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Neagu AN, Bruno P, Johnson KR, Ballestas G, Darie CC. Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era. Int J Mol Sci 2024; 25:4113. [PMID: 38612922 PMCID: PMC11012526 DOI: 10.3390/ijms25074113] [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: 03/03/2024] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024] Open
Abstract
Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, "Alexandru Ioan Cuza" University of Iași, Carol I bvd. 20A, 700505 Iasi, Romania
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biochemistry, Clarkson University, Potsdam, NY 13699-5810, USA
| | - Kaya R Johnson
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biochemistry, Clarkson University, Potsdam, NY 13699-5810, USA
| | - Gabriella Ballestas
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biochemistry, Clarkson University, Potsdam, NY 13699-5810, USA
| | - Costel C Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biochemistry, Clarkson University, Potsdam, NY 13699-5810, USA
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Chatterji S, Krzoska E, Thoroughgood CW, Saganty J, Liu P, Elsberger B, Abu-Eid R, Speirs V. Defining genomic, transcriptomic, proteomic, epigenetic, and phenotypic biomarkers with prognostic capability in male breast cancer: a systematic review. Lancet Oncol 2023; 24:e74-e85. [PMID: 36725152 DOI: 10.1016/s1470-2045(22)00633-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/15/2022] [Accepted: 10/10/2022] [Indexed: 02/02/2023]
Abstract
Although similar phenotypically, there is evidence that male and female breast cancer differ in their molecular landscapes. In this systematic review, we consolidated all existing prognostic biomarker data in male breast cancer spanning genetics, transcriptomics, proteomics, and epigenetics, and phenotypic features of prognostic value from articles published over a 29-year period (March 16, 1992, to May 1, 2021). We identified knowledge gaps in the existing literature, discussed limitations of the included studies, and outlined potential approaches for translational biomarker discovery and validation in male breast cancer. We also recognised STC2, DDX3, and DACH1 as underexploited markers of male-specific prognostic value in breast cancer. Finally, beyond describing the cumulative knowledge on the extensively researched markers oestrogen receptor-α, progesterone receptor, HER2, androgen receptor, and BRCA2, we highlighted ATM, CCND1, FGFR2, GATA3, HIF1-α, MDM2, TP53, and c-Myc as well studied predictors of poor survival that also aligned with several hallmarks of cancer.
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Affiliation(s)
- Subarnarekha Chatterji
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK; Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Emma Krzoska
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - John Saganty
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Peng Liu
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK; Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | | | - Rasha Abu-Eid
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK; Institute of Dentistry, University of Aberdeen, Aberdeen, UK
| | - Valerie Speirs
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK; Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK.
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Arna AB, Patel H, Singh RS, Vizeacoumar FS, Kusalik A, Freywald A, Vizeacoumar FJ, Wu Y. Synthetic lethal interactions of DEAD/H-box helicases as targets for cancer therapy. Front Oncol 2023; 12:1087989. [PMID: 36761420 PMCID: PMC9905851 DOI: 10.3389/fonc.2022.1087989] [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: 11/02/2022] [Accepted: 12/28/2022] [Indexed: 01/26/2023] Open
Abstract
DEAD/H-box helicases are implicated in virtually every aspect of RNA metabolism, including transcription, pre-mRNA splicing, ribosomes biogenesis, nuclear export, translation initiation, RNA degradation, and mRNA editing. Most of these helicases are upregulated in various cancers and mutations in some of them are associated with several malignancies. Lately, synthetic lethality (SL) and synthetic dosage lethality (SDL) approaches, where genetic interactions of cancer-related genes are exploited as therapeutic targets, are emerging as a leading area of cancer research. Several DEAD/H-box helicases, including DDX3, DDX9 (Dbp9), DDX10 (Dbp4), DDX11 (ChlR1), and DDX41 (Sacy-1), have been subjected to SL analyses in humans and different model organisms. It remains to be explored whether SDL can be utilized to identity druggable targets in DEAD/H-box helicase overexpressing cancers. In this review, we analyze gene expression data of a subset of DEAD/H-box helicases in multiple cancer types and discuss how their SL/SDL interactions can be used for therapeutic purposes. We also summarize the latest developments in clinical applications, apart from discussing some of the challenges in drug discovery in the context of targeting DEAD/H-box helicases.
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Affiliation(s)
- Ananna Bhadra Arna
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Hardikkumar Patel
- Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ravi Shankar Singh
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Frederick S. Vizeacoumar
- Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Anthony Kusalik
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Andrew Freywald
- Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Franco J. Vizeacoumar
- Division of Oncology, College of Medicine, University of Saskatchewan and Saskatchewan Cancer Agency, Saskatoon, SK, Canada,*Correspondence: Yuliang Wu, ; Franco J. Vizeacoumar,
| | - Yuliang Wu
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, SK, Canada,*Correspondence: Yuliang Wu, ; Franco J. Vizeacoumar,
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Mridha MF, Hamid MA, Monowar MM, Keya AJ, Ohi AQ, Islam MR, Kim JM. A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis. Cancers (Basel) 2021; 13:6116. [PMID: 34885225 PMCID: PMC8656730 DOI: 10.3390/cancers13236116] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/25/2021] [Accepted: 12/01/2021] [Indexed: 12/11/2022] Open
Abstract
Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.
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Affiliation(s)
- Muhammad Firoz Mridha
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Md. Abdul Hamid
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.A.H.); (M.M.M.)
| | - Muhammad Mostafa Monowar
- Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (M.A.H.); (M.M.M.)
| | - Ashfia Jannat Keya
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Abu Quwsar Ohi
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (A.J.K.); (A.Q.O.)
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh;
| | - Jong-Myon Kim
- Department of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan 680-749, Korea
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