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Shifat-E-Rabbi M, Ironside N, Pathan NS, Ozolek JA, Singh R, Pantanowitz L, Rohde GK. Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry. Cytometry A 2025; 107:98-110. [PMID: 39982036 DOI: 10.1002/cyto.a.24917] [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: 03/25/2024] [Revised: 10/25/2024] [Accepted: 01/27/2025] [Indexed: 02/22/2025]
Abstract
Alterations in nuclear morphology are useful adjuncts and even diagnostic tools used by pathologists in the diagnosis and grading of many tumors, particularly malignant tumors. Large datasets such as TCGA and the Human Protein Atlas, in combination with emerging machine learning and statistical modeling methods, such as feature extraction and deep learning techniques, can be used to extract meaningful knowledge from images of nuclei, particularly from cancerous tumors. Here, we describe a new technique based on the mathematics of optimal transport for modeling the information content related to nuclear chromatin structure directly from imaging data. In contrast to other techniques, our method represents the entire information content of each nucleus relative to a template nucleus using a transport-based morphometry (TBM) framework. We demonstrate that the model is robust to different staining patterns and imaging protocols, and can be used to discover meaningful and interpretable information within and across datasets and cancer types. In particular, we demonstrate morphological differences capable of distinguishing nuclear features along the spectrum from benign to malignant categories of tumors across different cancer tissue types, including tumors derived from liver parenchyma, thyroid gland, lung mesothelium, and skin epithelium. We believe these proof-of-concept calculations demonstrate that the TBM framework can provide the quantitative measurements necessary for performing meaningful comparisons across a wide range of datasets and cancer types that can potentially enable numerous cancer studies, technologies, and clinical applications and help elevate the role of nuclear morphometry into a more quantitative science.
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Affiliation(s)
- Mohammad Shifat-E-Rabbi
- Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh
| | - Natasha Ironside
- Imaging and Data Science Laboratory, Charlottesville, USA
- Department of Neurological Surgery, University of Virginia, Charlottesville, USA
| | - Naqib Sad Pathan
- Imaging and Data Science Laboratory, Charlottesville, USA
- Department of Electrical & Computer Engineering, University of Virginia, Charlottesville, USA
| | - John A Ozolek
- Department of Pathology, Anatomy, and Laboratory Medicine, West Virginia University, Morgantown, West Virginia, USA
| | | | - Liron Pantanowitz
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Gustavo K Rohde
- Imaging and Data Science Laboratory, Charlottesville, USA
- Department of Electrical & Computer Engineering, University of Virginia, Charlottesville, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, USA
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Han Y, Chen B, Bian J, Kang R, Shang X. Cancerous time estimation for interpreting the evolution of lung adenocarcinoma. Brief Bioinform 2024; 25:bbae520. [PMID: 39413800 PMCID: PMC11483137 DOI: 10.1093/bib/bbae520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 08/19/2024] [Accepted: 10/02/2024] [Indexed: 10/18/2024] Open
Abstract
The evolution of lung adenocarcinoma is accompanied by a multitude of gene mutations and dysfunctions, rendering its phenotypic state and evolutionary direction highly complex. To interpret the evolution of lung adenocarcinoma, various methods have been developed to elucidate the molecular pathogenesis and functional evolution processes. However, most of these methods are constrained by the absence of cancerous temporal information, and the challenges of heterogeneous characteristics. To handle these problems, in this study, a patient quasi-potential landscape method was proposed to estimate the cancerous time of phenotypic states' emergence during the evolutionary process. Subsequently, a total of 39 different oncogenetic paths were identified based on cancerous time and mutations, reflecting the molecular pathogenesis of the evolutionary process of lung adenocarcinoma. To interpret the evolution patterns of lung adenocarcinoma, three oncogenetic graphs were obtained as the common evolutionary patterns by merging the oncogenetic paths. Moreover, patients were evenly re-divided into early, middle, and late evolutionary stages according to cancerous time, and a feasible framework was developed to construct the functional evolution network of lung adenocarcinoma. A total of six significant functional evolution processes were identified from the functional evolution network based on the pathway enrichment analysis, which plays critical roles in understanding the development of lung adenocarcinoma.
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Affiliation(s)
- Yourui Han
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710012, China
| | - Bolin Chen
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710012, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710012, China
| | - Jun Bian
- Department of General Surgery, Xi’an Children’s Hospital, Xi’an Jiaotong University Affiliated Children’s Hospital, Xi’an 710003, China
| | - Ruiming Kang
- Rewise (Hangzhou) Information Technology Co., LTD, Hangzhou 310000, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710012, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710012, China
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Lózsa R, Németh E, Gervai JZ, Márkus BG, Kollarics S, Gyüre Z, Tóth J, Simon F, Szüts D. DNA mismatch repair protects the genome from oxygen-induced replicative mutagenesis. Nucleic Acids Res 2023; 51:11040-11055. [PMID: 37791890 PMCID: PMC10639081 DOI: 10.1093/nar/gkad775] [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: 05/16/2023] [Revised: 08/23/2023] [Accepted: 09/12/2023] [Indexed: 10/05/2023] Open
Abstract
DNA mismatch repair (MMR) corrects mismatched DNA bases arising from multiple sources including polymerase errors and base damage. By detecting spontaneous mutagenesis using whole genome sequencing of cultured MMR deficient human cell lines, we show that a primary role of MMR is the repair of oxygen-induced mismatches. We found an approximately twofold higher mutation rate in MSH6 deficient DLD-1 cells or MHL1 deficient HCT116 cells exposed to atmospheric conditions as opposed to mild hypoxia, which correlated with oxidant levels measured using electron paramagnetic resonance spectroscopy. The oxygen-induced mutations were dominated by T to C base substitutions and single T deletions found primarily on the lagging strand. A broad sequence context preference, dependence on replication timing and a lack of transcriptional strand bias further suggested that oxygen-induced mutations arise from polymerase errors rather than oxidative base damage. We defined separate low and high oxygen-specific MMR deficiency mutation signatures common to the two cell lines and showed that the effect of oxygen is observable in MMR deficient cancer genomes, where it best correlates with the contribution of mutation signature SBS21. Our results imply that MMR corrects oxygen-induced genomic mismatches introduced by a replicative process in proliferating cells.
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Affiliation(s)
- Rita Lózsa
- Institute of Enzymology, Research Centre for Natural Sciences, H-1117 Budapest, Hungary
| | - Eszter Németh
- Institute of Enzymology, Research Centre for Natural Sciences, H-1117 Budapest, Hungary
| | - Judit Z Gervai
- Institute of Enzymology, Research Centre for Natural Sciences, H-1117 Budapest, Hungary
| | - Bence G Márkus
- Stavropoulos Center for Complex Quantum Matter, Department of Physics and Astronomy, University of Notre Dame, Notre Dame, IN 46556, USA
- Institute for Solid State Physics and Optics, Wigner Research Centre for Physics, H-1525 Budapest, Hungary
- Department of Physics, Institute of Physics, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
| | - Sándor Kollarics
- Institute for Solid State Physics and Optics, Wigner Research Centre for Physics, H-1525 Budapest, Hungary
- Department of Physics, Institute of Physics, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
| | - Zsolt Gyüre
- Institute of Enzymology, Research Centre for Natural Sciences, H-1117 Budapest, Hungary
- Doctoral School of Molecular Medicine, Semmelweis University, H-1085 Budapest, Hungary
- Turbine Simulated Cell Technologies, H-1027 Budapest, Hungary
| | - Judit Tóth
- Institute of Enzymology, Research Centre for Natural Sciences, H-1117 Budapest, Hungary
- Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
| | - Ferenc Simon
- Institute for Solid State Physics and Optics, Wigner Research Centre for Physics, H-1525 Budapest, Hungary
- Department of Physics, Institute of Physics, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
| | - Dávid Szüts
- Institute of Enzymology, Research Centre for Natural Sciences, H-1117 Budapest, Hungary
- National Laboratory for Drug Research and Development, H-1117 Budapest, Hungary
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Hamamoto R, Takasawa K, Machino H, Kobayashi K, Takahashi S, Bolatkan A, Shinkai N, Sakai A, Aoyama R, Yamada M, Asada K, Komatsu M, Okamoto K, Kameoka H, Kaneko S. Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Brief Bioinform 2022; 23:bbac246. [PMID: 35788277 PMCID: PMC9294421 DOI: 10.1093/bib/bbac246] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/06/2022] [Accepted: 05/25/2022] [Indexed: 12/19/2022] Open
Abstract
The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Rina Aoyama
- Showa University Graduate School of Medicine School of Medicine
| | | | - Ken Asada
- RIKEN Center for Advanced Intelligence Project
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