1
|
Campbell CJ, Booth BW. The Influence of the Normal Mammary Microenvironment on Breast Cancer Cells. Cancers (Basel) 2023; 15:cancers15030576. [PMID: 36765535 PMCID: PMC9913214 DOI: 10.3390/cancers15030576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/09/2023] [Accepted: 01/14/2023] [Indexed: 01/20/2023] Open
Abstract
The tumor microenvironment is recognized as performing a critical role in tumor initiation, progression, and metastasis of many cancers, including breast cancer. The breast cancer microenvironment is a complex mixture of cells consisting of tumor cells, immune cells, fibroblasts, and vascular cells, as well as noncellular components, such as extracellular matrix and soluble products. The interactions between the tumor cells and the tumor microenvironment modulate tumor behavior and affect the responses of cancer patients to therapies. The interactions between tumor cells and the surrounding environment can include direct cell-to-cell contact or through intercellular signals over short and long distances. The intricate functions of the tumor microenvironment in breast cancer have led to increased research into the tumor microenvironment as a possible therapeutic target of breast cancer. Though expanded research has shown the clear importance of the tumor microenvironment, there is little focus on how normal mammary epithelial cells can affect breast cancer cells. Previous studies have shown the normal breast microenvironment can manipulate non-mammary stem cells and tumor-derived cancer stem cells to participate in normal mammary gland development. The tumorigenic cells lose their tumor-forming capacity and are "redirected" to divide into "normal", non-tumorigenic cells. This cellular behavior is "cancer cell redirection". This review will summarize the current literature on cancer cell redirection and the normal mammary microenvironment's influence on breast cancer cells.
Collapse
|
2
|
Ogle C, Reddick D, McKnight C, Biggs T, Pauly R, Ficklin SP, Feltus FA, Shannigrahi S. Named Data Networking for Genomics Data Management and Integrated Workflows. Front Big Data 2021; 4:582468. [PMID: 33748749 PMCID: PMC7968724 DOI: 10.3389/fdata.2021.582468] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Accepted: 01/04/2021] [Indexed: 11/25/2022] Open
Abstract
Advanced imaging and DNA sequencing technologies now enable the diverse biology community to routinely generate and analyze terabytes of high resolution biological data. The community is rapidly heading toward the petascale in single investigator laboratory settings. As evidence, the single NCBI SRA central DNA sequence repository contains over 45 petabytes of biological data. Given the geometric growth of this and other genomics repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous as they are not only large in the size but also stored in geographically distributed repositories in various repositories such as National Center for Biotechnology Information (NCBI), DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA’s GeneLab. In this work, we first systematically point out the data-management challenges of the genomics community. We then introduce Named Data Networking (NDN), a novel but well-researched Internet architecture, is capable of solving these challenges at the network layer. NDN performs all operations such as forwarding requests to data sources, content discovery, access, and retrieval using content names (that are similar to traditional filenames or filepaths) and eliminates the need for a location layer (the IP address) for data management. Utilizing NDN for genomics workflows simplifies data discovery, speeds up data retrieval using in-network caching of popular datasets, and allows the community to create infrastructure that supports operations such as creating federation of content repositories, retrieval from multiple sources, remote data subsetting, and others. Named based operations also streamlines deployment and integration of workflows with various cloud platforms. Our contributions in this work are as follows 1) we enumerate the cyberinfrastructure challenges of the genomics community that NDN can alleviate, and 2) we describe our efforts in applying NDN for a contemporary genomics workflow (GEMmaker) and quantify the improvements. The preliminary evaluation shows a sixfold speed up in data insertion into the workflow. 3) As a pilot, we have used an NDN naming scheme (agreed upon by the community and discussed in Section 4) to publish data from broadly used data repositories including the NCBI SRA. We have loaded the NDN testbed with these pre-processed genomes that can be accessed over NDN and used by anyone interested in those datasets. Finally, we discuss our continued effort in integrating NDN with cloud computing platforms, such as the Pacific Research Platform (PRP). The reader should note that the goal of this paper is to introduce NDN to the genomics community and discuss NDN’s properties that can benefit the genomics community. We do not present an extensive performance evaluation of NDN—we are working on extending and evaluating our pilot deployment and will present systematic results in a future work.
Collapse
Affiliation(s)
- Cameron Ogle
- School of Computing, Clemson University, Clemson, SC, United States
| | - David Reddick
- Department of Computer Science, Tennessee Tech University, Cookeville, TN, United States
| | - Coleman McKnight
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, United States
| | - Tyler Biggs
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Rini Pauly
- Biomedical Data Science and Informatics Program, Clemson, SC, United States
| | - Stephen P Ficklin
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - F Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, United States.,Biomedical Data Science and Informatics Program, Clemson, SC, United States.,Center for Human Genetics, Clemson University, Greenwood, SC, United States
| | - Susmit Shannigrahi
- Department of Computer Science, Tennessee Tech University, Cookeville, TN, United States
| |
Collapse
|
3
|
NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles. G3-GENES GENOMES GENETICS 2020; 10:2953-2963. [PMID: 32665353 PMCID: PMC7466957 DOI: 10.1534/g3.120.401067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.
Collapse
|
4
|
Frank-Kamenetskii A, Mook J, Reeves M, Boulanger CA, Meyer TJ, Ragle L, Jordan HC, Smith GH, Booth BW. Induction of phenotypic changes in HER2-postive breast cancer cells in vivo and in vitro. Oncotarget 2020; 11:2919-2929. [PMID: 32774772 PMCID: PMC7392627 DOI: 10.18632/oncotarget.27679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 06/30/2020] [Indexed: 11/30/2022] Open
Abstract
The influence of breast cancer cells on normal cells of the microenvironment, such as fibroblasts and macrophages, has been heavily studied but the influence of normal epithelial cells on breast cancer cells has not. Here using in vivo and in vitro models we demonstrate the impact epithelial cells and the mammary microenvironment can exert on breast cancer cells. Under specific conditions, signals that originate in epithelial cells can induce phenotypic and genotypic changes in cancer cells. We have termed this phenomenon "cancer cell redirection." Once breast cancer cells are redirected, either in vivo or in vitro, they lose their tumor forming capacity and undergo a genetic expression profile shift away from one that supports a cancer profile towards one that supports a non-tumorigenic epithelial profile. These findings indicate that epithelial cells and the normal microenvironment influence breast cancer cells and that under certain circumstances restrict proliferation of tumorigenic cells.
Collapse
Affiliation(s)
| | - Julia Mook
- Department of Biological Sciences, Clemson University, Clemson, SC, USA
| | - Meredith Reeves
- Department of Bioengineering, Clemson University, Clemson, SC, USA
| | - Corinne A. Boulanger
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Thomas J. Meyer
- CCR Collaborative Bioinformatics Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Lauren Ragle
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Gilbert H. Smith
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- These authors contributed equally to this work
| | - Brian W. Booth
- Department of Bioengineering, Clemson University, Clemson, SC, USA
- These authors contributed equally to this work
| |
Collapse
|
5
|
Kim SJ, Ha JW, Kim H. Genome-Wide Identification of Discriminative Genetic Variations in Beef and Dairy Cattle via an Information-Theoretic Approach. Genes (Basel) 2020; 11:genes11060678. [PMID: 32580275 PMCID: PMC7350245 DOI: 10.3390/genes11060678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 06/19/2020] [Accepted: 06/19/2020] [Indexed: 11/23/2022] Open
Abstract
Analyzing the associations between genotypic changes and phenotypic traits on a genome-wide scale can contribute to understanding the functional roles of distinct genetic variations during breed development. We performed a whole-genome analysis of Angus and Jersey cattle breeds using conditional mutual information, which is an information-theoretic method estimating the conditional independency among multiple factor variables. The proposed conditional mutual information-based approach allows breed-discriminative genetic variations to be explicitly identified from tens of millions of SNP (single nucleotide polymorphism) positions on a genome-wide scale while minimizing the usage of prior knowledge. Using this data-driven approach, we identified biologically relevant functional genes, including breed-specific variants for cattle traits such as beef and dairy production. The identified lipid-related genes were shown to be significantly associated with lipid and triglyceride metabolism, fat cell differentiation, and muscle development. In addition, we confirmed that milk-related genes are involved in mammary gland development, lactation, and mastitis-associated processes. Our results provide the distinct properties of Angus and Jersey cattle at a genome-wide level. Moreover, this study offers important insights into discovering unrevealed genetic variants for breed-specific traits and the identification of genetic signatures of diverse cattle breeds with respect to target breed-specific properties.
Collapse
Affiliation(s)
- Soo-Jin Kim
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea;
| | - Jung-Woo Ha
- Clova AI Research, NAVER Corp., Seongnam 13561, Korea;
| | - Heebal Kim
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea;
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
- C&K Genomics, Seoul 05836, Korea
- Correspondence: ; Tel.: +82-2880-4803
| |
Collapse
|
6
|
Frank-Kamenetskii A, Booth BW. Redirecting Normal and Cancer Stem Cells to a Mammary Epithelial Cell Fate. J Mammary Gland Biol Neoplasia 2019; 24:285-292. [PMID: 31732837 DOI: 10.1007/s10911-019-09439-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 10/31/2019] [Indexed: 12/21/2022] Open
Abstract
Tissue microenvironments, also known as stem cell niches, influence not only resident cells but also cells in surrounding tissues. Physical and biochemical intercellular signals originating from resident stem cells or non-stem cells participate in the homeostasis of the tissue regulating cell proliferation, differentiation, wound healing, tissue remodeling, and tumorigenesis. In recent publications it has been demonstrated that the normal mouse mammary microenvironment can provide development and differentiation guidance to not only resident mammary cells but also cells of non-mammary origin including tumor-derived cells. When placed in reforming mammary stem cell niches the non-mammary cells proliferate and differentiate along mammary epithelial cell lineages and contribute progeny to reforming mammary gland outgrowths. The tumor-derived cells that are redirected to assume mammary epithelial phenotypes lose their cancer-forming capacity and shift their gene expression profiles from a cancer profile towards a normal mammary epithelial expression profile. This review summarizes the recent discoveries regarding the ability of the normal mouse mammary microenvironment to dictate the cell fates of non-mammary cells introduced into mammary stem cell niches.
Collapse
Affiliation(s)
- Anastasia Frank-Kamenetskii
- Department of Bioengineering, Clemson University, 401-1 Rhodes Engineering Research Center, Clemson, SC, 29634, USA
| | - Brian W Booth
- Department of Bioengineering, Clemson University, 401-1 Rhodes Engineering Research Center, Clemson, SC, 29634, USA.
| |
Collapse
|