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Xiao Y, Elmasry M, Bai JDK, Chen A, Chen Y, Jackson B, Johnson JO, Gillies RJ, Prasanna P, Chen C, Damaghi M. Eco-evolutionary Guided Pathomic Analysis to Predict DCIS Upstaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.23.600274. [PMID: 38979368 PMCID: PMC11230267 DOI: 10.1101/2024.06.23.600274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Cancers evolve in a dynamic ecosystem. Thus, characterizing cancer's ecological dynamics is crucial to understanding cancer evolution and can lead to discovering novel biomarkers to predict disease progression. Ductal carcinoma in situ (DCIS) is an early-stage breast cancer characterized by abnormal epithelial cell growth confined within the milk ducts. Although there has been extensive research on genetic and epigenetic causes of breast carcinogenesis, none of these studies have successfully identified a biomarker for the progression and/or upstaging of DCIS. In this study, we show that ecological habitat analysis of hypoxia and acidosis biomarkers can significantly improve prediction of DCIS upstaging. First, we developed a novel eco-evolutionary designed approach to define habitats in the tumor intra-ductal microenvironment based on oxygen diffusion distance in our DCIS cohort of 84 patients. Then, we identify cancer cells with metabolic phenotypes attributed to their habitat conditions, such as the expression of CA9 indicating hypoxia responding phenotype, and LAMP2b indicating a hypoxia-induced acid adaptation. Traditionally these markers have shown limited predictive capabilities for DCIS upstaging, if any. However, when analyzed from an ecological perspective, their power to differentiate between indolent and upstaged DCIS increased significantly. Second, using eco-evolutionary guided computational and digital pathology techniques, we discovered distinct spatial patterns of these biomarkers and used the distribution of such patterns to predict patient upstaging. The patterns were characterized by both cellular features and spatial features. With a 5-fold validation on the biopsy cohort, we trained a random forest classifier to achieve the area under curve(AUC) of 0.74. Our results affirm the importance of using eco-evolutionary-designed approaches in biomarkers discovery studies in the era of digital pathology by demonstrating the role of eco-evolution dynamics in predicting cancer progression.
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Affiliation(s)
- Yujie Xiao
- Department of Applied Mathematics and Statistics, Stony Brook University, NY, USA
| | - Manal Elmasry
- Department of Pathology, Stony Brook Medicine, Stony Brook University, NY, USA
- Department of Pathology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Ji Dong K. Bai
- Department of Pathology, Stony Brook Medicine, Stony Brook University, NY, USA
| | - Andrew Chen
- Department of Pathology, Stony Brook Medicine, Stony Brook University, NY, USA
| | - Yuzhu Chen
- Department of Pathology, Stony Brook Medicine, Stony Brook University, NY, USA
| | | | | | | | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook University, NY, USA
| | - Chao Chen
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook University, NY, USA
| | - Mehdi Damaghi
- Department of Applied Mathematics and Statistics, Stony Brook University, NY, USA
- Department of Pathology, Stony Brook Medicine, Stony Brook University, NY, USA
- Department of Pathology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
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Guo X, Bian X, Li Y, Zhu X, Zhou X. The intricate dance of tumor evolution: Exploring immune escape, tumor migration, drug resistance, and treatment strategies. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167098. [PMID: 38412927 DOI: 10.1016/j.bbadis.2024.167098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/14/2024] [Accepted: 02/19/2024] [Indexed: 02/29/2024]
Abstract
Recent research has unveiled fascinating insights into the intricate mechanisms governing tumor evolution. These studies have illuminated how tumors adapt and proliferate by exploiting various factors, including immune evasion, resistance to therapeutic drugs, genetic mutations, and their ability to adapt to different environments. Furthermore, investigations into tumor heterogeneity and chromosomal aberrations have revealed the profound complexity that underlies the evolution of cancer. Emerging findings have also underscored the role of viral influences in the development and progression of cancer, introducing an additional layer of complexity to the field of oncology. Tumor evolution is a dynamic and complex process influenced by various factors, including immune evasion, drug resistance, tumor heterogeneity, and viral influences. Understanding these elements is indispensable for developing more effective treatments and advancing cancer therapies. A holistic approach to studying and addressing tumor evolution is crucial in the ongoing battle against cancer. The main goal of this comprehensive review is to explore the intricate relationship between tumor evolution and critical aspects of cancer biology. By delving into this complex interplay, we aim to provide a profound understanding of how tumors evolve, adapt, and respond to treatment strategies. This review underscores the pivotal importance of comprehending tumor evolution in shaping effective approaches to cancer treatment.
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Affiliation(s)
- Xiaojun Guo
- Department of Immunology, School of Medicine, Nantong University, Nantong, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xiaonan Bian
- Department of Immunology, School of Medicine, Nantong University, Nantong, China
| | - Yitong Li
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xiao Zhu
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China.
| | - Xiaorong Zhou
- Department of Immunology, School of Medicine, Nantong University, Nantong, China.
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Sunassee ED, Jardim-Perassi BV, Madonna MC, Ordway B, Ramanujam N. Metabolic Imaging as a Tool to Characterize Chemoresistance and Guide Therapy in Triple-Negative Breast Cancer (TNBC). Mol Cancer Res 2023; 21:995-1009. [PMID: 37343066 PMCID: PMC10592445 DOI: 10.1158/1541-7786.mcr-22-1004] [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: 12/16/2022] [Revised: 04/07/2023] [Accepted: 06/15/2023] [Indexed: 06/23/2023]
Abstract
After an initial response to chemotherapy, tumor relapse is frequent. This event is reflective of both the spatiotemporal heterogeneities of the tumor microenvironment as well as the evolutionary propensity of cancer cell populations to adapt to variable conditions. Because the cause of this adaptation could be genetic or epigenetic, studying phenotypic properties such as tumor metabolism is useful as it reflects molecular, cellular, and tissue-level dynamics. In triple-negative breast cancer (TNBC), the characteristic metabolic phenotype is a highly fermentative state. However, during treatment, the spatial and temporal dynamics of the metabolic landscape are highly unstable, with surviving populations taking on a variety of metabolic states. Thus, longitudinally imaging tumor metabolism provides a promising approach to inform therapeutic strategies, and to monitor treatment responses to understand and mitigate recurrence. Here we summarize some examples of the metabolic plasticity reported in TNBC following chemotherapy and review the current metabolic imaging techniques available in monitoring chemotherapy responses clinically and preclinically. The ensemble of imaging technologies we describe has distinct attributes that make them uniquely suited for a particular length scale, biological model, and/or features that can be captured. We focus on TNBC to highlight the potential of each of these technological advances in understanding evolution-based therapeutic resistance.
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Affiliation(s)
- Enakshi D. Sunassee
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | | | - Megan C. Madonna
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Bryce Ordway
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL 33612, USA
- Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Nirmala Ramanujam
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27708, USA
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Jiang P, Ning W, Shi Y, Liu C, Mo S, Zhou H, Liu K, Guo Y. FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction. Comput Struct Biotechnol J 2021; 19:4497-4509. [PMID: 34471495 PMCID: PMC8385177 DOI: 10.1016/j.csbj.2021.08.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/05/2021] [Accepted: 08/08/2021] [Indexed: 01/04/2023] Open
Abstract
As a novel lactate-derived post-translational modification (PTM), lysine lactylation (Kla) is involved in diverse biological processes, and participates in human tumorigenesis. Identification of Kla substrates with their exact sites is crucial for revealing the molecular mechanisms of lactylation. In contrast with labor-intensive and time-consuming experimental approaches, computational prediction of Kla could provide convenience and increased speed, but is still lacking. In this work, although current identified Kla sites are limited, we constructed the first Kla benchmark dataset and developed a few-shot learning-based architecture approach to leverage the power of small datasets and reduce the impact of imbalance and overfitting. A maximum 11.7% (0.745 versus 0.667) increase of area under the curve (AUC) value was achieved in contrast to conventional machine learning methods. We conducted a comprehensive survey of the performance by combining 8 sequence-based features and 3 structure-based features and tailored a multi-feature hybrid system for synergistic combination. This system achieved >16.2% improvement of the AUC value (0.889 versus 0.765) compared with single feature-based models for the prediction of Kla sites in silico. Taken few-shot learning and hybrid system together, we present our newly designed predictor named FSL-Kla, which is not only a cutting-edge tool for Kla site profile but also could generate candidates for further experimental approaches. The webserver of FSL-Kla is freely accessible for academic research at http://kla.zbiolab.cn/.
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Affiliation(s)
- Peiran Jiang
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Wanshan Ning
- MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yunshu Shi
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- Henan Provincial Cooperative Innovation Center for Cancer Chemoprevention, Zhengzhou, Henan 450001, China
| | - Chuan Liu
- State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Saijun Mo
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Haoran Zhou
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Kangdong Liu
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou, Henan 450001, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Yaping Guo
- Department of Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou, Henan 450001, China
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5
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Jiang P, Ning W, Shi Y, Liu C, Mo S, Zhou H, Liu K, Guo Y. FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction. Comput Struct Biotechnol J 2021. [DOI: 10.1016/j.csbj.2021.08.013\] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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6
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Persi E, Wolf YI, Horn D, Ruppin E, Demichelis F, Gatenby RA, Gillies RJ, Koonin EV. Mutation-selection balance and compensatory mechanisms in tumour evolution. Nat Rev Genet 2020; 22:251-262. [PMID: 33257848 DOI: 10.1038/s41576-020-00299-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2020] [Indexed: 12/11/2022]
Abstract
Intratumour heterogeneity and phenotypic plasticity, sustained by a range of somatic aberrations, as well as epigenetic and metabolic adaptations, are the principal mechanisms that enable cancers to resist treatment and survive under environmental stress. A comprehensive picture of the interplay between different somatic aberrations, from point mutations to whole-genome duplications, in tumour initiation and progression is lacking. We posit that different genomic aberrations generally exhibit a temporal order, shaped by a balance between the levels of mutations and selective pressures. Repeat instability emerges first, followed by larger aberrations, with compensatory effects leading to robust tumour fitness maintained throughout the tumour progression. A better understanding of the interplay between genetic aberrations, the microenvironment, and epigenetic and metabolic cellular states is essential for early detection and prevention of cancer as well as development of efficient therapeutic strategies.
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Affiliation(s)
- Erez Persi
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
| | - Yuri I Wolf
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - David Horn
- School of Physics and Astronomy, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Eytan Ruppin
- Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Francesca Demichelis
- Department for Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.,Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, NY, USA
| | - Robert A Gatenby
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
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