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Hariharan R, Hood L, Price ND. A data-driven approach to improve wellness and reduce recurrence in cancer survivors. Front Oncol 2024; 14:1397008. [PMID: 38665952 PMCID: PMC11044254 DOI: 10.3389/fonc.2024.1397008] [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: 03/06/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
For many cancer survivors, toxic side effects of treatment, lingering effects of the aftermath of disease and cancer recurrence adversely affect quality of life (QoL) and reduce healthspan. Data-driven approaches for quantifying and improving wellness in healthy individuals hold great promise for improving the lives of cancer survivors. The data-driven strategy will also guide personalized nutrition and exercise recommendations that may help prevent cancer recurrence and secondary malignancies in survivors.
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Affiliation(s)
- Ramkumar Hariharan
- College of Engineering, Northeastern University, Seattle, WA, United States
- Institute for Experiential Artificial Intelligence, Northeastern University, Boston, MA, United States
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, United States
- Buck Institute for Research on Aging, Novato, CA, United States
- Phenome Health, Seattle, WA, United States
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, WA, United States
- Thorne HealthTech, New York, NY, United States
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Yurkovich JT, Evans SJ, Rappaport N, Boore JL, Lovejoy JC, Price ND, Hood LE. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 2024; 25:286-302. [PMID: 38093095 DOI: 10.1038/s41576-023-00674-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 03/21/2024]
Abstract
Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
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Affiliation(s)
- James T Yurkovich
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Simon J Evans
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Noa Rappaport
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Jeffrey L Boore
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Jennifer C Lovejoy
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy E Hood
- Phenome Health, Seattle, WA, USA.
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.
- Institute for Systems Biology, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
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3
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Wang X, Wang L, Lin H, Zhu Y, Huang D, Lai M, Xi X, Huang J, Zhang W, Zhong T. Research progress of CTC, ctDNA, and EVs in cancer liquid biopsy. Front Oncol 2024; 14:1303335. [PMID: 38333685 PMCID: PMC10850354 DOI: 10.3389/fonc.2024.1303335] [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: 09/27/2023] [Accepted: 01/04/2024] [Indexed: 02/10/2024] Open
Abstract
Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vehicles (EVs) have received significant attention in recent times as emerging biomarkers and subjects of transformational studies. The three main branches of liquid biopsy have evolved from the three primary tumor liquid biopsy detection targets-CTC, ctDNA, and EVs-each with distinct benefits. CTCs are derived from circulating cancer cells from the original tumor or metastases and may display global features of the tumor. ctDNA has been extensively analyzed and has been used to aid in the diagnosis, treatment, and prognosis of neoplastic diseases. EVs contain tumor-derived material such as DNA, RNA, proteins, lipids, sugar structures, and metabolites. The three provide different detection contents but have strong complementarity to a certain extent. Even though they have already been employed in several clinical trials, the clinical utility of three biomarkers is still being studied, with promising initial findings. This review thoroughly overviews established and emerging technologies for the isolation, characterization, and content detection of CTC, ctDNA, and EVs. Also discussed were the most recent developments in the study of potential liquid biopsy biomarkers for cancer diagnosis, therapeutic monitoring, and prognosis prediction. These included CTC, ctDNA, and EVs. Finally, the potential and challenges of employing liquid biopsy based on CTC, ctDNA, and EVs for precision medicine were evaluated.
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Affiliation(s)
- Xiaoling Wang
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- The First School of Clinical Medicine, Gannan Medical University, Ganzhou, China
| | - Lijuan Wang
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- The First School of Clinical Medicine, Gannan Medical University, Ganzhou, China
| | - Haihong Lin
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- The First School of Clinical Medicine, Gannan Medical University, Ganzhou, China
| | - Yifan Zhu
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- The First School of Clinical Medicine, Gannan Medical University, Ganzhou, China
| | - Defa Huang
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Mi Lai
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xuxiang Xi
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Junyun Huang
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- The First School of Clinical Medicine, Gannan Medical University, Ganzhou, China
| | - Wenjuan Zhang
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- The First School of Clinical Medicine, Gannan Medical University, Ganzhou, China
| | - Tianyu Zhong
- Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- The First School of Clinical Medicine, Gannan Medical University, Ganzhou, China
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Pham TD, Sun X. Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients. Cancer Med 2023; 12:21502-21518. [PMID: 38014709 PMCID: PMC10726782 DOI: 10.1002/cam4.6672] [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: 08/25/2023] [Revised: 09/25/2023] [Accepted: 10/20/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5-year survival time of two patient groups: one with preoperative radiotherapy and one without. METHODS The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet-scattering network. Our study combines the strengths of these two convolution-based approaches to robustly extract image features related to protein expression. RESULTS The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve. CONCLUSION These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry Queen MaryUniversity of London Turner StreetLondonUK
| | - Xiao‐Feng Sun
- Division of Oncology Department of Biomedical and Clinical SciencesLinkoping UniversityLinkopingSweden
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Debernardi S, Blyuss O, Rycyk D, Srivastava K, Jeon CY, Cai H, Cai Q, Shu X, Crnogorac‐Jurcevic T. Urine biomarkers enable pancreatic cancer detection up to 2 years before diagnosis. Int J Cancer 2023; 152:769-780. [PMID: 36093581 PMCID: PMC9789171 DOI: 10.1002/ijc.34287] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/15/2022] [Accepted: 08/31/2022] [Indexed: 02/01/2023]
Abstract
The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19-9. This nested case-control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19-9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank-sum and Mann-Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19-9 up to 1 year before PDAC diagnosis (AUC = 0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC = 0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1 year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19-9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis.
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Affiliation(s)
- Silvana Debernardi
- Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer InstituteQueen Mary University of LondonLondonUK
| | - Oleg Blyuss
- Centre for Prevention, Detection and Diagnosis, Wolfson Institute of Population HealthQueen Mary University of LondonLondonUK
| | - Daria Rycyk
- Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer InstituteQueen Mary University of LondonLondonUK
| | - Kirtiman Srivastava
- Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer InstituteQueen Mary University of LondonLondonUK
| | - Christie Y. Jeon
- Department of MedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Hui Cai
- Division of Epidemiology, Department of MedicineVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Qiuyin Cai
- Division of Epidemiology, Department of MedicineVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Xiao‐Ou Shu
- Division of Epidemiology, Department of MedicineVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Tatjana Crnogorac‐Jurcevic
- Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer InstituteQueen Mary University of LondonLondonUK
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Nené NR, Ney A, Nazarenko T, Blyuss O, Johnston HE, Whitwell HJ, Sedlak E, Gentry-Maharaj A, Apostolidou S, Costello E, Greenhalf W, Jacobs I, Menon U, Hsuan J, Pereira SP, Zaikin A, Timms JF. Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning. COMMUNICATIONS MEDICINE 2023; 3:10. [PMID: 36670203 PMCID: PMC9860022 DOI: 10.1038/s43856-023-00237-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/04/2023] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough. METHODS The primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis. The data used is part of a nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 218 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 249 matched non-cancer controls. We develop a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets. RESULTS Here we show that with ensemble learning we can predict PDAC status with an AUC of 0.91 (95% CI 0.75-1.0), sensitivity of 92% (95% CI 0.54-1.0) at 90% specificity, up to 1 year prior to diagnosis, and at an AUC of 0.85 (95% CI 0.74-0.93) up to 2 years prior to diagnosis (sensitivity of 61%, 95% CI 0.17-0.83, at 90% specificity). CONCLUSIONS The ensemble modelling strategy explored here outperforms considerably biomarker combinations cited in the literature. Further developments in the selection of classifiers balancing performance and heterogeneity should further enhance the predictive capacity of the method.
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Affiliation(s)
- Nuno R Nené
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK.
- Institute for Women's Health, University College London, Cruciform Building 1.1, Gower Street, London, WC1E 6BT, UK.
| | - Alexander Ney
- Institute for Liver and Digestive Health, University College London, Upper 3rd Floor, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK
| | - Tatiana Nazarenko
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK
- Department of Mathematics, University College London, London, WC1H 0AY, UK
| | - Oleg Blyuss
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK
- Wolfson Institute of Population Health, Queen Mary University of London, Charterhouse Square, EC1M 6BQ, London, UK
| | - Harvey E Johnston
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK
- Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK
| | - Harry J Whitwell
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK
- National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Metabolism, Digestion and Reproduction, IRDB Building, Imperial College London, Hammersmith Campus, London, W12 0NN, UK
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Eva Sedlak
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, 90 High Holborn, 2nd Floor, London, WC1V 6LJ, UK
| | - Sophia Apostolidou
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, 90 High Holborn, 2nd Floor, London, WC1V 6LJ, UK
| | - Eithne Costello
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - William Greenhalf
- Liverpool Experimental Cancer Medicine Centre, University of Liverpool, Liverpool, L69 3GL, UK
| | - Ian Jacobs
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK
- University of New South Wales, Sydney, NSW, 2052, Australia
| | - Usha Menon
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, 90 High Holborn, 2nd Floor, London, WC1V 6LJ, UK
| | - Justin Hsuan
- Institute for Liver and Digestive Health, University College London, Upper 3rd Floor, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK
| | - Stephen P Pereira
- Institute for Liver and Digestive Health, University College London, Upper 3rd Floor, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK
| | - Alexey Zaikin
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK
- Department of Mathematics, University College London, London, WC1H 0AY, UK
| | - John F Timms
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, 84-86 Chenies Mews, London, WC1E 6HU, UK
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Trifonova OP, Maslov DL, Balashova EE, Lokhov PG. Current State and Future Perspectives on Personalized Metabolomics. Metabolites 2023; 13:metabo13010067. [PMID: 36676992 PMCID: PMC9863827 DOI: 10.3390/metabo13010067] [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: 12/05/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.
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Omenn GS, Magis AT, Price ND, Hood L. Personal Dense Dynamic Data Clouds Connect Systems Biomedicine to Scientific Wellness. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:315-334. [PMID: 35437729 DOI: 10.1007/978-1-0716-2265-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holistic, dynamic, integrative, cross-disciplinary approach to biological complexity that embraces experimentation, technology, computation, and clinical translation. Systems Medicine integrates genome analyses and longitudinal deep phenotyping with biological pathways and networks to understand mechanisms of disease, identify relevant blood biomarkers, define druggable molecular targets, and enhance the maintenance or restoration of wellness. Two programs initiated our understanding of data-driven population-based wellness. The Pioneer 100 Study of Scientific Wellness and the much larger Arivale commercial program that followed had two spectacular results: demonstrating the feasibility and utility of collecting longitudinal multiomic data, and then generating dense, dynamic data clouds for each individual to utilize actionable metrics for promoting health and preventing disease when combined with personalized coaching. Future developments in these domains will enable better population health and personal, preventive, predictive, participatory (P4) health care.
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Affiliation(s)
- Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA. .,Institute for Systems Biology, Seattle, WA, USA.
| | | | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA.,Onegevity, New York, New York, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA.,Providence Saint Joseph Healthcare System, Seattle, USA
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Omenn GS, Lane L, Overall CM, Paik YK, Cristea IM, Corrales FJ, Lindskog C, Weintraub S, Roehrl MHA, Liu S, Bandeira N, Srivastava S, Chen YJ, Aebersold R, Moritz RL, Deutsch EW. Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project. J Proteome Res 2021; 20:5227-5240. [PMID: 34670092 PMCID: PMC9340669 DOI: 10.1021/acs.jproteome.1c00590] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.
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Affiliation(s)
- Gilbert S Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | | | - Young-Ki Paik
- Yonsei Proteome Research Center and Yonsei University, Seoul 03722, Korea
| | - Ileana M Cristea
- Princeton University, Princeton, New Jersey 08544, United States
| | | | | | - Susan Weintraub
- University of Texas Health, San Antonio, San Antonio, Texas 78229-3900, United States
| | - Michael H A Roehrl
- Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | | | - Yu-Ju Chen
- National Taiwan University, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Ruedi Aebersold
- ETH-Zurich and University of Zurich, 8092 Zurich, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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10
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Wik L, Nordberg N, Broberg J, Björkesten J, Assarsson E, Henriksson S, Grundberg I, Pettersson E, Westerberg C, Liljeroth E, Falck A, Lundberg M. Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis. Mol Cell Proteomics 2021; 20:100168. [PMID: 34715355 PMCID: PMC8633680 DOI: 10.1016/j.mcpro.2021.100168] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 10/14/2021] [Accepted: 10/21/2021] [Indexed: 01/21/2023] Open
Abstract
Understanding the dynamics of the human proteome is crucial for developing biomarkers to be used as measurable indicators for disease severity and progression, patient stratification, and drug development. The Proximity Extension Assay (PEA) is a technology that translates protein information into actionable knowledge by linking protein-specific antibodies to DNA-encoded tags. In this report we demonstrate how we have combined the unique PEA technology with an innovative and automated sample preparation and high-throughput sequencing readout enabling parallel measurement of nearly 1500 proteins in 96 samples generating close to 150,000 data points per run. This advancement will have a major impact on the discovery of new biomarkers for disease prediction and prognosis and contribute to the development of the rapidly evolving fields of wellness monitoring and precision medicine.
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11
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Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer-A Comprehensive Review. Cancers (Basel) 2021; 13:cancers13112722. [PMID: 34072842 PMCID: PMC8198035 DOI: 10.3390/cancers13112722] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/20/2021] [Accepted: 05/27/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 90% of all pancreatic cancers, is an extremely aggressive and lethal disease. It is considered a silent killer due to a largely asymptomatic course and late clinical presentation. Earlier detection of the disease would likely have a great impact on changing the currently poor survival figures for this malignancy. In this comprehensive review, we assessed over 4000 reports on non-invasive PDAC biomarkers in the last decade. Applying the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, we selected and reviewed in more detail 49 relevant studies reporting on the most promising candidate biomarkers. In addition, we also highlight the present challenges and complexities of translating novel biomarkers into clinical use. Abstract Pancreatic ductal adenocarcinoma (PDAC) carries a deadly diagnosis, due in large part to delayed presentation when the disease is already at an advanced stage. CA19-9 is currently the most commonly utilized biomarker for PDAC; however, it lacks the necessary accuracy to detect precursor lesions or stage I PDAC. Novel biomarkers that could detect this malignancy with improved sensitivity (SN) and specificity (SP) would likely result in more curative resections and more effective therapeutic interventions, changing thus the present dismal survival figures. The aim of this study was to systematically and comprehensively review the scientific literature on non-invasive biomarkers in biofluids such as blood, urine and saliva that were attempting earlier PDAC detection. The search performed covered a period of 10 years (January 2010—August 2020). Data were extracted using keywords search in the three databases: MEDLINE, Web of Science and Embase. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied for study selection based on establishing the risk of bias and applicability concerns in Patient Selection, Index test (biomarker assay) and Reference Standard (standard-of-care diagnostic test). Out of initially over 4000 published reports, 49 relevant studies were selected and reviewed in more detail. In addition, we discuss the present challenges and complexities in the path of translating the discovered biomarkers into the clinical setting. Our systematic review highlighted several promising biomarkers that could, either alone or in combination with CA19-9, potentially improve earlier detection of PDAC. Overall, reviewed biomarker studies should aim to improve methodological and reporting quality, and novel candidate biomarkers should be investigated further in order to demonstrate their clinical usefulness. However, challenges and complexities in the path of translating the discovered biomarkers from the research laboratory to the clinical setting remain and would have to be addressed before a more realistic breakthrough in earlier detection of PDAC is achieved.
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