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Clarke R, Jones BC, Sevigny CM, Hilakivi-Clarke LA, Sengupta S. Experimental models of endocrine responsive breast cancer: strengths, limitations, and use. CANCER DRUG RESISTANCE (ALHAMBRA, CALIF.) 2021; 4:762-783. [PMID: 34532657 PMCID: PMC8442978 DOI: 10.20517/cdr.2021.33] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Breast cancers characterized by expression of estrogen receptor-alpha (ER; ESR1) represent approximately 70% of all new cases and comprise the largest molecular subtype of this disease. Despite this high prevalence, the number of adequate experimental models of ER+ breast cancer is relatively limited. Nonetheless, these models have proved very useful in advancing understanding of how cells respond to and resist endocrine therapies, and how the ER acts as a transcription factor to regulate cell fate signaling. We discuss the primary experimental models of ER+ breast cancer including 2D and 3D cultures of established cell lines, cell line- and patient-derived xenografts, and chemically induced rodent models, with a consideration of their respective general strengths and limitations. What can and cannot be learned easily from these models is also discussed, and some observations on how these models may be used more effectively are provided. Overall, despite their limitations, the panel of models currently available has enabled major advances in the field, and these models remain central to the ability to study mechanisms of therapy action and resistance and for hypothesis testing that would otherwise be intractable or unethical in human subjects.
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Affiliation(s)
- Robert Clarke
- The Hormel Institute and Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Austin, MN 55912, USA
| | - Brandon C Jones
- Department of Oncology, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Catherine M Sevigny
- Department of Oncology, Georgetown University Medical Center, Washington, DC 20057, USA.,The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Leena A Hilakivi-Clarke
- The Hormel Institute and Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Austin, MN 55912, USA
| | - Surojeet Sengupta
- The Hormel Institute and Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Austin, MN 55912, USA
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2
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Xu Q, Chen S, Hu Y, Huang W. Landscape of Immune Microenvironment Under Immune Cell Infiltration Pattern in Breast Cancer. Front Immunol 2021; 12:711433. [PMID: 34512634 PMCID: PMC8429934 DOI: 10.3389/fimmu.2021.711433] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 08/06/2021] [Indexed: 12/25/2022] Open
Abstract
Background Increasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy. Methods Multiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the “maftools” R package. Results Three different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed. Conclusion This work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.
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Affiliation(s)
- Qianhui Xu
- Department of Nephrology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.,Department of Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shaohuai Chen
- Department of Nephrology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuanbo Hu
- Department of Nephrology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wen Huang
- Department of Nephrology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
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3
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Bhattacharya A, Hamilton AM, Troester MA, Love MI. DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing. Nucleic Acids Res 2021; 49:e48. [PMID: 33524140 PMCID: PMC8096278 DOI: 10.1093/nar/gkab031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/21/2020] [Accepted: 01/12/2021] [Indexed: 12/13/2022] Open
Abstract
Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA
| | - Alina M Hamilton
- Department of Pathology and Laboratory Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
| | - Melissa A Troester
- Department of Pathology and Laboratory Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
- Department of Epidemiology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
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4
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Understanding breast cancer heterogeneity through non-genetic heterogeneity. Breast Cancer 2021; 28:777-791. [PMID: 33723745 DOI: 10.1007/s12282-021-01237-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 03/04/2021] [Indexed: 01/01/2023]
Abstract
Intricacy in treatment and diagnosis of breast cancer has been an obstacle due to genotype and phenotype heterogeneity. Understanding of non-genetic heterogeneity mechanisms along with considering role of genetic heterogeneity may fill the gaps in landscape painting of heterogeneity. The main factors contribute to non-genetic heterogeneity including: transcriptional pulsing/bursting or discontinuous transcriptions, stochastic partitioning of components at cell division and various signal transduction from tumor ecosystem. Throughout this review, we desired to provide a conceptual framework focused on non-genetic heterogeneity, which has been intended to offer insight into prediction, diagnosis and treatment of breast cancer.
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5
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Bhattacharya A, García-Closas M, Olshan AF, Perou CM, Troester MA, Love MI. A framework for transcriptome-wide association studies in breast cancer in diverse study populations. Genome Biol 2020; 21:42. [PMID: 32079541 PMCID: PMC7033948 DOI: 10.1186/s13059-020-1942-6] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/21/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. RESULTS We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. CONCLUSIONS We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Andrew F. Olshan
- Department of Epidemiology, University of North Carolina-Chapel Hill, Chapel Hill, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina-Chapel Hill, Chapel Hill, USA
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina-Chapel Hill, Chapel Hill, USA
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina-Chapel Hill, Chapel Hill, USA
| | - Melissa A. Troester
- Department of Epidemiology, University of North Carolina-Chapel Hill, Chapel Hill, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina-Chapel Hill, Chapel Hill, USA
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, USA
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, USA
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6
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Mapelli SN, Albino D, Mello-Grand M, Shinde D, Scimeca M, Bonfiglio R, Bonanno E, Chiorino G, Garcia-Escudero R, Catapano CV, Carbone GM. A Novel Prostate Cell Type-Specific Gene Signature to Interrogate Prostate Tumor Differentiation Status and Monitor Therapeutic Response (Running Title: Phenotypic Classification of Prostate Tumors). Cancers (Basel) 2020; 12:cancers12010176. [PMID: 31936761 PMCID: PMC7016595 DOI: 10.3390/cancers12010176] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 12/28/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023] Open
Abstract
In this study, we extracted prostate cell-specific gene sets (metagenes) to define the epithelial differentiation status of prostate cancers and, using a deconvolution-based strategy, interrogated thousands of primary and metastatic tumors in public gene profiling datasets. We identified a subgroup of primary prostate tumors with low luminal epithelial enrichment (LumElow). LumElow tumors were associated with higher Gleason score and mutational burden, reduced relapse-free and overall survival, and were more likely to progress to castration-resistant prostate cancer (CRPC). Using discriminant function analysis, we generate a predictive 10-gene classifier for clinical implementation. This mini-classifier predicted with high accuracy the luminal status in both primary tumors and CRPCs. Immunohistochemistry for COL4A1, a low-luminal marker, sustained the association of attenuated luminal phenotype with metastatic disease. We found also an association of LumE score with tumor phenotype in genetically engineered mouse models (GEMMs) of prostate cancer. Notably, the metagene approach led to the discovery of drugs that could revert the low luminal status in prostate cell lines and mouse models. This study describes a novel tool to dissect the intrinsic heterogeneity of prostate tumors and provide predictive information on clinical outcome and treatment response in experimental and clinical samples.
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Affiliation(s)
- Sarah N. Mapelli
- Institute of Oncology Research (IOR), Università della Svizzera italiana (USI), 6500 Bellinzona, Switzerland; (S.N.M.); (D.A.); (D.S.)
- Swiss Institute of Bioinformatics (SIB), 1015 Lausanne, Switzerland
| | - Domenico Albino
- Institute of Oncology Research (IOR), Università della Svizzera italiana (USI), 6500 Bellinzona, Switzerland; (S.N.M.); (D.A.); (D.S.)
| | - Maurizia Mello-Grand
- Laboratory of Cancer Genomics, Fondazione Edo ed Elvo Tempia Valenta, 13900 Biella, Italy; (M.M.-G.); (G.C.)
| | - Dheeraj Shinde
- Institute of Oncology Research (IOR), Università della Svizzera italiana (USI), 6500 Bellinzona, Switzerland; (S.N.M.); (D.A.); (D.S.)
| | - Manuel Scimeca
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (M.S.); (R.B.); (E.B.)
| | - Rita Bonfiglio
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (M.S.); (R.B.); (E.B.)
| | - Elena Bonanno
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (M.S.); (R.B.); (E.B.)
| | - Giovanna Chiorino
- Laboratory of Cancer Genomics, Fondazione Edo ed Elvo Tempia Valenta, 13900 Biella, Italy; (M.M.-G.); (G.C.)
| | - Ramon Garcia-Escudero
- Molecular Oncology Unit, CIEMAT, 28040 Madrid, Spain
- Biomedicine Research Institute, Hospital 12 octubre, 28040 Madrid, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), 28040 Madrid, Spain
- Correspondence: (R.G.-E.); (C.V.C.); (G.M.C.); Tel.: +41-918210074 (G.M.C.); Fax: +41-918200397 (G.M.C.)
| | - Carlo V. Catapano
- Institute of Oncology Research (IOR), Università della Svizzera italiana (USI), 6500 Bellinzona, Switzerland; (S.N.M.); (D.A.); (D.S.)
- Swiss Institute of Bioinformatics (SIB), 1015 Lausanne, Switzerland
- Department of Oncology, Faculty of Biology and Medicine, University of Lausanne, 1011 Lausanne, Switzerland
- Correspondence: (R.G.-E.); (C.V.C.); (G.M.C.); Tel.: +41-918210074 (G.M.C.); Fax: +41-918200397 (G.M.C.)
| | - Giuseppina M. Carbone
- Institute of Oncology Research (IOR), Università della Svizzera italiana (USI), 6500 Bellinzona, Switzerland; (S.N.M.); (D.A.); (D.S.)
- Correspondence: (R.G.-E.); (C.V.C.); (G.M.C.); Tel.: +41-918210074 (G.M.C.); Fax: +41-918200397 (G.M.C.)
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7
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Zeng C, Stroup EK, Zhang Z, Chiu BCH, Zhang W. Towards precision medicine: advances in 5-hydroxymethylcytosine cancer biomarker discovery in liquid biopsy. Cancer Commun (Lond) 2019; 39:12. [PMID: 30922396 PMCID: PMC6440138 DOI: 10.1186/s40880-019-0356-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 03/19/2019] [Indexed: 12/13/2022] Open
Abstract
Robust and clinically convenient biomarkers for cancer diagnosis, early detection, and prognosis have great potential to improve patient survival and are the key to precision medicine. The advent of next-generation sequencing technologies enables a more sensitive and comprehensive profiling of genetic and epigenetic information in tumor-derived materials. Researchers are now able to monitor the dynamics of tumorigenesis in new dimensions, such as using circulating cell-free DNA (cfDNA) and tumor DNA (ctDNA). Mutation-based assays in liquid biopsy cannot always provide consistent results across studies due partly to intra- and inter-tumoral heterogeneity as well as technical limitations. In contrast, epigenetic analysis of patient-derived cfDNA is a promising alternative, especially for early detection and disease surveillance, because epigenetic modifications are tissue-specific and reflect the dynamic process of cancer progression. Therefore, cfDNA-based epigenetic assays are emerging to be a highly sensitive, minimally invasive tool for cancer diagnosis and prognosis with great potential in future precise care of cancer patients. The major obstacle for applying epigenetic analysis of cfDNA, however, has been the lack of enabling techniques with high sensitivity and technical robustness. In this review, we summarized the advances in epigenome-wide profiling of 5-hydroxymethylcytosine (5hmC) in cfDNA, focusing on the detection approaches and potential role as biomarkers in different cancer types.
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Affiliation(s)
- Chang Zeng
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Emily Kunce Stroup
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Zhou Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA
| | - Brian C-H Chiu
- Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA
| | - Wei Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA. .,The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. .,Institute of Precision Medicine, Jining Medical University, Jining, 272067, Shandong, P. R. China.
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8
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Zhu X, Li HD, Xu Y, Guo L, Wu FX, Duan G, Wang J. A Hybrid Clustering Algorithm for Identifying Cell Types from Single-Cell RNA-Seq Data. Genes (Basel) 2019; 10:E98. [PMID: 30700040 PMCID: PMC6409843 DOI: 10.3390/genes10020098] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 01/24/2019] [Accepted: 01/25/2019] [Indexed: 02/01/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has recently brought new insight into cell differentiation processes and functional variation in cell subtypes from homogeneous cell populations. A lack of prior knowledge makes unsupervised machine learning methods, such as clustering, suitable for analyzing scRNA-seq . However, there are several limitations to overcome, including high dimensionality, clustering result instability, and parameter adjustment complexity. In this study, we propose a method by combining structure entropy and k nearest neighbor to identify cell subpopulations in scRNA-seq data. In contrast to existing clustering methods for identifying cell subtypes, minimized structure entropy results in natural communities without specifying the number of clusters. To investigate the performance of our model, we applied it to eight scRNA-seq datasets and compared our method with three existing methods (nonnegative matrix factorization, single-cell interpretation via multikernel learning, and structural entropy minimization principle). The experimental results showed that our approach achieves, on average, better performance in these datasets compared to the benchmark methods.
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Affiliation(s)
- Xiaoshu Zhu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
- School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi 537000, China.
| | - Hong-Dong Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
| | - Yunpei Xu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
| | - Lilu Guo
- School of Computer Science and Engineering, Yulin Normal University, Yulin, Guangxi 537000, China.
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada.
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
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9
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Abstract
The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is "bottom-up integration" mode with follow-up manual integration, and the other one is "top-down integration" mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.
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Affiliation(s)
- Xiang-Tian Yu
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy Science, Shanghai, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy Science, Shanghai, China.
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10
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Jiao Z, Jiang Z, Wang J, Xu H, Zhang Q, Liu S, Du N, Zhang Y, Qiu H. Whole‑genome scale identification of methylation markers specific for cerebral palsy in monozygotic discordant twins. Mol Med Rep 2017; 16:9423-9430. [PMID: 29039597 PMCID: PMC5779998 DOI: 10.3892/mmr.2017.7800] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Accepted: 09/09/2017] [Indexed: 12/23/2022] Open
Abstract
Cerebral palsy (CP) is a severe type of brain disease affecting movement and posture. Although CP has strong genetic and environmental components, considerable differences in the methylome between monozygotic (MZ) twins discordant for CP implicates epigenetic contributors as well. In order to determine the differences in methylation in patients with CP without interference of the interindividual genomic variation, four pairs of MZ twins discordant for CP were profiled for DNA methylation changes using reduced representation bisulfite sequencing on the genomic-scale. Similar DNA methylation patterns were observed in all samples. However, MZ twins demonstrated higher correlations and closer evolutionary associations compared with the other samples, indicating a stable methylome of MZ twins. A total of 190 differentially methylated genes (DMGs) were identified using Student's t-test, of which 37 genes were hypermethylated in the CP group while the remainders were hypomethylated compared with control group. The identified DMGs were enriched in several cerebral abnormalities, including cerebral cortical atrophy and cerebral atrophy, suggesting that the occurrence of CP may be associated with the methylation alterations. The neighboring genes of DMGs in the protein-protein interaction network were enriched in numerous important functions in essential processes. The results of the present study identified important genes that may epigenetically contribute to the occurrence and development of CP in MZ twins, suggesting that the different prevalence of CP in identical twins may be associated with DNA methylation alterations.
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Affiliation(s)
- Zhe Jiao
- Department of Epidemiology, School of Public Health, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Zhimei Jiang
- Heilongjiang Cerebral Palsy Treatment and Management Center, Jiamusi University, Jiamusi, Heilongjiang 154007, P.R. China
| | - Jingtao Wang
- School of Basic Medicine, Jiamusi University, Jiamusi, Heilongjiang 154007, P.R. China
| | - Hui Xu
- School of Basic Medicine, Jiamusi University, Jiamusi, Heilongjiang 154007, P.R. China
| | - Qiang Zhang
- School of Public Health, Jiamusi University, Jiamusi, Heilongjiang 154007, P.R. China
| | - Shuang Liu
- School of Basic Medicine, Jiamusi University, Jiamusi, Heilongjiang 154007, P.R. China
| | - Ning Du
- School of Public Health, Jiamusi University, Jiamusi, Heilongjiang 154007, P.R. China
| | - Yuanyuan Zhang
- School of Public Health, Jiamusi University, Jiamusi, Heilongjiang 154007, P.R. China
| | - Hongbin Qiu
- Department of Epidemiology, School of Public Health, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
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11
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Liu H, Li S, Wang X, Zhu J, Wei Y, Wang Y, Wen Y, Wang L, Huang Y, Zhang B, Shang S, Zhang Y. DNA methylation dynamics: identification and functional annotation. Brief Funct Genomics 2016; 15:470-484. [PMID: 27515490 DOI: 10.1093/bfgp/elw029] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
DNA methylation is an epigenetic modification of cytosines that undergoes dynamic changes in a temporal, spatial and cell-type-specific manner. Recent advances in technology have permitted the profiling of high-throughput methylomes in large numbers of biological samples. Various computational tools have been developed to identify and analyze DNA methylation dynamics in a variety of critical biological processes. As DNA methylation is becoming increasingly viewed as a dynamic process, the mechanisms governing DNA methylation dynamics and its roles in the transcriptional regulatory network are of great interest. It has been reported that DNA methylation dynamics plays essential roles in multiple biological processes, including development and cancer. As a functional event, the dynamics of DNA methylation have become increasingly relevant to many researchers. Here, we review state-of-the-art advances at three levels (genome-wide identification, regulatory mechanism investigation and the functional annotation) in the field of DNA methylation dynamics, as well as the future perspective of DNA methylation dynamics.
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