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Qin C, Li T, Lin C, Zhao B, Li Z, Zhao Y, Wang W. The systematic role of pancreatic cancer exosomes: distant communication, liquid biopsy and future therapy. Cancer Cell Int 2024; 24:264. [PMID: 39054529 PMCID: PMC11271018 DOI: 10.1186/s12935-024-03456-5] [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: 05/02/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024] Open
Abstract
Pancreatic cancer remains one of the most lethal diseases worldwide. Cancer-derived exosomes, benefiting from the protective role of the lipid membrane, exhibit remarkable stability in the circulatory system. These exosomes, released by tumor microenvironment, contain various biomolecules such as proteins, RNAs, and lipids that plays a pivotal role in mediating distant communication between the local pancreatic tumor and other organs or tissues. They facilitate the transfer of oncogenic factors to distant sites, contributing to the compromised body immune system, distant metastasis, diabetes, cachexia, and promoting a microenvironment conducive to tumor growth and metastasis in pancreatic cancer patients. Beyond their intrinsic roles, circulating exosomes in peripheral blood can be detected to facilitate accurate liquid biopsy. This approach offers a novel and promising method for the diagnosis and management of pancreatic cancer. Consequently, circulating exosomes are not only crucial mediators of systemic cell-cell communication during pancreatic cancer progression but also hold great potential as precise tools for pancreatic cancer management and treatment. Exosome-based liquid biopsy and therapy represent promising advancements in the diagnosis and treatment of pancreatic cancer. Exosomes can serve as drug delivery vehicles, enhancing the targeting and efficacy of anticancer treatments, modulating the immune system, and facilitating gene editing to suppress tumor growth. Ongoing research focuses on biomarker identification, drug delivery systems, and clinical trials to validate the safety and efficacy of exosome-based therapies, offering new possibilities for early diagnosis and precision treatment in pancreatic cancer. Leveraging the therapeutic potential of exosomes, including their ability to deliver targeted drugs and modulate immune responses, opens new avenues for innovative treatment strategies.
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Affiliation(s)
- Cheng Qin
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tianyu Li
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chen Lin
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bangbo Zhao
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zeru Li
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yutong Zhao
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weibin Wang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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2
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Till JE, McDaniel L, Chang C, Long Q, Pfeiffer SM, Lyman JP, Padrón LJ, Maurer DM, Yu JX, Spencer CN, Gherardini PF, Da Silva DM, LaVallee TM, Abbott C, Chen RO, Boyle SM, Bhagwat N, Cannas S, Sagreiya H, Li W, Yee SS, Abdalla A, Wang Z, Yin M, Ballinger D, Wissel P, Eads J, Karasic T, Schneider C, O'Dwyer P, Teitelbaum U, Reiss KA, Rahma OE, Fisher GA, Ko AH, Wainberg ZA, Wolff RA, O'Reilly EM, O'Hara MH, Cabanski CR, Vonderheide RH, Carpenter EL. Circulating KRAS G12D but not G12V is associated with survival in metastatic pancreatic ductal adenocarcinoma. Nat Commun 2024; 15:5763. [PMID: 38982051 PMCID: PMC11233636 DOI: 10.1038/s41467-024-49915-5] [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: 12/05/2023] [Accepted: 06/18/2024] [Indexed: 07/11/2024] Open
Abstract
While high circulating tumor DNA (ctDNA) levels are associated with poor survival for multiple cancers, variant-specific differences in the association of ctDNA levels and survival have not been examined. Here we investigate KRAS ctDNA (ctKRAS) variant-specific associations with overall and progression-free survival (OS/PFS) in first-line metastatic pancreatic ductal adenocarcinoma (mPDAC) for patients receiving chemoimmunotherapy ("PRINCE", NCT03214250), and an independent cohort receiving standard of care (SOC) chemotherapy. For PRINCE, higher baseline plasma levels are associated with worse OS for ctKRAS G12D (log-rank p = 0.0010) but not G12V (p = 0.7101), even with adjustment for clinical covariates. Early, on-therapy clearance of G12D (p = 0.0002), but not G12V (p = 0.4058), strongly associates with OS for PRINCE. Similar results are obtained for the SOC cohort, and for PFS in both cohorts. These results suggest ctKRAS G12D but not G12V as a promising prognostic biomarker for mPDAC and that G12D clearance could also serve as an early biomarker of response.
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Affiliation(s)
- Jacob E Till
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | | | - Changgee Chang
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Qi Long
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jaclyn P Lyman
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Lacey J Padrón
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Deena M Maurer
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Jia Xin Yu
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | | | | | - Diane M Da Silva
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | | | | | | | | | - Neha Bhagwat
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Samuele Cannas
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Hersh Sagreiya
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Wenrui Li
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Stephanie S Yee
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Aseel Abdalla
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Zhuoyang Wang
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Melinda Yin
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Dominique Ballinger
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Wissel
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Eads
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas Karasic
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Charles Schneider
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Peter O'Dwyer
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Ursina Teitelbaum
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | - Kim A Reiss
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Andrew H Ko
- University of California, San Francisco, San Francisco, CA, USA
| | - Zev A Wainberg
- University of California, Los Angeles, Los Angeles, CA, USA
| | - Robert A Wolff
- The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Mark H O'Hara
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Erica L Carpenter
- Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA, USA.
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3
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Fu X, Ma W, Zuo Q, Qi Y, Zhang S, Zhao Y. Application of machine learning for high-throughput tumor marker screening. Life Sci 2024; 348:122634. [PMID: 38685558 DOI: 10.1016/j.lfs.2024.122634] [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: 01/16/2024] [Revised: 03/26/2024] [Accepted: 04/10/2024] [Indexed: 05/02/2024]
Abstract
High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.
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Affiliation(s)
- Xingxing Fu
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Wanting Ma
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Qi Zuo
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Yanfei Qi
- Centenary Institute, The University of Sydney, Sydney, NSW 2050, Australia
| | - Shubiao Zhang
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China.
| | - Yinan Zhao
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
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4
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Hou H, Zhang R, Li J. Artificial intelligence in the clinical laboratory. Clin Chim Acta 2024; 559:119724. [PMID: 38734225 DOI: 10.1016/j.cca.2024.119724] [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: 04/17/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/13/2024]
Abstract
Laboratory medicine has become a highly automated medical discipline. Nowadays, artificial intelligence (AI) applied to laboratory medicine is also gaining more and more attention, which can optimize the entire laboratory workflow and even revolutionize laboratory medicine in the future. However, only a few commercially available AI models are currently approved for use in clinical laboratories and have drawbacks such as high cost, lack of accuracy, and the need for manual review of model results. Furthermore, there are a limited number of literature reviews that comprehensively address the research status, challenges, and future opportunities of AI applications in laboratory medicine. Our article begins with a brief introduction to AI and some of its subsets, then reviews some AI models that are currently being used in clinical laboratories or that have been described in emerging studies, and explains the existing challenges associated with their application and possible solutions, finally provides insights into the future opportunities of the field. We highlight the current status of implementation and potential applications of AI models in different stages of the clinical testing process.
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Affiliation(s)
- Hanjing Hou
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, PR China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, PR China.
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5
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Nicolò E, Gianni C, Pontolillo L, Serafini MS, Munoz-Arcos LS, Andreopoulou E, Curigliano G, Reduzzi C, Cristofanilli M. Circulating tumor cells et al.: towards a comprehensive liquid biopsy approach in breast cancer. TRANSLATIONAL BREAST CANCER RESEARCH : A JOURNAL FOCUSING ON TRANSLATIONAL RESEARCH IN BREAST CANCER 2024; 5:10. [PMID: 38751670 PMCID: PMC11093063 DOI: 10.21037/tbcr-23-55] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 03/21/2024] [Indexed: 05/18/2024]
Abstract
Liquid biopsy has emerged as a crucial tool in managing breast cancer (BC) patients, offering a minimally invasive approach to detect circulating tumor biomarkers. Until recently, the majority of the studies in BC focused on evaluating a single liquid biopsy analyte, primarily circulating tumor DNA and circulating tumor cells (CTCs). Despite the proven prognostic and predictive value of CTCs, their low abundance when detected using enrichment methods, especially in the early stages, poses a significant challenge. It is becoming evident that combining diverse circulating biomarkers, each representing different facets of tumor biology, has the potential to enhance the management of patients with BC. This article emphasizes the importance of considering these biomarkers as complementary/synergistic rather than competitive, recognizing their ability to contribute to a comprehensive disease profile. The review provides an overview of the clinical significance of simultaneously analyzing CTCs and other biomarkers, including cell-free circulating DNA, extracellular vesicles, non-canonical CTCs, cell-free RNAs, and non-malignant cells. Such a comprehensive liquid biopsy approach holds promise not only in BC but also in other cancer types, offering opportunities for early detection, prognostication, and therapy monitoring. However, addressing associated challenges, such as refining detection methods and establishing standardized protocols, is crucial for realizing the full potential of liquid biopsy in transforming our understanding and approach to BC. As the field evolves, collaborative efforts will be instrumental in unlocking the revolutionary impact of liquid biopsy in BC research and management.
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Affiliation(s)
- Eleonora Nicolò
- Department of Medicine, Division of Hematology-Oncology, Weill Cornell Medicine, New York, NY, USA
- Department of Oncology and Hematology-Oncology, University of Milan, Milan, Italy
- Division of Early Drug Development, European Institute of Oncology IRCCS, Milan, Italy
| | - Caterina Gianni
- Department of Medicine, Division of Hematology-Oncology, Weill Cornell Medicine, New York, NY, USA
- Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Meldola, Italy
| | - Letizia Pontolillo
- Department of Medicine, Division of Hematology-Oncology, Weill Cornell Medicine, New York, NY, USA
- Medical Oncology Department, Catholic University of Sacred Heart, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Mara Serena Serafini
- Department of Medicine, Division of Hematology-Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Laura Sofia Munoz-Arcos
- Department of Medicine, Division of Hematology-Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Eleni Andreopoulou
- Department of Medicine, Division of Hematology-Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Giuseppe Curigliano
- Department of Oncology and Hematology-Oncology, University of Milan, Milan, Italy
- Division of Early Drug Development, European Institute of Oncology IRCCS, Milan, Italy
| | - Carolina Reduzzi
- Department of Medicine, Division of Hematology-Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Massimo Cristofanilli
- Department of Medicine, Division of Hematology-Oncology, Weill Cornell Medicine, New York, NY, USA
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6
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Lu S, Yang J, Gu Y, He D, Wu H, Sun W, Xu D, Li C, Guo C. Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors. ACS Sens 2024; 9:1134-1148. [PMID: 38363978 DOI: 10.1021/acssensors.3c02670] [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: 02/18/2024]
Abstract
Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.
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Affiliation(s)
- Shasha Lu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Jianyu Yang
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Yu Gu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Dongyuan He
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Haocheng Wu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Wei Sun
- College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Changming Li
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Chunxian Guo
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
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7
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Ben-Ami R, Wang QL, Zhang J, Supplee JG, Fahrmann JF, Lehmann-Werman R, Brais LK, Nowak J, Yuan C, Loftus M, Babic A, Irajizad E, Davidi T, Zick A, Hubert A, Neiman D, Piyanzin S, Gal-Rosenberg O, Horn A, Shemer R, Glaser B, Boos N, Jajoo K, Lee L, Clancy TE, Rubinson DA, Ng K, Chabot JA, Kastrinos F, Kluger M, Aguirre AJ, Jänne PA, Bardeesy N, Stanger B, O'Hara MH, Till J, Maitra A, Carpenter EL, Bullock AJ, Genkinger J, Hanash SM, Paweletz CP, Dor Y, Wolpin BM. Protein biomarkers and alternatively methylated cell-free DNA detect early stage pancreatic cancer. Gut 2024; 73:639-648. [PMID: 38123998 PMCID: PMC10958271 DOI: 10.1136/gutjnl-2023-331074] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVE Pancreatic ductal adenocarcinoma (PDAC) is commonly diagnosed at an advanced stage. Liquid biopsy approaches may facilitate detection of early stage PDAC when curative treatments can be employed. DESIGN To assess circulating marker discrimination in training, testing and validation patient cohorts (total n=426 patients), plasma markers were measured among PDAC cases and patients with chronic pancreatitis, colorectal cancer (CRC), and healthy controls. Using CA19-9 as an anchor marker, measurements were made of two protein markers (TIMP1, LRG1) and cell-free DNA (cfDNA) pancreas-specific methylation at 9 loci encompassing 61 CpG sites. RESULTS Comparative methylome analysis identified nine loci that were differentially methylated in exocrine pancreas DNA. In the training set (n=124 patients), cfDNA methylation markers distinguished PDAC from healthy and CRC controls. In the testing set of 86 early stage PDAC and 86 matched healthy controls, CA19-9 had an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI 0.83 to 0.94), which was increased by adding TIMP1 (AUC 0.92; 95% CI 0.88 to 0.96; p=0.06), LRG1 (AUC 0.92; 95% CI 0.88 to 0.96; p=0.02) or exocrine pancreas-specific cfDNA methylation markers at nine loci (AUC 0.92; 95% CI 0.88 to 0.96; p=0.02). In the validation set of 40 early stage PDAC and 40 matched healthy controls, a combined panel including CA19-9, TIMP1 and a 9-loci cfDNA methylation panel had greater discrimination (AUC 0.86, 95% CI 0.77 to 0.95) than CA19-9 alone (AUC 0.82; 95% CI 0.72 to 0.92). CONCLUSION A combined panel of circulating markers including proteins and methylated cfDNA increased discrimination compared with CA19-9 alone for early stage PDAC.
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Affiliation(s)
- Roni Ben-Ami
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Qiao-Li Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Jinming Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Julianna G Supplee
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Johannes F Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Roni Lehmann-Werman
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lauren K Brais
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathan Nowak
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Chen Yuan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Maureen Loftus
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Ana Babic
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Ehsan Irajizad
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tal Davidi
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - Aviad Zick
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - Ayala Hubert
- Sharett Institute of Oncology, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - Daniel Neiman
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sheina Piyanzin
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ofer Gal-Rosenberg
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Amit Horn
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ruth Shemer
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Benjamin Glaser
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Endocrinology and Metabolism, Hadassah Medical Center, Jerusalem, Israel
| | - Natalia Boos
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Kunal Jajoo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Linda Lee
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Thomas E Clancy
- Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Douglas A Rubinson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - John A Chabot
- Department of Surgery, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA
| | - Fay Kastrinos
- Division of Digestive and Liver Diseases, Columbia University Irving Medical Cancer and the Vagelos College of Physicians and Surgeons, New York, New York, USA
| | - Michael Kluger
- Department of Surgery, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA
| | - Andrew J Aguirre
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Pasi A Jänne
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nabeel Bardeesy
- Massachusetts General Hospital Cancer Center, Center for Cancer Research, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ben Stanger
- Department of Medicine, Division of Gastroenterology, Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Mark H O'Hara
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jacob Till
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anirban Maitra
- Department of Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA
| | - Erica L Carpenter
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrea J Bullock
- Division of Hematology and Oncology, Beth-Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Jeanine Genkinger
- Department of epidemiology, Mailman school of public health, Columbia university, New York, New York, USA
- Herbert Irving Comprehensive Cancer Center, Columbia university Irving Medical Center, New York, New York, USA
| | - Samir M Hanash
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Cloud P Paweletz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Yuval Dor
- Department of Developmental Biology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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8
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Daher H, Punchayil SA, Ismail AAE, Fernandes RR, Jacob J, Algazzar MH, Mansour M. Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis. Cureus 2024; 16:e56583. [PMID: 38646386 PMCID: PMC11031195 DOI: 10.7759/cureus.56583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2024] [Indexed: 04/23/2024] Open
Abstract
Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.
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Affiliation(s)
- Hisham Daher
- Internal Medicine, University of Debrecen, Debrecen, HUN
| | - Sneha A Punchayil
- Internal Medicine, University Hospital of North Tees, Stockton-on-Tees, GBR
| | | | | | - Joel Jacob
- General Medicine, Diana Princess of Wales Hospital, Grimsby, GBR
| | | | - Mohammad Mansour
- General Medicine, University of Debrecen, Debrecen, HUN
- General Medicine, Jordan University Hospital, Amman, JOR
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9
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Wang F, Li X, Li M, Liu W, Lu L, Li Y, Chen X, Yang S, Liu T, Cheng W, Weng L, Wang H, Lu D, Yao Q, Wang Y, Wu J, Wittkop T, Faham M, Zhou H, Hu H, Jin H, Hu Z, Ma D, Cheng X. Ultra-short cell-free DNA fragments enhance cancer early detection in a multi-analyte blood test combining mutation, protein and fragmentomics. Clin Chem Lab Med 2024; 62:168-177. [PMID: 37678194 DOI: 10.1515/cclm-2023-0541] [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: 05/22/2023] [Accepted: 08/21/2023] [Indexed: 09/09/2023]
Abstract
OBJECTIVES Cancer morbidity and mortality can be reduced if the cancer is detected early. Cell-free DNA (cfDNA) fragmentomics emerged as a novel epigenetic biomarker for early cancer detection, however, it is still at its infancy and requires technical improvement. We sought to apply a single-strand DNA sequencing technology, for measuring genetic and fragmentomic features of cfDNA and evaluate the performance in detecting multiple cancers. METHODS Blood samples of 364 patients from six cancer types (colorectal, esophageal, gastric, liver, lung, and ovarian cancers) and 675 healthy individuals were included in this study. Circulating tumor DNA mutations, cfDNA fragmentomic features and a set of protein biomarkers were assayed. Sensitivity and specificity were reported by cancer types and stages. RESULTS Circular Ligation Amplification and sequencing (CLAmp-seq), a single-strand DNA sequencing technology, yielded a population of ultra-short fragments (<100 bp) than double-strand DNA preparation protocols and reveals a more significant size difference between cancer and healthy cfDNA fragments (25.84 bp vs. 16.05 bp). Analysis of the subnucleosomal peaks in ultra-short cfDNA fragments indicates that these peaks are regulatory element "footprints" and correlates with gene expression and cancer stages. At 98 % specificity, a prediction model using ctDNA mutations alone showed an overall sensitivity of 46 %; sensitivity reaches 60 % when protein is added, sensitivity further increases to 66 % when fragmentomics is also integrated. More improvements observed for samples representing earlier cancer stages than later ones. CONCLUSIONS These results suggest synergistic properties of protein, genetic and fragmentomics features in the identification of early-stage cancers.
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Affiliation(s)
- Fenfen Wang
- Gynecological Oncology Department, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, P.R. China
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, P.R. China
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Hangzhou, P.R. China
| | - Xinxing Li
- Department of Gastrointestinal Surgery, Tongji Hospital Medical College of Tongji University, Shanghai, P.R. China
| | - Mengxing Li
- Department of Thoracic Surgery, Changhai Hospital, Second Military Medical University, Shanghai, P.R. China
| | - Wendi Liu
- Department of Hepatobiliary Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, P.R. China
| | - Lingjia Lu
- Gynecological Oncology Department, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, P.R. China
| | - Yang Li
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, P.R. China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine for Reproductive Health Research, Hangzhou, P.R. China
- Women's Reproductive Health Key Laboratory of Zhejiang Province, Hangzhou, P.R. China
| | - Xiaojing Chen
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, P.R. China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine for Reproductive Health Research, Hangzhou, P.R. China
- Women's Reproductive Health Key Laboratory of Zhejiang Province, Hangzhou, P.R. China
| | - Siqi Yang
- Women's Reproductive Health Key Laboratory of Zhejiang Province, Hangzhou, P.R. China
| | - Tao Liu
- Department of Thoracic Surgery, Changhai Hospital, Second Military Medical University, Shanghai, P.R. China
| | - Wen Cheng
- Department of Thoracic Surgery, Changhai Hospital, Second Military Medical University, Shanghai, P.R. China
| | - Li Weng
- Department of Research and Development, AccuraGen Inc., San Jose, CA, USA
| | - Hongyan Wang
- Department of Research and Development, Shanghai Yunsheng Medical Laboratory Co., Ltd., Shanghai, P.R. China
| | - Dongsheng Lu
- Department of Bioinformatics, Shanghai Yunsheng Medical Laboratory Co., Ltd., Shanghai, P.R. China
| | - Qianqian Yao
- Department of Medical Science, Shanghai Yunsheng Medical Laboratory Co., Ltd., Shanghai, P.R. China
| | - Yingyu Wang
- Department of Bioinformatics, AccuraGen Inc., San Jose, CA, USA
| | - Johnny Wu
- Department of Bioinformatics, AccuraGen Inc., San Jose, CA, USA
| | - Tobias Wittkop
- Department of Bioinformatics, AccuraGen Inc., San Jose, CA, USA
| | | | - Huabang Zhou
- Department of Hepatobiliary Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, P.R. China
| | - Heping Hu
- Department of Hepatobiliary Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, P.R. China
| | - Hai Jin
- Department of Thoracic Surgery, Shanghai Changhai Hospital, Shanghai, P.R. China
| | - Zhiqian Hu
- Department of Gastrointestinal Surgery, Tongji Hospital Medical College of Tongji University, Shanghai, P.R. China
- Department of General Surgery, Changzheng Hospital Naval Medical University, Shanghai, P.R. China
| | - Ding Ma
- Department of Obstetrics and Gynaecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Xiaodong Cheng
- Gynecological Oncology Department, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, P.R. China
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, P.R. China
- Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Hangzhou, P.R. China
- Zhejiang Provincial Key Laboratory of Traditional Chinese Medicine for Reproductive Health Research, Hangzhou, P.R. China
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10
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Zhou X, Liu M, Sun L, Cao Y, Tan S, Luo G, Liu T, Yao Y, Xiao W, Wan Z, Tang J. Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer. J Transl Med 2023; 21:927. [PMID: 38129848 PMCID: PMC10740240 DOI: 10.1186/s12967-023-04774-4] [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: 06/08/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND No residual disease (R0 resection) after debulking surgery is the most critical independent prognostic factor for advanced ovarian cancer (AOC). There is an unmet clinical need for selecting primary or interval debulking surgery in AOC patients using existing prediction models. METHODS RNA sequencing of circulating small extracellular vesicles (sEVs) was used to discover the differential expression microRNAs (DEMs) profile between any residual disease (R0, n = 17) and no residual disease (non-R0, n = 20) in AOC patients. We further analyzed plasma samples of AOC patients collected before surgery or neoadjuvant chemotherapy via TaqMan qRT-PCR. The combined risk model of residual disease was developed by logistic regression analysis based on the discovery-validation sets. RESULTS Using a comprehensive plasma small extracellular vesicles (sEVs) microRNAs (miRNAs) profile in AOC, we identified and optimized a risk prediction model consisting of plasma sEVs-derived 4-miRNA and CA-125 with better performance in predicting R0 resection. Based on 360 clinical human samples, this model was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and it has favorable calibration and discrimination ability (AUC:0.903; sensitivity:0.897; specificity:0.910; PPV:0.926; NPV:0.871). The quantitative evaluation of Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) suggested that the additional predictive power of the combined model was significantly improved contrasted with CA-125 or 4-miRNA alone (NRI = 0.471, IDI = 0.538, p < 0.001; NRI = 0.122, IDI = 0.185, p < 0.01). CONCLUSION Overall, we established a reliable, non-invasive, and objective detection method composed of circulating tumor-derived sEVs 4-miRNA plus CA-125 to preoperatively anticipate the high-risk AOC patients of residual disease to optimize clinical therapy.
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Affiliation(s)
- Xiaofang Zhou
- Department of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, People's Republic of China
- Department of Oncology, Xiangya Cancer Center, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China
| | - Mu Liu
- Department of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, People's Republic of China
| | - Lijuan Sun
- Department of Gynecology and Obstetrics, The Central Hospital of Shaoyang, Shaoyang, 422000, People's Republic of China
| | - Yumei Cao
- Department of Gynecology and Obstetrics, The Central Hospital of Shaoyang, Shaoyang, 422000, People's Republic of China
| | - Shanmei Tan
- Department of Gynecology and Obstetrics, The First People's Hospital of Huaihua, The Affiliated Huaihua Hospital of University of South China, Huaihua, 418000, People's Republic of China
| | - Guangxia Luo
- Department of Gynecology and Obstetrics, The First People's Hospital of Huaihua, The Affiliated Huaihua Hospital of University of South China, Huaihua, 418000, People's Republic of China
| | - Tingting Liu
- Department of Gynecology and Obstetrics, The First People's Hospital of Changde, Changde, 415000, People's Republic of China
| | - Ying Yao
- Department of Gynecology and Obstetrics, The First People's Hospital of Yueyang, Yueyang, 414000, People's Republic of China
| | - Wangli Xiao
- Department of Gynecology and Obstetrics, The First People's Hospital of Yueyang, Yueyang, 414000, People's Republic of China
| | - Ziqing Wan
- Department of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, People's Republic of China
| | - Jie Tang
- Department of Gynecologic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, People's Republic of China.
- Department of Gynecologic Oncology, Hunan Gynecologic Cancer Research Center, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Address: 283 Tongzipo Road, Yuelu District, Changsha, 410013, People's Republic of China.
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11
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Batool SM, Yekula A, Khanna P, Hsia T, Gamblin AS, Ekanayake E, Escobedo AK, You DG, Castro CM, Im H, Kilic T, Garlin MA, Skog J, Dinulescu DM, Dudley J, Agrawal N, Cheng J, Abtin F, Aberle DR, Chia D, Elashoff D, Grognan T, Krysan K, Oh SS, Strom C, Tu M, Wei F, Xian RR, Skates SJ, Zhang DY, Trinh T, Watson M, Aft R, Rawal S, Agarwal A, Kesmodel SB, Yang C, Shen C, Hochberg FH, Wong DTW, Patel AA, Papadopoulos N, Bettegowda C, Cote RJ, Srivastava S, Lee H, Carter BS, Balaj L. The Liquid Biopsy Consortium: Challenges and opportunities for early cancer detection and monitoring. Cell Rep Med 2023; 4:101198. [PMID: 37716353 PMCID: PMC10591039 DOI: 10.1016/j.xcrm.2023.101198] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 12/01/2022] [Accepted: 08/22/2023] [Indexed: 09/18/2023]
Abstract
The emerging field of liquid biopsy stands at the forefront of novel diagnostic strategies for cancer and other diseases. Liquid biopsy allows minimally invasive molecular characterization of cancers for diagnosis, patient stratification to therapy, and longitudinal monitoring. Liquid biopsy strategies include detection and monitoring of circulating tumor cells, cell-free DNA, and extracellular vesicles. In this review, we address the current understanding and the role of existing liquid-biopsy-based modalities in cancer diagnostics and monitoring. We specifically focus on the technical and clinical challenges associated with liquid biopsy and biomarker development being addressed by the Liquid Biopsy Consortium, established through the National Cancer Institute. The Liquid Biopsy Consortium has developed new methods/assays and validated existing methods/technologies to capture and characterize tumor-derived circulating cargo, as well as addressed existing challenges and provided recommendations for advancing biomarker assays.
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Affiliation(s)
| | - Anudeep Yekula
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Prerna Khanna
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tiffaney Hsia
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Austin S Gamblin
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Emil Ekanayake
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ana K Escobedo
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dong Gil You
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Cesar M Castro
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hyungsoon Im
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tugba Kilic
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Johan Skog
- Exosome Diagnostics Inc., Waltham, MA, USA
| | | | - Jonathan Dudley
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Jordan Cheng
- University of California Los Angeles, Los Angeles, CA, USA
| | | | | | - David Chia
- University of California Los Angeles, Los Angeles, CA, USA
| | - David Elashoff
- University of California Los Angeles, Los Angeles, CA, USA
| | | | | | - Scott S Oh
- University of California Los Angeles, Los Angeles, CA, USA
| | - Charles Strom
- University of California Los Angeles, Los Angeles, CA, USA
| | - Michael Tu
- Liquid Diagnostics LLC., Los Angeles, CA, USA
| | - Fang Wei
- University of California Los Angeles, Los Angeles, CA, USA
| | - Rena R Xian
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Steven J Skates
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Thi Trinh
- Yale University School of Medicine, New Haven, CT, USA
| | - Mark Watson
- Washington University School of Medicine, St. Louis, MO, USA
| | - Rebecca Aft
- Washington University School of Medicine, St. Louis, MO, USA
| | - Siddarth Rawal
- Washington University School of Medicine, St. Louis, MO, USA; Circulogix Inc., St. Louis, MO, USA
| | | | | | | | - Cheng Shen
- California Institute of Technology, Pasadena, CA, USA
| | | | - David T W Wong
- University of California Los Angeles, Los Angeles, CA, USA
| | | | | | | | - Richard J Cote
- Washington University School of Medicine, St. Louis, MO, USA; Circulogix Inc., St. Louis, MO, USA
| | - Sudhir Srivastava
- Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Hakho Lee
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bob S Carter
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Leonora Balaj
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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12
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Wang K, Wang X, Pan Q, Zhao B. Liquid biopsy techniques and pancreatic cancer: diagnosis, monitoring, and evaluation. Mol Cancer 2023; 22:167. [PMID: 37803304 PMCID: PMC10557192 DOI: 10.1186/s12943-023-01870-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 09/25/2023] [Indexed: 10/08/2023] Open
Abstract
Pancreatic cancer (PC) is one of the most common malignancies. Surgical resection is a potential curative approach for PC, but most patients are unsuitable for operations when at the time of diagnosis. Even with surgery, some patients may still experience tumour metastasis during the operation or shortly after surgery, as precise prognosis evaluation is not always possible. If patients miss the opportunity for surgery and resort to chemotherapy, they may face the challenging issue of chemotherapy resistance. In recent years, liquid biopsy has shown promising prospects in disease diagnosis, treatment monitoring, and prognosis assessment. As a noninvasive detection method, liquid biopsy offers advantages over traditional diagnostic procedures, such as tissue biopsy, in terms of both cost-effectiveness and convenience. The information provided by liquid biopsy helps clinical practitioners understand the molecular mechanisms underlying tumour occurrence and development, enabling the formulation of more precise and personalized treatment decisions for each patient. This review introduces molecular biomarkers and detection methods in liquid biopsy for PC, including circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), noncoding RNAs (ncRNAs), and extracellular vesicles (EVs) or exosomes. Additionally, we summarize the applications of liquid biopsy in the early diagnosis, treatment response, resistance assessment, and prognostic evaluation of PC.
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Affiliation(s)
- Kangchun Wang
- Department of Organ Transplantation and Hepatobiliary, The First Affiliated Hospital of China Medical University, Shenyang, 110001, China
| | - Xin Wang
- Movement System Injury and Repair Research Center, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Qi Pan
- Department of Organ Transplantation and Hepatobiliary, The First Affiliated Hospital of China Medical University, Shenyang, 110001, China.
| | - Bei Zhao
- Department of Ultrasound, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
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13
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Shen H, Zaitseva D, Yang Z, Forsythe L, Joergensen S, Zone AI, Shehu J, Maghraoui S, Ghorbani A, Davila A, Issadore D, Abella BS. Brain-derived extracellular vesicles as serologic markers of brain injury following cardiac arrest: A pilot feasibility study. Resuscitation 2023; 191:109937. [PMID: 37591443 PMCID: PMC10528050 DOI: 10.1016/j.resuscitation.2023.109937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/19/2023]
Abstract
AIM Assessment of neurologic injury within the immediate hours following out-of-hospital cardiac arrest (OHCA) resuscitation remains a major clinical challenge. Extracellular vesicles (EVs), small bodies derived from cytosolic contents during injury, may provide the opportunity for "liquid biopsy" within hours following resuscitation, as they contain proteins and RNA linked to cell type of origin. We evaluated whether micro-RNA (miRNA) from serologic EVs were associated with post-arrest neurologic outcome. METHODS We obtained serial blood samples in an OHCA cohort. Using novel microfluidic techniques to isolate EVs based on EV surface marker GluR2 (present on excitatory neuronal dendrites enriched in hippocampal tissue), we employed reverse transcription quantitative polymerase chain reaction (RT-qPCR) methods to measure a panel of miRNAs and tested association with dichotomized modified Rankin Score (mRS) at discharge. RESULTS EVs were assessed in 27 post-arrest patients between 7/3/2019 and 7/21/2022; 9 patients experienced good outcomes. Several miRNA species including miR-124 were statistically associated with mRS at discharge when measured within 6 hours of resuscitation (AUC = 0.84 for miR-124, p < 0.05). In a Kendall ranked correlation analysis, miRNA associations with outcome were not strongly correlated with standard serologic marker measurements, or amongst themselves, suggesting that miRNA provide distinct information from common protein biomarkers. CONCLUSIONS This study explores the associations between miRNAs from neuron-derived EVs (NDEs) and circulating protein biomarkers within 6 hours with neurologic outcome, suggesting a panel of very early biomarker may be useful during clinical care. Future work will be required to test larger cohorts with a broader panel of miRNA species.
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Affiliation(s)
- Hanfei Shen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Daria Zaitseva
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zijian Yang
- Department of Radiology, Stanford University, Palo Alto, CA, USA
| | - Liam Forsythe
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Joergensen
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alea I Zone
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joana Shehu
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Maghraoui
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anahita Ghorbani
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Antonio Davila
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA; School of Nursing, University of Pennsylvania, Philadelphia, PA, USA
| | - David Issadore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin S Abella
- Penn Acute Research Collaboration, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA; Center for Resuscitation Science, Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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14
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Abu-Khudir R, Hafsa N, Badr BE. Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning. Diagnostics (Basel) 2023; 13:3091. [PMID: 37835833 PMCID: PMC10572229 DOI: 10.3390/diagnostics13193091] [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: 06/01/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.
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Affiliation(s)
- Rasha Abu-Khudir
- Chemistry Department, College of Science, King Faisal University, P.O. Box 380, Hofuf 31982, Al-Ahsa, Saudi Arabia
- Chemistry Department, Biochemistry Branch, Faculty of Science, Tanta University, Tanta 31527, Egypt
| | - Noor Hafsa
- Computer Science Department, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Hofuf 31982, Al-Ahsa, Saudi Arabia;
| | - Badr E. Badr
- Egyptian Ministry of Labor, Training and Research Department, Tanta 31512, Egypt;
- Botany Department, Microbiology Unit, Faculty of Science, Tanta University, Tanta 31527, Egypt
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15
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Kim HJ, Rames MJ, Goncalves F, Kirschbaum CW, Roskams-Hieter B, Spiliotopoulos E, Briand J, Doe A, Estabrook J, Wagner JT, Demir E, Mills G, Ngo TTM. Selective enrichment of plasma cell-free messenger RNA in cancer-associated extracellular vesicles. Commun Biol 2023; 6:885. [PMID: 37644220 PMCID: PMC10465482 DOI: 10.1038/s42003-023-05232-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 08/09/2023] [Indexed: 08/31/2023] Open
Abstract
Extracellular vesicles (EVs) have been shown as key mediators of extracellular small RNA transport. However, carriers of cell-free messenger RNA (cf-mRNA) in human biofluids and their association with cancer remain poorly understood. Here, we performed a transcriptomic analysis of size-fractionated plasma from lung cancer, liver cancer, multiple myeloma, and healthy donors. Morphology and size distribution analysis showed the successful separation of large and medium particles from other soluble plasma protein fractions. We developed a strategy to purify and sequence ultra-low amounts of cf-mRNA from particle and protein enriched subpopulations with the implementation of RNA spike-ins to control for technical variability and to normalize for intrinsic drastic differences in cf-mRNA amount carried in each plasma fraction. We found that the majority of cf-mRNA was enriched and protected in EVs with remarkable stability in RNase-rich environments. We observed specific enrichment patterns of cancer-associated cf-mRNA in each particle and protein enriched subpopulation. The EV-enriched differentiating genes were associated with specific biological pathways, such as immune systems, liver function, and toxic substance regulation in lung cancer, liver cancer, and multiple myeloma, respectively. Our results suggest that dissecting the complexity of EV subpopulations illuminates their biological significance and offers a promising liquid biopsy approach.
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Affiliation(s)
- Hyun Ji Kim
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Matthew J Rames
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Florian Goncalves
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - C Ward Kirschbaum
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Breeshey Roskams-Hieter
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Elias Spiliotopoulos
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Josephine Briand
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Aaron Doe
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Joseph Estabrook
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Computational Biology Program, Oregon Health and Science University, Portland, OR, USA
| | - Josiah T Wagner
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Molecular Genomics Laboratory, Providence Health and Services, Portland, OR, USA
| | - Emek Demir
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Computational Biology Program, Oregon Health and Science University, Portland, OR, USA
- Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, USA
| | - Gordon Mills
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Thuy T M Ngo
- Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
- Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, USA.
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
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16
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Lin AA, Shen H, Spychalski G, Carpenter EL, Issadore D. Modeling and optimization of parallelized immunomagnetic nanopore sorting for surface marker specific isolation of extracellular vesicles from complex media. Sci Rep 2023; 13:13292. [PMID: 37587235 PMCID: PMC10432479 DOI: 10.1038/s41598-023-39746-7] [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: 05/09/2023] [Accepted: 07/30/2023] [Indexed: 08/18/2023] Open
Abstract
The isolation of specific subpopulations of extracellular vesicles (EVs) based on their expression of surface markers poses a significant challenge due to their nanoscale size (< 800 nm), their heterogeneous surface marker expression, and the vast number of background EVs present in clinical specimens (1010-1012 EVs/mL in blood). Highly parallelized nanomagnetic sorting using track etched magnetic nanopore (TENPO) chips has achieved precise immunospecific sorting with high throughput and resilience to clogging. However, there has not yet been a systematic study of the design parameters that control the trade-offs in throughput, target EV recovery, and ability to discard background EVs in this approach. We combine finite-element simulation and experimental characterization of TENPO chips to elucidate design rules to isolate EV subpopulations from blood. We demonstrate the utility of this approach by reducing device background > 10× relative to prior published designs without sacrificing recovery of the target EVs by selecting pore diameter, number of membranes placed in series, and flow rate. We compare TENPO-isolated EVs to those of gold-standard methods of EV isolation and demonstrate its utility for wide application and modularity by targeting subpopulations of EVs from multiple models of disease including lung cancer, pancreatic cancer, and liver cancer.
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Affiliation(s)
- Andrew A Lin
- Department of Bioengineering, University of Pennsylvania, 210 S. 33rd St., Philadelphia, PA, 19104, USA
- Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA, 19104, USA
| | - Hanfei Shen
- Department of Bioengineering, University of Pennsylvania, 210 S. 33rd St., Philadelphia, PA, 19104, USA
| | - Griffin Spychalski
- Department of Bioengineering, University of Pennsylvania, 210 S. 33rd St., Philadelphia, PA, 19104, USA
- Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA, 19104, USA
| | - Erica L Carpenter
- Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA, 19104, USA
| | - David Issadore
- Department of Bioengineering, University of Pennsylvania, 210 S. 33rd St., Philadelphia, PA, 19104, USA.
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17
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Gao L, Lin Y, Yue P, Li S, Zhang Y, Mi N, Bai M, Fu W, Xia Z, Jiang N, Cao J, Yang M, Ma Y, Zhang F, Zhang C, Leung JW, He S, Yuan J, Meng W, Li X. Identification of a novel bile marker clusterin and a public online prediction platform based on deep learning for cholangiocarcinoma. BMC Med 2023; 21:294. [PMID: 37553571 PMCID: PMC10408060 DOI: 10.1186/s12916-023-02990-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 07/20/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor, and its diagnosis is still a challenge. This study aimed to identify a novel bile marker for CCA diagnosis based on proteomics and establish a diagnostic model with deep learning. METHODS A total of 644 subjects (236 CCA and 408 non-CCA) from two independent centers were divided into discovery, cross-validation, and external validation sets for the study. Candidate bile markers were identified by three proteomics data and validated on 635 clinical humoral specimens and 121 tissue specimens. A diagnostic multi-analyte model containing bile and serum biomarkers was established in cross-validation set by deep learning and validated in an independent external cohort. RESULTS The results of proteomics analysis and clinical specimen verification showed that bile clusterin (CLU) was significantly higher in CCA body fluids. Based on 376 subjects in the cross-validation set, ROC analysis indicated that bile CLU had a satisfactory diagnostic power (AUC: 0.852, sensitivity: 73.6%, specificity: 90.1%). Building on bile CLU and 63 serum markers, deep learning established a diagnostic model incorporating seven factors (CLU, CA19-9, IBIL, GGT, LDL-C, TG, and TBA), which showed a high diagnostic utility (AUC: 0.947, sensitivity: 90.3%, specificity: 84.9%). External validation in an independent cohort (n = 259) resulted in a similar accuracy for the detection of CCA. Finally, for the convenience of operation, a user-friendly prediction platform was built online for CCA. CONCLUSIONS This is the largest and most comprehensive study combining bile and serum biomarkers to differentiate CCA. This diagnostic model may potentially be used to detect CCA.
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Affiliation(s)
- Long Gao
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Yanyan Lin
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
- Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine Transformation, Lanzhou, 730030, Gansu, China
| | - Ping Yue
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
- Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine Transformation, Lanzhou, 730030, Gansu, China
| | - Shuyan Li
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yong Zhang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
- Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine Transformation, Lanzhou, 730030, Gansu, China
| | - Ningning Mi
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Mingzhen Bai
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Wenkang Fu
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Zhili Xia
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Ningzu Jiang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Jie Cao
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Man Yang
- Clinical Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Yanni Ma
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Fanxiang Zhang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Chao Zhang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
| | - Joseph W Leung
- Division of Gastroenterology, UC Davis Medical Center and Sacramento VA Medical Center, Sacramento, CA, 95817, USA
| | - Shun He
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Jinqiu Yuan
- Clinical Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
| | - Wenbo Meng
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China.
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China.
- Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine Transformation, Lanzhou, 730030, Gansu, China.
| | - Xun Li
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China
- Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine Transformation, Lanzhou, 730030, Gansu, China
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18
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Lin AA, Shen H, Spychalski G, Carpenter EL, Issadore D. Parallelized immunomagnetic nanopore sorting: modeling, scaling, and optimization of surface marker specific isolation of extracellular vesicles from complex media. RESEARCH SQUARE 2023:rs.3.rs-2913647. [PMID: 37292737 PMCID: PMC10246262 DOI: 10.21203/rs.3.rs-2913647/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The isolation of specific subpopulations of extracellular vesicles (EVs) based on their expression of surface markers poses a significant challenge due to their nanoscale size (< 800 nm), their heterogeneous surface marker expression, and the vast number of background EVs present in clinical specimens (10 10 -10 12 EVs/mL in blood). Highly parallelized nanomagnetic sorting using track etched magnetic nanopore (TENPO) chips has achieved precise immunospecific sorting with high throughput and resilience to clogging. However, there has not yet been a systematic study of the design parameters that control the trade-offs in throughput, target EV recovery, and specificity in this approach. We combine finite-element simulation and experimental characterization of TENPO chips to elucidate design rules to isolate EV subpopulations from blood. We demonstrate the utility of this approach by increasing specificity > 10x relative to prior published designs without sacrificing recovery of the target EVs by selecting pore diameter, number of membranes placed in series, and flow rate. We compare TENPO-isolated EVs to those of gold-standard methods of EV isolation and demonstrate its utility for wide application and modularity by targeting subpopulations of EVs from multiple models of disease including lung cancer, pancreatic cancer, and liver cancer.
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19
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Xu X, Bhandari K, Xu C, Morris K, Ding WQ. miR-18a and miR-106a Signatures in Plasma Small EVs Are Promising Biomarkers for Early Detection of Pancreatic Ductal Adenocarcinoma. Int J Mol Sci 2023; 24:7215. [PMID: 37108374 PMCID: PMC10138951 DOI: 10.3390/ijms24087215] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Pancreatic cancer is the third leading cause of cancer-related death in the United States. Pancreatic ductal adenocarcinoma (PDAC) is the major form of pancreatic cancer with the worst outcomes. Early detection is key to improving the overall survival rate of PDAC patients. Recent studies have demonstrated that microRNA (miRNA) signatures in plasma small extracellular vesicles (EVs) are potential biomarkers for the early detection of PDAC. However, published results are inconsistent due to the heterogeneity of plasma small EVs and the methods used for small EV isolation. We have recently refined the process of plasma small EV isolation using double filtration and ultracentrifugation. In the present study, we applied this protocol and analyzed plasma small EV miRNA signatures by small RNA sequencing and quantitative RT-PCR in a pilot cohort, consisting of patients with early-stage PDAC, and age- and gender-matched healthy subjects (n = 20). We found, via small RNA sequencing, that there are several miRNAs enriched in plasma small EVs of PDAC patients, and the levels of miR-18a and miR-106a were confirmed by quantitative RT-PCR to be significantly elevated in patients with early-stage PDAC compared with age- and gender-matched healthy subjects. Furthermore, using an immunoaffinity-based plasma small EV isolation approach, we confirmed that the levels of miR-18a and miR-106a in plasma small EVs were significantly higher in PDAC patients versus the healthy subjects. We thus conclude that the levels of miR-18a and miR-106a in plasma small EVs are promising biomarkers for the early detection of PDAC.
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Affiliation(s)
- Xiaohui Xu
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (X.X.)
| | - Kritisha Bhandari
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (X.X.)
| | - Chao Xu
- Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Katherine Morris
- Department of Surgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73126, USA
| | - Wei-Qun Ding
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (X.X.)
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20
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Di Sario G, Rossella V, Famulari ES, Maurizio A, Lazarevic D, Giannese F, Felici C. Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review. Front Genet 2023; 14:1152470. [PMID: 37077538 PMCID: PMC10109350 DOI: 10.3389/fgene.2023.1152470] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. "Classical" tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.
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21
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Zhao Y, Tang J, Jiang K, Liu SY, Aicher A, Heeschen C. Liquid biopsy in pancreatic cancer - Current perspective and future outlook. Biochim Biophys Acta Rev Cancer 2023; 1878:188868. [PMID: 36842769 DOI: 10.1016/j.bbcan.2023.188868] [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: 09/12/2022] [Revised: 01/14/2023] [Accepted: 01/16/2023] [Indexed: 02/27/2023]
Abstract
Pancreatic cancer is a lethal condition with a rising incidence and often presents at an advanced stage, contributing to abysmal five-year survival rates. Unspecific symptoms and the current lack of biomarkers and screening tools hamper early diagnosis. New technologies for liquid biopsies and their respective evaluation in pancreatic cancer patients have emerged over recent years. The term liquid biopsy summarizes the sampling and analysis of circulating tumor cells (CTCs), small extracellular vesicles (sEVs), and tumor DNA (ctDNA) from body fluids. The major advantages of liquid biopsies rely on their minimal invasiveness and repeatability, allowing serial sampling for dynamic insights to aid diagnosis, particularly early detection, risk stratification, and precision medicine in pancreatic cancer. However, liquid biopsies have not yet developed into a new pillar for clinicians' routine armamentarium. Here, we summarize recent findings on the use of liquid biopsy in pancreatic cancer patients. We discuss current challenges and future perspectives of this potentially powerful alternative to conventional tissue biopsies.
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Affiliation(s)
- Yaru Zhao
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiajia Tang
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ke Jiang
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shin-Yi Liu
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan; Research and Development Center for Immunology, China Medical University, Taichung, Taiwan
| | - Alexandra Aicher
- Precision Immunotherapy, Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Christopher Heeschen
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Key Laboratory of Oncogenes and Related Genes, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Pancreatic Cancer Heterogeneity, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
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22
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Bracht JWP, Los M, van Eijndhoven MAJ, Bettin B, van der Pol E, Pegtel DM, Nieuwland R. Platelet removal from human blood plasma improves detection of extracellular vesicle-associated miRNA. J Extracell Vesicles 2023; 12:e12302. [PMID: 36788785 PMCID: PMC9929339 DOI: 10.1002/jev2.12302] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/12/2022] [Accepted: 01/04/2023] [Indexed: 02/16/2023] Open
Abstract
Human blood plasma prepared by centrifugation contains not only extracellular vesicles (EVs) but also platelets and erythrocyte ghosts (ery-ghosts). Here we studied whether analysis of miRNA associated with plasma EVs (EV-miRNA) is affected by the presence of platelets and ery-ghosts. EDTA blood was collected from healthy donors (n = 3), and plasma was prepared by the centrifugation protocol recommended by the International Society on Thrombosis and Haemostasis (ISTH), and by a centrifugation protocol from an EV-miRNA expert lab (non-ISTH protocol). EVs were isolated from plasma by size-exclusion chromatography CL-2B (SEC2B), and concentrations of platelets, activated platelets, ery-ghosts and EVs (150-1000 nm) were measured by calibrated flow cytometry. Two EV-associated miRNAs (let7a-5p and miR-21-5p), and one platelet-associated miRNA (miR-223-3p), were measured by qRT-PCR. Measurements were performed with and without filtration using 0.8 μm track-etched filters to remove platelets and ery-ghosts from plasma and EV-enriched SEC fractions. Plasma prepared by both centrifugation protocols contained platelets and ery-ghosts, which co-migrated with EVs into the EV-enriched SEC2B fractions. Filtration removed platelets and ery-ghosts (>97%; p ≤ 0.05) and did not affect the EV concentrations (p > 0.17). The miRNA concentrations were 2-4-fold overestimated due to the presence of platelets but not ery-ghosts. Thus, filtration of human plasma is expected to improve comparability and reproducibility of quantitative EV-miRNA studies. Therefore, we recommend to measure and report the plasma concentration of platelets for EV-miRNA studies, and to filter plasma before downstream analyses or storage in biobanks.
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Affiliation(s)
- Jillian W. P. Bracht
- Amsterdam UMC location University of Amsterdam, Vesicle Observation Centre, Laboratory of Experimental Clinical Chemistry, Department of Clinical Chemistry, Meibergdreef 9AmsterdamThe Netherlands
- Cancer Centre Amsterdam, Imaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic SyndromesAmsterdamThe Netherlands
| | - Mandy Los
- Amsterdam UMC location University of Amsterdam, Vesicle Observation Centre, Laboratory of Experimental Clinical Chemistry, Department of Clinical Chemistry, Meibergdreef 9AmsterdamThe Netherlands
| | - Monique A. J. van Eijndhoven
- Cancer Centre Amsterdam, Imaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Pathology, Boelelaan 1117AmsterdamThe Netherlands
| | - Britta Bettin
- Amsterdam UMC location University of Amsterdam, Vesicle Observation Centre, Laboratory of Experimental Clinical Chemistry, Department of Clinical Chemistry, Meibergdreef 9AmsterdamThe Netherlands
- Amsterdam UMC location University of Amsterdam, Department of Biomedical Engineering and Physics, Meibergdreef 9AmsterdamThe Netherlands
| | - Edwin van der Pol
- Amsterdam UMC location University of Amsterdam, Vesicle Observation Centre, Laboratory of Experimental Clinical Chemistry, Department of Clinical Chemistry, Meibergdreef 9AmsterdamThe Netherlands
- Cancer Centre Amsterdam, Imaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic SyndromesAmsterdamThe Netherlands
- Amsterdam UMC location University of Amsterdam, Department of Biomedical Engineering and Physics, Meibergdreef 9AmsterdamThe Netherlands
| | - D. Michiel Pegtel
- Cancer Centre Amsterdam, Imaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Pathology, Boelelaan 1117AmsterdamThe Netherlands
| | - Rienk Nieuwland
- Amsterdam UMC location University of Amsterdam, Vesicle Observation Centre, Laboratory of Experimental Clinical Chemistry, Department of Clinical Chemistry, Meibergdreef 9AmsterdamThe Netherlands
- Cancer Centre Amsterdam, Imaging and BiomarkersAmsterdamThe Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic SyndromesAmsterdamThe Netherlands
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23
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Predicting chemoresponsiveness in epithelial ovarian cancer patients using circulating small extracellular vesicle-derived plasma gelsolin. J Ovarian Res 2023; 16:14. [PMID: 36642715 PMCID: PMC9841140 DOI: 10.1186/s13048-022-01086-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/20/2022] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Resistance to chemotherapy continues to be a challenge when treating epithelial ovarian cancer (EOC), contributing to low patient survival rates. While CA125, the conventional EOC biomarker, has been useful in monitoring patients' response to therapy, there are no biomarkers used to predict treatment response prior to chemotherapy. Previous work in vitro showed that plasma gelsolin (pGSN) is highly expressed in chemoresistant EOC cell lines, where it is secreted in small extracellular vesicles (sEVs). Whether sEVs from tumour cells are secreted into the circulation of EOC patients and could be used to predict patient chemoresponsiveness is yet to be determined. This study aims to identify if sEV-pGSN in the circulation could be a predictive biomarker for chemoresistance in EOC. METHODS Sandwich ELISA was used to measure pGSN concentrations from plasma samples of 96 EOC patients (primarily high grade serous EOC). sEVs were isolated using ExoQuick ULTRA and characterized using western blot, nanoparticle tracking analysis, and electron microscopy after which pGSN was measured from the sEVs. Patients were stratified as platinum sensitive or resistant groups based on first progression free interval (PFI) of 6 or 12 months. RESULTS Total circulating pGSN was significantly decreased and sEV-pGSN increased in patients with a PFI ≤ 12 months (chemoresistant) compared to those with a PFI > 12 months (chemosensitive). The ratio of total pGSN to sEV-pGSN further differentiated these groups and was a strong predictive marker for chemoresistance (sensitivity: 73.91%, specificity: 72.46%). Predetermined CA125 was not different between chemosensitive and chemoresistant groups and was not predictive of chemoresponsiveness prior to treatment. When CA125 was combined with the ratio of total pGSN/sEV-pGSN, it was a significant predictor of chemoresponsiveness, but the test performance was not as robust as the total pGSN/sEV-pGSN alone. CONCLUSIONS Total pGSN/sEV-pGSN was the best predictor of chemoresponsiveness prior to treatment, outperforming the individual biomarkers (CA125, total pGSN, and sEV-pGSN). This multianalyte predictor of chemoresponsiveness could help to inform physicians' treatment and follow up plan at the time of EOC diagnosis, thus improving patients' outcomes.
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24
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Rackles E, Lopez PH, Falcon-Perez JM. Extracellular vesicles as source for the identification of minimally invasive molecular signatures in glioblastoma. Semin Cancer Biol 2022; 87:148-159. [PMID: 36375777 DOI: 10.1016/j.semcancer.2022.11.004] [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: 07/01/2022] [Revised: 10/21/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022]
Abstract
The analysis of extracellular vesicles (EVs) as a source of cancer biomarkers is an emerging field since low-invasive biomarkers are highly demanded. EVs constitute a heterogeneous population of small membrane-contained vesicles that are present in most of body fluids. They are released by all cell types, including cancer cells and their cargo consists of nucleic acids, proteins and metabolites and varies depending on the biological-pathological state of the secretory cell. Therefore, EVs are considered as a potential source of reliable biomarkers for cancer. EV biomarkers in liquid biopsy can be a valuable tool to complement current medical technologies for cancer diagnosis, as their sampling is minimally invasive and can be repeated over time to monitor disease progression. In this review, we highlight the advances in EV biomarker research for cancer diagnosis, prognosis, and therapy monitoring. We especially focus on EV derived biomarkers for glioblastoma. The diagnosis and monitoring of glioblastoma still relies on imaging techniques, which are not sufficient to reflect the highly heterogenous and invasive nature of glioblastoma. Therefore, we discuss how the use of EV biomarkers could overcome the challenges faced in diagnosis and monitoring of glioblastoma.
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Affiliation(s)
- Elisabeth Rackles
- Exosomes Laboratory, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Derio, Spain.
| | - Patricia Hernández Lopez
- Exosomes Laboratory, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Derio, Spain.
| | - Juan M Falcon-Perez
- Exosomes Laboratory, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Derio, Spain; Metabolomics Platform, CIC bioGUNE, Bizkaia Technology Park, 48160 Derio, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (Ciberehd), Madrid, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
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25
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Sha M, Kunduzi B, Froghi S, Quaglia A, Davidson B, Fusai GK. Role of circulating exosomal biomarkers and their diagnostic accuracy in pancreatic cancer. JGH Open 2022; 7:30-39. [PMID: 36660044 PMCID: PMC9840196 DOI: 10.1002/jgh3.12848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 11/07/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022]
Abstract
Background and Aim New biomarkers have the potential to facilitate early diagnosis of pancreatic cancer (PC). Circulating exosomes are cell-derived protein complexes containing RNA that can be used as indicators of cancer development. The aim of this review is to evaluate the current literature involving PC patient groups for highly accurate exosomal biomarkers. Methods The literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eight-hundred and seventy-five studies were identified across various databases (Ovid MEDLINE, Embase, and Cochrane) published between 2009 and 2020. Nine studies fulfilled the inclusion criteria: human PC patients, diagnosis as outcome of interest, serum biomarker of exosomal content, reporting of diagnostic values, and disease progress. Area under the curve (AUC) of the exosomal biomarker was compared against that of CA19-9. Results Nine papers were reviewed for relevant outcomes based on the inclusion criteria. These studies involved 565 participants (331 PC, 234 controls; male/female ratio 1.21; mean age 64.1). Tumor staging was reported in all studies, with 45.6% of PC patients diagnosed with early-stage PC (T1-2). The mRNA panel (ARG1, CD63, CK18, Erbb3, GAPDH, H3F3A, KRAS, ODC1) and GPC 1 reported the highest performing sensitivity and specificity at 100% each. The microRNA panel (miR-10b, miR-21, miR-30c, miR-181a, and miR-let7a), mRNA panel (ARG1, CD63, CK18, Erbb3, GAPDH, H3F3A, KRAS, ODC1), and GPC 1 showed a perfect AUC of 1.0. Five studies compared the AUC of the exosomal biomarker against CA19-9, each being superior to that of CA19-9. Conclusion The potential of exosomal biomarkers remains promising in PC diagnosis. Standardization of future studies will allow for larger comparative analyses and overcoming contrasting findings.
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Affiliation(s)
- Menazir Sha
- University College London, Medical SchoolLondonUK,Division of Surgery and Interventional Sciences/UCLRoyal Free HospitalLondonUK
| | | | - Saied Froghi
- Division of Surgery and Interventional Sciences/UCLRoyal Free HospitalLondonUK,Department of HPB and Liver TransplantationRoyal Free HospitalLondonUK
| | | | - Brian Davidson
- Division of Surgery and Interventional Sciences/UCLRoyal Free HospitalLondonUK,Department of HPB and Liver TransplantationRoyal Free HospitalLondonUK
| | - Giuseppe K Fusai
- Division of Surgery and Interventional Sciences/UCLRoyal Free HospitalLondonUK,Department of HPB and Liver TransplantationRoyal Free HospitalLondonUK
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26
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Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Theranostics 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
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Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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Matulić M, Gršković P, Petrović A, Begić V, Harabajsa S, Korać P. miRNA in Molecular Diagnostics. Bioengineering (Basel) 2022; 9:bioengineering9090459. [PMID: 36135005 PMCID: PMC9495386 DOI: 10.3390/bioengineering9090459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/05/2022] [Accepted: 09/07/2022] [Indexed: 11/17/2022] Open
Abstract
MicroRNAs are a class of small non-coding RNA molecules that regulate gene expression on post-transcriptional level. Their biogenesis consists of a complex series of sequential processes, and they regulate expression of many genes involved in all cellular processes. Their function is essential for maintaining the homeostasis of a single cell; therefore, their aberrant expression contributes to development and progression of many diseases, especially malignant tumors and viral infections. Moreover, they can be associated with certain states of a specific disease, obtained in the least invasive manner for patients and analyzed with basic molecular methods used in clinical laboratories. Because of this, they have a promising potential to become very useful biomarkers and potential tools in personalized medicine approaches. In this review, miRNAs biogenesis, significance in cancer and infectious diseases, and current available test and methods for their detection are summarized.
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Affiliation(s)
- Maja Matulić
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
| | - Paula Gršković
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
| | - Andreja Petrović
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
- Institute of Clinical Pathology and Cytology, Merkur University Hospital, 10000 Zagreb, Croatia
| | - Valerija Begić
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
- Primary School “Sesvetski Kraljevec”, 10361 Sesvetski Kraljevec, Croatia
| | - Suzana Harabajsa
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
- Department of Pathology and Cytology, Division of Pulmonary Cytology Jordanovac, University Hospital Centre Zagreb, 10000 Zagreb, Croatia
| | - Petra Korać
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
- Correspondence: ; Tel.: +385-1-4606-278
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Morales RTT, Ko J. Future of Digital Assays to Resolve Clinical Heterogeneity of Single Extracellular Vesicles. ACS NANO 2022; 16:11619-11645. [PMID: 35904433 PMCID: PMC10174080 DOI: 10.1021/acsnano.2c04337] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Extracellular vesicles (EVs) are complex lipid membrane vehicles with variable expressions of molecular cargo, composed of diverse subpopulations that participate in the intercellular signaling of biological responses in disease. EV-based liquid biopsies demonstrate invaluable clinical potential for overhauling current practices of disease management. Yet, EV heterogeneity is a major needle-in-a-haystack challenge to translate their use into clinical practice. In this review, existing digital assays will be discussed to analyze EVs at a single vesicle resolution, and future opportunities to optimize the throughput, multiplexing, and sensitivity of current digital EV assays will be highlighted. Furthermore, this review will outline the challenges and opportunities that impact the clinical translation of single EV technologies for disease diagnostics and treatment monitoring.
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Affiliation(s)
- Renee-Tyler T Morales
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Jina Ko
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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29
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Iyer V, Yang Z, Ko J, Weissleder R, Issadore D. Advancing microfluidic diagnostic chips into clinical use: a review of current challenges and opportunities. LAB ON A CHIP 2022; 22:3110-3121. [PMID: 35674283 PMCID: PMC9798730 DOI: 10.1039/d2lc00024e] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Microfluidic diagnostic (μDX) technologies miniaturize sensors and actuators to the length-scales that are relevant to biology: the micrometer scale to interact with cells and the nanometer scale to interrogate biology's molecular machinery. This miniaturization allows measurements of biomarkers of disease (cells, nanoscale vesicles, molecules) in clinical samples that are not detectable using conventional technologies. There has been steady progress in the field over the last three decades, and a recent burst of activity catalyzed by the COVID-19 pandemic. In this time, an impressive and ever-growing set of technologies have been successfully validated in their ability to measure biomarkers in clinical samples, such as blood and urine, with sensitivity and specificity not possible using conventional tests. Despite our field's many accomplishments to date, very few of these technologies have been successfully commercialized and brought to clinical use where they can fulfill their promise to improve medical care. In this paper, we identify three major technological trends in our field that we believe will allow the next generation of μDx to have a major impact on the practice of medicine, and which present major opportunities for those entering the field from outside disciplines: 1. the combination of next generation, highly multiplexed μDx technologies with machine learning to allow complex patterns of multiple biomarkers to be decoded to inform clinical decision points, for which conventional biomarkers do not necessarily exist. 2. The use of micro/nano devices to overcome the limits of binding affinity in complex backgrounds in both the detection of sparse soluble proteins and nucleic acids in blood and rare circulating extracellular vesicles. 3. A suite of recent technologies that obviate the manual pre-processing and post-processing of samples before they are measured on a μDX chip. Additionally, we discuss economic and regulatory challenges that have stymied μDx translation to the clinic, and highlight strategies for successfully navigating this challenging space.
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Affiliation(s)
- Vasant Iyer
- Electrical and Systems Engineering Department, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
| | - Zijian Yang
- Mechanical Engineering Department, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jina Ko
- Bioengineering Department, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital/Harvard Medical School, 185 Cambridge Street, Boston, Massachusetts, USA
| | - David Issadore
- Electrical and Systems Engineering Department, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
- Bioengineering Department, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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30
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Chapin WJ, Till JE, Hwang WT, Eads JR, Karasic TB, O'Dwyer PJ, Schneider CJ, Teitelbaum UR, Romeo J, Black TA, Christensen TE, Redlinger Tabery C, Anderson A, Slade M, LaRiviere M, Yee SS, Reiss KA, O'Hara MH, Carpenter EL. Multianalyte Prognostic Signature Including Circulating Tumor DNA and Circulating Tumor Cells in Patients With Advanced Pancreatic Adenocarcinoma. JCO Precis Oncol 2022; 6:e2200060. [PMID: 35939771 PMCID: PMC9384952 DOI: 10.1200/po.22.00060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/24/2022] [Accepted: 06/15/2022] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Pancreatic ductal adenocarcinoma (PDAC) is associated with a poor prognosis. Multianalyte signatures, including liquid biopsy and traditional clinical variables, have shown promise for improving prognostication in other solid tumors but have not yet been rigorously assessed for PDAC. MATERIALS AND METHODS We performed a prospective cohort study of patients with newly diagnosed locally advanced pancreatic cancer (LAPC) or metastatic PDAC (mPDAC) who were planned to undergo systemic therapy. We collected peripheral blood before systemic therapy and assessed circulating tumor cells (CTCs), cell-free DNA concentration (cfDNA), and circulating tumor KRAS (ctKRAS)-variant allele fraction (VAF). Association of variables with overall survival (OS) was assessed in univariate and multivariate survival analysis, and comparisons were made between models containing liquid biopsy variables combined with traditional clinical prognostic variables versus models containing traditional clinical prognostic variables alone. RESULTS One hundred four patients, 40 with LAPC and 64 with mPDAC, were enrolled. CTCs, cfDNA concentration, and ctKRAS VAF were all significantly higher in patients with mPDAC than patients with LAPC. ctKRAS VAF (cube root; 0.05 unit increments; hazard ratio, 1.11; 95% CI, 1.03 to 1.21; P = .01), and CTCs ≥ 1/mL (hazard ratio, 2.22; 95% CI, 1.34 to 3.69; P = .002) were significantly associated with worse OS in multivariate analysis while cfDNA concentration was not. A model selected by backward selection containing traditional clinical variables plus liquid biopsy variables had better discrimination of OS compared with a model containing traditional clinical variables alone (optimism-corrected Harrell's C-statistic 0.725 v 0.681). CONCLUSION A multianalyte prognostic signature containing CTCs, ctKRAS, and cfDNA concentration outperformed a model containing traditional clinical variables alone suggesting that CTCs, ctKRAS, and cfDNA provide prognostic information complementary to traditional clinical variables in advanced PDAC.
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Affiliation(s)
- William J. Chapin
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jacob E. Till
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Wei-Ting Hwang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA
| | - Jennifer R. Eads
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Thomas B. Karasic
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Peter J. O'Dwyer
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Charles J. Schneider
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ursina R. Teitelbaum
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Janae Romeo
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Taylor A. Black
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Theresa E. Christensen
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Colleen Redlinger Tabery
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | | | - Michael LaRiviere
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Stephanie S. Yee
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kim A. Reiss
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mark H. O'Hara
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Erica L. Carpenter
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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31
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Huang CJ, Huang WY, Chen CY, Chao YJ, Chiang NJ, Shan YS. Cancer-cell-derived cell-free DNA can predict distant metastasis earlier in pancreatic cancer: a prospective cohort study. Ther Adv Med Oncol 2022; 14:17588359221106558. [PMID: 35747164 PMCID: PMC9210094 DOI: 10.1177/17588359221106558] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 05/25/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Carbohydrate antigen 19-9 (CA19-9) is the only biomarker for monitoring responses during treatments of pancreatic cancer, but its accuracy for disease outcome is controversial. Fluid biopsy is a new method for diagnosis and monitoring treatment response. In this study, we investigate the usefulness of cell-free DNA (cfDNA) in predicting disease progression during the treatment of pancreatic cancer. Methods: Biopsy-proved advanced pancreatic cancer patients who received systemic chemotherapy were enrolled after signed informed consent. CA19-9 and cfDNA in blood were measured before and after every two cycles of treatments, and the disease progression was monitored by computed tomography (CT) with 3-month interval. Results: In total, 74 patients and 148 blood samples were enrolled in this study. Patients whose average blood cfDNA concentration of >9.71 ng/mL before and after first two courses of chemotherapy would subsequently show new distant metastasis (NDM) on CT scans 3 months later. The accuracy was 94.37% (AUC 0.9705, p < 0.0001) and the progression-free survival (PFS) and overall survival (OS) of patients with cfDNA concentration of >9.71 ng/mL were worse than those patients with cfDNA concentration of <9.71 ng/mL (median PFS: 95 days versus 322 days, p < 0.0001; median OS: 150 days versus 431 days, p < 0.0001). The cfDNA concentration of >9.71 ng/mL is a predictor for PFS, OS, and distant metastasis-free survival by multivariate analysis. Comparison of KRAS G12 variants detected by next-generation sequencing from tumor tissue issue and remnant DNA of cfDNA showed that increased cfDNA was primarily derived from cancer cells. Conclusion: The cancer-cell-derived cfDNA levels could be served as a powerful biomarker for prediction of NDM in patients with advanced/metastatic pancreatic cancer.
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Affiliation(s)
- Chien-Jui Huang
- Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan
| | - Wen-Yen Huang
- Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chien-Yu Chen
- School of Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ying-Jui Chao
- Division of General Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Nai-Jung Chiang
- Department of Oncology, Taipei Veterans General Hospital, Taipei
| | - Yan-Shen Shan
- Division of General Surgery, Department of Surgery, National Cheng Kung University Hospital, Tainan
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Zhang W, Wang L, Li D, Campbell DH, Walsh BJ, Packer NH, Dong Q, Wang E, Wang Y. Phenotypic profiling of pancreatic ductal adenocarcinoma plasma-derived small extracellular vesicles for cancer diagnosis and cancer stage prediction: a proof-of-concept study. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:2255-2265. [PMID: 35612592 DOI: 10.1039/d2ay00536k] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Circulating pancreatic ductal adenocarcinoma (PDAC) derived small extracellular vesicles (sEVs) are nano-sized membranous vesicles secreted from PDAC cells and released into surrounding body fluids, such as blood. The use of plasma-derived sEVs for cancer diagnosis is particularly appealing in biomedical research because the sEVs reflect some key features (e.g. genetic and phenotypic status) related to the organs from which they originate. For example, the surface membrane proteins and their expression level on sEVs were reported to be related to the presence and progression of PDAC. However, difficulty in sEVs isolation and lack of ultrasensitive assays for simultaneous analysis of multiple protein biomarkers on patient plasma-derived sEVs hinder their application in the clinic. In our previous study, we have demonstrated the application of magnetic beads (MBs) and surface-enhanced Raman scattering (SERS) assay for phenotypic analysis of cancer cells-derived sEVs using different cell lines. To further demonstrate the clinical application of the proposed assay, we have profiled the sEVs' phenotypes (relative expression of biomarker Glypican 1, EpCAM and CD44V6) of healthy donors and PDAC patients to enable simultaneous detection of multiple surface membrane proteins on plasma-derived sEVs. We discovered that the PDAC sEVs' phenotype signatures had high accuracy for PDAC diagnosis (100%) and showed strong correlation with cancer stages, which were further validated by the imaging techniques (e.g. computerized tomography and magnetic resonance imaging) and also the correlation of cancer stages with CA19-9 (gold standard biomarker) and the sEVs' phenotype signatures. The present proof-of-concept study thus provides an initial investigation of using the proposed SERS assay for PDAC diagnosis and early cancer stage prediction in the clinic.
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Affiliation(s)
- Wei Zhang
- ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia.
| | - Ling Wang
- Department of Gynecology and Obstetrics, The Second Hospital of Jilin University, Changchun 130041, Jilin, P. R. China
| | - Dan Li
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.
| | | | - Bradley J Walsh
- Minomic International Ltd, Macquarie Park, NSW 2113, Australia
| | - Nicolle H Packer
- ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia.
| | - Qing Dong
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.
| | - Erkang Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.
| | - Yuling Wang
- ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia.
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Liu S, Wang J. Current and Future Perspectives of Cell-Free DNA in Liquid Biopsy. Curr Issues Mol Biol 2022; 44:2695-2709. [PMID: 35735625 PMCID: PMC9222159 DOI: 10.3390/cimb44060184] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/01/2022] [Accepted: 06/09/2022] [Indexed: 11/16/2022] Open
Abstract
A liquid biopsy is a minimally invasive or non-invasive method to analyze a range of tumor material in blood or other body fluids, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), messenger RNA (mRNA), microRNA (miRNA), and exosomes, which is a very promising technology. Among these cancer biomarkers, plasma cfDNA is the most widely used in clinical practice. Compared with a tissue biopsy of traditional cancer diagnosis, in assessing tumor heterogeneity, a liquid biopsy is more reliable because all tumor sites release cfDNA into the blood. Therefore, a cfDNA liquid biopsy is less invasive and comprehensive. Moreover, the development of next-generation sequencing technology makes cfDNA sequencing more sensitive than a tissue biopsy, with higher clinical applicability and wider application. In this publication, we aim to review the latest perspectives of cfDNA liquid biopsy clinical significance and application in cancer diagnosis, treatment, and prognosis. We introduce the sequencing techniques and challenges of cfDNA detection, analysis, and clinical applications, and discuss future research directions.
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Yang Z, Atiyas Y, Shen H, Siedlik MJ, Wu J, Beard K, Fonar G, Dolle JP, Smith DH, Eberwine JH, Meaney DF, Issadore DA. Ultrasensitive Single Extracellular Vesicle Detection Using High Throughput Droplet Digital Enzyme-Linked Immunosorbent Assay. NANO LETTERS 2022; 22:4315-4324. [PMID: 35588529 PMCID: PMC9593357 DOI: 10.1021/acs.nanolett.2c00274] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Extracellular vesicles (EVs) have attracted enormous attention for their diagnostic and therapeutic potential. However, it has proven challenging to achieve the sensitivity to detect individual nanoscale EVs, the specificity to distinguish EV subpopulations, and a sufficient throughput to study EVs among an enormous background. To address this fundamental challenge, we developed a droplet-based optofluidic platform to quantify specific individual EV subpopulations at high throughput. The key innovation of our platform is parallelization of droplet generation, processing, and analysis to achieve a throughput (∼20 million droplets/min) more than 100× greater than typical microfluidics. We demonstrate that the improvement in throughput enables EV quantification at a limit of detection = 9EVs/μL, a >100× improvement over gold standard methods. Additionally, we demonstrate the clinical potential of this system by detecting human EVs in complex media. Building on this work, we expect this technology will allow accurate quantification of rare EV subpopulations for broad biomedical applications.
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Affiliation(s)
- Zijian Yang
- Department of Mechanical Engineering and Applied Mechanics, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Yasemin Atiyas
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Hanfei Shen
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Michael J Siedlik
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Jingyu Wu
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Kryshawna Beard
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Gennadiy Fonar
- Center for Brain Injury and Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Jean Pierre Dolle
- Center for Brain Injury and Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Douglas H Smith
- Center for Brain Injury and Repair, Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - James H Eberwine
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - David F Meaney
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - David A Issadore
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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35
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Lin AA, Nimgaonkar V, Issadore D, Carpenter EL. Extracellular Vesicle-Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer. Annu Rev Biomed Data Sci 2022; 5:269-292. [PMID: 35562850 DOI: 10.1146/annurev-biodatasci-122120-113218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Andrew A Lin
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; .,Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Vivek Nimgaonkar
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - David Issadore
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Erica L Carpenter
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
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Ferguson S, Yang KS, Weissleder R. Single extracellular vesicle analysis for early cancer detection. Trends Mol Med 2022; 28:681-692. [DOI: 10.1016/j.molmed.2022.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 12/25/2022]
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Circulating Nucleic Acids as Novel Biomarkers for Pancreatic Ductal Adenocarcinoma. Cancers (Basel) 2022; 14:cancers14082027. [PMID: 35454933 PMCID: PMC9031361 DOI: 10.3390/cancers14082027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/12/2022] [Accepted: 04/15/2022] [Indexed: 02/01/2023] Open
Abstract
Despite considerable advancements in the clinical management of PDAC it remains a significant cause of mortality. PDAC is often diagnosed at advanced stages due to vague symptoms associated with early-stage disease and a lack of reliable diagnostic biomarkers. Late diagnosis results in a high proportion of cases being ineligible for surgical resection, the only potentially curative therapy for PDAC. Furthermore, a lack of prognostic biomarkers impedes clinician's ability to properly assess the efficacy of therapeutic interventions. Advances in our ability to detect circulating nucleic acids allows for the advent of novel biomarkers for PDAC. Tumor derived circulating and exosomal nucleic acids allow for the detection of PDAC-specific mutations through a non-invasive blood sample. Such biomarkers could expand upon the currently limited repertoire of tests available. This review outlines recent developments in the use of molecular techniques for the detection of these nucleic acids and their potential roles, alongside current techniques, in the diagnosis, prognosis and therapeutic governance of PDAC.
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Keup C, Kimmig R, Kasimir-Bauer S. Combinatorial Power of cfDNA, CTCs and EVs in Oncology. Diagnostics (Basel) 2022; 12:870. [PMID: 35453918 PMCID: PMC9031112 DOI: 10.3390/diagnostics12040870] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/18/2022] [Accepted: 03/28/2022] [Indexed: 01/01/2023] Open
Abstract
Liquid biopsy is a promising technique for clinical management of oncological patients. The diversity of analytes circulating in the blood useable for liquid biopsy testing is enormous. Circulating tumor cells (CTCs), cell-free DNA (cfDNA) and extracellular vesicles (EVs), as well as blood cells and other soluble components in the plasma, were shown as liquid biopsy analytes. A few studies directly comparing two liquid biopsy analytes showed a benefit of one analyte over the other, while most authors concluded the benefit of the additional analyte. Only three years ago, the first studies to examine the value of a characterization of more than two liquid biopsy analytes from the same sample were conducted. We attempt to reflect on the recent development of multimodal liquid biopsy testing in this review. Although the analytes and clinical purposes of the published multimodal studies differed significantly, the additive value of the analytes was concluded in almost all projects. Thus, the blood components, as liquid biopsy reservoirs, are complementary rather than competitive, and orthogonal data sets were even shown to harbor synergistic effects. The unmistakable potential of multimodal liquid biopsy testing, however, is dampened by its clinical utility, which is yet to be proven, the lack of methodical standardization and insufficiently mature reimbursement, logistics and data handling.
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Affiliation(s)
- Corinna Keup
- Department of Gynecology and Obstetrics, University Hospital of Essen, Hufelandstr. 55, 45122 Essen, Germany
| | - Rainer Kimmig
- Department of Gynecology and Obstetrics, University Hospital of Essen, Hufelandstr. 55, 45122 Essen, Germany
| | - Sabine Kasimir-Bauer
- Department of Gynecology and Obstetrics, University Hospital of Essen, Hufelandstr. 55, 45122 Essen, Germany
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Liu Y, Li S, Liu Y. Machine Learning-Driven Multiobjective Optimization: An Opportunity of Microfluidic Platforms Applied in Cancer Research. Cells 2022; 11:905. [PMID: 35269527 PMCID: PMC8909684 DOI: 10.3390/cells11050905] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/27/2022] [Accepted: 03/02/2022] [Indexed: 12/24/2022] Open
Abstract
Cancer metastasis is one of the primary reasons for cancer-related fatalities. Despite the achievements of cancer research with microfluidic platforms, understanding the interplay of multiple factors when it comes to cancer cells is still a great challenge. Crosstalk and causality of different factors in pathogenesis are two important areas in need of further research. With the assistance of machine learning, microfluidic platforms can reach a higher level of detection and classification of cancer metastasis. This article reviews the development history of microfluidics used for cancer research and summarizes how the utilization of machine learning benefits cancer studies, particularly in biomarker detection, wherein causality analysis is useful. To optimize microfluidic platforms, researchers are encouraged to use causality analysis when detecting biomarkers, analyzing tumor microenvironments, choosing materials, and designing structures.
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Affiliation(s)
- Yi Liu
- School of Engineering, Dali University, Dali 671000, China;
| | - Sijing Li
- School of Engineering, Dali University, Dali 671000, China;
| | - Yaling Liu
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA 18015, USA
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Rapid Multiplex Strip Test for the Detection of Circulating Tumor DNA Mutations for Liquid Biopsy Applications. BIOSENSORS 2022; 12:bios12020097. [PMID: 35200357 PMCID: PMC8869478 DOI: 10.3390/bios12020097] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/26/2022] [Accepted: 02/01/2022] [Indexed: 01/16/2023]
Abstract
In the era of personalized medicine, molecular profiling of patient tumors has become the standard practice, especially for patients with advanced disease. Activating point mutations of the KRAS proto-oncogene are clinically relevant for many types of cancer, including colorectal cancer (CRC). While several approaches have been developed for tumor genotyping, liquid biopsy has been gaining much attention in the clinical setting. Analysis of circulating tumor DNA for genetic alterations has been challenging, and many methodologies with both advantages and disadvantages have been developed. We here developed a gold nanoparticle-based rapid strip test that has been applied for the first time for the multiplex detection of KRAS mutations in circulating tumor DNA (ctDNA) of CRC patients. The method involved ctDNA isolation, PCR-amplification of the KRAS gene, multiplex primer extension (PEXT) reaction, and detection with a multiplex strip test. We have optimized the efficiency and specificity of the multiplex strip test in synthetic DNA targets, in colorectal cancer cell lines, in tissue samples, and in blood-derived ctDNA from patients with advanced colorectal cancer. The proposed strip test achieved rapid and easy multiplex detection (normal allele and three major single-point mutations) of the clinically relevant KRAS mutations in ctDNA in blood samples of CRC patients with high specificity and repeatability. This multiplex strip test represents a minimally invasive, rapid, low-cost, and promising diagnostic tool for the detection of clinically relevant mutations in cancer patients.
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Tomeva E, Switzeny OJ, Heitzinger C, Hippe B, Haslberger AG. Comprehensive Approach to Distinguish Patients with Solid Tumors from Healthy Controls by Combining Androgen Receptor Mutation p.H875Y with Cell-Free DNA Methylation and Circulating miRNAs. Cancers (Basel) 2022; 14:cancers14020462. [PMID: 35053623 PMCID: PMC8774173 DOI: 10.3390/cancers14020462] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/13/2022] [Accepted: 01/16/2022] [Indexed: 02/01/2023] Open
Abstract
Liquid biopsy-based tests emerge progressively as an important tool for cancer diagnostics and management. Currently, researchers focus on a single biomarker type and one tumor entity. This study aimed to create a multi-analyte liquid biopsy test for the simultaneous detection of several solid cancers. For this purpose, we analyzed cell-free DNA (cfDNA) mutations and methylation, as well as circulating miRNAs (miRNAs) in plasma samples from 97 patients with cancer (20 bladder, 9 brain, 30 breast, 28 colorectal, 29 lung, 19 ovarian, 12 pancreas, 27 prostate, 23 stomach) and 15 healthy controls via real-time qPCR. Androgen receptor p.H875Y mutation (AR) was detected for the first time in bladder, lung, stomach, ovarian, brain, and pancreas cancer, all together in 51.3% of all cancer samples and in none of the healthy controls. A discriminant function model, comprising cfDNA mutations (COSM10758, COSM18561), cfDNA methylation markers (MLH1, MDR1, GATA5, SFN) and miRNAs (miR-17-5p, miR-20a-5p, miR-21-5p, miR-26a-5p, miR-27a-3p, miR-29c-3p, miR-92a-3p, miR-101-3p, miR-133a-3p, miR-148b-3p, miR-155-5p, miR-195-5p) could further classify healthy and tumor samples with 95.4% accuracy, 97.9% sensitivity, 80% specificity. This multi-analyte liquid biopsy-based test may help improve the simultaneous detection of several cancer types and underlines the importance of combining genetic and epigenetic biomarkers.
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Affiliation(s)
- Elena Tomeva
- HealthBioCare GmbH, A-1090 Vienna, Austria; (E.T.); (O.J.S.); (B.H.)
| | | | - Clemens Heitzinger
- Center for Artificial Intelligence and Machine Learning (CAIML), TU Wien, A-1040 Vienna, Austria;
| | - Berit Hippe
- HealthBioCare GmbH, A-1090 Vienna, Austria; (E.T.); (O.J.S.); (B.H.)
- Department of Nutritional Sciences, University of Vienna, A-1090 Vienna, Austria
| | - Alexander G. Haslberger
- Department of Nutritional Sciences, University of Vienna, A-1090 Vienna, Austria
- Correspondence:
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Nikas IP, Mountzios G, Sydney GI, Ioakim KJ, Won JK, Papageorgis P. Evaluating Pancreatic and Biliary Neoplasms with Small Biopsy-Based Next Generation Sequencing (NGS): Doing More with Less. Cancers (Basel) 2022; 14:cancers14020397. [PMID: 35053560 PMCID: PMC8773813 DOI: 10.3390/cancers14020397] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/05/2022] [Accepted: 01/10/2022] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Pancreatic cancer and cholangiocarcinoma are aggressive diseases mostly diagnosed at an advanced and inoperable stage. This review presents the value of next-generation sequencing (NGS) when performed on small biopsies—including fine-needle aspiration/biopsy samples, brushings, pancreatic juice and bile, and also blood—in the field of pancreatobiliary neoplasia. NGS could guide physicians while evaluating pancreatic solid and cystic lesions or suspicious biliary strictures, performing surveillance in high-risk individuals, or monitoring the disease and assessing prognosis in already diagnosed cancer patients. Evidence suggests that NGS performed on small biopsies is a robust tool for the diagnosis and pre-operative risk stratification of pancreatic and biliary lesions, whereas it also carries significant prognostic and therapeutic value. However, effective standardization of the pre-analytical and analytical assay parameters used for each clinical scenario is needed to fully implement NGS into routine practice and provide more personalized management in patients with suspected or established pancreatobiliary neoplasia. Abstract Pancreatic cancer and cholangiocarcinoma are lethal diseases mainly diagnosed at an inoperable stage. As pancreatobiliary surgical specimens are often unavailable for further molecular testing, this review aimed to highlight the diagnostic, prognostic, and therapeutic impact of next-generation sequencing (NGS) performed on distinct small biopsies, including endoscopic ultrasound fine-needle aspirations and biopsies of pancreatic solid and cystic lesions, biliary duct brushings, and also “liquid biopsies” such as the pancreatic juice, bile, and blood. NGS could clarify indeterminate pancreatic lesions or biliary strictures, for instance by identifying TP53 or SMAD4 mutations indicating high-grade dysplasia or cancer. It could also stratify pancreatic cystic lesions, by distinguishing mucinous from non-mucinous cysts and identifying high-risk cysts that should be excised in surgically fit patients, whereas the combination of cytology, elevated cystic CEA levels and NGS could improve the overall diagnostic accuracy. When NGS is performed on the pancreatic juice, it could stratify high-risk patients under surveillance. On the plasma, it could dynamically monitor the disease course and response to therapy. Notably, the circulating tumor DNA (ctDNA) levels have been associated with staging, grading, and survival. Lastly, NGS has shown potential in identifying potentially actionable molecular alterations. In conclusion, NGS applied on small biopsies could carry significant diagnostic, prognostic, and therapeutic value.
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Affiliation(s)
- Ilias P. Nikas
- School of Medicine, European University Cyprus, Nicosia 2404, Cyprus; (G.I.S.); (K.J.I.)
- Correspondence:
| | - Giannis Mountzios
- Fourth Department of Medical Oncology and Clinical Trials Unit, Henry Dunant Hospital Center, 11526 Athens, Greece;
| | - Guy I. Sydney
- School of Medicine, European University Cyprus, Nicosia 2404, Cyprus; (G.I.S.); (K.J.I.)
- Department of Internal Medicine, Southern Illinois University School of Medicine, Springfield, IL 62702, USA
| | - Kalliopi J. Ioakim
- School of Medicine, European University Cyprus, Nicosia 2404, Cyprus; (G.I.S.); (K.J.I.)
- Department of Internal Medicine, Limassol General Hospital, Limassol 4131, Cyprus
| | - Jae-Kyung Won
- Department of Pathology, Seoul National University Hospital and College of Medicine, Seoul 03080, Korea;
| | - Panagiotis Papageorgis
- Tumor Microenvironment, Metastasis and Experimental Therapeutics Laboratory, Basic and Translational Cancer Research Center, Department of Life Sciences, European University Cyprus, Nicosia 2404, Cyprus;
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Yan TB, Huang JQ, Huang SY, Ahir BK, Li LM, Mo ZN, Zhong JH. Advances in the Detection of Pancreatic Cancer Through Liquid Biopsy. Front Oncol 2021; 11:801173. [PMID: 34993149 PMCID: PMC8726483 DOI: 10.3389/fonc.2021.801173] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/06/2021] [Indexed: 01/27/2023] Open
Abstract
Pancreatic cancer refers to the development of malignant tumors in the pancreas: it is associated with high mortality rates and mostly goes undetected in its early stages for lack of symptoms. Currently, surgical treatment is the only effective way to improve the survival of pancreatic cancer patients. Therefore, it is crucial to diagnose the disease as early as possible in order to improve the survival rate of patients with pancreatic cancer. Liquid biopsy is a unique in vitro diagnostic technique offering the advantage of earlier detection of tumors. Although liquid biopsies have shown promise for screening for certain cancers, whether they are effective for early diagnosis of pancreatic cancer is unclear. Therefore, we reviewed relevant literature indexed in PubMed and collated updates and information on advances in the field of liquid biopsy with respect to the early diagnosis of pancreatic cancer.
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Affiliation(s)
- Tian-Bao Yan
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Jia-Qi Huang
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Shi-Yun Huang
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Bhavesh K. Ahir
- Section of Hematology and Oncology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - Long-Man Li
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Zeng-Nan Mo
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Jian-Hong Zhong
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- *Correspondence: Jian-Hong Zhong,
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Anti-Cancer Role and Therapeutic Potential of Extracellular Vesicles. Cancers (Basel) 2021; 13:cancers13246303. [PMID: 34944923 PMCID: PMC8699603 DOI: 10.3390/cancers13246303] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 02/07/2023] Open
Abstract
Cell-cell communication is an important mechanism in biological processes. Extracellular vesicles (EVs), also referred to as exosomes, microvesicles, and prostasomes, are microvesicles secreted by a variety of cells. EVs are nanometer-scale vesicles composed of a lipid bilayer and contain biological functional molecules, such as microRNAs (miRNAs), mRNAs, and proteins. In this review, "EVs" is used as a comprehensive term for vesicles that are secreted from cells. EV research has been developing over the last four decades. Many studies have suggested that EVs play a crucial role in cell-cell communication. Importantly, EVs contribute to cancer malignancy mechanisms such as carcinogenesis, proliferation, angiogenesis, metastasis, and escape from the immune system. EVs derived from cancer cells and their microenvironments are diverse, change in nature depending on the condition. As EVs are thought to be secreted into body fluids, they have the potential to serve as diagnostic markers for liquid biopsy. In addition, cells can encapsulate functional molecules in EVs. Hence, the characteristics of EVs make them suitable for use in drug delivery systems and novel cancer treatments. In this review, the potential of EVs as anti-cancer therapeutics is discussed.
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Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021; 194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 02/06/2023]
Abstract
Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.
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Affiliation(s)
- Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Jeeson Kim
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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Wang Y, Tan H, Yu T, Ma X, Chen X, Jing F, Zou L, Shi H. The identification of gene signatures in patients with extranodal NK/T-cell lymphoma from a pair of twins. BMC Cancer 2021; 21:1303. [PMID: 34872521 PMCID: PMC8650233 DOI: 10.1186/s12885-021-09023-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 11/17/2021] [Indexed: 02/08/2023] Open
Abstract
Background There is no unified treatment standard for patients with extranodal NK/T-cell lymphoma (ENKTL). Cancer neoantigens are the result of somatic mutations and cancer-specific. Increased number of somatic mutations are associated with anti-cancer effects. Screening out ENKTL-specific neoantigens on the surface of cancer cells relies on the understanding of ENKTL mutation patterns. Hence, it is imperative to identify ENKTL-specific genes for ENKTL diagnosis, the discovery of tumor-specific neoantigens and the development of novel therapeutic strategies. We investigated the gene signatures of ENKTL patients. Methods We collected the peripheral blood of a pair of twins for sequencing to identify unique variant genes. One of the twins is diagnosed with ENKTL. Seventy samples were analyzed by Robust Multi-array Analysis (RMA). Two methods (elastic net and Support Vector Machine-Recursive Feature Elimination) were used to select unique genes. Next, we performed functional enrichment analysis and pathway enrichment analysis. Then, we conducted single-sample gene set enrichment analysis of immune infiltration and validated the expression of the screened markers with limma packages. Results We screened out 126 unique variant genes. Among them, 11 unique genes were selected by the combination of elastic net and Support Vector Machine-Recursive Feature Elimination. Subsequently, GO and KEGG analysis indicated the biological function of identified unique genes. GSEA indicated five immunity-related pathways with high signature scores. In patients with ENKTL and the group with high signature scores, a proportion of functional immune cells are all of great infiltration. We finally found that CDC27, ZNF141, FCGR2C and NES were four significantly differential genes in ENKTL patients. ZNF141, FCGR2C and NES were upregulated in patients with ENKTL, while CDC27 was significantly downregulated. Conclusion We identified four ENKTL markers (ZNF141, FCGR2C, NES and CDC27) in patients with extranodal NK/T-cell lymphoma.
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Affiliation(s)
- Yang Wang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No. 17, Block 3, Southern Renmin Road, Chengdu, Sichuan, 610041, PR China
| | - Huaicheng Tan
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No. 17, Block 3, Southern Renmin Road, Chengdu, Sichuan, 610041, PR China
| | - Ting Yu
- Department of Pathology and Laboratory of Pathology, State Key Laboratory of Biotherapy, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoxuan Chen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Fangqi Jing
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Liqun Zou
- Department of Head and Neck Cancer, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Huashan Shi
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China. .,Department of Radiotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
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Olmedillas-López S, Olivera-Salazar R, García-Arranz M, García-Olmo D. Current and Emerging Applications of Droplet Digital PCR in Oncology: An Updated Review. Mol Diagn Ther 2021; 26:61-87. [PMID: 34773243 DOI: 10.1007/s40291-021-00562-2] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2021] [Indexed: 12/14/2022]
Abstract
In the era of personalized medicine and targeted therapies for the management of patients with cancer, ultrasensitive detection methods for tumor genotyping, such as next-generation sequencing or droplet digital polymerase chain reaction (ddPCR), play a significant role. In the search for less invasive strategies for diagnosis, prognosis and disease monitoring, the number of publications regarding liquid biopsy approaches using ddPCR has increased substantially in recent years. There is a long list of malignancies in which ddPCR provides a reliable and accurate tool for detection of nucleic acid-based markers derived from cell-free DNA, cell-free RNA, circulating tumor cells, extracellular vesicles or exosomes when isolated from whole blood, plasma and serum, helping to anticipate tumor relapse or unveil intratumor heterogeneity and clonal evolution in response to treatment. This updated review describes recent developments in ddPCR platforms and provides a general overview about the major applications of liquid biopsy in blood, including its utility for molecular response and minimal residual disease monitoring in hematological malignancies or the therapeutic management of patients with colorectal or lung cancer, particularly for the selection and monitoring of treatment with tyrosine kinase inhibitors. Although plasma is the main source of genetic material for tumor genomic profiling, liquid biopsy by ddPCR is being investigated in a wide variety of biologic fluids, such as cerebrospinal fluid, urine, stool, ocular fluids, sputum, saliva, bronchoalveolar lavage, pleural effusion, mucin, peritoneal fluid, fine needle aspirate, bile or pancreatic juice. The present review focuses on these "alternative" sources of genetic material and their analysis by ddPCR in different kinds of cancers.
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Affiliation(s)
- Susana Olmedillas-López
- New Therapies Laboratory, Health Research Institute-Fundación Jiménez Díaz University Hospital (IIS-FJD), Avda. Reyes Católicos, 2, 28040, Madrid, Spain.
| | - Rocío Olivera-Salazar
- New Therapies Laboratory, Health Research Institute-Fundación Jiménez Díaz University Hospital (IIS-FJD), Avda. Reyes Católicos, 2, 28040, Madrid, Spain
| | - Mariano García-Arranz
- New Therapies Laboratory, Health Research Institute-Fundación Jiménez Díaz University Hospital (IIS-FJD), Avda. Reyes Católicos, 2, 28040, Madrid, Spain.,Department of Surgery, School of Medicine, Universidad Autónoma de Madrid (UAM), 28029, Madrid, Spain
| | - Damián García-Olmo
- New Therapies Laboratory, Health Research Institute-Fundación Jiménez Díaz University Hospital (IIS-FJD), Avda. Reyes Católicos, 2, 28040, Madrid, Spain.,Department of Surgery, School of Medicine, Universidad Autónoma de Madrid (UAM), 28029, Madrid, Spain.,Department of Surgery, Fundación Jiménez Díaz University Hospital (FJD), 28040, Madrid, Spain
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48
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Herman DS, Rhoads DD, Schulz WL, Durant TJS. Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review. Clin Chem 2021; 67:1466-1482. [PMID: 34557917 DOI: 10.1093/clinchem/hvab165] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 07/26/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. CONTENT In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. SUMMARY AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.
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Affiliation(s)
- Daniel S Herman
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel D Rhoads
- Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH, USA.,Department of Pathology, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Wade L Schulz
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Thomas J S Durant
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
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Beard K, Yang Z, Haber M, Flamholz M, Diaz-Arrastia R, Sandsmark D, Meaney DF, Issadore D. Extracellular vesicles as distinct biomarker reservoirs for mild traumatic brain injury diagnosis. Brain Commun 2021; 3:fcab151. [PMID: 34622206 PMCID: PMC8491985 DOI: 10.1093/braincomms/fcab151] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 05/14/2021] [Accepted: 05/20/2021] [Indexed: 01/08/2023] Open
Abstract
Mild traumatic brain injury does not currently have a clear molecular diagnostic panel to either confirm the injury or to guide its treatment. Current biomarkers for traumatic brain injury rely mainly on detecting circulating proteins in blood that are associated with degenerating neurons, which are less common in mild traumatic brain injury, or with broad inflammatory cascades which are produced in multiple tissues and are thus not brain specific. To address this issue, we conducted an observational cohort study designed to measure a protein panel in two compartments—plasma and brain-derived extracellular vesicles—with the following hypotheses: (i) each compartment provides independent diagnostic information and (ii) algorithmically combining these compartments accurately classifies clinical mild traumatic brain injury. We evaluated this hypothesis using plasma samples from mild (Glasgow coma scale scores 13–15) traumatic brain injury patients (n = 47) and healthy and orthopaedic control subjects (n = 46) to evaluate biomarkers in brain-derived extracellular vesicles and plasma. We used our Track Etched Magnetic Nanopore technology to isolate brain-derived extracellular vesicles from plasma based on their expression of GluR2, combined with the ultrasensitive digital enzyme-linked immunosorbent assay technique, Single-Molecule Array. We quantified extracellular vesicle-packaged and plasma levels of biomarkers associated with two categories of traumatic brain injury pathology: neurodegeneration and neuronal/glial damage (ubiquitin C-terminal hydrolase L1, glial fibrillary acid protein, neurofilament light and Tau) and inflammation (interleukin-6, interleukin-10 and tumour necrosis factor alpha). We found that GluR2+ extracellular vesicles have distinct biomarker distributions than those present in the plasma. As a proof of concept, we showed that using a panel of biomarkers comprised of both plasma and GluR2+ extracellular vesicles, injured patients could be accurately classified versus non-injured patients.
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Affiliation(s)
- Kryshawna Beard
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zijian Yang
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Margalit Haber
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Miranda Flamholz
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ramon Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle Sandsmark
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David F Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Neurosurgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - David Issadore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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50
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Montezuma D, Teixeira-Marques A, Jerónimo C, Henrique R. Epigenetic extracellular vesicle-based biomarkers for urological malignancies: is the hope worth the hype? Epigenomics 2021; 13:1514-1521. [PMID: 34617453 DOI: 10.2217/epi-2021-0333] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Diana Montezuma
- Cancer Biology & Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal.,Doctoral Programme in Medical Sciences, School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, Porto, 4050-513, Portugal
| | - Ana Teixeira-Marques
- Cancer Biology & Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal.,Master in Medicine & Molecular Oncology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, Porto, 4200-319, Portugal
| | - Carmen Jerónimo
- Cancer Biology & Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal.,Department of Pathology & Molecular Immunology, ICBAS, School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, Porto, 4050-513, Portugal
| | - Rui Henrique
- Cancer Biology & Epigenetics Group, Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), R. Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal.,Department of Pathology & Molecular Immunology, ICBAS, School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, Porto, 4050-513, Portugal.,Department of Pathology, Portuguese Oncology Institute of Porto (IPOP), R. Dr. António Bernardino de Almeida, Porto, 4200-072, Portugal
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