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Bonni S, Brindley DN, Chamberlain MD, Daneshvar-Baghbadorani N, Freywald A, Hemmings DG, Hombach-Klonisch S, Klonisch T, Raouf A, Shemanko CS, Topolnitska D, Visser K, Vizeacoumar FJ, Wang E, Gibson SB. Breast Tumor Metastasis and Its Microenvironment: It Takes Both Seed and Soil to Grow a Tumor and Target It for Treatment. Cancers (Basel) 2024; 16:911. [PMID: 38473273 DOI: 10.3390/cancers16050911] [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: 01/09/2024] [Revised: 02/12/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
Metastasis remains a major challenge in treating breast cancer. Breast tumors metastasize to organ-specific locations such as the brain, lungs, and bone, but why some organs are favored over others remains unclear. Breast tumors also show heterogeneity, plasticity, and distinct microenvironments. This contributes to treatment failure and relapse. The interaction of breast cancer cells with their metastatic microenvironment has led to the concept that primary breast cancer cells act as seeds, whereas the metastatic tissue microenvironment (TME) is the soil. Improving our understanding of this interaction could lead to better treatment strategies for metastatic breast cancer. Targeted treatments for different subtypes of breast cancers have improved overall patient survival, even with metastasis. However, these targeted treatments are based upon the biology of the primary tumor and often these patients' relapse, after therapy, with metastatic tumors. The advent of immunotherapy allowed the immune system to target metastatic tumors. Unfortunately, immunotherapy has not been as effective in metastatic breast cancer relative to other cancers with metastases, such as melanoma. This review will describe the heterogeneic nature of breast cancer cells and their microenvironments. The distinct properties of metastatic breast cancer cells and their microenvironments that allow interactions, especially in bone and brain metastasis, will also be described. Finally, we will review immunotherapy approaches to treat metastatic breast tumors and discuss future therapeutic approaches to improve treatments for metastatic breast cancer.
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Affiliation(s)
- Shirin Bonni
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB T2N 4N1, Canada
- The Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - David N Brindley
- Department of Biochemistry, University of Alberta, Edmonton, AB T6G 2H7, Canada
- Cancer Research Institute of Northern Alberta, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - M Dean Chamberlain
- Division of Oncology, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 0W8, Canada
- Saskatchewan Cancer Agency, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Nima Daneshvar-Baghbadorani
- Division of Oncology, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 0W8, Canada
- Saskatchewan Cancer Agency, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Andrew Freywald
- Department of Pathology, Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Denise G Hemmings
- Cancer Research Institute of Northern Alberta, University of Alberta, Edmonton, AB T6G 2E1, Canada
- Department of Obstetrics and Gynecology, University of Alberta, Edmonton, AB T6G 2S2, Canada
- Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB T6G 2E1, Canada
- Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Sabine Hombach-Klonisch
- Department of Human Anatomy and Cell Science, Faculty of Health Sciences, College of Medicine, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Thomas Klonisch
- Department of Human Anatomy and Cell Science, Faculty of Health Sciences, College of Medicine, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Afshin Raouf
- Department of Immunology, Faculty of Medicine, University of Manitoba, Winnipeg, MB R3E OT5, Canada
- Cancer Care Manitoba Research Institute, Cancer Care Manitoba, Winnipeg, MB R3E OV9, Canada
| | - Carrie Simone Shemanko
- The Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Biological Sciences, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
| | - Diana Topolnitska
- Department of Immunology, Faculty of Medicine, University of Manitoba, Winnipeg, MB R3E OT5, Canada
- Cancer Care Manitoba Research Institute, Cancer Care Manitoba, Winnipeg, MB R3E OV9, Canada
| | - Kaitlyn Visser
- Cancer Research Institute of Northern Alberta, University of Alberta, Edmonton, AB T6G 2E1, Canada
- Department of Obstetrics and Gynecology, University of Alberta, Edmonton, AB T6G 2S2, Canada
- Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB T6G 2E1, Canada
- Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Franco J Vizeacoumar
- Division of Oncology, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 0W8, Canada
- Saskatchewan Cancer Agency, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Edwin Wang
- Department of Biochemistry and Molecular Biology, Medical Genetics, and Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Spencer B Gibson
- Department of Oncology, University of Alberta, Edmonton, AB T6G 2R3, Canada
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Milanese JS, Marcotte R, Costain WJ, Kablar B, Drouin S. Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview. ADVANCES IN ANATOMY, EMBRYOLOGY, AND CELL BIOLOGY 2023; 236:21-55. [PMID: 37955770 DOI: 10.1007/978-3-031-38215-4_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
The ability to assess various cellular events consequent to perturbations, such as genetic mutations, disease states and therapies, has been recently revolutionized by technological advances in multiple "omics" fields. The resulting deluge of information has enabled and necessitated the development of tools required to both process and interpret the data. While of tremendous value to basic researchers, the amount and complexity of the data has made it extremely difficult to manually draw inference and identify factors key to the study objectives. The challenges of data reduction and interpretation are being met by the development of increasingly complex tools that integrate disparate knowledge bases and synthesize coherent models based on current biological understanding. This chapter presents an example of how genomics data can be integrated with biological network analyses to gain further insight into the developmental consequences of genetic perturbations. State of the art methods for conducting similar studies are discussed along with modern methods used to analyze and interpret the data.
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Affiliation(s)
| | - Richard Marcotte
- Human Health Therapeutics, National Research Council of Canada , Montreal, QC, Canada
| | - Willard J Costain
- Human Health Therapeutics, National Research Council of Canada, Ottawa, ON, Canada
| | - Boris Kablar
- Department of Medical Neuroscience, Anatomy and Pathology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Simon Drouin
- Human Health Therapeutics, National Research Council of Canada , Montreal, QC, Canada.
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Gupta P, Jindal A, Ahuja G, Jayadeva, Sengupta D. A new deep learning technique reveals the exclusive functional contributions of individual cancer mutations. J Biol Chem 2022; 298:102177. [PMID: 35753349 PMCID: PMC9304782 DOI: 10.1016/j.jbc.2022.102177] [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: 02/21/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 11/26/2022] Open
Abstract
Cancers are caused by genomic alterations that may be inherited, induced by environmental carcinogens, or caused due to random replication errors. Postinduction of carcinogenicity, mutations further propagate and drastically alter the cancer genomes. Although a subset of driver mutations has been identified and characterized to date, most cancer-related somatic mutations are indistinguishable from germline variants or other noncancerous somatic mutations. Thus, such overlap impedes appreciation of many deleterious but previously uncharacterized somatic mutations. The major bottleneck arises due to patient-to-patient variability in mutational profiles, making it difficult to associate specific mutations with a given disease outcome. Here, we describe a newly developed technique Continuous Representation of Codon Switches (CRCS), a deep learning-based method that allows us to generate numerical vector representations of mutations, thereby enabling numerous machine learning-based tasks. We demonstrate three major applications of CRCS; first, we show how CRCS can help detect cancer-related somatic mutations in the absence of matched normal samples, which has applications in cell-free DNA–based assessment of tumor mutation burden. Second, the proposed approach also enables identification and exploration of driver genes; our analyses implicate DMD, RSK4, OFD1, WDR44, and AFF2 as potential cancer drivers. Finally, we used CRCS to score individual mutations in a tumor sample, which was found to be predictive of patient survival in bladder urothelial carcinoma, hepatocellular carcinoma, and lung adenocarcinoma. Taken together, we propose CRCS as a valuable computational tool for analysis of the functional significance of individual cancer mutations.
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Affiliation(s)
- Prashant Gupta
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, Delhi 110016, India
| | - Aashi Jindal
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, Delhi 110016, India
| | - Gaurav Ahuja
- Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Jayadeva
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, Delhi 110016, India.
| | - Debarka Sengupta
- Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India; Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi 110020, India; Center for Artificial Intelligence, Indraprastha Institute of Information Technology, Delhi 110020, India.
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Chen X, Zhou C, Wang CC, Zhao Y. Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization. Brief Bioinform 2021; 22:6353837. [PMID: 34404088 DOI: 10.1093/bib/bbab328] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/24/2021] [Accepted: 07/26/2021] [Indexed: 12/14/2022] Open
Abstract
Mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment. Given the fact that exploring new SM-miRNA associations through biological experiments is extremely expensive, several computing models have been constructed to reveal the possible SM-miRNA associations. Here, we built a computing model of Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA). Specifically, we first constructed a heterogeneous SM-miRNA network utilizing miRNA similarity, SM similarity, confirmed SM-miRNA associations and defined a matrix to represent the heterogeneous network. Then, we constructed a model to complete this matrix by minimizing its nuclear norm. The Alternating Direction Method of Multipliers was adopted to minimize the nuclear norm and obtain predicted scores. The main innovation lies in two aspects. During completion, we limited all elements of the matrix within the interval of (0,1) to make sure they have practical significance. Besides, instead of strictly fitting all known elements, a regularization term was incorporated to tolerate the noise in integrated similarities. Furthermore, four kinds of cross-validations on two datasets and two types of case studies were performed to evaluate the predictive performance of BNNRSMMA. Finally, BNNRSMMA attained areas under the curve of 0.9822 (0.8433), 0.9793 (0.8852), 0.8253 (0.7350) and 0.9758 ± 0.0029 (0.8759 ± 0.0041) under global leave-one-out cross-validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation based on Dataset 1(Dataset 2), respectively. With regard to case studies, plenty of predicted associations have been verified by experimental literatures. All these results confirmed that BNNRSMMA is a reliable tool for inferring associations.
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Affiliation(s)
- Xing Chen
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
| | - Chi Zhou
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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