1
|
Chen S, Huang M, Zhang L, Huang Q, Wang Y, Liang Y. Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis. Comput Struct Biotechnol J 2024; 23:369-383. [PMID: 38226313 PMCID: PMC10788202 DOI: 10.1016/j.csbj.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 01/17/2024] Open
Abstract
Background Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis. Methods We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Findings The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line. Conclusion In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.
Collapse
Affiliation(s)
- Shuzhao Chen
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
- Department of Thyroid and Breast Surgery, Clinical Research Center, The First Affiliated Hospital of Shantou University Medical College (SUMC), Shantou, Guangdong, China
| | - Mayan Huang
- Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
| | - Limei Zhang
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
| | - Qianqian Huang
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
| | - Yun Wang
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
| | - Yang Liang
- Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, Guangdong, China
| |
Collapse
|
2
|
Xu H, Gitto SB, Ho GY, Medvedev S, Shield-Artin K, Kim H, Beard S, Kinose Y, Wang X, Barker HE, Ratnayake G, Hwang WT, Hansen RJ, Strouse B, Milutinovic S, Hassig C, Wakefield MJ, Vandenberg CJ, Scott CL, Simpkins F. CHK1 inhibitor SRA737 is active in PARP inhibitor resistant and CCNE1 amplified ovarian cancer. iScience 2024; 27:109978. [PMID: 39021796 PMCID: PMC11253285 DOI: 10.1016/j.isci.2024.109978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 04/05/2024] [Accepted: 05/11/2024] [Indexed: 07/20/2024] Open
Abstract
High-grade serous ovarian cancers (HGSOCs) with homologous recombination deficiency (HRD) are initially responsive to poly (ADP-ribose) polymerase inhibitors (PARPi), but resistance ultimately emerges. HGSOC with CCNE1 amplification (CCNE1 amp) are associated with resistance to PARPi and platinum treatments. High replication stress in HRD and CCNE1 amp HGSOC leads to increased reliance on checkpoint kinase 1 (CHK1), a key regulator of cell cycle progression and the replication stress response. Here, we investigated the anti-tumor activity of the potent, highly selective, orally bioavailable CHK1 inhibitor (CHK1i), SRA737, in both acquired PARPi-resistant BRCA1/2 mutant and CCNE1 amp HGSOC models. We demonstrated that SRA737 increased replication stress and induced subsequent cell death in vitro. SRA737 monotherapy in vivo prolonged survival in CCNE1 amp models, suggesting a potential biomarker for CHK1i therapy. Combination SRA737 and PARPi therapy increased tumor regression in both PARPi-resistant and CCNE1 amp patient-derived xenograft models, warranting further study in these HGSOC subgroups.
Collapse
Affiliation(s)
- Haineng Xu
- Ovarian Cancer Research Center, Division of Gynecology Oncology, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah B. Gitto
- Ovarian Cancer Research Center, Division of Gynecology Oncology, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Gwo-Yaw Ho
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sergey Medvedev
- Ovarian Cancer Research Center, Division of Gynecology Oncology, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kristy Shield-Artin
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Hyoung Kim
- Ovarian Cancer Research Center, Division of Gynecology Oncology, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sally Beard
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
| | - Yasuto Kinose
- Ovarian Cancer Research Center, Division of Gynecology Oncology, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xiaolei Wang
- Ovarian Cancer Research Center, Division of Gynecology Oncology, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Holly E. Barker
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia
| | | | - Wei-Ting Hwang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Australian Ovarian Cancer Study
- Research Division, Peter MacCallum Cancer Centre, 305 Grattan Street, Melbourne, VIC 3000, Australia
- Centre for Cancer Research, The Westmead Institute for Medical Research, Sydney, NSW 2145, Australia
| | - Ryan J. Hansen
- Centre for Cancer Research, The Westmead Institute for Medical Research, Sydney, NSW 2145, Australia
| | - Bryan Strouse
- Sierra Oncology, Inc, 885 West Georgia Street, Suite 2150, Vancouver, BC V6C 3E8, Canada
| | - Snezana Milutinovic
- Sierra Oncology, Inc, 885 West Georgia Street, Suite 2150, Vancouver, BC V6C 3E8, Canada
| | - Christian Hassig
- Sierra Oncology, Inc, 885 West Georgia Street, Suite 2150, Vancouver, BC V6C 3E8, Canada
| | - Matthew J. Wakefield
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
- Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Cassandra J. Vandenberg
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Clare L. Scott
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia
- The Royal Women’s Hospital, Parkville, VIC 3052, Australia
- Department of Obstetrics and Gynaecology, University of Melbourne, Parkville, VIC 3010, Australia
- Sir Peter MacCallum Cancer Centre Department of Oncology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Fiona Simpkins
- Ovarian Cancer Research Center, Division of Gynecology Oncology, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
3
|
Santos R, Moreno-Torres V, Pintos I, Corral O, de Mendoza C, Soriano V, Corpas M. Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients. GIGABYTE 2024; 2024:gigabyte127. [PMID: 38948510 PMCID: PMC11211761 DOI: 10.46471/gigabyte.127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 06/04/2024] [Indexed: 07/02/2024] Open
Abstract
Despite the advances in genetic marker identification associated with severe COVID-19, the full genetic characterisation of the disease remains elusive. This study explores imputation in low-coverage whole genome sequencing for a severe COVID-19 patient cohort. We generated a dataset of 79 imputed variant call format files using the GLIMPSE1 tool, each containing an average of 9.5 million single nucleotide variants. Validation revealed a high imputation accuracy (squared Pearson correlation ≍0.97) across sequencing platforms, showcasing GLIMPSE1's ability to confidently impute variants with minor allele frequencies as low as 2% in individuals with Spanish ancestry. We carried out a comprehensive analysis of the patient cohort, examining hospitalisation and intensive care utilisation, sex and age-based differences, and clinical phenotypes using a standardised set of medical terms developed to characterise severe COVID-19 symptoms. The methods and findings presented here can be leveraged for future genomic projects to gain vital insights into health challenges like COVID-19.
Collapse
Affiliation(s)
- Renato Santos
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Víctor Moreno-Torres
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Madrid, Spain
| | - Ilduara Pintos
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Madrid, Spain
| | - Octavio Corral
- Health Sciences School & Medical Centre, Universidad Internacional La Rioja (UNIR), Madrid, Spain
| | - Carmen de Mendoza
- Puerta de Hierro University Hospital & Research Institute, Majadahonda, Madrid, Spain
| | - Vicente Soriano
- Health Sciences School & Medical Centre, Universidad Internacional La Rioja (UNIR), Madrid, Spain
| | - Manuel Corpas
- School of Life Sciences, University of Westminster, London, UK
| |
Collapse
|
4
|
Carraro C, Montgomery JV, Klimmt J, Paquet D, Schultze JL, Beyer MD. Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs. Front Mol Neurosci 2024; 17:1414886. [PMID: 38952421 PMCID: PMC11215216 DOI: 10.3389/fnmol.2024.1414886] [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: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.
Collapse
Affiliation(s)
- Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jessica V. Montgomery
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| | - Julien Klimmt
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
- Immunogenomics & Neurodegeneration, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| |
Collapse
|
5
|
Kashyap MK, Karathia H, Kumar D, Vera Alvarez R, Forero-Forero JV, Moreno E, Lujan JV, Amaya-Chanaga CI, Vidal NM, Yu Z, Ghia EM, Lengerke-Diaz PA, Achinko D, Choi MY, Rassenti LZ, Mariño-Ramírez L, Mount SM, Hannenhalli S, Kipps TJ, Castro JE. Aberrant spliceosome activity via elevated intron retention and upregulation and phosphorylation of SF3B1 in chronic lymphocytic leukemia. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102202. [PMID: 38846999 PMCID: PMC11154714 DOI: 10.1016/j.omtn.2024.102202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Abstract
Splicing factor 3b subunit 1 (SF3B1) is the largest subunit and core component of the spliceosome. Inhibition of SF3B1 was associated with an increase in broad intron retention (IR) on most transcripts, suggesting that IR can be used as a marker of spliceosome inhibition in chronic lymphocytic leukemia (CLL) cells. Furthermore, we separately analyzed exonic and intronic mapped reads on annotated RNA-sequencing transcripts obtained from B cells (n = 98 CLL patients) and healthy volunteers (n = 9). We measured intron/exon ratio to use that as a surrogate for alternative RNA splicing (ARS) and found that 66% of CLL-B cell transcripts had significant IR elevation compared with normal B cells (NBCs) and that correlated with mRNA downregulation and low expression levels. Transcripts with the highest IR levels belonged to biological pathways associated with gene expression and RNA splicing. A >2-fold increase of active pSF3B1 was observed in CLL-B cells compared with NBCs. Additionally, when the CLL-B cells were treated with macrolides (pladienolide-B), a significant decrease in pSF3B1, but not total SF3B1 protein, was observed. These findings suggest that IR/ARS is increased in CLL, which is associated with SF3B1 phosphorylation and susceptibility to SF3B1 inhibitors. These data provide additional support to the relevance of ARS in carcinogenesis and evidence of pSF3B1 participation in this process.
Collapse
Affiliation(s)
- Manoj Kumar Kashyap
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
- Division of Hematology Oncology, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Amity Stem Cell Institute, Amity Medical School, Amity University Haryana, Panchgaon (Manesar), Gurugram (HR) 122413, India
| | - Hiren Karathia
- Advanced Biomedical Computational Science and National Center for Advancing Translational Sciences, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
- Greenwood Genetic Center, Greenwood, SC, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA
| | - Deepak Kumar
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Roberto Vera Alvarez
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Eider Moreno
- Department of Internal Medicine, Division of Hematology-Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Juliana Velez Lujan
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | | | - Newton Medeiros Vidal
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Zhe Yu
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
| | - Emanuela M. Ghia
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
- Division of Hematology Oncology, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Novel Therapeutics, University of California, San Diego, La Jolla, CA 92037, USA
| | - Paula A. Lengerke-Diaz
- Department of Internal Medicine, Division of Hematology-Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Daniel Achinko
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Michael Y. Choi
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
- Division of Hematology Oncology, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Novel Therapeutics, University of California, San Diego, La Jolla, CA 92037, USA
| | - Laura Z. Rassenti
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
- Division of Hematology Oncology, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Novel Therapeutics, University of California, San Diego, La Jolla, CA 92037, USA
| | - Leonardo Mariño-Ramírez
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Stephen M. Mount
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - Sridhar Hannenhalli
- Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Thomas J. Kipps
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
- Division of Hematology Oncology, Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Novel Therapeutics, University of California, San Diego, La Jolla, CA 92037, USA
| | - Januario E. Castro
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093-0820, USA
- Department of Internal Medicine, Division of Hematology-Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| |
Collapse
|
6
|
Stillger MN, Li MJ, Hönscheid P, von Neubeck C, Föll MC. Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma. Proteomics 2024; 24:e2300001. [PMID: 38402423 DOI: 10.1002/pmic.202300001] [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] [Revised: 02/10/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.
Collapse
Affiliation(s)
- Maren Nicole Stillger
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Mujia Jenny Li
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Institute for Pharmaceutical Sciences, University of Freiburg, Freiburg, Germany
| | - Pia Hönscheid
- Institute of Pathology, Faculty of Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases, Partner Site Dresden, German Cancer Research Center Heidelberg, Dresden, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cläre von Neubeck
- Department of Particle Therapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| |
Collapse
|
7
|
Rujano MA, Boiten JW, Ohmann C, Canham S, Contrino S, David R, Ewbank J, Filippone C, Connellan C, Custers I, van Nuland R, Mayrhofer MT, Holub P, Álvarez EG, Bacry E, Hughes N, Freeberg MA, Schaffhauser B, Wagener H, Sánchez-Pla A, Bertolini G, Panagiotopoulou M. Sharing sensitive data in life sciences: an overview of centralized and federated approaches. Brief Bioinform 2024; 25:bbae262. [PMID: 38836701 DOI: 10.1093/bib/bbae262] [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: 02/06/2024] [Revised: 04/19/2024] [Indexed: 06/06/2024] Open
Abstract
Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.
Collapse
Affiliation(s)
- Maria A Rujano
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| | - Jan-Willem Boiten
- Foundation Lygature, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Christian Ohmann
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| | - Steve Canham
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| | - Sergio Contrino
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| | - Romain David
- European Research Infrastructure on Highly Pathogenic Agents (ERINHA AISBL), rue du Trône 98/Boîte 4B, 1050, Brussels, Belgium
| | - Jonathan Ewbank
- European Research Infrastructure on Highly Pathogenic Agents (ERINHA AISBL), rue du Trône 98/Boîte 4B, 1050, Brussels, Belgium
| | - Claudia Filippone
- European Research Infrastructure on Highly Pathogenic Agents (ERINHA AISBL), rue du Trône 98/Boîte 4B, 1050, Brussels, Belgium
| | - Claire Connellan
- European Research Infrastructure on Highly Pathogenic Agents (ERINHA AISBL), rue du Trône 98/Boîte 4B, 1050, Brussels, Belgium
| | - Ilse Custers
- Foundation Lygature, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Rick van Nuland
- Foundation Lygature, Jaarbeursplein 6, 3521 AL, Utrecht, The Netherlands
| | - Michaela Th Mayrhofer
- Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-ERIC), Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Petr Holub
- Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-ERIC), Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Eva García Álvarez
- Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-ERIC), Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Emmanuel Bacry
- Health Data Hub (HDH), rue Georges Pitard 9, 75015, Paris, France
| | - Nigel Hughes
- Janssen Research and Development, Antwerpseweg 15, 2340, Beerse, Belgium
| | - Mallory A Freeberg
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridgeshire, United Kingdom
| | - Birgit Schaffhauser
- Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 21, 1011, Lausanne, Switzerland
| | - Harald Wagener
- Center for Digital Health, BIH@Charité University Medicine, Anna-Louisa-Karsch-Straße 2, 10178, Berlin, Germany
| | - Alex Sánchez-Pla
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Diagonal 643, 08028, Barcelona, Spain
| | - Guido Bertolini
- Laboratory of Clinical Epidemiology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via GB Camozzi 3, 24020, Ranica (Bergamo), Italy
| | - Maria Panagiotopoulou
- European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France
| |
Collapse
|
8
|
Jeanson F, Gibson SJ, Alper P, Bernier A, Woolley JP, Mietchen D, Strug A, Becker R, Kamerling P, Sanchez Gonzalez MDC, Mah N, Novakowski A, Wilkinson MD, Benhamed OM, Landi A, Krog GP, Müller H, Riaz U, Veal C, Holub P, van Enckevort E, Brookes AJ. Getting your DUCs in a row - standardising the representation of Digital Use Conditions. Sci Data 2024; 11:464. [PMID: 38719839 PMCID: PMC11078994 DOI: 10.1038/s41597-024-03280-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 04/18/2024] [Indexed: 05/12/2024] Open
Abstract
Improving patient care and advancing scientific discovery requires responsible sharing of research data, healthcare records, biosamples, and biomedical resources that must also respect applicable use conditions. Defining a standard to structure and manage these use conditions is a complex and challenging task. This is exemplified by a near unlimited range of asset types, a high variability of applicable conditions, and differing applications at the individual or collective level. Furthermore, the specifics and granularity required are likely to vary depending on the ultimate contexts of use. All these factors confound alignment of institutional missions, funding objectives, regulatory and technical requirements to facilitate effective sharing. The presented work highlights the complexity and diversity of the problem, reviews the current state of the art, and emphasises the need for a flexible and adaptable approach. We propose Digital Use Conditions (DUC) as a framework that addresses these needs by leveraging existing standards, striking a balance between expressiveness versus ambiguity, and considering the breadth of applicable information with their context of use.
Collapse
Grants
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- 825575 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Societal Challenges | H2020 Health (H2020 Societal Challenges - Health, Demographic Change and Well-being)
- Algerian Ministry of Higher Education and Scientific Research
- The European Reference Network on Rare Multisystemic Vascular Diseases (VASCERN), which is partly co-funded by the European Union within the framework of the EU4Health programme – “VASCERN Action Grant".
Collapse
Affiliation(s)
- Francis Jeanson
- Centre for Analytics, Ontario Brain Institute, Toronto, Canada.
| | - Spencer J Gibson
- Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Pinar Alper
- Luxembourg National Data Service, Esch-sur-Alzette, Luxembourg
| | - Alexander Bernier
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - J Patrick Woolley
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Andrzej Strug
- Medical Laboratory Diagnostics Department, Medical University of Gdańsk, Gdańsk, Poland
| | - Regina Becker
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Pim Kamerling
- Center for Radiology and Nuclear Medicine, VASCERN ERN /Radboud University Medical Center, Nijmegen, Netherlands
| | | | - Nancy Mah
- Biomedical Data & Bioethics Group, Fraunhofer Institute for Biomedical Engineering, Sulzbach/Saar, Germany
| | | | - Mark D Wilkinson
- Departamento de Biotecnología-Biología Vegetal, ETSI Agronómica, Alimentaria y de Biosistemas, Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA/CSIC), Universidad Politécnica de Madrid, Madrid, Spain
| | - Oussama Mohammed Benhamed
- Departamento de Biotecnología-Biología Vegetal, ETSI Agronómica, Alimentaria y de Biosistemas, Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA/CSIC), Universidad Politécnica de Madrid, Madrid, Spain
| | - Annalisa Landi
- Research, Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, Bari, Italy
| | | | | | - Umar Riaz
- Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Colin Veal
- Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Petr Holub
- BBMRI-ERIC, Graz, Austria
- Institute of Computer Science, Masaryk University, Brno, Czechia
| | - Esther van Enckevort
- University of Groningen, Groningen, Netherlands
- Department of Genetics, University Medical Center Groningen, Groningen, Netherlands
| | | |
Collapse
|
9
|
Block I, Burton M, Sørensen KP, Larsen MJ, Do TTN, Bak M, Cold S, Thomassen M, Tan Q, Kruse TA. Ensemble-based classification using microRNA expression identifies a breast cancer patient subgroup with an ultralow long-term risk of metastases. Cancer Med 2024; 13:e7089. [PMID: 38676390 PMCID: PMC11053369 DOI: 10.1002/cam4.7089] [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: 03/02/2023] [Revised: 06/28/2023] [Accepted: 01/18/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Current clinical markers overestimate the recurrence risk in many lymph node negative (LNN) breast cancer (BC) patients such that a majority of these low-risk patients unnecessarily receive systemic treatments. We tested if differential microRNA expression in primary tumors allows reliable identification of indolent LNN BC patients to provide an improved classification tool for overtreatment reduction in this patient group. METHODS We collected freshly frozen primary tumors of 80 LNN BC patients with recurrence and 80 recurrence-free patients (mean follow-up: 20.9 years). The study comprises solely systemically untreated patients to exclude that administered treatments confound the metastasis status. Samples were pairwise matched for clinical-pathological characteristics to minimize dependence of current markers. Patients were classified into risk-subgroups according to the differential microRNA expression of their tumors via classification model building with cross-validation using seven classification methods and a voting scheme. The methodology was validated using available data of two independent cohorts (n = 123, n = 339). RESULTS Of the 80 indolent patients (who would all likely receive systemic treatments today) our ultralow-risk classifier correctly identified 37 while keeping a sensitivity of 100% in the recurrence group. Multivariable logistic regression analysis confirmed independence of voting results from current clinical markers. Application of the method in two validation cohorts confirmed successful classification of ultralow-risk BC patients with significantly prolonged recurrence-free survival. CONCLUSION Profiles of differential microRNAs expression can identify LNN BC patients who could spare systemic treatments demanded by currently applied classifications. However, further validation studies are required for clinical implementation of the applied methodology.
Collapse
Affiliation(s)
- Ines Block
- Department of Clinical GeneticsOdense University HospitalOdenseDenmark
- Present address:
Department of Mathematics and Computer ScienceUniversity of MarburgMarburgGermany
| | - Mark Burton
- Department of Clinical GeneticsOdense University HospitalOdenseDenmark
- Human Genetics, Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
- Clinical Genome CenterUniversity of Southern Denmark and Region of Southern DenmarkOdenseDenmark
| | | | - Martin J. Larsen
- Department of Clinical GeneticsOdense University HospitalOdenseDenmark
- Human Genetics, Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
| | - Thi T. N. Do
- Department of Clinical GeneticsOdense University HospitalOdenseDenmark
- Human Genetics, Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
| | - Martin Bak
- Department of PathologyOdense University HospitalOdenseDenmark
- Department of PathologyHospital of Southwest JutlandEsbjergDenmark
| | - Søren Cold
- Department of OncologyOdense University HospitalOdenseDenmark
| | - Mads Thomassen
- Department of Clinical GeneticsOdense University HospitalOdenseDenmark
- Human Genetics, Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
- Clinical Genome CenterUniversity of Southern Denmark and Region of Southern DenmarkOdenseDenmark
| | - Qihua Tan
- Human Genetics, Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
- Clinical Genome CenterUniversity of Southern Denmark and Region of Southern DenmarkOdenseDenmark
- Epidemiology, Department of Public HealthUniversity of Southern DenmarkOdenseDenmark
| | - Torben A. Kruse
- Department of Clinical GeneticsOdense University HospitalOdenseDenmark
- Human Genetics, Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
- Clinical Genome CenterUniversity of Southern Denmark and Region of Southern DenmarkOdenseDenmark
| |
Collapse
|
10
|
Fűr GM, Nemes K, Magó É, Benő AÁ, Topolcsányi P, Moldvay J, Pongor LS. Applied models and molecular characteristics of small cell lung cancer. Pathol Oncol Res 2024; 30:1611743. [PMID: 38711976 PMCID: PMC11070512 DOI: 10.3389/pore.2024.1611743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/03/2024] [Indexed: 05/08/2024]
Abstract
Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care.
Collapse
Affiliation(s)
- Gabriella Mihalekné Fűr
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Kolos Nemes
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Éva Magó
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
- Genome Integrity and DNA Repair Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Alexandra Á. Benő
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Petronella Topolcsányi
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| | - Judit Moldvay
- Department of Pulmonology, Szeged University Szent-Gyorgyi Albert Medical School, Szeged, Hungary
- 1st Department of Pulmonology, National Koranyi Institute of Pulmonology, Budapest, Hungary
| | - Lőrinc S. Pongor
- Cancer Genomics and Epigenetics Core Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
| |
Collapse
|
11
|
Eklund N, Engels C, Neumann M, Strug A, van Enckevort E, Baber R, Bloemers M, Debucquoy A, van der Lugt A, Müller H, Parkkonen L, Quinlan PR, Urwin E, Holub P, Silander K, Anton G. Update of the Minimum Information About BIobank Data Sharing (MIABIS) Core Terminology to the 3 rd Version. Biopreserv Biobank 2024. [PMID: 38497765 DOI: 10.1089/bio.2023.0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
Introduction: The Minimum Information About BIobank Data Sharing (MIABIS) is a biobank-specific terminology enabling the sharing of biobank-related data for different purposes across a wide range of database implementations. After 4 years in use and with the first version of the individual-level MIABIS component Sample, Sample donor, and Event, it was necessary to revise the terminology, especially to include biobanks that work more in the data domain than with samples. Materials & Methods: Nine use-cases representing different types of biobanks, studies, and networks participated in the development work. They represent types of data, specific sample types, or levels of organization that were not included earlier in MIABIS. To support our revision of the Biobank entity, we conducted a survey of European biobanks to chart the services they provide. An important stakeholder group for biobanks include researchers as the main users of biobanks. To be able to render MIABIS more researcher-friendly, we collected different sample/data requests to analyze the terminology adjustment needs in detail. During the update process, the Core terminology was iteratively reviewed by a large group of experts until a consensus was reached. Results: With this update, MIABIS was adjusted to encompass data-driven biobanks and to include data collections, while also describing the services and capabilities biobanks offer to their users, besides the retrospective samples. The terminology was also extended to accommodate sample and data collections of nonhuman origin. Additionally, a set of organizational attributes was compiled to describe networks. Discussion: The usability of MIABIS Core v3 was increased by extending it to cover more topics of the biobanking domain. Additionally, the focus was on a more general terminology and harmonization of attributes with the individual-level entities Sample, Sample donor, and Event to keep the overall terminology minimal. With this work, the internal semantics of the MIABIS terminology was improved.
Collapse
Affiliation(s)
- Niina Eklund
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Cäcilia Engels
- German Biobank Node (GBN), Charité - Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Charité University Hospital Berlin, Berlin, Germany
| | | | - Andrzej Strug
- Department of Medical Laboratory Diagnostics, Medical University of Gdansk, Gdansk, Poland
| | - Esther van Enckevort
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ronny Baber
- Leipzig Medical Biobank, Leipzig, Germany and Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig Medical Center, Leipzig, Germany
| | - Margreet Bloemers
- ZonMw Organisation for Health Research and Development, the Hague, The Netherlands
| | | | | | | | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | | | - Esmond Urwin
- University of Nottingham, Nottingham, United Kingdom
| | | | - Kaisa Silander
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | | |
Collapse
|
12
|
Yuan X, Su J, Wang J, Dai B, Sun Y, Zhang K, Li Y, Chuan J, Tang C, Yu Y, Gong Q. Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases. Sci Rep 2024; 14:2845. [PMID: 38310124 PMCID: PMC10838329 DOI: 10.1038/s41598-024-53461-x] [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: 08/30/2023] [Accepted: 01/31/2024] [Indexed: 02/05/2024] Open
Abstract
Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3-91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.
Collapse
Affiliation(s)
- Xiao Yuan
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Jieqiong Su
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Jing Wang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Bing Dai
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yanfang Sun
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Keke Zhang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yinghua Li
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, Guangdong, China
| | - Jun Chuan
- Genetalks Biotech. Co., Ltd., Changsha, Hunan, China
| | - Chunyan Tang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yan Yu
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China.
| | - Qiang Gong
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China.
| |
Collapse
|
13
|
Lagorce D, Lebreton E, Matalonga L, Hongnat O, Chahdil M, Piscia D, Paramonov I, Ellwanger K, Köhler S, Robinson P, Graessner H, Beltran S, Lucano C, Hanauer M, Rath A. Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report. Eur J Hum Genet 2024; 32:182-189. [PMID: 37926714 PMCID: PMC10853199 DOI: 10.1038/s41431-023-01486-7] [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: 05/17/2023] [Revised: 09/13/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Rare diseases (RD) have a prevalence of not more than 1/2000 persons in the European population, and are characterised by the difficulty experienced in obtaining a correct and timely diagnosis. According to Orphanet, 72.5% of RD have a genetic origin although 35% of them do not yet have an identified causative gene. A significant proportion of patients suspected to have a genetic RD receive an inconclusive exome/genome sequencing. Working towards the International Rare Diseases Research Consortium (IRDiRC)'s goal for 2027 to ensure that all people living with a RD receive a diagnosis within one year of coming to medical attention, the Solve-RD project aims to identify the molecular causes underlying undiagnosed RD. As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing submitted cases with other submitted cases and with known RD in Orphanet. Three complementary approaches based on phenotypic similarity calculations using the Human Phenotype Ontology (HPO), the Orphanet Rare Diseases Ontology (ORDO) and the HPO-ORDO Ontological Module (HOOM) were developed; genomic data reanalysis was performed by the RD-Connect Genome-Phenome Analysis Platform (GPAP). The methodology was tested in 4 exemplary cases discussed with experts from European Reference Networks. Variants of interest (pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity calculations. Diagnostic hypotheses were validated in 42.1% of them and needed further exploration in another 10.9%. Based on the promising results, we are devising an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort.
Collapse
Affiliation(s)
- David Lagorce
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, 75014, Paris, France.
| | - Emeline Lebreton
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, 75014, Paris, France
| | - Leslie Matalonga
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona, 08028, Spain
| | - Oscar Hongnat
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, 75014, Paris, France
| | - Maroua Chahdil
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, 75014, Paris, France
| | - Davide Piscia
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona, 08028, Spain
| | - Ida Paramonov
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona, 08028, Spain
| | - Kornelia Ellwanger
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | | | - Peter Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Holm Graessner
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Sergi Beltran
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona, 08028, Spain
| | - Caterina Lucano
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, 75014, Paris, France
| | - Marc Hanauer
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, 75014, Paris, France
| | - Ana Rath
- INSERM, US14 - Orphanet, Plateforme Maladies Rares, 75014, Paris, France
| |
Collapse
|
14
|
Yang Z, Guan F, Bronk L, Zhao L. Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective. Pharmacol Ther 2024; 254:108591. [PMID: 38286161 DOI: 10.1016/j.pharmthera.2024.108591] [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: 08/23/2023] [Revised: 12/02/2023] [Accepted: 01/04/2024] [Indexed: 01/31/2024]
Abstract
Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.
Collapse
Affiliation(s)
- Zhi Yang
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi'an, China
| | - Fada Guan
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT 06510, United States of America
| | - Lawrence Bronk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi'an, China.
| |
Collapse
|
15
|
Zech M, Winkelmann J. Next-generation sequencing and bioinformatics in rare movement disorders. Nat Rev Neurol 2024; 20:114-126. [PMID: 38172289 DOI: 10.1038/s41582-023-00909-9] [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: 11/29/2023] [Indexed: 01/05/2024]
Abstract
The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.
Collapse
Affiliation(s)
- Michael Zech
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
| | - Juliane Winkelmann
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.
- Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany.
- Munich Cluster for Systems Neurology, SyNergy, Munich, Germany.
- DZPG, Deutsches Zentrum für Psychische Gesundheit, Munich, Germany.
| |
Collapse
|
16
|
Ge F, Arif M, Yan Z, Alahmadi H, Worachartcheewan A, Shoombuatong W. Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations. ACS OMEGA 2024; 9:2032-2047. [PMID: 38250421 PMCID: PMC10795160 DOI: 10.1021/acsomega.3c07662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 01/23/2024]
Abstract
Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.
Collapse
Affiliation(s)
- Fang Ge
- State
Key Laboratory of Organic Electronics and lnformation Displays &
lnstitute of Advanced Materials (IAM), Nanjing University of Posts
& Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
| | - Muhammad Arif
- College
of Science and Engineering, Hamad Bin Khalifa
University, Doha 34110, Qatar
| | - Zihao Yan
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Hanin Alahmadi
- College
of Computer Science and Engineering, Taibah
University, Madinah 344, Saudi Arabia
| | - Apilak Worachartcheewan
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Watshara Shoombuatong
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
| |
Collapse
|
17
|
George N, Fexova S, Fuentes AM, Madrigal P, Bi Y, Iqbal H, Kumbham U, Nolte N, Zhao L, Thanki A, Yu I, Marugan Calles J, Erdos K, Vilmovsky L, Kurri S, Vathrakokoili-Pournara A, Osumi-Sutherland D, Prakash A, Wang S, Tello-Ruiz M, Kumari S, Ware D, Goutte-Gattat D, Hu Y, Brown N, Perrimon N, Vizcaíno JA, Burdett T, Teichmann S, Brazma A, Papatheodorou I. Expression Atlas update: insights from sequencing data at both bulk and single cell level. Nucleic Acids Res 2024; 52:D107-D114. [PMID: 37992296 PMCID: PMC10767917 DOI: 10.1093/nar/gkad1021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023] Open
Abstract
Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.
Collapse
Affiliation(s)
- Nancy George
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Silvie Fexova
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Alfonso Munoz Fuentes
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Pedro Madrigal
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Yalan Bi
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Haider Iqbal
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Upendra Kumbham
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Nadja Francesca Nolte
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Lingyun Zhao
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Anil S Thanki
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Iris D Yu
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Jose C Marugan Calles
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Karoly Erdos
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Liora Vilmovsky
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Sandeep R Kurri
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | | | - David Osumi-Sutherland
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Ananth Prakash
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Shengbo Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Marcela K Tello-Ruiz
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Sunita Kumari
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Doreen Ware
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
- USDA ARS NEA, Plant Soil & Nutrition Laboratory Research Unit, Ithaca, NY 14853, USA
| | - Damien Goutte-Gattat
- FlyBase-Cambridge, Department of Physiology, Development and Neuroscience, University of Cambridge Downing Street, Cambridge CB2 3DY, UK
| | - Yanhui Hu
- Perrimon Lab, Department of Genetics, Harvard Medical School, Boston MA 02115, USA
| | - Nick Brown
- FlyBase-Cambridge, Department of Physiology, Development and Neuroscience, University of Cambridge Downing Street, Cambridge CB2 3DY, UK
| | - Norbert Perrimon
- Perrimon Lab, Department of Genetics, Harvard Medical School, Boston MA 02115, USA
- FlyBase-Harvard Biological Laboratories, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Sarah Teichmann
- Wellcome Trust Sanger Institute. Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| |
Collapse
|
18
|
Ouwerkerk J, Rasche H, Spalding JD, Hiltemann S, Stubbs AP. FAIR data retrieval for sensitive clinical research data in Galaxy. Gigascience 2024; 13:giad099. [PMID: 38280189 PMCID: PMC10821763 DOI: 10.1093/gigascience/giad099] [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: 06/28/2023] [Revised: 10/16/2023] [Accepted: 11/01/2023] [Indexed: 01/29/2024] Open
Abstract
BACKGROUND In clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized "omics" platform for FAIR data analysis. RESULTS To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow. CONCLUSIONS We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy.
Collapse
Affiliation(s)
- Jasper Ouwerkerk
- Clinical Bioinformatics Group, Department of Pathology, Erasmus Medical Center, 3015 CN, Rotterdam, the Netherlands
| | - Helena Rasche
- Clinical Bioinformatics Group, Department of Pathology, Erasmus Medical Center, 3015 CN, Rotterdam, the Netherlands
| | | | - Saskia Hiltemann
- Clinical Bioinformatics Group, Department of Pathology, Erasmus Medical Center, 3015 CN, Rotterdam, the Netherlands
| | - Andrew P Stubbs
- Clinical Bioinformatics Group, Department of Pathology, Erasmus Medical Center, 3015 CN, Rotterdam, the Netherlands
| |
Collapse
|
19
|
Dumschott K, Dörpholz H, Laporte MA, Brilhaus D, Schrader A, Usadel B, Neumann S, Arnaud E, Kranz A. Ontologies for increasing the FAIRness of plant research data. FRONTIERS IN PLANT SCIENCE 2023; 14:1279694. [PMID: 38098789 PMCID: PMC10720748 DOI: 10.3389/fpls.2023.1279694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/15/2023] [Indexed: 12/17/2023]
Abstract
The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.
Collapse
Affiliation(s)
- Kathryn Dumschott
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Hannah Dörpholz
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| | - Marie-Angélique Laporte
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Dominik Brilhaus
- Data Science and Management & Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Andrea Schrader
- Data Science and Management & Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Cologne, Germany
| | - Björn Usadel
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
- Institute for Biological Data Science & Cluster of Excellence on Plant Sciences (CEPLAS), Faculty of Mathematics and Life Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Steffen Neumann
- Program Center MetaCom, Leibniz Institute of Plant Biochemistry, Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
| | - Elizabeth Arnaud
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Angela Kranz
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics) & Bioeconomy Science Center (BioSC), CEPLAS, Forschungszentrum Jülich, Jülich, Germany
| |
Collapse
|
20
|
Brennan K, Espín-Pérez A, Chang S, Bedi N, Saumyaa S, Shin JH, Plevritis SK, Gevaert O, Sunwoo JB, Gentles AJ. Loss of p53-DREAM-mediated repression of cell cycle genes as a driver of lymph node metastasis in head and neck cancer. Genome Med 2023; 15:98. [PMID: 37978395 PMCID: PMC10656821 DOI: 10.1186/s13073-023-01236-w] [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: 01/14/2023] [Accepted: 09/20/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND The prognosis for patients with head and neck cancer (HNC) is poor and has improved little in recent decades, partially due to lack of therapeutic options. To identify effective therapeutic targets, we sought to identify molecular pathways that drive metastasis and HNC progression, through large-scale systematic analyses of transcriptomic data. METHODS We performed meta-analysis across 29 gene expression studies including 2074 primary HNC biopsies to identify genes and transcriptional pathways associated with survival and lymph node metastasis (LNM). To understand the biological roles of these genes in HNC, we identified their associated cancer pathways, as well as the cell types that express them within HNC tumor microenvironments, by integrating single-cell RNA-seq and bulk RNA-seq from sorted cell populations. RESULTS Patient survival-associated genes were heterogenous and included drivers of diverse tumor biological processes: these included tumor-intrinsic processes such as epithelial dedifferentiation and epithelial to mesenchymal transition, as well as tumor microenvironmental factors such as T cell-mediated immunity and cancer-associated fibroblast activity. Unexpectedly, LNM-associated genes were almost universally associated with epithelial dedifferentiation within malignant cells. Genes negatively associated with LNM consisted of regulators of squamous epithelial differentiation that are expressed within well-differentiated malignant cells, while those positively associated with LNM represented cell cycle regulators that are normally repressed by the p53-DREAM pathway. These pro-LNM genes are overexpressed in proliferating malignant cells of TP53 mutated and HPV + ve HNCs and are strongly associated with stemness, suggesting that they represent markers of pre-metastatic cancer stem-like cells. LNM-associated genes are deregulated in high-grade oral precancerous lesions, and deregulated further in primary HNCs with advancing tumor grade and deregulated further still in lymph node metastases. CONCLUSIONS In HNC, patient survival is affected by multiple biological processes and is strongly influenced by the tumor immune and stromal microenvironments. In contrast, LNM appears to be driven primarily by malignant cell plasticity, characterized by epithelial dedifferentiation coupled with EMT-independent proliferation and stemness. Our findings postulate that LNM is initially caused by loss of p53-DREAM-mediated repression of cell cycle genes during early tumorigenesis.
Collapse
Affiliation(s)
- Kevin Brennan
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.
| | - Almudena Espín-Pérez
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Serena Chang
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, USA
| | - Nikita Bedi
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, USA
| | - Saumyaa Saumyaa
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, USA
| | - June Ho Shin
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, USA
| | - Sylvia K Plevritis
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - John B Sunwoo
- Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, USA
| | - Andrew J Gentles
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Pathology, Stanford University, Stanford, CA, USA.
| |
Collapse
|
21
|
Bustos MA, Yokoe T, Shoji Y, Kobayashi Y, Mizuno S, Murakami T, Zhang X, Sekhar SC, Kim S, Ryu S, Knarr M, Vasilev SA, DiFeo A, Drapkin R, Hoon DSB. MiR-181a targets STING to drive PARP inhibitor resistance in BRCA- mutated triple-negative breast cancer and ovarian cancer. Cell Biosci 2023; 13:200. [PMID: 37932806 PMCID: PMC10626784 DOI: 10.1186/s13578-023-01151-y] [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: 06/13/2023] [Accepted: 10/24/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Poly (ADP-ribose) polymerase inhibitors (PARPi) are approved for the treatment of BRCA-mutated breast cancer (BC), including triple-negative BC (TNBC) and ovarian cancer (OvCa). A key challenge is to identify the factors associated with PARPi resistance; although, previous studies suggest that platinum-based agents and PARPi share similar resistance mechanisms. METHODS Olaparib-resistant (OlaR) cell lines were analyzed using HTG EdgeSeq miRNA Whole Transcriptomic Analysis (WTA). Functional assays were performed in three BRCA-mutated TNBC cell lines. In-silico analysis were performed using multiple databases including The Cancer Genome Atlas, the Genotype-Tissue Expression, The Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, and Gene Omnibus Expression. RESULTS High miR-181a levels were identified in OlaR TNBC cell lines (p = 0.001) as well as in tumor tissues from TNBC patients (p = 0.001). We hypothesized that miR-181a downregulates the stimulator of interferon genes (STING) and the downstream proinflammatory cytokines to mediate PARPi resistance. BRCA1 mutated TNBC cell lines with miR-181a-overexpression were more resistant to olaparib and showed downregulation in STING and the downstream genes controlled by STING. Extracellular vesicles derived from PARPi-resistant TNBC cell lines horizontally transferred miR-181a to parental cells which conferred PARPi-resistance and targeted STING. In clinical settings, STING levels were positively correlated with interferon gamma (IFNG) response scores (p = 0.01). In addition, low IFNG response scores were associated with worse response to neoadjuvant treatment including PARPi for high-risk HER2 negative BC patients (p = 0.001). OlaR TNBC cell lines showed resistance to platinum-based drugs. OvCa cell lines resistant to platinum showed resistance to olaparib. Knockout of miR-181a significantly improved olaparib sensitivity in OvCa cell lines (p = 0.001). CONCLUSION miR-181a is a key factor controlling the STING pathway and driving PARPi and platinum-based drug resistance in TNBC and OvCa. The miR-181a-STING axis can be used as a potential marker for predicting PARPi responses in TNBC and OvCa tumors.
Collapse
Affiliation(s)
- Matias A Bustos
- Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), 2200 Santa Monica Blvd, Santa Monica, CA, 90404, USA
| | - Takamichi Yokoe
- Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), 2200 Santa Monica Blvd, Santa Monica, CA, 90404, USA
| | - Yoshiaki Shoji
- Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), 2200 Santa Monica Blvd, Santa Monica, CA, 90404, USA
| | - Yuta Kobayashi
- Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), 2200 Santa Monica Blvd, Santa Monica, CA, 90404, USA
| | - Shodai Mizuno
- Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), 2200 Santa Monica Blvd, Santa Monica, CA, 90404, USA
| | - Tomohiro Murakami
- Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), 2200 Santa Monica Blvd, Santa Monica, CA, 90404, USA
| | - Xiaoqing Zhang
- Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), 2200 Santa Monica Blvd, Santa Monica, CA, 90404, USA
| | - Sreeja C Sekhar
- Department of Obstetrics & Gynecology, University Michigan, Ann Arbor, MI, 48109, USA
- Department of Pathology, Rogel Cancer Center, University Michigan, Ann Arbor, MI, 48109, USA
| | - SooMin Kim
- Department of Genome Sequencing, SJCI at Providence SJHC, Santa Monica, CA, 90404, USA
| | - Suyeon Ryu
- Department of Genome Sequencing, SJCI at Providence SJHC, Santa Monica, CA, 90404, USA
| | - Matthew Knarr
- Department of Obstetrics and Gynecology, Perelman School of Medicine, Penn Ovarian Cancer Research Center, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Steven A Vasilev
- Department of Gynecologic Oncology Research, SJCI at SJHC, Santa Monica, CA, 90404, USA
| | - Analisa DiFeo
- Department of Obstetrics & Gynecology, University Michigan, Ann Arbor, MI, 48109, USA
- Department of Pathology, Rogel Cancer Center, University Michigan, Ann Arbor, MI, 48109, USA
| | - Ronny Drapkin
- Department of Obstetrics and Gynecology, Perelman School of Medicine, Penn Ovarian Cancer Research Center, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Dave S B Hoon
- Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), 2200 Santa Monica Blvd, Santa Monica, CA, 90404, USA.
- Department of Genome Sequencing, SJCI at Providence SJHC, Santa Monica, CA, 90404, USA.
| |
Collapse
|
22
|
Liosis KC, Marouf AA, Rokne JG, Ghosh S, Bismar TA, Alhajj R. Genomic Biomarker Discovery in Disease Progression and Therapy Response in Bladder Cancer Utilizing Machine Learning. Cancers (Basel) 2023; 15:4801. [PMID: 37835496 PMCID: PMC10571566 DOI: 10.3390/cancers15194801] [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: 07/24/2023] [Revised: 08/20/2023] [Accepted: 09/09/2023] [Indexed: 10/15/2023] Open
Abstract
Cancer in all its forms of expression is a major cause of death. To identify the genomic reason behind cancer, discovery of biomarkers is needed. In this paper, genomic data of bladder cancer are examined for the purpose of biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either at a very low level based on the genome sequence itself, or more abstractly such as measuring the level of gene expression for different disease groups. The latter method is pivotal for this work, since the available datasets consist of RNA sequencing data, transformed to gene expression levels, as well as data on a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan-Meier curves, and heatmaps for the experiments leading to biomarker discovery. The experiments have led to the discovery of two gene signatures capable of predicting therapy response and disease progression with considerable accuracy for bladder cancer patients which correlates well with clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner.
Collapse
Affiliation(s)
- Konstantinos Christos Liosis
- Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ahmed Al Marouf
- Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Jon G Rokne
- Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Sunita Ghosh
- Department of Medical Oncology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R7, Canada
- Departments of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2J5, Canada
| | - Tarek A Bismar
- Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Departments of Oncology, Biochemistry and Molecular Biology, Cumming School of Medicine, Calgary, AB T2N 4N1, Canada
- Tom Baker Cancer Center, Arnie Charbonneau Cancer Institute, Calgary, AB T2N 4N1, Canada
- Prostate Cancer Center, Calgary, AB T2V 1P9, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada
- Department of Computer Engineering, Istanbul Medipol University, Istanbul 34810, Turkey
- Department of Heath Informatics, University of Southern Denmark, 5230 Odense, Denmark
| |
Collapse
|
23
|
Joshi E, Biddanda A, Popoola J, Yakubu A, Osakwe O, Attipoe D, Dogbo E, Salako B, Nash O, Salako O, Oyedele O, Eze-Echesi G, Fatumo S, Ene-Obong A, O’Dushlaine C. Whole-genome sequencing across 449 samples spanning 47 ethnolinguistic groups provides insights into genetic diversity in Nigeria. CELL GENOMICS 2023; 3:100378. [PMID: 37719143 PMCID: PMC10504631 DOI: 10.1016/j.xgen.2023.100378] [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: 01/12/2023] [Revised: 02/23/2023] [Accepted: 07/13/2023] [Indexed: 09/19/2023]
Abstract
African populations have been drastically underrepresented in genomics research, and failure to capture the genetic diversity across the numerous ethnolinguistic groups (ELGs) found on the continent has hindered the equity of precision medicine initiatives globally. Here, we describe the whole-genome sequencing of 449 Nigerian individuals across 47 unique self-reported ELGs. Population structure analysis reveals genetic differentiation among our ELGs, consistent with previous findings. From the 36 million SNPs and insertions or deletions (indels) discovered in our dataset, we provide a high-level catalog of both novel and medically relevant variation present across the ELGs. These results emphasize the value of this resource for genomics research, with added granularity by representing multiple ELGs from Nigeria. Our results also underscore the potential of using these cohorts with larger sample sizes to improve our understanding of human ancestry and health in Africa.
Collapse
Affiliation(s)
- Esha Joshi
- 54gene, Inc., 1100 15th St. NW, Washington, DC 20005, USA
| | - Arjun Biddanda
- 54gene, Inc., 1100 15th St. NW, Washington, DC 20005, USA
| | - Jumi Popoola
- 54gene, Inc., 1100 15th St. NW, Washington, DC 20005, USA
| | - Aminu Yakubu
- 54gene, Inc., 1100 15th St. NW, Washington, DC 20005, USA
| | | | - Delali Attipoe
- 54gene, Inc., 1100 15th St. NW, Washington, DC 20005, USA
| | - Estelle Dogbo
- 54gene, Inc., 1100 15th St. NW, Washington, DC 20005, USA
| | | | - Oyekanmi Nash
- Center for Genomics Research and Innovation, National Agency for Biotechnology Development, Abuja 09004, Nigeria
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja 09004, Nigeria
| | - Omolola Salako
- College of Medicine University of Lagos, Lagos 101233, Nigeria
| | | | | | - Segun Fatumo
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja 09004, Nigeria
- The African Computational Genomics (TAGC) Research Group, MRC/UVRI and London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | | |
Collapse
|
24
|
Souza VGP, Forder A, Telkar N, Stewart GL, Carvalho RF, Mur LAJ, Lam WL, Reis PP. Identifying New Contributors to Brain Metastasis in Lung Adenocarcinoma: A Transcriptomic Meta-Analysis. Cancers (Basel) 2023; 15:4526. [PMID: 37760494 PMCID: PMC10526208 DOI: 10.3390/cancers15184526] [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: 08/15/2023] [Revised: 09/07/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
Lung tumors frequently metastasize to the brain. Brain metastasis (BM) is common in advanced cases, and a major cause of patient morbidity and mortality. The precise molecular mechanisms governing BM are still unclear, in part attributed to the rarity of BM specimens. In this work, we compile a unique transcriptomic dataset encompassing RNA-seq, microarray, and single-cell analyses from BM samples obtained from patients with lung adenocarcinoma (LUAD). By integrating this comprehensive dataset, we aimed to enhance understanding of the molecular landscape of BM, thereby facilitating the identification of novel and efficient treatment strategies. We identified 102 genes with significantly deregulated expression levels in BM tissues, and discovered transcriptional alterations affecting the key driver 'hub' genes CD69 (a type II C-lectin receptor) and GZMA (Granzyme A), indicating an important role of the immune system in the development of BM from primary LUAD. Our study demonstrated a BM-specific gene expression pattern and revealed the presence of dendritic cells and neutrophils in BM, suggesting an immunosuppressive tumor microenvironment. These findings highlight key drivers of LUAD-BM that may yield therapeutic targets to improve patient outcomes.
Collapse
Affiliation(s)
- Vanessa G. P. Souza
- Molecular Oncology Laboratory, Experimental Research Unit (UNIPEX), Faculty of Medicine, São Paulo State University (UNESP), Botucatu 18618-687, SP, Brazil
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; (A.F.); (N.T.); (G.L.S.); (W.L.L.)
| | - Aisling Forder
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; (A.F.); (N.T.); (G.L.S.); (W.L.L.)
| | - Nikita Telkar
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; (A.F.); (N.T.); (G.L.S.); (W.L.L.)
- British Columbia Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - Greg L. Stewart
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; (A.F.); (N.T.); (G.L.S.); (W.L.L.)
| | - Robson F. Carvalho
- Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, SP, Brazil;
| | - Luis A. J. Mur
- Department of Life Science, Aberystwyth University, Aberystwyth, Wales SY23 3FL, UK;
| | - Wan L. Lam
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; (A.F.); (N.T.); (G.L.S.); (W.L.L.)
| | - Patricia P. Reis
- Molecular Oncology Laboratory, Experimental Research Unit (UNIPEX), Faculty of Medicine, São Paulo State University (UNESP), Botucatu 18618-687, SP, Brazil
- Department of Surgery and Orthopedics, Faculty of Medicine, São Paulo State University (UNESP), Botucatu 18618-687, SP, Brazil
| |
Collapse
|
25
|
Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, Wang L, Yang X, Yang M, Liu G. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023; 4:e315. [PMID: 37533767 PMCID: PMC10390758 DOI: 10.1002/mco2.315] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 08/04/2023] Open
Abstract
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.
Collapse
Affiliation(s)
- Chongyang Chen
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
| | - Jing Wang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Donghui Pan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xinyu Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Yuping Xu
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Junjie Yan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Lizhen Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xifei Yang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Min Yang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Gong‐Ping Liu
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
- Department of PathophysiologySchool of Basic MedicineKey Laboratory of Ministry of Education of China and Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| |
Collapse
|
26
|
Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [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: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
Collapse
Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| |
Collapse
|
27
|
Sadhuka S, Fridman D, Berger B, Cho H. Assessing transcriptomic reidentification risks using discriminative sequence models. Genome Res 2023; 33:1101-1112. [PMID: 37541758 PMCID: PMC10538488 DOI: 10.1101/gr.277699.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 04/19/2023] [Indexed: 08/06/2023]
Abstract
Gene expression data provide molecular insights into the functional impact of genetic variation, for example, through expression quantitative trait loci (eQTLs). With an improving understanding of the association between genotypes and gene expression comes a greater concern that gene expression profiles could be matched to genotype profiles of the same individuals in another data set, known as a linking attack. Prior works show such a risk could analyze only a fraction of eQTLs that is independent owing to restrictive model assumptions, leaving the full extent of this risk incompletely understood. To address this challenge, we introduce the discriminative sequence model (DSM), a novel probabilistic framework for predicting a sequence of genotypes based on gene expression data. By modeling the joint distribution over all known eQTLs in a genomic region, DSM improves the power of linking attacks with necessary calibration for linkage disequilibrium and redundant predictive signals. We show greater linking accuracy of DSM compared with existing approaches across a range of attack scenarios and data sets including up to 22,288 individuals, suggesting that DSM helps uncover a substantial additional risk overlooked by previous studies. Our work provides a unified framework for assessing the privacy risks of sharing diverse omics data sets beyond transcriptomics.
Collapse
Affiliation(s)
- Shuvom Sadhuka
- Computer Science and AI Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Daniel Fridman
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Bonnie Berger
- Computer Science and AI Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Hyunghoon Cho
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;
| |
Collapse
|
28
|
Statzer C, Luthria K, Sharma A, Kann MG, Ewald CY. The Human Extracellular Matrix Diseasome Reveals Genotype-Phenotype Associations with Clinical Implications for Age-Related Diseases. Biomedicines 2023; 11:biomedicines11041212. [PMID: 37189830 DOI: 10.3390/biomedicines11041212] [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: 03/09/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
The extracellular matrix (ECM) is earning an increasingly relevant role in many disease states and aging. The analysis of these disease states is possible with the GWAS and PheWAS methodologies, and through our analysis, we aimed to explore the relationships between polymorphisms in the compendium of ECM genes (i.e., matrisome genes) in various disease states. A significant contribution on the part of ECM polymorphisms is evident in various types of disease, particularly those in the core-matrisome genes. Our results confirm previous links to connective-tissue disorders but also unearth new and underexplored relationships with neurological, psychiatric, and age-related disease states. Through our analysis of the drug indications for gene-disease relationships, we identify numerous targets that may be repurposed for age-related pathologies. The identification of ECM polymorphisms and their contributions to disease will play an integral role in future therapeutic developments, drug repurposing, precision medicine, and personalized care.
Collapse
Affiliation(s)
- Cyril Statzer
- Department of Health Sciences and Technology, Institute of Translational Medicine, Eidgenössische Technische Hochschule Zürich, Schwerzenbach, CH-8603 Zurich, Switzerland
| | - Karan Luthria
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Arastu Sharma
- Department of Health Sciences and Technology, Institute of Translational Medicine, Eidgenössische Technische Hochschule Zürich, Schwerzenbach, CH-8603 Zurich, Switzerland
| | - Maricel G Kann
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Collin Y Ewald
- Department of Health Sciences and Technology, Institute of Translational Medicine, Eidgenössische Technische Hochschule Zürich, Schwerzenbach, CH-8603 Zurich, Switzerland
| |
Collapse
|
29
|
Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem. Neuroinformatics 2023; 21:89-100. [PMID: 36520344 PMCID: PMC9931855 DOI: 10.1007/s12021-022-09577-4] [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] [Accepted: 02/23/2022] [Indexed: 12/23/2022]
Abstract
We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management.
Collapse
|
30
|
Evaluation of Epigenetic Age Acceleration Scores and Their Associations with CVD-Related Phenotypes in a Population Cohort. BIOLOGY 2022; 12:biology12010068. [PMID: 36671760 PMCID: PMC9855929 DOI: 10.3390/biology12010068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 01/04/2023]
Abstract
We evaluated associations between nine epigenetic age acceleration (EAA) scores and 18 cardiometabolic phenotypes using an Eastern European ageing population cohort richly annotated for a diverse set of phenotypes (subsample, n = 306; aged 45-69 years). This was implemented by splitting the data into groups with positive and negative EAAs. We observed strong association between all EAA scores and sex, suggesting that any analysis of EAAs should be adjusted by sex. We found that some sex-adjusted EAA scores were significantly associated with several phenotypes such as blood levels of gamma-glutamyl transferase and low-density lipoprotein, smoking status, annual alcohol consumption, multiple carotid plaques, and incident coronary heart disease status (not necessarily the same phenotypes for different EAAs). We demonstrated that even after adjusting EAAs for sex, EAA-phenotype associations remain sex-specific, which should be taken into account in any downstream analysis involving EAAs. The obtained results suggest that in some EAA-phenotype associations, negative EAA scores (i.e., epigenetic age below chronological age) indicated more harmful phenotype values, which is counterintuitive. Among all considered epigenetic clocks, GrimAge was significantly associated with more phenotypes than any other EA scores in this Russian sample.
Collapse
|
31
|
TogoVar: A comprehensive Japanese genetic variation database. Hum Genome Var 2022; 9:44. [PMID: 36509753 PMCID: PMC9744889 DOI: 10.1038/s41439-022-00222-9] [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: 09/01/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 12/14/2022] Open
Abstract
TogoVar ( https://togovar.org ) is a database that integrates allele frequencies derived from Japanese populations and provides annotations for variant interpretation. First, a scheme to reanalyze individual-level genome sequence data deposited in the Japanese Genotype-phenotype Archive (JGA), a controlled-access database, was established to make allele frequencies publicly available. As more Japanese individual-level genome sequence data are deposited in JGA, the sample size employed in TogoVar is expected to increase, contributing to genetic study as reference data for Japanese populations. Second, public datasets of Japanese and non-Japanese populations were integrated into TogoVar to easily compare allele frequencies in Japanese and other populations. Each variant detected in Japanese populations was assigned a TogoVar ID as a permanent identifier. Third, these variants were annotated with molecular consequence, pathogenicity, and literature information for interpreting and prioritizing variants. Here, we introduce the newly developed TogoVar database that compares allele frequencies among Japanese and non-Japanese populations and describes the integrated annotations.
Collapse
|
32
|
Tanizawa Y, Fujisawa T, Kodama Y, Kosuge T, Mashima J, Tanjo T, Nakamura Y. DNA Data Bank of Japan (DDBJ) update report 2022. Nucleic Acids Res 2022; 51:D101-D105. [PMID: 36420889 PMCID: PMC9825463 DOI: 10.1093/nar/gkac1083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 10/24/2022] [Accepted: 11/22/2022] [Indexed: 11/26/2022] Open
Abstract
The Bioinformation and DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp) maintains database archives that cover a wide range of fields in life sciences. As a founding member of the International Nucleotide Sequence Database Collaboration (INSDC), our primary mission is to collect and distribute nucleotide sequence data, as well as their study and sample information, in collaboration with the National Center for Biotechnology Information in the United States and the European Bioinformatics Institute. In addition to INSDC resources, the Center operates databases for functional genomics (GEA: Genomic Expression Archive), metabolomics (MetaboBank), and human genetic and phenotypic data (JGA: Japanese Genotype-Phenotype Archive). These databases are built on the supercomputer of the National Institute of Genetics, whose remaining computational capacity is actively utilized by domestic researchers for large-scale biological data analyses. Here, we report our recent updates and the activities of our services.
Collapse
Affiliation(s)
- Yasuhiro Tanizawa
- To whom correspondence should be addressed. Tel: +55 981 6859; Fax: +55 981 6889;
| | - Takatomo Fujisawa
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Yuichi Kodama
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Takehide Kosuge
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Jun Mashima
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Tomoya Tanjo
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| | - Yasukazu Nakamura
- Bioinformation and DDBJ Center, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
| |
Collapse
|
33
|
van der Vlugt M, Carvalho B, Fliers J, Montazeri N, Rausch C, Grobbee EJ, Engeland MV, Spaander MCW, Meijer GA, Dekker E. Missed colorectal cancers in a fecal immunochemical test-based screening program: Molecular profiling of interval carcinomas. World J Gastrointest Oncol 2022; 14:2195-2207. [PMID: 36438700 PMCID: PMC9694267 DOI: 10.4251/wjgo.v14.i11.2195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/06/2022] [Accepted: 10/03/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND For optimizing fecal immunochemical test (FIT)-based screening programs, reducing the rate of missed colorectal cancers (CRCs) by FIT (FIT-interval CRCs) is an important aspect. Knowledge of the molecular make-up of these missed lesions could facilitate more accurate detection of all (precursor) lesions.
AIM To compare the molecular make-up of FIT-interval CRCs to lesions that are detected by FIT [screen-detected CRCs (SD-CRCs)].
METHODS FIT-interval CRCs observed in a Dutch pilot-program of FIT-based screening were compared to a control group of SD-CRCs in a 1:2 ratio, resulting in 27 FIT-interval CRC and 54 SD-CRCs. Molecular analyses included microsatellite instability (MSI), CpG island methylator phenotype (CIMP), DNA sequence mutations and copy number alterations (CNAs).
RESULTS Although no significant differences were reached, FIT-interval CRCs were more often CIMP positive and MSI positive (33% CIMP in FIT-interval CRCs vs 21% in SD-CRCs (P = 0.274); 19% MSI in FIT-interval CRCs vs 12% in SD-CRCs (P = 0.469)), and showed more often serrated pathway associated features such as BRAF (30% vs 12%, P = 0.090) and PTEN (15% vs 2.4%, P = 0.063) mutations. APC mutations, a classic feature of the adenoma-carcinoma-sequence, were more abundant in SD-CRCs (68% vs 40% in FIT-interval CRCs P = 0.035). Regarding CNAs differences between the two groups; FIT-interval CRCs less often showed gains at the regions 8p11.22-q24.3 (P = 0.009), and more often gains at 20p13-p12.1 (P = 0.039).
CONCLUSION Serrated pathway associated molecular features seem to be more common in FIT-interval CRCs, while classic adenoma carcinoma pathway associated molecular features seem to be more common in SD-CRCs. This indicates that proximal serrated lesions may be overrepresented among FIT-interval CRCs.
Collapse
Affiliation(s)
- Manon van der Vlugt
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam 1105 AZ, Netherlands
| | - Beatriz Carvalho
- Department of Pathology, Netherlands Cancer Institute, Amsterdam 1066 CX, Netherlands
| | - Joelle Fliers
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam 1105 AZ, Netherlands
| | - Nahid Montazeri
- Biostatistics Unit, Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam 1105 AZ, Netherlands
| | - Christian Rausch
- Department of Pathology, Netherlands Cancer Institute, Amsterdam 1066 CX, Netherlands
| | - Esmée J Grobbee
- Department of Gastroenterology and Hepatology, Erasmus MC University Medical Center, Rotterdam 3015 CN, Netherlands
| | - Manon van Engeland
- Department of Pathology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
| | - Manon C W Spaander
- Department of Gastroenterology and Hepatology, Erasmus MC University Medical Center, Rotterdam 3015 CN, Netherlands
| | - Gerrit A Meijer
- Department of Pathology, Netherlands Cancer Institute, Amsterdam 1066 CX, Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam 1105 AZ, Netherlands
| |
Collapse
|
34
|
Wilson SL, Shen SY, Harmon L, Burgener JM, Triche T, Bratman SV, De Carvalho DD, Hoffman MM. Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls. CELL REPORTS METHODS 2022; 2:100294. [PMID: 36160046 PMCID: PMC9499995 DOI: 10.1016/j.crmeth.2022.100294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 05/17/2022] [Accepted: 08/19/2022] [Indexed: 06/16/2023]
Abstract
Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). We developed a set of synthetic spike-in DNA controls for cfMeDIP-seq to provide a simple and inexpensive reference for quantitative normalization. We designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G + C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). Using 0.01 ng of spike-in controls enables training a generalized linear model that absolutely quantifies methylated cfDNA in MeDIP-seq experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases.
Collapse
Affiliation(s)
- Samantha L. Wilson
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Shu Yi Shen
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | | | - Justin M. Burgener
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Tim Triche
- Van Andel Institute, Grand Rapids, MI, USA
| | - Scott V. Bratman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Daniel D. De Carvalho
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Michael M. Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| |
Collapse
|
35
|
Investigation of Anti-Liver Cancer Activity of the Herbal Drug FDY003 Using Network Pharmacology. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:5765233. [PMID: 36118098 PMCID: PMC9481369 DOI: 10.1155/2022/5765233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 08/10/2022] [Indexed: 11/18/2022]
Abstract
Globally, liver cancer (LC) is the sixth-most frequently occurring and the second-most fatal malignancy, responsible for 0.83 million deaths annually. Although the application of herbal drugs in cancer therapies has increased, their anti-LC activity and relevant mechanisms have not been fully studied from a systems perspective. To address these issues, we conducted a system-perspective network pharmacological investigation into the activity and mechanisms underlying the action of the herbal drug. FDY003 reduced the viability of human LC treatment. FDY003 reduced the viability of human LC cells and elevated their chemosensitivity. There were a total of 16 potential bioactive chemical components in FDY003 and they had 91 corresponding targets responsible for the pathological processes in LC. These FDY003 targets were functionally involved in regulating the survival, proliferation, apoptosis, and cell cycle of LC cells. Additionally, we found that FDY003 may target key signaling cascades connected to diverse LC pathological mechanisms, namely, PI3K-Akt, focal adhesion, IL-17, FoxO, MAPK, and TNF pathways. Overall, this study contributed to integrative mechanistic insights into the anti-LC potential of FDY003.
Collapse
|
36
|
Poolman TM, Townsend‐Nicholson A, Cain A. Teaching genomics to life science undergraduates using cloud computing platforms with open datasets. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2022; 50:446-449. [PMID: 35972192 PMCID: PMC9804627 DOI: 10.1002/bmb.21646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/08/2022] [Accepted: 06/30/2022] [Indexed: 06/15/2023]
Abstract
The final year of a biochemistry degree is usually a time to experience research. However, laboratory-based research projects were not possible during COVID-19. Instead, we used open datasets to provide computational research projects in metagenomics to biochemistry undergraduates (80 students with limited computing experience). We aimed to give the students a chance to explore any dataset, rather than use a small number of artificial datasets (~60 published datasets were used). To achieve this, we utilized Google Colaboratory (Colab), a virtual computing environment. Colab was used as a framework to retrieve raw sequencing data (analyzed with QIIME2) and generate visualizations. Setting up the environment requires no prior experience; all students have the same drive structure and notebooks can be shared (for synchronous sessions). We also used the platform to combine multiple datasets, perform a meta-analysis, and allowed the students to analyze large datasets with 1000s of subjects and factors. Projects that required increased computational resources were integrated with Google Cloud Compute. In future, all research projects can include some aspects of reanalyzing public data, providing students with data science experience. Colab is also an excellent environment in which to develop data skills in multiple languages (e.g., Perl, Python, Julia).
Collapse
Affiliation(s)
- Toryn M. Poolman
- Structural & Molecular Biology Faculty of Life SciencesUCLLondonUK
| | | | - Amanda Cain
- Structural & Molecular Biology Faculty of Life SciencesUCLLondonUK
| |
Collapse
|
37
|
Wan JCM, Stephens D, Luo L, White JR, Stewart CM, Rousseau B, Tsui DWY, Diaz LA. Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA. Nat Commun 2022; 13:4953. [PMID: 35999207 PMCID: PMC9399180 DOI: 10.1038/s41467-022-32598-1] [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: 09/17/2021] [Accepted: 08/08/2022] [Indexed: 11/11/2022] Open
Abstract
Mutational signatures accumulate in somatic cells as an admixture of endogenous and exogenous processes that occur during an individual’s lifetime. Since dividing cells release cell-free DNA (cfDNA) fragments into the circulation, we hypothesize that plasma cfDNA might reflect mutational signatures. Point mutations in plasma whole genome sequencing (WGS) are challenging to identify through conventional mutation calling due to low sequencing coverage and low mutant allele fractions. In this proof of concept study of plasma WGS at 0.3–1.5x coverage from 215 patients and 227 healthy individuals, we show that both pathological and physiological mutational signatures may be identified in plasma. By applying machine learning to mutation profiles, patients with stage I-IV cancer can be distinguished from healthy individuals with an Area Under the Curve of 0.96. Interrogating mutational processes in plasma may enable earlier cancer detection, and might enable the assessment of cancer risk and etiology. Detection of mutational signatures in cell-free DNA (cfDNA) is challenging due to low sequence coverage and low mutant allele fractions. Here, the authors identify mutational signatures in plasma whole genome sequencing of cancer patients and use machine learning to distinguish them from healthy individuals.
Collapse
Affiliation(s)
- Jonathan C M Wan
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Dennis Stephens
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Lingqi Luo
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - James R White
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Resphera Biosciences, Baltimore, MD, 21231, USA
| | - Caitlin M Stewart
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Meyer Cancer Center, Weill Cornell Medical College, New York, NY, 10065, USA.,New York Genome Center, New York, NY, 10013, USA
| | - Benoît Rousseau
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Dana W Y Tsui
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,PetDx Inc., San Diego, CA, USA
| | - Luis A Diaz
- Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
| |
Collapse
|
38
|
Lu Z, Ni K, Wang Y, Zhou Y, Li Y, Yan J, Song Q, Liu M, Xu Y, Yu Z, Guo T, Ma L. An in-library ligation strategy and its application in CRISPR/Cas9 screening of high-order gRNA combinations. Nucleic Acids Res 2022; 50:6575-6586. [PMID: 35670669 PMCID: PMC9226518 DOI: 10.1093/nar/gkac458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 11/14/2022] Open
Abstract
Simultaneous targeting multiple genes is a big advantage of CRISPR (clustered regularly interspaced short palindromic repeats) genome editing but challenging to achieve in CRISPR screening. The crosstalk among genes or gene products is a common and fundamental mechanism to ensure cellular stability and functional diversity. However, the screening approach to map high-order gene combinations to the interesting phenotype is still lacking. Here, we developed a universal in-library ligation strategy and applied it to generate multiplexed CRISPR library, which could perturb four pre-designed targets in a cell. We conducted in vivo CRISPR screening for potential guide RNA (gRNA) combinations inducing anti-tumor immune responses. Simultaneously disturbing a combination of three checkpoints in CD8+ T cells was demonstrated to be more effective than disturbing Pdcd1 only for T cell activation in the tumor environment. This study developed a novel in-library ligation strategy to facilitate the multiplexed CRISPR screening, which could extend our ability to explore the combinatorial outcomes from coordinated gene behaviors.
Collapse
Affiliation(s)
- Zhike Lu
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Ke Ni
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Yingying Wang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Yangfan Zhou
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Yini Li
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Jianfeng Yan
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Qingkai Song
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Min Liu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Yujun Xu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Zhenxing Yu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Lijia Ma
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| |
Collapse
|
39
|
Opportunities and challenges for the use of common controls in sequencing studies. Nat Rev Genet 2022; 23:665-679. [PMID: 35581355 DOI: 10.1038/s41576-022-00487-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2022] [Indexed: 01/02/2023]
Abstract
Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.
Collapse
|
40
|
de Krijger M, Carvalho B, Rausch C, Bolijn AS, Delis-van Diemen PM, Tijssen M, van Engeland M, Mostafavi N, Bogie RMM, Dekker E, Masclee AAM, Verheij J, Meijer GA, Ponsioen CY. Genetic Profiling of Colorectal Carcinomas of Patients with Primary Sclerosing Cholangitis and Inflammatory Bowel Disease. Inflamm Bowel Dis 2022; 28:1309-1320. [PMID: 35554535 PMCID: PMC9434447 DOI: 10.1093/ibd/izac087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Indexed: 12/09/2022]
Abstract
BACKGROUND Patients with primary sclerosing cholangitis (PSC) and inflammatory bowel disease (IBD) run a 10-fold increased risk of developing colorectal cancer (CRC) compared to patients with IBD only. The aim of this study was to perform an extensive screen of known carcinogenic genomic alterations in patients with PSC-IBD, and to investigate whether such changes occur already in nondysplastic mucosa. METHODS Archival cancer tissue and nondysplastic mucosa from resection specimens of 19 patients with PSC-IBD-CRC were characterized, determining DNA copy-number variations, microsatellite instability (MSI), mutations on 48 cancer genes, and CpG island methylator phenotype (CIMP). Genetic profiles were compared with 2 published cohorts of IBD-associated CRC (IBD-CRC; n = 11) and sporadic CRC (s-CRC; n = 100). RESULTS Patterns of chromosomal aberrations in PSC-IBD-CRC were similar to those observed in IBD-CRC and s-CRC, MSI occurred only once. Mutation frequencies were comparable between the groups, except for mutations in KRAS, which were less frequent in PSC-IBD-CRC (5%) versus IBD-CRC (38%) and s-CRC (31%; P = .034), and in APC, which were less frequent in PSC-IBD-CRC (5%) and IBD-CRC (0%) versus s-CRC (50%; P < .001). Cases of PSC-IBD-CRC were frequently CIMP positive (44%), at similar levels to cases of s-CRC (34%; P = .574) but less frequent than in cases with IBD-CRC (90%; P = .037). Similar copy number aberrations and mutations were present in matched cancers and adjacent mucosa in 5/15 and 7/11 patients, respectively. CONCLUSIONS The excess risk of CRC in patients with PSC-IBD was not explained by copy number aberrations, mutations, MSI, nor CIMP status, in cancer tissue, nor in adjacent mucosa. These findings set the stage for further exome-wide and epigenetic studies.
Collapse
Affiliation(s)
- Manon de Krijger
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands,Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Beatriz Carvalho
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Christian Rausch
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Anne S Bolijn
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | | | - Marianne Tijssen
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Manon van Engeland
- Department of Pathology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Nahid Mostafavi
- Biostatistics Unit of Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Roel M M Bogie
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Ad A M Masclee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Joanne Verheij
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Gerrit A Meijer
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Cyriel Y Ponsioen
- Address Correspondence to: Cyriel Y. Ponsioen, MD, PhD, Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands ()
| |
Collapse
|
41
|
Exploration of the System-Level Mechanisms of the Herbal Drug FDY003 for Pancreatic Cancer Treatment: A Network Pharmacological Investigation. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:7160209. [PMID: 35591866 PMCID: PMC9113891 DOI: 10.1155/2022/7160209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/12/2022] [Indexed: 11/18/2022]
Abstract
Pancreatic cancer (PC) is the most lethal cancer with the lowest survival rate globally. Although the prescription of herbal drugs against PC is gaining increasing attention, their polypharmacological therapeutic mechanisms are yet to be fully understood. Based on network pharmacology, we explored the anti-PC properties and system-level mechanisms of the herbal drug FDY003. FDY003 decreased the viability of human PC cells and strengthened their chemosensitivity. Network pharmacological analysis of FDY003 indicated the presence of 16 active phytochemical components and 123 PC-related pharmacological targets. Functional enrichment analysis revealed that the PC-related targets of FDY003 participate in the regulation of cell growth and proliferation, cell cycle process, cell survival, and cell death. In addition, FDY003 was shown to target diverse key pathways associated with PC pathophysiology, namely, the PIK3-Akt, MAPK, FoxO, focal adhesion, TNF, p53, HIF-1, and Ras pathways. Our network pharmacological findings advance the mechanistic understanding of the anti-PC properties of FDY003 from a system perspective.
Collapse
|
42
|
Fernández-Orth D, Rueda M, Singh B, Moldes M, Jene A, Ferri M, Vasallo C, Fromont LA, Navarro A, Rambla J. A quality control portal for sequencing data deposited at the European genome-phenome archive. Brief Bioinform 2022; 23:6570012. [PMID: 35438138 PMCID: PMC9116225 DOI: 10.1093/bib/bbac136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/01/2022] [Accepted: 03/23/2022] [Indexed: 11/15/2022] Open
Abstract
Since its launch in 2008, the European Genome-Phenome Archive (EGA) has been leading the archiving and distribution of human identifiable genomic data. In this regard, one of the community concerns is the potential usability of the stored data, as of now, data submitters are not mandated to perform any quality control (QC) before uploading their data and associated metadata information. Here, we present a new File QC Portal developed at EGA, along with QC reports performed and created for 1 694 442 files [Fastq, sequence alignment map (SAM)/binary alignment map (BAM)/CRAM and variant call format (VCF)] submitted at EGA. QC reports allow anonymous EGA users to view summary-level information regarding the files within a specific dataset, such as quality of reads, alignment quality, number and type of variants and other features. Researchers benefit from being able to assess the quality of data prior to the data access decision and thereby, increasing the reusability of data (https://ega-archive.org/blog/data-upcycling-powered-by-ega/).
Collapse
Affiliation(s)
- Dietmar Fernández-Orth
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Manuel Rueda
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Babita Singh
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Mauricio Moldes
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Aina Jene
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Marta Ferri
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Claudia Vasallo
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Lauren A Fromont
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Arcadi Navarro
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| | - Jordi Rambla
- European Genome-phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology Dr. Aiguader 88, Barcelona, 08003 Spain
| |
Collapse
|
43
|
van der Velde KJ, Singh G, Kaliyaperumal R, Liao X, de Ridder S, Rebers S, Kerstens HHD, de Andrade F, van Reeuwijk J, De Gruyter FE, Hiltemann S, Ligtvoet M, Weiss MM, van Deutekom HWM, Jansen AML, Stubbs AP, Vissers LELM, Laros JFJ, van Enckevort E, Stemkens D, 't Hoen PAC, Beliën JAM, van Gijn ME, Swertz MA. FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research. Sci Data 2022; 9:169. [PMID: 35418585 PMCID: PMC9008059 DOI: 10.1038/s41597-022-01265-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 03/25/2022] [Indexed: 11/08/2022] Open
Abstract
The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .
Collapse
Affiliation(s)
- K Joeri van der Velde
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Gurnoor Singh
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Rajaram Kaliyaperumal
- Leiden University Medical Center, Department of Human Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - XiaoFeng Liao
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Sander de Ridder
- Amsterdam University Medical Center, University of Amsterdam, Department of Pathology, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Susanne Rebers
- The Netherlands Cancer Institute, Division of Molecular Pathology, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Hindrik H D Kerstens
- Prinses Máxima Center for Pediatric Oncology, Kemmeren group, Heidelberglaan 25, 3584 CS, Utrecht, The Netherlands
| | - Fernanda de Andrade
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Jeroen van Reeuwijk
- Radboud University Medical Center, Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Fini E De Gruyter
- University Medical Center Utrecht, Department of Genetics, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Saskia Hiltemann
- Erasmus Medical Center, Department of Pathology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Maarten Ligtvoet
- Nictiz - Dutch competence centre for electronic exchange of health and care information, Oude Middenweg 55, 2491 AC, The Hague, The Netherlands
| | - Marjan M Weiss
- Radboud University Medical Center, Department of Human Genetics, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Hanneke W M van Deutekom
- University Medical Center Utrecht, Department of Genetics, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Anne M L Jansen
- University Medical Center Utrecht, Department of Pathology, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Andrew P Stubbs
- Erasmus Medical Center, Department of Pathology, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Lisenka E L M Vissers
- Radboud University Medical Center, Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Jeroen F J Laros
- Leiden University Medical Center, Department of Human Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Leiden University Medical Center, Department of Clinical Genetics, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
- Rijksinstituut voor Volksgezondheid en Milieu, Antonie van Leeuwenhoeklaan 9, 3721 MA, Bilthoven, The Netherlands
| | - Esther van Enckevort
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Daphne Stemkens
- VSOP - Patient Alliance for Rare and Genetic Diseases The Netherlands, Koninginnelaan 23, 3762 DA, Soest, The Netherlands
| | - Peter A C 't Hoen
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Center for Molecular and Biomolecular Informatics, Geert Grooteplein 28, 6525 GA, Nijmegen, The Netherlands
| | - Jeroen A M Beliën
- Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Department of Pathology, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Mariëlle E van Gijn
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands
| | - Morris A Swertz
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
- University of Groningen and University Medical Center Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.
| |
Collapse
|
44
|
Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo, Brazil. Nat Commun 2022; 13:1004. [PMID: 35246524 PMCID: PMC8897431 DOI: 10.1038/s41467-022-28648-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 01/21/2022] [Indexed: 02/07/2023] Open
Abstract
As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~2 million are absent from large public databases. WGS enables identification of ~2,000 previously undescribed mobile element insertions without previous description, nearly 5 Mb of genomic segments absent from the human genome reference, and over 140 alleles from HLA genes absent from public resources. We reclassify and curate pathogenicity assertions for nearly four hundred variants in genes associated with dominantly-inherited Mendelian disorders and calculate the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observe that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS. Whole genome sequencing (WGS) data on non-European and admixed individuals remains scarce. Here, the authors analyse WGS data from 1,171 admixed elderly Brazilians from a census cohort, characterising population-specific genetic variation and exploring the clinical utility of this expanded dataset.
Collapse
|
45
|
Farooq A, Trøen G, Delabie J, Wang J. Integrating whole genome sequencing, methylation, gene expression, topological associated domain information in regulatory mutation prediction: a study of follicular lymphoma. Comput Struct Biotechnol J 2022; 20:1726-1742. [PMID: 35495111 PMCID: PMC9024376 DOI: 10.1016/j.csbj.2022.03.023] [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: 01/07/2022] [Revised: 03/22/2022] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
A major challenge in human genetics is of the analysis of the interplay between genetic and epigenetic factors in a multifactorial disease like cancer. Here, a novel methodology is proposed to investigate genome-wide regulatory mechanisms in cancer, as studied with the example of follicular Lymphoma (FL). In a first phase, a new machine-learning method is designed to identify Differentially Methylated Regions (DMRs) by computing six attributes. In a second phase, an integrative data analysis method is developed to study regulatory mutations in FL, by considering differential methylation information together with DNA sequence variation, differential gene expression, 3D organization of genome (e.g., topologically associated domains), and enriched biological pathways. Resulting mutation block-gene pairs are further ranked to find out the significant ones. By this approach, BCL2 and BCL6 were identified as top-ranking FL-related genes with several mutation blocks and DMRs acting on their regulatory regions. Two additional genes, CDCA4 and CTSO, were also found in top rank with significant DNA sequence variation and differential methylation in neighboring areas, pointing towards their potential use as biomarkers for FL. This work combines both genomic and epigenomic information to investigate genome-wide gene regulatory mechanisms in cancer and contribute to devising novel treatment strategies.
Collapse
|
46
|
Yuan X, Wang J, Dai B, Sun Y, Zhang K, Chen F, Peng Q, Huang Y, Zhang X, Chen J, Xu X, Chuan J, Mu W, Li H, Fang P, Gong Q, Zhang P. Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases. Brief Bioinform 2022; 23:6521702. [PMID: 35134823 PMCID: PMC8921623 DOI: 10.1093/bib/bbac019] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/10/2022] [Accepted: 01/13/2022] [Indexed: 12/31/2022] Open
Abstract
It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.
Collapse
Affiliation(s)
- Xiao Yuan
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China.,Genetalks Biotech. Co., Ltd., Changsha, China
| | - Jing Wang
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Bing Dai
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Yanfang Sun
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Keke Zhang
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Fangfang Chen
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Qian Peng
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Yixuan Huang
- Beijing Geneworks Technology Co., Ltd., Beijing, China
| | - Xinlei Zhang
- Reproductive & Genetics Hospital of Citic & Xiangya, Changsha, China
| | - Junru Chen
- Genetalks Biotech. Co., Ltd., Changsha, China
| | - Xilin Xu
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Jun Chuan
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Wenbo Mu
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Huiyuan Li
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Ping Fang
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Qiang Gong
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Peng Zhang
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| |
Collapse
|
47
|
Cervantes-Gracia K, Chahwan R, Husi H. Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach. Front Genet 2022; 13:828786. [PMID: 35186042 PMCID: PMC8855827 DOI: 10.3389/fgene.2022.828786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/12/2022] [Indexed: 12/24/2022] Open
Abstract
The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.
Collapse
Affiliation(s)
| | - Richard Chahwan
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- *Correspondence: Richard Chahwan, ; Holger Husi,
| | - Holger Husi
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
- Division of Biomedical Sciences, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom
- *Correspondence: Richard Chahwan, ; Holger Husi,
| |
Collapse
|
48
|
Luque J, Mendes I, Gómez B, Morte B, Heredia ML, Herreras E, Corrochano V, Bueren J, Gallano P, Artuch R, Fillat C, Pérez‐Jurado LA, Montoliu L, Carracedo Á, Millán JM, Webb SM, Palau F, Lapunzina P, Aguado C, Aguado C, Albiñana V, Alías L, Almoguera B, Alonso J, Alonso‐Ferreira V, Alvarez‐Mora MI, Alvarez‐Mora MI, Antiñolo G, Arbones ML, Arenas J, Arjona E, Armangue T, Armstrong J, Arnedo M, Artuch R, Masó AA, Avila‐Fernandez A, Ayuso C, Badell I, Badenas C, Baeza ML, Baiget M, Balcells S, Ballesta‐Martínez MJ, Barahona M, Barros F, Bartoccioni PC, Bayona‐Bafaluy MP, Sanz SB, Bernabéu C, Bernal S, Blanco‐Kelly F, Blázquez A, Bodoy S, Bogliolo M, Borralleras C, Borrego S, Botella LM, Pieri FB, Bovolenta P, Bravo‐Gil N, Brea A, Bueno‐Lozano G, Bueren J, Bustamante A, Caballero T, Camacho‐Macorra C, Cámara Y, Camats‐Tarruella N, Barrio ÁC, Campuzano V, Cantarero L, Cantó J, Caparrós‐Martín JA, Cardellach F, Carmona R, Carracedo Á, Carretero M, Casado M, Casado JA, Casasnovas C, Cascón A, Casino P, Castaño L, Castilla‐Vallmanya L, Catala A, Cayuela ML, Cediel R, Cervera J, Codina‐Solà M, Contreras J, Cormand B, Corominas R, Corral J, Corrochano V, Cortés‐Rodríguez A, Corton M, Costa‐Roger M, Cozar M, Crespo I, Crispi F, Cruz R, Cuezva JM, Cuscó I, Dalmau J, Cima S, Luna S, De Luna N, Oyarzabal Sanz A, Campo M, Castillo I, Molina LDP, Pozo ÁD, Río M, Delmiro A, Desviat LR, Dierssen M, Domínguez‐González C, Domínguez‐Ruiz M, Dopazo J, Errasti E, Escámez MJ, Estañ MC, Esteban J, Estévez R, Ezquieta B, Fernández L, Fernández A, Fernández‐Cancio M, Fernàndez‐Castillo N, Jose PF, Fillat C, Fons C, Fort J, Fourcade S, Fraga MF, Gallano P, Gallardo E, García M, García‐Arumí E, García‐Bravo M, García‐Cazorla A, García‐Consuegra I, Garcia‐Garcia FJ, García‐García G, García‐Giménez JL, Garcia‐Gimeno MA, García‐Miñaur S, García‐Redondo A, García‐Silva MT, García‐Villoria J, Santiago FG, Garrabou G, Garrido G, Garrido‐Pérez N, Gaztambide S, Gil‐Campos M, Giroud‐Gerbetant J, Glover G, Gómez B, Gómez‐Puertas P, Gonzalez‐Cabo P, Gonzalez‐Casacuberta I, Pozo MG, González‐Quereda L, González‐Quintana A, Gort L, Gougeard N, Gratacos E, Grau JM, Grinberg D, Güenechea G, Guerrero R, Guillén‐Navarro E, Guitart‐Mampel M, Gutiérrez‐Arumí A, Heath K, Heredia M, Hernández‐Chico C, Herreras E, Hoenicka J, Homs A, Jimenez‐Estrada JA, Jimenez‐Mallebrera C, Jou C, Juarez‐Flores DL, Lapunzina P, Larcher F, Lasa A, Lassaletta L, Latorre‐Pellicer A, Linares D, Llacer JL, Llames S, Lopez‐Gallardo E, López‐Laso E, López‐Lera A, Lopez‐Lopez D, López‐Sánchez M, Heredia ML, Granados EL, Lorda‐Sanchez I, Lozano ML, Luque J, Madrigal I, García CM, Mansilla E, Marco‐Marín C, Marfany G, Marina A, Martí R, Martí S, Martin Y, Martín MA, Martín‐Hernandez E, Martin‐Merida I, Martínez R, Martínez‐Azorín F, Martinez‐Delgado B, Martínez‐Gil N, Martínez‐Glez VM, Martínez‐Momblán MA, Martínez‐Romero MC, Fernández PM, Santamaría LM, Martorell L, Meade P, Meana Á, Medina MÁ, Mendes I, Méndez‐Vidal C, Millán JM, Minguez P, Minguillón J, Mirra S, Molla B, Moltó E, Montero R, Montoliu L, Montoya J, Morán M, Moren C, Moreno M, Moreno JC, Moreno‐Galdó A, Moreno‐Pelayo MÁ, Mori MA, Morin M, Morte B, Mulero V, Muñoz‐Pujol G, Murillas R, Murillo‐Cuesta S, Nascimento A, Navarro S, Navas P, Nevado J, Nicolas A, Nieto MÁ, O’Callaghan M, Olavarrieta L, Ormazabal A, Ortiz‐Romero P, Osorio A, Páez D, Palacín M, Palacios‐Verdú MG, Palau F, Palencia‐Campos A, Pallardó FV, Palomares M, Peña‐Chilet M, Pérez B, Perez‐Florido J, Pérez‐García D, Perez‐Jimenez E, Pérez‐Jurado LA, Perkins JR, Perona R, Pie J, Pinós T, Pinto S, Potrony M, Puig S, Puig‐Butille JA, Puisac B, Pujol R, Pujol A, Quintana Ó, Rabionet R, Ramos FJ, Ranea JAG, Reina‐Castillón J, Resmini E, Ribes A, Rica I, Richard E, Riera P, Río P, Riveiro‐Alvarez R, Rivera J, Rivera‐Barahona A, Robledo M, Rodriguez‐Aguilera JC, Rosa LR, Rodríguez‐Palmero A, Rodriguez‐Pombo P, Rodriguez‐Revenga L, Rodríguez‐Santiago B, Rodríguez‐Sureda V, Alba MR, Cordoba SR, Romá‐Mateo C, Rubio V, Ruiz Á, Ruiz M, Ruiz‐Arenas C, Ruiz‐Perez VL, Ruiz‐Pesini E, Ruiz‐Ponte C, Rullo J, Sabater L, Salazar J, Salido E, Sanchez‐Jimeno C, Cuesta AMS, Soler MJS, Santacatterina F, Santamarina M, Santos A, Santos‐Ocaña C, Simarro FS, Sanz P, Sastre L, Schlüter A, Segovia JC, Segura‐Puimedon M, Seoane P, Serra‐Juhe C, Serrano M, Serratosa JM, Sevilla T, Surrallés J, Tahsin‐Swafiri S, Tell‐Martí G, Tenorio‐Castaño JA, Tizzano E, Tobias E, Tort F, Trujillano L, Trujillo‐Tiebas MJ, Ugalde C, Ugarteburu O, Urreizti R, Urrutia I, Valencia M, Vallcorba P, Vallespín E, Varela‐Nieto I, Vega A, Vélez‐Santamaria V, Vílchez JJ, Villa O, Villamar M, Webb SM, Zubeldia JM, Zurita O. CIBERER: Spanish National Network for Research on Rare Diseases: a highly productive collaborative initiative. Clin Genet 2022; 101:481-493. [PMID: 35060122 PMCID: PMC9305285 DOI: 10.1111/cge.14113] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 11/30/2022]
Abstract
CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low‐prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15‐year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research.
Collapse
Affiliation(s)
- Juan Luque
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
| | - Ingrid Mendes
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
| | - Beatriz Gómez
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
| | - Beatriz Morte
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
| | - Miguel López Heredia
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
| | - Enrique Herreras
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
| | - Virginia Corrochano
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
| | - Juan Bueren
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Hematopoietic Innovative Therapies Division, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Instituto de Investigaciones Sanitarias Fundación Jiménez Díaz (IIS‐FJD), Madrid Spain
| | - Pía Gallano
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Genetics Department, Hospital de la Santa Creu i Sant Pau Barcelona Spain
- Institut de Recerca Hospital de la Santa Creu i Sant Pau (IIB Sant Pau), Barcelona Spain
- Universitat Autònoma de Barcelona Barcelona Spain
| | - Rafael Artuch
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Clinical Biochemistry Department, Institut de Recerca Sant Joan de Déu Barcelona Spain
| | - Cristina Fillat
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona Spain
- Universitat de Barcelona Barcelona Spain
| | - Luis A. Pérez‐Jurado
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Department of Experimental and Health Sciences Universitat Pompeu Fabra (UPF), Barcelona Spain
- Genetics Service, Hospital del Mar Barcelona Spain
- Hospital del Mar Research Institute (IMIM), Barcelona Spain
| | - Lluis Montoliu
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Department of Molecular and Cellular Biology, National Centre for Biotechnology (CNB‐CSIC), Madrid Spain
| | - Ángel Carracedo
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Grupo de Medicina Xenómica, Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela Santiago de Compostela Spain
- Fundación Pública Galega de Medicina Xenómica (SERGAS), IDIS Santiago de Compostela Spain
| | - José M. Millán
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Unidad de Genética, Hospital Universitario y Politécnico La Fe Valencia Spain
- Biomedicina Molecular Celular y Genómica, Instituto Investigación Sanitaria La Fe Valencia Spain
| | - Susan M. Webb
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Hospital S Pau, Dept Medicine/Endocrinology, IIB‐Sant Pau, Research Center for Pituitary Diseases Barcelona Spain
- Departamento de Medicina Universitat Autònoma de Barcelona Barcelona Spain
| | - Francesc Palau
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- Department of Genetic and Molecular Medicine, Hospital Sant Joan de Deu Barcelona Spain
- Laboratory of Neurogenetics and Molecular Medicine ‐ IPER, Institut de Recerca Sant Joan de Déu Barcelona Spain
- Institute of Medicine & Dermatology, Hospital Clínic Barcelona Spain
- Division of Pediatrics University of Barcelona School of Medicine Barcelona Spain
| | - Pablo Lapunzina
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III Madrid Spain
- INGEMM‐Instituto de Genética Médica y Molecular, Hospital Universitario La Paz Madrid Spain
- Instituto de Investigación Sanitaria Hospital La Paz (IdiPAZ), Madrid Spain
- ERN‐ITHACA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
49
|
Genome and transcriptome profiling of spontaneous preterm birth phenotypes. Sci Rep 2022; 12:1003. [PMID: 35046466 PMCID: PMC8770724 DOI: 10.1038/s41598-022-04881-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 12/23/2021] [Indexed: 12/27/2022] Open
Abstract
Preterm birth (PTB) occurs before 37 weeks of gestation. Risk factors include genetics and infection/inflammation. Different mechanisms have been reported for spontaneous preterm birth (SPTB) and preterm birth following preterm premature rupture of membranes (PPROM). This study aimed to identify early pregnancy biomarkers of SPTB and PPROM from the maternal genome and transcriptome. Pregnant women were recruited at the Liverpool Women’s Hospital. Pregnancy outcomes were categorised as SPTB, PPROM (≤ 34 weeks gestation, n = 53), high-risk term (HTERM, ≥ 37 weeks, n = 126) or low-risk (no history of SPTB/PPROM) term (LTERM, ≥ 39 weeks, n = 188). Blood samples were collected at 16 and 20 weeks gestation from which, genome (UK Biobank Axiom array) and transcriptome (Clariom D Human assay) data were acquired. PLINK and R were used to perform genetic association and differential expression analyses and expression quantitative trait loci (eQTL) mapping. Several significant molecular signatures were identified across the analyses in preterm cases. Genome-wide significant SNP rs14675645 (ASTN1) was associated with SPTB whereas microRNA-142 transcript and PPARG1-FOXP3 gene set were associated with PPROM at week 20 of gestation and is related to inflammation and immune response. This study has determined genomic and transcriptomic candidate biomarkers of SPTB and PPROM that require validation in diverse populations.
Collapse
|
50
|
Kahl I, Mense J, Finke C, Boller AL, Lorber C, Győrffy B, Greve B, Götte M, Espinoza-Sánchez NA. The cell cycle-related genes RHAMM, AURKA, TPX2, PLK1, and PLK4 are associated with the poor prognosis of breast cancer patients. J Cell Biochem 2022; 123:581-600. [PMID: 35014077 DOI: 10.1002/jcb.30205] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 01/02/2023]
Abstract
Breast cancer is the third most common type of cancer diagnosed. Cell cycle is a complex but highly organized and controlled process, in which normal cells sense mitogenic growth signals that instruct them to enter and progress through their cell cycle. This process culminates in cell division generating two daughter cells with identical amounts of genetic material. Uncontrolled proliferation is one of the hallmarks of cancer. In this study, we analyzed the expression of the cell cycle-related genes receptor for hyaluronan (HA)-mediated motility (RHAMM), AURKA, TPX2, PLK1, and PLK4 and correlated them with the prognosis in a collective of 3952 breast cancer patients. A high messenger RNA expression of all studied genes correlated with a poor prognosis. Stratifying the patients according to the expression of hormonal receptors, we found that in patients with estrogen and progesterone receptor-positive and human epithelial growth factor receptor 2-negative tumors, and Luminal A and Luminal B tumors, the expression of the five analyzed genes correlates with worse survival. qPCR analysis of a panel of breast cancer cell lines representative of major molecular subtypes indicated a predominant expression in the luminal subtype. In vitro experiments showed that radiation influences the expression of the five analyzed genes both in luminal and triple-negative model cell lines. Functional analysis of MDA-MB-231 cells showed that small interfering RNA knockdown of PLK4 and TPX2 and pharmacological inhibition of PLK1 had an impact on the cell cycle and colony formation. Looking for a potential upstream regulation by microRNAs, we observed a differential expression of RHAMM, AURKA, TPX2, PLK1, and PLK4 after transfecting the MDA-MB-231 cells with three different microRNAs. Survival analysis of miR-34c-5p, miR-375, and miR-142-3p showed a different impact on the prognosis of breast cancer patients. Our study suggests that RHAMM, AURKA, TPX2, PLK1, and PLK4 can be used as potential targets for treatment or as a prognostic value in breast cancer patients.
Collapse
Affiliation(s)
- Iris Kahl
- Department of Gynecology and Obstetrics, Münster University Hospital, Münster, Germany
| | - Julian Mense
- Department of Gynecology and Obstetrics, Münster University Hospital, Münster, Germany
| | - Christopher Finke
- Department of Gynecology and Obstetrics, Münster University Hospital, Münster, Germany
| | - Anna-Lena Boller
- Department of Gynecology and Obstetrics, Münster University Hospital, Münster, Germany
| | - Clara Lorber
- Department of Gynecology and Obstetrics, Münster University Hospital, Münster, Germany
| | - Balázs Győrffy
- Department of Bioinformatics, Semmelweis University, Budapest, Hungary.,Cancer Biomarker Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - Burkhard Greve
- Department of Radiotherapy-Radiooncology, Münster University Hospital, Münster, Germany
| | - Martin Götte
- Department of Gynecology and Obstetrics, Münster University Hospital, Münster, Germany
| | - Nancy A Espinoza-Sánchez
- Department of Gynecology and Obstetrics, Münster University Hospital, Münster, Germany.,Department of Radiotherapy-Radiooncology, Münster University Hospital, Münster, Germany
| |
Collapse
|