectious Diseases, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, PR China; Hepatology Unit, Shenzhen Hospital, Southern Medical University, Shenzhen, PR China.
| | - Hong-Yang Wang
- National Center of Liver Cancer, Navel Medical University, Shanghai, 210822, PR China; International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute/Hospital, Shanghai, 200438, PR China; Key Laboratory of Signaling Regulation and Targeting Therapy of Liver Cancer (SMMU), Ministry of Education, Shanghai, 200438, PR China; Shanghai Key Laboratory of Hepatobiliary Tumor Biology (EHBH), Shanghai, 200438, PR China.
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24
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Zhang K, Fu R, Liu R, Su Z. Circulating cell-free DNA-based multi-cancer early detection. Trends Cancer 2024; 10:161-174. [PMID: 37709615 DOI: 10.1016/j.trecan.2023.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/03/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023]
Abstract
Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.
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Affiliation(s)
- Kai Zhang
- Department of Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Chaoyang District, Beijing 100021, China
| | - Ruiqing Fu
- Singlera Genomics Ltd, Shanghai 201203, China
| | - Rui Liu
- Singlera Genomics Ltd, Shanghai 201203, China
| | - Zhixi Su
- Singlera Genomics Ltd, Shanghai 201203, China.
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25
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Xie K, Hou Y, Zhou X. Deep centroid: a general deep cascade classifier for biomedical omics data classification. Bioinformatics 2024; 40:btae039. [PMID: 38305432 PMCID: PMC10868341 DOI: 10.1093/bioinformatics/btae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/13/2024] [Accepted: 01/30/2024] [Indexed: 02/03/2024] Open
Abstract
MOTIVATION Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent biases across diverse sources. These factors limit the performance of traditional machine learning models, particularly when applied to independent datasets. RESULTS To address these challenges, we propose a novel classifier, Deep Centroid, which combines the stability of the nearest centroid classifier and the strong fitting ability of the deep cascade strategy. Deep Centroid is an ensemble learning method with a multi-layer cascade structure, consisting of feature scanning and cascade learning stages that can dynamically adjust the training scale. We apply Deep Centroid to three precision medicine applications-cancer early diagnosis, cancer prognosis, and drug sensitivity prediction-using cell-free DNA fragmentations, gene expression profiles, and DNA methylation data. Experimental results demonstrate that Deep Centroid outperforms six traditional machine learning models in all three applications, showcasing its potential in biological omics data classification. Furthermore, functional annotations reveal that the features scanned by the model exhibit biological significance, indicating its interpretability from a biological perspective. Our findings underscore the promising application of Deep Centroid in the classification of biomedical omics data, particularly in the field of precision medicine. AVAILABILITY AND IMPLEMENTATION Deep Centroid is available at both github (github.com/xiexiexiekuan/DeepCentroid) and Figshare (https://figshare.com/articles/software/Deep_Centroid_A_General_Deep_Cascade_Classifier_for_Biomedical_Omics_Data_Classification/24993516).
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Affiliation(s)
- Kuan Xie
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, People’s Republic of China
| | - Yuying Hou
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, People’s Republic of China
| | - Xionghui Zhou
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, People’s Republic of China
- Key Laboratory of Smart Farming for Agricultural Animals, Huazhong Agricultural University, Wuhan 430070, People’s Republic of China
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26
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Shen H, Liu J, Chen K, Li X. Language model enables end-to-end accurate detection of cancer from cell-free DNA. Brief Bioinform 2024; 25:bbae053. [PMID: 38385880 PMCID: PMC10883418 DOI: 10.1093/bib/bbae053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/28/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
We present a language model Affordable Cancer Interception and Diagnostics (ACID) that can achieve high classification performance in the diagnosis of cancer exclusively from using raw cfDNA sequencing reads. We formulate ACID as an autoregressive language model. ACID is pretrained with language sentences that are obtained from concatenation of raw sequencing reads and diagnostic labels. We benchmark ACID against three methods. On testing set subjected to whole-genome sequencing, ACID significantly outperforms the best benchmarked method in diagnosis of cancer [Area Under the Receiver Operating Curve (AUROC), 0.924 versus 0.853; P < 0.001] and detection of hepatocellular carcinoma (AUROC, 0.981 versus 0.917; P < 0.001). ACID can achieve high accuracy with just 10 000 reads per sample. Meanwhile, ACID achieves the best performance on testing sets that were subjected to bisulfite sequencing compared with benchmarked methods. In summary, we present an affordable, simple yet efficient end-to-end paradigm for cancer detection using raw cfDNA sequencing reads.
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Affiliation(s)
- Hongru Shen
- Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jilei Liu
- Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xiangchun Li
- Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
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27
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Maansson CT, Thomsen LS, Meldgaard P, Nielsen AL, Sorensen BS. Integration of Cell-Free DNA End Motifs and Fragment Lengths Can Identify Active Genes in Liquid Biopsies. Int J Mol Sci 2024; 25:1243. [PMID: 38279243 PMCID: PMC10815977 DOI: 10.3390/ijms25021243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 01/28/2024] Open
Abstract
Multiple studies have shown that cell-free DNA (cfDNA) from cancer patients differ in both fragment length and fragment end motif (FEM) from healthy individuals, yet there is a lack of understanding of how the two factors combined are associated with cancer and gene transcription. In this study, we conducted cfDNA fragmentomics evaluations using plasma from lung cancer patients (n = 12) and healthy individuals (n = 7). A personal gene expression profile was established from plasma using H3K36me3 cell-free chromatin immunoprecipitation sequencing (cfChIP-seq). The genes with the highest expression displayed an enrichment of short cfDNA fragments (median = 19.99%, IQR: 16.94-27.13%, p < 0.0001) compared to the genes with low expression. Furthermore, distinct GC-rich FEMs were enriched after cfChIP. Combining the frequency of short cfDNA fragments with the presence of distinct FEMs resulted in an even further enrichment of the most expressed genes (median = 37.85%, IQR: 30.10-39.49%, p < 0.0001). An in vitro size selection of <150 bp cfDNA could isolate cfDNA representing active genes and the size-selection enrichment correlated with the cfChIP-seq enrichment (Spearman r range: 0.499-0.882, p < 0.0001). This study expands the knowledge regarding cfDNA fragmentomics and sheds new light on how gene activity is associated with both cfDNA fragment lengths and distinct FEMs.
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Affiliation(s)
- Christoffer Trier Maansson
- Department of Clinical Biochemistry, Aarhus University Hospital, 8200 Aarhus, Denmark; (C.T.M.)
- Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark;
| | - Louise Skov Thomsen
- Department of Clinical Biochemistry, Aarhus University Hospital, 8200 Aarhus, Denmark; (C.T.M.)
| | - Peter Meldgaard
- Department of Oncology, Aarhus University Hospital, 8200 Aarhus, Denmark;
| | | | - Boe Sandahl Sorensen
- Department of Clinical Biochemistry, Aarhus University Hospital, 8200 Aarhus, Denmark; (C.T.M.)
- Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark
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28
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Li Y, Xu J, Chen C, Lu Z, Wan D, Li D, Li JS, Sorg AJ, Roberts CC, Mahajan S, Gallant MA, Pinkoviezky I, Cui Y, Taggart DJ, Li W. Multimodal epigenetic sequencing analysis (MESA) of cell-free DNA for non-invasive colorectal cancer detection. Genome Med 2024; 16:9. [PMID: 38225592 PMCID: PMC10790422 DOI: 10.1186/s13073-023-01280-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 12/22/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Detecting human cancers through cell-free DNA (cfDNA) in blood is a sensitive and non-invasive option. However, capturing multiple forms of epigenetic information remains a technical and financial challenge. METHODS To address this, we developed multimodal epigenetic sequencing analysis (MESA), a flexible and sensitive approach to capturing and integrating a diverse range of epigenetic features in cfDNA using a single experimental assay, i.e., non-disruptive bisulfite-free methylation sequencing, such as Enzymatic Methyl-seq. MESA enables simultaneous inference of four epigenetic modalities: cfDNA methylation, nucleosome occupancy, nucleosome fuzziness, and windowed protection score for regions surrounding gene promoters and polyadenylation sites. RESULTS When applied to 690 cfDNA samples from 3 colorectal cancer clinical cohorts, MESA's novel modalities, which include nucleosome fuzziness, and genomic features, including polyadenylation sites, improve cancer detection beyond the traditional epigenetic markers of promoter DNA methylation. CONCLUSIONS Together, MESA stands as a major advancement in the field by utilizing comprehensive and complementary epigenetic profiles of cfDNA for effective non-invasive cancer detection.
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Affiliation(s)
- Yumei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA
- School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, P. R. China
| | | | - Chaorong Chen
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Zhenhai Lu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P. R. China
| | - Desen Wan
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P. R. China
| | - Diange Li
- Guangzhou Youze Biological Pharmaceutical Technology Company Ltd, Guangzhou, 510005, P. R. China
| | - Jason S Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA
| | | | | | | | | | | | - Ya Cui
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA
| | | | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA.
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29
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Luo Y, Zhang H, Li L, Lin Y, Wang X, Chen W, Tao Y, Ou R, Zhou W, Zheng F, Jin Y, Cheng F, Zhu H, Zhang Y, Jin X. Heat inactivation does not alter host plasma cell-free DNA characteristics in infectious disease research. Clin Chim Acta 2024; 553:117751. [PMID: 38163539 DOI: 10.1016/j.cca.2023.117751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/28/2023] [Accepted: 12/28/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Cell-free DNA (cfDNA) is a promising analyte for non-invasive liquid biopsy, carrying abundant signatures for disease diagnosis and monitoring. In infectious disease researches, blood plasma samples are routinely heat-inactivated before proceeding with downstream analyses. However, the effects of heat inactivation on cfDNA fragmentomic analysis remain largely unclear, potentially introducing biases or altering the characteristics of cfDNA. METHODS We performed a comprehensive investigation of cfDNA concentrations and fragmentomics in 21 plasma samples from 7 healthy individuals, by comparing the sample group without the heat inactivation to those exposed to once or twice heat-inactivation at 56 °C for 30 min and following freeze-thaw. RESULTS Plasma samples with once and twice heat inactivation displayed no significant deviations in primary characteristics, including cfDNA concentrations, size profiles, end motif features, and genome-wide distributions, compared to samples without heat treatment. CONCLUSIONS Heat-inactivated cfDNA can be utilized for liquid biopsy in infectious disease researches, without substantial impact on cfDNA concentrations and fragmentomic properties. This study provides essential insights into the effects of heat inactivation on cfDNA properties and will contribute to the development of reliable non-invasive biomarkers for infectious disease.
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Affiliation(s)
- Yuxue Luo
- School of Medicine, South China University of Technology, Guangzhou 510006, Guangdong, China
| | | | - Lingguo Li
- BGI-Shenzhen, Shenzhen 518083, Guangdong, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yu Lin
- BGI-Shenzhen, Shenzhen 518083, Guangdong, China
| | - Xinxin Wang
- BGI-Shenzhen, Shenzhen 518083, Guangdong, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Wei Chen
- School of Medicine, South China University of Technology, Guangzhou 510006, Guangdong, China; BGI-Shenzhen, Shenzhen 518083, Guangdong, China
| | - Ye Tao
- BGI-Shenzhen, Shenzhen 518083, Guangdong, China
| | - Rijing Ou
- BGI-Shenzhen, Shenzhen 518083, Guangdong, China
| | - Wenwen Zhou
- BGI-Shenzhen, Shenzhen 518083, Guangdong, China
| | - Fang Zheng
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Yan Jin
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Fanjun Cheng
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | | | - Yan Zhang
- BGI-Shenzhen, Shenzhen 518083, Guangdong, China.
| | - Xin Jin
- School of Medicine, South China University of Technology, Guangzhou 510006, Guangdong, China; BGI-Shenzhen, Shenzhen 518083, Guangdong, China; Shenzhen Key Laboratory of Transomics Biotechnologies, BGI-Shenzhen, Shenzhen 518083, China.
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30
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Spiegl B, Kapidzic F, Röner S, Kircher M, Speicher M. GCparagon: evaluating and correcting GC biases in cell-free DNA at the fragment level. NAR Genom Bioinform 2023; 5:lqad102. [PMID: 38025047 PMCID: PMC10657415 DOI: 10.1093/nargab/lqad102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/18/2023] [Accepted: 11/07/2023] [Indexed: 12/01/2023] Open
Abstract
Analyses of cell-free DNA (cfDNA) are increasingly being employed for various diagnostic and research applications. Many technologies aim to increase resolution, e.g. for detecting early-stage cancer or minimal residual disease. However, these efforts may be confounded by inherent base composition biases of cfDNA, specifically the over - and underrepresentation of guanine (G) and cytosine (C) sequences. Currently, there is no universally applicable tool to correct these effects on sequencing read-level data. Here, we present GCparagon, a two-stage algorithm for computing and correcting GC biases in cfDNA samples. In the initial step, length and GC base count parameters are determined. Here, our algorithm minimizes the inclusion of known problematic genomic regions, such as low-mappability regions, in its calculations. In the second step, GCparagon computes weights counterbalancing the distortion of cfDNA attributes (correction matrix). These fragment weights are added to a binary alignment map (BAM) file as alignment tags for individual reads. The GC correction matrix or the tagged BAM file can be used for downstream analyses. Parallel computing allows for a GC bias estimation below 1 min. We demonstrate that GCparagon vastly improves the analysis of regulatory regions, which frequently show specific GC composition patterns and will contribute to standardized cfDNA applications.
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Affiliation(s)
- Benjamin Spiegl
- Institute of Human Genetics, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, 8010 Graz, Austria
| | - Faruk Kapidzic
- Institute of Human Genetics, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, 8010 Graz, Austria
| | - Sebastian Röner
- Exploratory Diagnostic Sciences, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Martin Kircher
- Exploratory Diagnostic Sciences, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
- Institute of Human Genetics, University Medical Center Schleswig-Holstein (UKSH), University of Lübeck, 23562 Lübeck, Germany
| | - Michael R Speicher
- Institute of Human Genetics, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
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31
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Hallermayr A, Keßler T, Steinke-Lange V, Heitzer E, Holinski-Feder E, Speicher M. The utility of liquid biopsy in clinical genetic diagnosis of cancer and monogenic mosaic disorders. MED GENET-BERLIN 2023; 35:275-284. [PMID: 38835734 PMCID: PMC11006364 DOI: 10.1515/medgen-2023-2066] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Liquid biopsy for minimally invasive diagnosis and monitoring of cancer patients is progressing toward routine clinical practice. With the implementation of highly sensitive next-generation sequencing (NGS) based assays for the analysis of cfDNA, however, consideration of the utility of liquid biopsy for clinical genetic testing is critical. While the focus of liquid biopsy for cancer diagnosis is the detection of circulating tumor DNA (ctDNA) as a fraction of total cell-free DNA (cfDNA), cfDNA analysis reveals both somatic mosaic tumor and germline variants and clonal hematopoiesis. Here we outline advantages and limitations of mosaic and germline variant detection as well as the impact of clonal hematopoiesis on liquid biopsy in cancer diagnosis. We also evaluate the potential of cfDNA analysis for the molecular diagnosis of monogenic mosaic disorders.
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Affiliation(s)
| | - Thomas Keßler
- MGZ - Medizinisch Genetisches Zentrum München Germany
| | | | - Ellen Heitzer
- Medical University of Graz Institute of Human Genetics, Diagnostic and Research Center for Molecular Biomedicine (Austria) Graz Austria
| | | | - Michael Speicher
- Medical University of Graz Institute of Human Genetics, Diagnostic and Research Center for Molecular Biomedicine (Austria), Neue Stiftingtalstraße 2 Graz Austria
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He W, Zhang Y, Wu K, Wang Y, Zhao X, Lv L, Ren C, Lu J, Yang J, Yin A, Liu G. Epigenetic phenotype of plasma cell-free DNA in the prediction of early-onset preeclampsia. J OBSTET GYNAECOL 2023; 43:2282100. [PMID: 38038254 DOI: 10.1080/01443615.2023.2282100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 11/06/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND In the current study, we sought to characterise the methylation haplotypes and nucleosome positioning patterns of placental DNA and plasma cell-free DNA of pregnant women with early-onset preeclampsia using whole genome bisulphite sequencing (WGBS) and methylation capture bisulphite sequencing (MCBS) and further develop and examine the diagnostic performance of a generalised linear model (GLM) by incorporating the epigenetic features for early-onset preeclampsia. METHODS This case-control study recruited pregnant women aged at least 18 years who delivered their babies at our Hospital. In addition, non-pregnant women with no previous history of diseases were included. Placental samples of the villous parenchyma were taken at the time of delivery and venous blood was drawn from pregnant women during non-invasive prenatal testing at 12-15 weeks of pregnancy and nonpregnant women during the physical check-up. WGBS and MCBS were carried out of extracted genomic DNA. Then, we established the GLM by incorporating preeclampsia-specific methylation haplotypes and nucleosome positioning patterns and examined the diagnostic performance of the model by receiver operating characteristic (ROC) curve analysis. RESULTS The study included 135 pregnant women and 50 non-pregnant women. Our high-depth MCBS revealed notably different DNA methylation and nucleosome positioning patterns between women with and without preeclampsia. Preeclampsia-specific hypermethylated sites were found predominantly in the promoter regions and particularly enriched in CTCF on the X chromosome. Totally, 2379 preeclampsia-specific methylation haplotypes were found across the entire genome. ROC analysis showed that the area under the ROC curve (AUC) was 0.938 (95%CI 0.877, 1.000). At a GLM cut-off of 0.341, the AUC was the maximum, with a sensitivity of 95.6% and a specificity of 89.7%. CONCLUSION Pregnant women with early-onset preeclampsia exhibit DNA methylation and nucleosome positioning patterns in placental and plasma DNA.
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Affiliation(s)
- Wei He
- The First Affiliated Hospital of Jinan University, Guangzhou, China
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Yi Zhang
- Euler Technology, Beijing, China
- Peking-Tsinghua Center of Life Sciences, Beijing, China
- School of Life Sciences, Peking University, Beijing, China
| | - Kai Wu
- Euler Technology, Beijing, China
| | - Yunan Wang
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Xin Zhao
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Lijuan Lv
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Congmian Ren
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Jiaqi Lu
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Jiexia Yang
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Aihua Yin
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Guocheng Liu
- Department of Obstetrics, Guangdong Women and Children Hospital, Guangzhou, China
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Keup C, Kimmig R, Kasimir-Bauer S. The Diversity of Liquid Biopsies and Their Potential in Breast Cancer Management. Cancers (Basel) 2023; 15:5463. [PMID: 38001722 PMCID: PMC10670968 DOI: 10.3390/cancers15225463] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/10/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
Analyzing blood as a so-called liquid biopsy in breast cancer (BC) patients has the potential to adapt therapy management. Circulating tumor cells (CTCs), extracellular vesicles (EVs), cell-free DNA (cfDNA) and other blood components mirror the tumoral heterogeneity and could support a range of clinical decisions. Multi-cancer early detection tests utilizing blood are advancing but are not part of any clinical routine yet. Liquid biopsy analysis in the course of neoadjuvant therapy has potential for therapy (de)escalation.Minimal residual disease detection via serial cfDNA analysis is currently on its way. The prognostic value of blood analytes in early and metastatic BC is undisputable, but the value of these prognostic biomarkers for clinical management is controversial. An interventional trial confirmed a significant outcome benefit when therapy was changed in case of newly emerging cfDNA mutations under treatment and thus showed the clinical utility of cfDNA analysis for therapy monitoring. The analysis of PIK3CA or ESR1 variants in plasma of metastatic BC patients to prescribe targeted therapy with alpesilib or elacestrant has already arrived in clinical practice with FDA-approved tests available and is recommended by ASCO. The translation of more liquid biopsy applications into clinical practice is still pending due to a lack of knowledge of the analytes' biology, lack of standards and difficulties in proving clinical utility.
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Affiliation(s)
- Corinna Keup
- Department of Gynecology and Obstetrics, University Hospital of Essen, 45147 Essen, Germany
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Kim J, Hong SP, Lee S, Lee W, Lee D, Kim R, Park YJ, Moon S, Park K, Cha B, Kim JI. Multidimensional fragmentomic profiling of cell-free DNA released from patient-derived organoids. Hum Genomics 2023; 17:96. [PMID: 37898819 PMCID: PMC10613368 DOI: 10.1186/s40246-023-00533-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/11/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND Fragmentomics, the investigation of fragmentation patterns of cell-free DNA (cfDNA), has emerged as a promising strategy for the early detection of multiple cancers in the field of liquid biopsy. However, the clinical application of this approach has been hindered by a limited understanding of cfDNA biology. Furthermore, the prevalence of hematopoietic cell-derived cfDNA in plasma complicates the in vivo investigation of tissue-specific cfDNA other than that of hematopoietic origin. While conventional two-dimensional cell lines have contributed to research on cfDNA biology, their limited representation of in vivo tissue contexts underscores the need for more robust models. In this study, we propose three-dimensional organoids as a novel in vitro model for studying cfDNA biology, focusing on multifaceted fragmentomic analyses. RESULTS We established nine patient-derived organoid lines from normal lung airway, normal gastric, and gastric cancer tissues. We then extracted cfDNA from the culture medium of these organoids in both proliferative and apoptotic states. Using whole-genome sequencing data from cfDNA, we analyzed various fragmentomic features, including fragment size, footprints, end motifs, and repeat types at the end. The distribution of cfDNA fragment sizes in organoids, especially in apoptosis samples, was similar to that found in plasma, implying occupancy by mononucleosomes. The footprints determined by sequencing depth exhibited distinct patterns depending on fragment sizes, reflecting occupancy by a variety of DNA-binding proteins. Notably, we discovered that short fragments (< 118 bp) were exclusively enriched in the proliferative state and exhibited distinct fragmentomic profiles, characterized by 3 bp palindromic end motifs and specific repeats. CONCLUSIONS In conclusion, our results highlight the utility of in vitro organoid models as a valuable tool for studying cfDNA biology and its associated fragmentation patterns. This, in turn, will pave the way for further enhancements in noninvasive cancer detection methodologies based on fragmentomics.
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Affiliation(s)
- Jaeryuk Kim
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seung-Pyo Hong
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seyoon Lee
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Woochan Lee
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dakyung Lee
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Rokhyun Kim
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Young Jun Park
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungji Moon
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyunghyuk Park
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Bukyoung Cha
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jong-Il Kim
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea.
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea.
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Crispin-Ortuzar M, Woitek R, Reinius MAV, Moore E, Beer L, Bura V, Rundo L, McCague C, Ursprung S, Escudero Sanchez L, Martin-Gonzalez P, Mouliere F, Chandrananda D, Morris J, Goranova T, Piskorz AM, Singh N, Sahdev A, Pintican R, Zerunian M, Rosenfeld N, Addley H, Jimenez-Linan M, Markowetz F, Sala E, Brenton JD. Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer. Nat Commun 2023; 14:6756. [PMID: 37875466 PMCID: PMC10598212 DOI: 10.1038/s41467-023-41820-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/20/2023] [Indexed: 10/26/2023] Open
Abstract
High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.
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Affiliation(s)
- Mireia Crispin-Ortuzar
- Department of Oncology, University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
| | - Ramona Woitek
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Centre for Medical Image Analysis and AI (MIAAI), Danube Private University, Krems, Austria
| | - Marika A V Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Elizabeth Moore
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Lucian Beer
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Vlad Bura
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Leonardo Rundo
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA, Italy
| | - Cathal McCague
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Stephan Ursprung
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Lorena Escudero Sanchez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Paula Martin-Gonzalez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Florent Mouliere
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Pathology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - James Morris
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Teodora Goranova
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Anna M Piskorz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Naveena Singh
- Department of Cellular Pathology, Barts Health NHS Trust, London, UK
| | - Anju Sahdev
- Department of Radiology, Barts Health NHS Trust, London, UK
| | - Roxana Pintican
- "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Radiology, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - Marta Zerunian
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome-Sant'Andrea University Hospital, Rome, Italy
| | - Nitzan Rosenfeld
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Helen Addley
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Dipartimento di Scienze Radiologiche ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Western Balkans University, Tirana, Albania
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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36
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van der Pol Y, Moldovan N, Ramaker J, Bootsma S, Lenos KJ, Vermeulen L, Sandhu S, Bahce I, Pegtel DM, Wong SQ, Dawson SJ, Chandrananda D, Mouliere F. The landscape of cell-free mitochondrial DNA in liquid biopsy for cancer detection. Genome Biol 2023; 24:229. [PMID: 37828498 PMCID: PMC10571306 DOI: 10.1186/s13059-023-03074-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 09/26/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Existing methods to detect tumor signal in liquid biopsy have focused on the analysis of nuclear cell-free DNA (cfDNA). However, non-nuclear cfDNA and in particular mitochondrial DNA (mtDNA) has been understudied. We hypothesize that an increase in mtDNA in plasma could reflect the presence of cancer, and that leveraging cell-free mtDNA could enhance cancer detection. RESULTS We survey 203 healthy and 664 cancer plasma samples from three collection centers covering 12 cancer types with whole genome sequencing to catalogue the plasma mtDNA fraction. The mtDNA fraction is increased in individuals with cholangiocarcinoma, colorectal, liver, pancreatic, or prostate cancer, in comparison to that in healthy individuals. We detect almost no increase of mtDNA fraction in individuals with other cancer types. The mtDNA fraction in plasma correlates with the cfDNA tumor fraction as determined by somatic mutations and/or copy number aberrations. However, the mtDNA fraction is also elevated in a fraction of patients without an apparent increase in tumor-derived cfDNA. A predictive model integrating mtDNA and copy number analysis increases the area under the curve (AUC) from 0.73 when using copy number alterations alone to an AUC of 0.81. CONCLUSIONS The mtDNA signal retrieved by whole genome sequencing has the potential to boost the detection of cancer when combined with other tumor-derived signals in liquid biopsies.
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Affiliation(s)
- Ymke van der Pol
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Pathology, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Norbert Moldovan
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Pathology, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Jip Ramaker
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Pathology, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Sanne Bootsma
- Amsterdam UMC Location University of Amsterdam, Center for Experimental and Molecular Medicine, Laboratory for Experimental Oncology and Radiobiology, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Kristiaan J Lenos
- Amsterdam UMC Location University of Amsterdam, Center for Experimental and Molecular Medicine, Laboratory for Experimental Oncology and Radiobiology, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Louis Vermeulen
- Amsterdam UMC Location University of Amsterdam, Center for Experimental and Molecular Medicine, Laboratory for Experimental Oncology and Radiobiology, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Shahneen Sandhu
- Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum, Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Idris Bahce
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Pulmonology, Amsterdam, the Netherlands
| | - D Michiel Pegtel
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Pathology, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Stephen Q Wong
- Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum, Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Sarah-Jane Dawson
- Peter MacCallum Cancer Centre, Melbourne, Australia.
- Sir Peter MacCallum, Department of Oncology, University of Melbourne, Melbourne, Australia.
- Centre for Cancer Research, University of Melbourne, Melbourne, Australia.
| | - Dineika Chandrananda
- Peter MacCallum Cancer Centre, Melbourne, Australia.
- Sir Peter MacCallum, Department of Oncology, University of Melbourne, Melbourne, Australia.
| | - Florent Mouliere
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Pathology, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands.
- Cancer Research UK Cancer Biomarker Centre, Manchester, UK.
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37
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Akshintala S, Sundby RT, Bernstein D, Glod JW, Kaplan RN, Yohe ME, Gross AM, Derdak J, Lei H, Pan A, Dombi E, Palacio-Yance I, Herrera KR, Miettinen MM, Chen HX, Steinberg SM, Helman LJ, Mascarenhas L, Widemann BC, Navid F, Shern JF, Heske CM. Phase I trial of Ganitumab plus Dasatinib to Cotarget the Insulin-Like Growth Factor 1 Receptor and Src Family Kinase YES in Rhabdomyosarcoma. Clin Cancer Res 2023; 29:3329-3339. [PMID: 37398992 PMCID: PMC10529967 DOI: 10.1158/1078-0432.ccr-23-0709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/05/2023] [Accepted: 06/29/2023] [Indexed: 07/04/2023]
Abstract
PURPOSE Antibodies against insulin-like growth factor (IGF) type 1 receptor have shown meaningful but transient tumor responses in patients with rhabdomyosarcoma (RMS). The SRC family member YES has been shown to mediate IGF type 1 receptor (IGF-1R) antibody acquired resistance, and cotargeting IGF-1R and YES resulted in sustained responses in murine RMS models. We conducted a phase I trial of the anti-IGF-1R antibody ganitumab combined with dasatinib, a multi-kinase inhibitor targeting YES, in patients with RMS (NCT03041701). PATIENTS AND METHODS Patients with relapsed/refractory alveolar or embryonal RMS and measurable disease were eligible. All patients received ganitumab 18 mg/kg intravenously every 2 weeks. Dasatinib dose was 60 mg/m2/dose (max 100 mg) oral once daily [dose level (DL)1] or 60 mg/m2/dose (max 70 mg) twice daily (DL2). A 3+3 dose escalation design was used, and maximum tolerated dose (MTD) was determined on the basis of cycle 1 dose-limiting toxicities (DLT). RESULTS Thirteen eligible patients, median age 18 years (range 8-29) enrolled. Median number of prior systemic therapies was 3; all had received prior radiation. Of 11 toxicity-evaluable patients, 1/6 had a DLT at DL1 (diarrhea) and 2/5 had a DLT at DL2 (pneumonitis, hematuria) confirming DL1 as MTD. Of nine response-evaluable patients, one had a confirmed partial response for four cycles, and one had stable disease for six cycles. Genomic studies from cell-free DNA correlated with disease response. CONCLUSIONS The combination of dasatinib 60 mg/m2/dose daily and ganitumab 18 mg/kg every 2 weeks was safe and tolerable. This combination had a disease control rate of 22% at 5 months.
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Affiliation(s)
- Srivandana Akshintala
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - R. Taylor Sundby
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Donna Bernstein
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - John W. Glod
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Rosandra N. Kaplan
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Marielle E. Yohe
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
- Laboratory of Cell and Developmental Signaling, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Frederick, Maryland
| | - Andrea M. Gross
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Joanne Derdak
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Haiyan Lei
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Alexander Pan
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Eva Dombi
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Isabel Palacio-Yance
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Kailey R. Herrera
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Markku M. Miettinen
- Laboratory of Pathology, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Helen X. Chen
- Cancer Therapy Evaluation Program (CTEP), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Seth M. Steinberg
- Biostatistics and Data Management, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Lee J. Helman
- Cancer and Blood Disease Institute, Children’s Hospital Los Angeles (CHLA), Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, California
- The Osteosarcoma Institute, Dallas, Texas
| | - Leo Mascarenhas
- Cancer and Blood Disease Institute, Children’s Hospital Los Angeles (CHLA), Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Brigitte C. Widemann
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Fariba Navid
- Cancer and Blood Disease Institute, Children’s Hospital Los Angeles (CHLA), Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Jack F. Shern
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
| | - Christine M. Heske
- Pediatric Oncology Branch, Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland
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38
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Medina JE, Dracopoli NC, Bach PB, Lau A, Scharpf RB, Meijer GA, Andersen CL, Velculescu VE. Cell-free DNA approaches for cancer early detection and interception. J Immunother Cancer 2023; 11:e006013. [PMID: 37696619 PMCID: PMC10496721 DOI: 10.1136/jitc-2022-006013] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2023] [Indexed: 09/13/2023] Open
Abstract
Rapid advancements in the area of early cancer detection have brought us closer to achieving the goals of finding cancer early enough to treat or cure it, while avoiding harms of overdiagnosis. We evaluate progress in the development of early cancer detection tests in the context of the current principles for cancer screening. We review cell-free DNA (cfDNA)-based approaches using mutations, methylation, or fragmentomes for early cancer detection. Lastly, we discuss the challenges in demonstrating clinical utility of these tests before integration into routine clinical care.
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Affiliation(s)
- Jamie E Medina
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | - Anna Lau
- Delfi Diagnostics Inc, Baltimore, Maryland, USA
| | - Robert B Scharpf
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Helzer KT, Sharifi MN, Sperger JM, Shi Y, Annala M, Bootsma ML, Reese SR, Taylor A, Kaufmann KR, Krause HK, Schehr JL, Sethakorn N, Kosoff D, Kyriakopoulos C, Burkard ME, Rydzewski NR, Yu M, Harari PM, Bassetti M, Blitzer G, Floberg J, Sjöström M, Quigley DA, Dehm SM, Armstrong AJ, Beltran H, McKay RR, Feng FY, O'Regan R, Wisinski KB, Emamekhoo H, Wyatt AW, Lang JM, Zhao SG. Fragmentomic analysis of circulating tumor DNA-targeted cancer panels. Ann Oncol 2023; 34:813-825. [PMID: 37330052 PMCID: PMC10527168 DOI: 10.1016/j.annonc.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/30/2023] [Accepted: 06/06/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA), and multiple cfDNA-targeted sequencing panels are now commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner. PATIENTS AND METHODS We used machine learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer and non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, n = 198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, n = 320). Each cohort was split 70%/30% into training and validation sets. RESULTS In the UW cohort, training cross-validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross-validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer versus non-cancer area under the curve was 0.99. CONCLUSIONS To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost.
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Affiliation(s)
- K T Helzer
- Department of Human Oncology, University of Wisconsin, Madison
| | - M N Sharifi
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - J M Sperger
- Department of Medicine, University of Wisconsin, Madison, USA
| | - Y Shi
- Department of Human Oncology, University of Wisconsin, Madison
| | - M Annala
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, Canada; Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, Finland
| | - M L Bootsma
- Department of Human Oncology, University of Wisconsin, Madison
| | - S R Reese
- Department of Human Oncology, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - A Taylor
- Department of Medicine, University of Wisconsin, Madison, USA
| | - K R Kaufmann
- Department of Medicine, University of Wisconsin, Madison, USA
| | - H K Krause
- Department of Medicine, University of Wisconsin, Madison, USA
| | - J L Schehr
- Carbone Cancer Center, University of Wisconsin, Madison
| | - N Sethakorn
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - D Kosoff
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - C Kyriakopoulos
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - M E Burkard
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - N R Rydzewski
- Department of Human Oncology, University of Wisconsin, Madison
| | - M Yu
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison
| | - P M Harari
- Department of Human Oncology, University of Wisconsin, Madison; Carbone Cancer Center, University of Wisconsin, Madison
| | - M Bassetti
- Department of Human Oncology, University of Wisconsin, Madison; Carbone Cancer Center, University of Wisconsin, Madison
| | - G Blitzer
- Department of Human Oncology, University of Wisconsin, Madison; Carbone Cancer Center, University of Wisconsin, Madison
| | - J Floberg
- Department of Human Oncology, University of Wisconsin, Madison; Carbone Cancer Center, University of Wisconsin, Madison
| | - M Sjöström
- Department of Radiation Oncology, University of California San Francisco, San Francisco; Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco
| | - D A Quigley
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco; Departments of Epidemiology and Biostatistics; Urology, University of California San Francisco, San Francisco
| | - S M Dehm
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis
| | - A J Armstrong
- Duke Cancer Institute Center for Prostate and Urologic Cancers, Department of Medicine, Duke University, Durham
| | - H Beltran
- Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston
| | - R R McKay
- Moores Cancer Center, University of California San Diego, La Jolla
| | - F Y Feng
- Department of Radiation Oncology, University of California San Francisco, San Francisco; Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco; Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis; Division of Hematology and Oncology, Department of Medicine, University of California San Francisco, San Francisco
| | - R O'Regan
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA; Department of Medicine, University of Rochester, Rochester, USA
| | - K B Wisinski
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - H Emamekhoo
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - A W Wyatt
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, Canada; Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, Canada
| | - J M Lang
- Carbone Cancer Center, University of Wisconsin, Madison; Department of Medicine, University of Wisconsin, Madison, USA
| | - S G Zhao
- Department of Human Oncology, University of Wisconsin, Madison; Carbone Cancer Center, University of Wisconsin, Madison; William S. Middleton Memorial Veterans' Hospital, Madison, USA.
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Pasca S, Guo MZ, Wang S, Stokvis K, Shedeck A, Pallavajjala A, Shams C, Pallavajjala R, DeZern AE, Varadhan R, Gocke CD, Jones RJ, Gondek LP. Cell-free DNA measurable residual disease as a predictor of postallogeneic hematopoietic cell transplant outcomes. Blood Adv 2023; 7:4660-4670. [PMID: 37276081 PMCID: PMC10448421 DOI: 10.1182/bloodadvances.2023010416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/25/2023] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
The measurable residual disease (MRD) assessment provides an attractive predictor of allogeneic hematopoietic cell transplnat (alloHCT) outcomes. Cell-free DNA (cfDNA) has been applied to diagnosis, early detection, and disease burden monitoring in various tumors, but its utility as an MRD test in myeloid malignancies has not been systematically evaluated. We sought to determine the differential sensitivity between bone marrow (BM) and cfDNA MRD and to assess the effect of cfDNA MRD on alloHCT outcomes. The technical and clinical validation cohorts, including 82 patients participating in clinical trials (Bone Marrow Transplant Clinical Trials Network-0201 and 0402), were used. Ultradeep error-corrected targeted sequencing was performed on plasma and BM-derived DNA. We demonstrated that 94.6% (range, 93.9-95.3) of cfDNA was derived from hematopoietic tissue. The mutant allele fraction was congruent between BM and cfDNA (rho = 0.8; P < .0001); however, cfDNA seemed to be more sensitive in detecting clones with a variant allele frequency (VAF) of <0.26%. cfDNA-MRD clearance by day 90 after alloHCT (D90) was associated with improved relapse-free survival (RFS, median survival not reached vs 5.5 months; P < .0001) and overall survival (OS, median survival not reached vs 7.3 months; P < .0001) when compared with patients with persistent MRD. Irrespective of pre-alloHCT MRD, D90 cfDNA MRD was associated with inferior 2-year OS (16.7% vs 84.8%; P < .0001) and RFS (16.7% vs 80.7%; P < .0001). cfDNA seems to be an accurate, minimally invasive alternative to BM aspirates in MRD assessment and confers important prognostic implications in patients with myeloid malignancies undergoing alloHCT.
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Affiliation(s)
- Sergiu Pasca
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Matthew Z. Guo
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Shiyu Wang
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Kristin Stokvis
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Audra Shedeck
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Aparna Pallavajjala
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Cynthia Shams
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Roshni Pallavajjala
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Amy E. DeZern
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Ravi Varadhan
- Division of Biostatistics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Christopher D. Gocke
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Richard J. Jones
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Lukasz P. Gondek
- Division of Hematological Malignancies, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
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Chicard M, Iddir Y, Masliah Planchon J, Combaret V, Attignon V, Saint-Charles A, Frappaz D, Faure-Conter C, Beccaria K, Varlet P, Geoerger B, Baulande S, Pierron G, Bouchoucha Y, Doz F, Delattre O, Waterfall JJ, Bourdeaut F, Schleiermacher G. Cell-Free DNA Extracted from CSF for the Molecular Diagnosis of Pediatric Embryonal Brain Tumors. Cancers (Basel) 2023; 15:3532. [PMID: 37444642 DOI: 10.3390/cancers15133532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Liquid biopsies are revolutionary tools used to detect tumor-specific genetic alterations in body fluids, including the use of cell-free DNA (cfDNA) for molecular diagnosis in cancer patients. In brain tumors, cerebrospinal fluid (CSF) cfDNA might be more informative than plasma cfDNA. Here, we assess the use of CSF cfDNA in pediatric embryonal brain tumors (EBT) for molecular diagnosis. METHODS The CSF cfDNA of pediatric patients with medulloblastoma (n = 18), ATRT (n = 3), ETMR (n = 1), CNS NB FOXR2 (n = 2) and pediatric EBT NOS (n = 1) (mean cfDNA concentration 48 ng/mL; range 4-442 ng/mL) and matched tumor genomic DNA were sequenced by WES and/or a targeted sequencing approach to determine single-nucleotide variations (SNVs) and copy number alterations (CNA). A specific capture covering transcription start sites (TSS) of genes of interest was also used for nucleosome footprinting in CSF cfDNA. RESULTS 15/25 CSF cfDNA samples yielded informative results, with informative CNA and SNVs in 11 and 15 cases, respectively. For cases with paired tumor and CSF cfDNA WES (n = 15), a mean of 83 (range 1-160) shared SNVs were observed, including SNVs in classical medulloblastoma genes such as SMO and KMT2D. Interestingly, tumor-specific SNVs (mean 18; range 1-62) or CSF-specific SNVs (mean 5; range 0-25) were also observed, suggesting clonal heterogeneity. The TSS panel resulted in differential coverage profiles across all 112 studied genes in 7 cases, indicating distinct promoter accessibility. CONCLUSION CSF cfDNA sequencing yielded informative results in 60% (15/25) of all cases, with informative results in 83% (15/18) of all cases analyzed by WES. These results pave the way for the implementation of these novel approaches for molecular diagnosis and minimal residual disease monitoring.
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Affiliation(s)
- Mathieu Chicard
- Recherche Translationelle en Oncologie Pédiatrique (RTOP), INSERM U830 Cancer, Heterogeneity, Instability and Plasticity, Department of Translational Research, Institut Curie Research Center, PSL Research University, 75005 Paris, France
| | - Yasmine Iddir
- Recherche Translationelle en Oncologie Pédiatrique (RTOP), INSERM U830 Cancer, Heterogeneity, Instability and Plasticity, Department of Translational Research, Institut Curie Research Center, PSL Research University, 75005 Paris, France
| | - Julien Masliah Planchon
- Unité de Génétique Somatique, Service de Génétique, Institut Curie Hospital Group, 75005 Paris, France
| | - Valérie Combaret
- Plateforme de Génomique des Cancers, Centre Léon Bérard, 69008 Lyon, France
- Laboratoire de Recherche Translationnelle, Centre Léon-Bérard, 69373 Lyon, France
| | - Valéry Attignon
- Plateforme de Génomique des Cancers, Centre Léon Bérard, 69008 Lyon, France
- Laboratoire de Recherche Translationnelle, Centre Léon-Bérard, 69373 Lyon, France
| | - Alexandra Saint-Charles
- Recherche Translationelle en Oncologie Pédiatrique (RTOP), INSERM U830 Cancer, Heterogeneity, Instability and Plasticity, Department of Translational Research, Institut Curie Research Center, PSL Research University, 75005 Paris, France
| | - Didier Frappaz
- Department of Pediatric Clinical Trials and Department of Pediatric Neuro-Oncology, Institut d'Hématologie et d'Oncologie Pédiatrique, 69008 Lyon, France
| | - Cécile Faure-Conter
- Department of Pediatric Clinical Trials and Department of Pediatric Neuro-Oncology, Institut d'Hématologie et d'Oncologie Pédiatrique, 69008 Lyon, France
| | - Kévin Beccaria
- Department of Pediatric Neurosurgery, Hôpital Necker-Enfants Malades, Assistance Publique Hôpitaux de Paris-Université Paris Cité, 75015 Paris, France
| | - Pascale Varlet
- GHU Psychiatrie et Neurosciences, Site Sainte-Anne, 75014 Paris, France
| | - Birgit Geoerger
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, 94805 Villejuif, France
| | - Sylvain Baulande
- Institut Curie Genomics of Excellence (ICGex) Platform, Institut Curie Research Center, 75005 Paris, France
| | - Gaelle Pierron
- Unité de Génétique Somatique, Service de Génétique, Institut Curie Hospital Group, 75005 Paris, France
| | - Yassine Bouchoucha
- SIREDO Integrated Pediatric Oncology Center, Institut Curie Hospital Group, 75005 Paris, France
| | - François Doz
- SIREDO Integrated Pediatric Oncology Center, Institut Curie Hospital Group, 75005 Paris, France
- Faculty of Medicine, Université Paris Cité, 75005 Paris, France
| | - Olivier Delattre
- SIREDO Integrated Pediatric Oncology Center, Institut Curie Hospital Group, 75005 Paris, France
- Diversity and Plasticity of Childhood Tumors Laboratory, INSERM U830 Cancer, Heterogeneity, Instability and Plasticity, Institut Curie Research Center, PSL Research University, 75005 Paris, France
| | - Joshua J Waterfall
- Integrative Functional Genomics of Cancer Laboratory, INSERM U830 Cancer, Heterogeneity, Instability and Plasticity, PSL Research University, 75005 Paris, France
- Department of Translational Research, Institut Curie Research Center, PSL Research University, 75005 Paris, France
| | - Franck Bourdeaut
- Recherche Translationelle en Oncologie Pédiatrique (RTOP), INSERM U830 Cancer, Heterogeneity, Instability and Plasticity, Department of Translational Research, Institut Curie Research Center, PSL Research University, 75005 Paris, France
- SIREDO Integrated Pediatric Oncology Center, Institut Curie Hospital Group, 75005 Paris, France
| | - Gudrun Schleiermacher
- Recherche Translationelle en Oncologie Pédiatrique (RTOP), INSERM U830 Cancer, Heterogeneity, Instability and Plasticity, Department of Translational Research, Institut Curie Research Center, PSL Research University, 75005 Paris, France
- SIREDO Integrated Pediatric Oncology Center, Institut Curie Hospital Group, 75005 Paris, France
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Rose KM, Huelster HL, Meeks JJ, Faltas BM, Sonpavde GP, Lerner SP, Ross JS, Spiess PE, Grass GD, Jain RK, Kamat AM, Vosoughi A, Wang L, Wang X, Li R. Circulating and urinary tumour DNA in urothelial carcinoma - upper tract, lower tract and metastatic disease. Nat Rev Urol 2023; 20:406-419. [PMID: 36977797 DOI: 10.1038/s41585-023-00725-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2023] [Indexed: 03/30/2023]
Abstract
Precision medicine has transformed the way urothelial carcinoma is managed. However, current practices are limited by the availability of tissue samples for genomic profiling and the spatial and temporal molecular heterogeneity observed in many studies. Among rapidly advancing genomic sequencing technologies, non-invasive liquid biopsy has emerged as a promising diagnostic tool to reproduce tumour genomics, and has shown potential to be integrated in several aspects of clinical care. In urothelial carcinoma, liquid biopsies such as plasma circulating tumour DNA (ctDNA) and urinary tumour DNA (utDNA) have been investigated as a surrogates for tumour biopsies and might bridge many shortfalls currently faced by clinicians. Both ctDNA and utDNA seem really promising in urothelial carcinoma diagnosis, staging and prognosis, response to therapy monitoring, detection of minimal residual disease and surveillance. The use of liquid biopsies in patients with urothelial carcinoma could further advance precision medicine in this population, facilitating personalized patient monitoring through non-invasive assays.
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Affiliation(s)
- Kyle M Rose
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Heather L Huelster
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Joshua J Meeks
- Department of Urology, Northwestern University, Chicago, IL, USA
| | - Bishoy M Faltas
- Department of Hematology/Oncology, Weill-Cornell Medicine, New York, NY, USA
| | - Guru P Sonpavde
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Seth P Lerner
- Department of Urology, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey S Ross
- Foundation Medicine, Inc, Cambridge, MA, USA
- Departments of Urology and Pathology, Upstate Medical University, Syracuse, NY, USA
| | - Philippe E Spiess
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - G Daniel Grass
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Rohit K Jain
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Ashish M Kamat
- Department of Urology, MD Anderson Cancer Center, Houston, TX, USA
| | - Aram Vosoughi
- Department of Anatomic Pathology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Liang Wang
- Department of Tumour Biology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Xuefeng Wang
- Department of Biostatistics/Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Roger Li
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA.
- Department of Immunology, H. Lee Moffitt Cancer Center, Tampa, FL, USA.
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Meriranta L, Pitkänen E, Leppä S. Blood has never been thicker: Cell-free DNA fragmentomics in the liquid biopsy toolbox of B-cell lymphomas. Semin Hematol 2023; 60:132-141. [PMID: 37455222 DOI: 10.1053/j.seminhematol.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/30/2023] [Accepted: 06/24/2023] [Indexed: 07/18/2023]
Abstract
Liquid biopsies utilizing plasma circulating tumor DNA (ctDNA) are anticipated to revolutionize decision-making in cancer care. In the field of lymphomas, ctDNA-based blood tests represent the forefront of clinically applicable tools to harness decades of genomic research for disease profiling, quantification, and detection. More recently, the discovery of nonrandom fragmentation patterns in cell-free DNA (cfDNA) has opened another avenue of liquid biopsy research beyond mutational interrogation of ctDNA. Through examination of structural features, nucleotide content, and genomic distribution of massive numbers of plasma cfDNA molecules, the study of fragmentomics aims at identifying new tools that augment existing ctDNA-based analyses and discover new ways to profile cancer from blood tests. Indeed, the characterization of aberrant lymphoma ctDNA fragment patterns and harnessing them with powerful machine-learning techniques are expected to unleash the potential of nonmutant molecules for liquid biopsy purposes. In this article, we review cfDNA fragmentomics as an emerging approach in the ctDNA research of B-cell lymphomas. We summarize the biology behind the formation of cfDNA fragment patterns and discuss the preanalytical and technical limitations faced with current methodologies. Then we go through the advances in the field of lymphomas and envision what other noninvasive tools based on fragment characteristics could be explored. Last, we place fragmentomics as one of the facets of ctDNA analyses in emerging multiview and multiomics liquid biopsies. We pay attention to the unknowns in the field of cfDNA fragmentation biology that warrant further mechanistic investigation to provide rational background for the development of these precision oncology tools and understanding of their limitations.
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Affiliation(s)
- Leo Meriranta
- Applied Tumor Genomics, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Oncology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland.
| | - Esa Pitkänen
- Applied Tumor Genomics, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland; Institute for Molecular Medicine Finland (FIMM), HILIFE, Helsinki, Finland
| | - Sirpa Leppä
- Applied Tumor Genomics, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Oncology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland.
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Ren J, Liu Y, Zhu X, Wang X, Li Y, Liu Y, Hu W, Zhang X, Wang J. OCRFinder: a noise-tolerance machine learning method for accurately estimating open chromatin regions. Front Genet 2023; 14:1184744. [PMID: 37323658 PMCID: PMC10267440 DOI: 10.3389/fgene.2023.1184744] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 05/19/2023] [Indexed: 06/17/2023] Open
Abstract
Open chromatin regions are the genomic regions associated with basic cellular physiological activities, while chromatin accessibility is reported to affect gene expressions and functions. A basic computational problem is to efficiently estimate open chromatin regions, which could facilitate both genomic and epigenetic studies. Currently, ATAC-seq and cfDNA-seq (plasma cell-free DNA sequencing) are two popular strategies to detect OCRs. As cfDNA-seq can obtain more biomarkers in one round of sequencing, it is considered more effective and convenient. However, in processing cfDNA-seq data, due to the dynamically variable chromatin accessibility, it is quite difficult to obtain the training data with pure OCRs or non-OCRs, and leads to a noise problem for either feature-based approaches or learning-based approaches. In this paper, we propose a learning-based OCR estimation approach with a noise-tolerance design. The proposed approach, named OCRFinder, incorporates the ideas of ensemble learning framework and semi-supervised strategy to avoid potential overfitting of noisy labels, which are the false positives on OCRs and non-OCRs. Compared to different noise control strategies and state-of-the-art approaches, OCRFinder achieved higher accuracies and sensitivities in the experiments. In addition, OCRFinder also has an excellent performance in ATAC-seq or DNase-seq comparison experiments.
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Affiliation(s)
- Jiayi Ren
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
| | - Yuqian Liu
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
| | - Xiaoyan Zhu
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
| | - Xuwen Wang
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
| | - Yifei Li
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
| | - Yuxin Liu
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
| | - Wenqing Hu
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
| | - Xuanping Zhang
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
| | - Jiayin Wang
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Engineering Research Center of Medical and Health Big Data, Xi’an Jiaotong University, Xi’an, China
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45
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Semenkovich NP, Szymanski JJ, Earland N, Chauhan PS, Pellini B, Chaudhuri AA. Genomic approaches to cancer and minimal residual disease detection using circulating tumor DNA. J Immunother Cancer 2023; 11:e006284. [PMID: 37349125 PMCID: PMC10314661 DOI: 10.1136/jitc-2022-006284] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2023] [Indexed: 06/24/2023] Open
Abstract
Liquid biopsies using cell-free circulating tumor DNA (ctDNA) are being used frequently in both research and clinical settings. ctDNA can be used to identify actionable mutations to personalize systemic therapy, detect post-treatment minimal residual disease (MRD), and predict responses to immunotherapy. ctDNA can also be isolated from a range of different biofluids, with the possibility of detecting locoregional MRD with increased sensitivity if sampling more proximally than blood plasma. However, ctDNA detection remains challenging in early-stage and post-treatment MRD settings where ctDNA levels are minuscule giving a high risk for false negative results, which is balanced with the risk of false positive results from clonal hematopoiesis. To address these challenges, researchers have developed ever-more elegant approaches to lower the limit of detection (LOD) of ctDNA assays toward the part-per-million range and boost assay sensitivity and specificity by reducing sources of low-level technical and biological noise, and by harnessing specific genomic and epigenomic features of ctDNA. In this review, we highlight a range of modern assays for ctDNA analysis, including advancements made to improve the signal-to-noise ratio. We further highlight the challenge of detecting ultra-rare tumor-associated variants, overcoming which will improve the sensitivity of post-treatment MRD detection and open a new frontier of personalized adjuvant treatment decision-making.
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Affiliation(s)
- Nicholas P Semenkovich
- Division of Endocrinology, Metabolism, and Lipid Research, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jeffrey J Szymanski
- Division of Cancer Biology, Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Noah Earland
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Pradeep S Chauhan
- Division of Cancer Biology, Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Bruna Pellini
- Department of Thoracic Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
- Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Aadel A Chaudhuri
- Division of Cancer Biology, Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, Missouri, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
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Jiang X, Li Z, Mehmood A, Wang H, Wang Q, Chu Y, Mao X, Zhao J, Jiang M, Zhao B, Lin G, Wang E, Wei D. A Self-attention Graph Convolutional Network for Precision Multi-tumor Early Diagnostics with DNA Methylation Data. Interdiscip Sci 2023:10.1007/s12539-023-00563-1. [PMID: 37247186 DOI: 10.1007/s12539-023-00563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 05/30/2023]
Abstract
DNA methylation-based precision tumor early diagnostics is emerging as state-of-the-art technology that could capture early cancer signs 3 ~ 5 years in advance, even for clinically homogenous groups. Presently, the sensitivity of early detection for many tumors is ~ 30%, which needs significant improvement. Nevertheless, based on the genome-wide DNA methylation data, one could comprehensively characterize tumors' entire molecular genetic landscape and their subtle differences. Therefore, novel high-performance methods must be modeled by considering unbiased information using excessively available DNA methylation data. To fill this gap, we have designed a computational model involving a self-attention graph convolutional network and multi-class classification support vector machine to identify the 11 most common cancers using DNA methylation data. The self-attention graph convolutional network automatically learns key methylation sites in a data-driven way. Then, multi-tumor early diagnostics is realized by training a multi-class classification support vector machine based on the selected methylation sites. We evaluated our model's performance through several data sets of experiments, and our results demonstrate the effectiveness of the selected key methylation sites, which are highly relevant for blood diagnosis. The pipeline of the self-attention graph convolutional network based computational framework.
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Affiliation(s)
- Xue Jiang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhiqi Li
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Aamir Mehmood
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Heng Wang
- International School of Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, China
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xueying Mao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Zhao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Mingming Jiang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Bowen Zhao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Guanning Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Edwin Wang
- Department of Biochemistry and Molecular Biology, Medical Genetics, and Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada.
| | - Dongqing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
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Zhang Z, Pi X, Gao C, Zhang J, Xia L, Yan X, Hu X, Yan Z, Zhang S, Wei A, Guo Y, Liu J, Li A, Liu X, Zhang W, Liu Y, Xie D. Integrated fragmentomic profile and 5-Hydroxymethylcytosine of capture-based low-pass sequencing data enables pan-cancer detection via cfDNA. Transl Oncol 2023; 34:101694. [PMID: 37209526 DOI: 10.1016/j.tranon.2023.101694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/09/2023] [Accepted: 05/14/2023] [Indexed: 05/22/2023] Open
Abstract
BACKGROUND Using epigenetic markers and fragmentomics of cell-free DNA for cancer detection has been proven applicable. METHODS We further investigated the diagnostic potential of combining two features (epigenetic markers and fragmentomic information) of cell-free DNA for detecting various types of cancers. To do this, we extracted cfDNA fragmentomic features from 191 whole-genome sequencing data and studied them in 396 low-pass 5hmC sequencing data, which included four common cancer types and control samples. RESULTS In our analysis of 5hmC sequencing data from cancer samples, we observed aberrant ultra-long fragments (220-500 bp) that differed from normal samples in terms of both size and coverage profile. These fragments played a significant role in predicting cancer. Leveraging the ability to detect cfDNA hydroxymethylation and fragmentomic markers simultaneously in low-pass 5hmC sequencing data, we developed an integrated model that incorporated 63 features representing both fragmentomic features and hydroxymethylation signatures. This model achieved high sensitivity and specificity for pan-cancer detection (88.52% and 82.35%, respectively). CONCLUSION We showed that fragmentomic information in 5hmC sequencing data is an ideal marker for cancer detection and that it shows high performance in low-pass sequencing data.
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Affiliation(s)
- Zhidong Zhang
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | - Xuenan Pi
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | - Chang Gao
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | - Jun Zhang
- Tailai Inc., Shanghai 200233, P. R. China
| | - Lin Xia
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | | | - Xinlei Hu
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | - Ziyue Yan
- Tailai Inc., Shanghai 200233, P. R. China
| | - Shuxin Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | - Ailin Wei
- Guang'an People's Hospital, Guang'an, China
| | - Yuer Guo
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | - Jingfeng Liu
- Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou 350025, Fujian Province, P. R. China
| | - Ang Li
- Department of Pancreatic Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | - Xiaolong Liu
- Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou 350025, Fujian Province, P. R. China
| | - Wei Zhang
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of the Second Military Medical University, Shanghai 200433, P. R. China
| | - Yanhui Liu
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China
| | - Dan Xie
- Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, P. R. China.
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48
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Li Y, Jiang G, Wu W, Yang H, Jin Y, Wu M, Liu W, Yang A, Chervova O, Zhang S, Zheng L, Zhang X, Du F, Kanu N, Wu L, Yang F, Wang J, Chen K. Multi-omics integrated circulating cell-free DNA genomic signatures enhanced the diagnostic performance of early-stage lung cancer and postoperative minimal residual disease. EBioMedicine 2023; 91:104553. [PMID: 37027928 PMCID: PMC10102814 DOI: 10.1016/j.ebiom.2023.104553] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Liquid biopsy is a promising non-invasive alternative for cancer screening and minimal residual disease (MRD) detection, although there are some concerns regarding its clinical applications. We aimed to develop an accurate detection platform based on liquid biopsy for both cancer screening and MRD detection in patients with lung cancer (LC), which is also applicable to clinical use. METHODS We applied a modified whole-genome sequencing (WGS) -based High-performance Infrastructure For MultIomics (HIFI) method for LC screening and postoperative MRD detection by combining the hyper-co-methylated read approach and the circulating single-molecule amplification and resequencing technology (cSMART2.0). FINDINGS For early screening of LC, the LC score model was constructed using the support vector machine, which showed sensitivity (51.8%) at high specificity (96.3%) and achieved an AUC of 0.912 in the validation set prospectively enrolled from multiple centers. The screening model achieved detection efficiency with an AUC of 0.906 in patients with lung adenocarcinoma and outperformed other clinical models in solid nodule cohort. When applied the HIFI model to real social population, a negative predictive value (NPV) of 99.92% was achieved in Chinese population. Additionally, the MRD detection rate improved significantly by combining results from WGS and cSMART2.0, with sensitivity of 73.7% at specificity of 97.3%. INTERPRETATION In conclusion, the HIFI method is promising for diagnosis and postoperative monitoring of LC. FUNDING This study was supported by CAMS Innovation Fund for Medical Sciences, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Beijing Natural Science Foundation and Peking University People's Hospital.
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49
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Jin X, Wang Y, Xu J, Li Y, Cheng F, Luo Y, Zhou H, Lin S, Xiao F, Zhang L, Lin Y, Zhang Z, Jin Y, Zheng F, Chen W, Zhu A, Tao Y, Zhao J, Kuo T, Li Y, Li L, Wen L, Ou R, Li F, Lin L, Zhang Y, Sun J, Yuan H, Zhuang Z, Sun H, Chen Z, Li J, Zhuo J, Chen D, Zhang S, Sun Y, Wei P, Yuan J, Xu T, Yang H, Wang J, Xu X, Zhong N, Xu Y, Sun K, Zhao J. Plasma cell-free DNA promise monitoring and tissue injury assessment of COVID-19. Mol Genet Genomics 2023; 298:823-836. [PMID: 37059908 PMCID: PMC10104435 DOI: 10.1007/s00438-023-02014-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 03/25/2023] [Indexed: 04/16/2023]
Abstract
Coronavirus 2019 (COVID-19) is a complex disease that affects billions of people worldwide. Currently, effective etiological treatment of COVID-19 is still lacking; COVID-19 also causes damages to various organs that affects therapeutics and mortality of the patients. Surveillance of the treatment responses and organ injury assessment of COVID-19 patients are of high clinical value. In this study, we investigated the characteristic fragmentation patterns and explored the potential in tissue injury assessment of plasma cell-free DNA in COVID-19 patients. Through recruitment of 37 COVID-19 patients, 32 controls and analysis of 208 blood samples upon diagnosis and during treatment, we report gross abnormalities in cfDNA of COVID-19 patients, including elevated GC content, altered molecule size and end motif patterns. More importantly, such cfDNA fragmentation characteristics reflect patient-specific physiological changes during treatment. Further analysis on cfDNA tissue-of-origin tracing reveals frequent tissue injuries in COVID-19 patients, which is supported by clinical diagnoses. Hence, our work demonstrates and extends the translational merit of cfDNA fragmentation pattern as valuable analyte for effective treatment monitoring, as well as tissue injury assessment in COVID-19.
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Affiliation(s)
- Xin Jin
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China.
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Yanqun Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Jinjin Xu
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
| | - Yimin Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Fanjun Cheng
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Yuxue Luo
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
| | - Haibo Zhou
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, 511500, Guangdong, China
| | - Shanwen Lin
- Yangjiang People's Hospital, Yangjiang, 529500, Guangdong, China
| | - Fei Xiao
- Department of Infectious Diseases, Guangdong Provincial Key Laboratory of Biomedical Imaging, Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, 519000, Guangdong Province, China
| | - Lu Zhang
- Institute of Infectious Disease, Guangzhou Eighth People's Hospital of Guangzhou Medical University, Guangzhou, 510060, Guangdong, China
| | - Yu Lin
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
| | - Zhaoyong Zhang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Yan Jin
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Fang Zheng
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Wei Chen
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
| | - Airu Zhu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Ye Tao
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
| | - Jingxian Zhao
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Tingyou Kuo
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, Guangdong, China
| | - Yuming Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Lingguo Li
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, Guangdong, China
| | - Liyan Wen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Rijing Ou
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
| | - Fang Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Long Lin
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, Guangdong, China
| | - Yanjun Zhang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Jing Sun
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Hao Yuan
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, Guangdong, China
| | - Zhen Zhuang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Haixi Sun
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
| | - Zhao Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Jie Li
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, Guangdong, China
| | - Jianfen Zhuo
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | | | - Shengnan Zhang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Yuzhe Sun
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
| | - Peilan Wei
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Jinwei Yuan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Tian Xu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
- Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, BGI-Shenzhen, Shenzhen, 518120, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, 518083, Guangdong, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen, 518120, China
| | - Nanshan Zhong
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Yonghao Xu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China.
| | - Jincun Zhao
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
- Institute of Infectious Disease, Guangzhou Eighth People's Hospital of Guangzhou Medical University, Guangzhou, 510060, Guangdong, China.
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50
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Bae M, Kim G, Lee TR, Ahn JM, Park H, Park SR, Song KB, Jun E, Oh D, Lee JW, Park YS, Song KW, Byeon JS, Kim BH, Sohn JH, Kim MH, Kim GM, Chie EK, Kang HC, Kong SY, Woo SM, Lee JE, Ryu JM, Lee J, Kim D, Ki CS, Cho EH, Choi JK. Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA. Nat Commun 2023; 14:2017. [PMID: 37037826 PMCID: PMC10085982 DOI: 10.1038/s41467-023-37768-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 03/22/2023] [Indexed: 04/12/2023] Open
Abstract
Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets covering 2125 patient samples of 9 cancer types and 1241 normal control samples, and also a reference dataset for background variant filtering based on 20,529 low-depth healthy samples. An external cfDNA dataset consisting of 208 cancer and 214 normal control samples is used for additional evaluation. Accuracy for cancer detection and tissue-of-origin localization is achieved using our algorithm, which incorporates cancer type-specific profiles of mutation distribution and chromatin organization in tumor tissues as model references. Our integrative model detects early-stage cancers, including those of pancreatic origin, with high sensitivity that is comparable to that of late-stage detection. Model interpretation reveals the contribution of cancer type-specific genomic and epigenomic features. Our methodologies may lay the groundwork for accurate cfDNA-based cancer diagnosis, especially at early stages.
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Affiliation(s)
- Mingyun Bae
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Gyuhee Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Tae-Rim Lee
- Genome Research Center, GC Genome, Yongin, Republic of Korea
| | - Jin Mo Ahn
- Genome Research Center, GC Genome, Yongin, Republic of Korea
| | - Hyunwook Park
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Sook Ryun Park
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ki Byung Song
- Division of Hepato-Biliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Eunsung Jun
- Division of Hepato-Biliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Dongryul Oh
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeong-Won Lee
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Young Sik Park
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Ki-Won Song
- Division of Hepatopancreatobiliary Surgery and Liver Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Bo Hyun Kim
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang, Republic of Korea
| | - Joo Hyuk Sohn
- Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
- AIMA, Inc., Avison Biomedical Research Center, Seoul, Republic of Korea
| | - Min Hwan Kim
- Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Gun Min Kim
- Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyun-Cheol Kang
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sun-Young Kong
- Department of Laboratory Medicine, National Cancer Center, Goyang, Republic of Korea
| | - Sang Myung Woo
- Center for Liver and Pancreatobiliary Cancer, National Cancer Center, Goyang, Republic of Korea
| | - Jeong Eon Lee
- Department of Surgery, Samsung Medical Center, Seoul, Republic of Korea
| | - Jai Min Ryu
- Department of Surgery, Samsung Medical Center, Seoul, Republic of Korea
| | - Junnam Lee
- Genome Research Center, GC Genome, Yongin, Republic of Korea
| | - Dasom Kim
- Genome Research Center, GC Genome, Yongin, Republic of Korea
| | - Chang-Seok Ki
- Genome Research Center, GC Genome, Yongin, Republic of Korea
| | - Eun-Hae Cho
- Genome Research Center, GC Genome, Yongin, Republic of Korea.
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
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