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Patrício A, Costa RS, Henriques R. Pattern-centric transformation of omics data grounded on discriminative gene associations aids predictive tasks in TCGA while ensuring interpretability. Biotechnol Bioeng 2024; 121:2881-2892. [PMID: 38859573 DOI: 10.1002/bit.28758] [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: 10/04/2023] [Revised: 02/07/2024] [Accepted: 05/18/2024] [Indexed: 06/12/2024]
Abstract
The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of molecular features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. We propose a transformation that moves data centered on molecules (e.g., transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant co-expression patterns. To this end, the proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and increase predictive performance of various classifiers across multiple omics layers. Results suggest that omics data transformations from gene-centric to pattern-centric data supports both prediction tasks and human interpretation, notably contributing to precision medicine applications.
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Affiliation(s)
- André Patrício
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal
| | - Rafael S Costa
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal
| | - Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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2
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Deng EZ, Marino GB, Clarke DJB, Diamant I, Resnick AC, Ma W, Wang P, Ma'ayan A. Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics. CELL REPORTS METHODS 2024:100839. [PMID: 39127042 DOI: 10.1016/j.crmeth.2024.100839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/06/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024]
Abstract
The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.
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Affiliation(s)
- Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Ido Diamant
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Adam C Resnick
- Center for Data Driven Discovery in Biomedicine, Division of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
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3
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Zhu W, Huang H, Hu Z, Gu Y, Zhang R, Shu H, Liu H, Sun X. Comprehensive Transcriptome Analysis Expands lncRNA Functional Profiles in Breast Cancer. Int J Mol Sci 2024; 25:8456. [PMID: 39126025 PMCID: PMC11313538 DOI: 10.3390/ijms25158456] [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: 07/09/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
Breast cancer is a heterogeneous disease that arises as a multi-stage process involving multiple cell types. Patients diagnosed with the same clinical stage and pathological classification may have different prognoses and therapeutic responses due to alterations in molecular genetics. As an essential marker for the molecular subtyping of breast cancer, long non-coding RNAs (lncRNAs) play a crucial role in gene expression regulation, cell differentiation, and the maintenance of genomic stability. Here, we developed a modular framework for lncRNA identification and applied it to a breast cancer cohort to identify novel lncRNAs not previously annotated. To investigate the potential biological function, regulatory mechanisms, and clinical relevance of the novel lncRNAs, we elucidated the genomic and chromatin features of these lncRNAs, along with the associated protein-coding genes and putative enhancers involved in the breast cancer regulatory networks. Furthermore, we uncovered that the expression patterns of novel and annotated lncRNAs identified in breast cancer were related to the hormone response in the PAM50 subtyping criterion, as well as the immune response and progression states of breast cancer across different immune cells and immune checkpoint genes. Collectively, the comprehensive identification and functional analysis of lncRNAs revealed that these lncRNAs play an essential role in breast cancer by altering gene expression and participating in the regulatory networks, contributing to a better insight into breast cancer heterogeneity and potential avenues for therapeutic intervention.
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Affiliation(s)
| | | | | | | | | | | | | | - Xiao Sun
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China
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4
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Li S, Garb BF, Qin T, Soppe S, Lopez E, Patil S, D’Silva NJ, Rozek LS, Sartor MA. Tumor Subtype Classification Tool for HPV-associated Head and Neck Cancers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.05.601906. [PMID: 39026719 PMCID: PMC11257489 DOI: 10.1101/2024.07.05.601906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Importance Molecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized and have been shown to have distinct mechanisms of carcinogenesis, clinical outcomes, and potentially differing optimal treatment strategies. Currently, no standardized method exists to subtype a new HPV+ HNSCC tumor. Our paper introduces a machine learning-based classifier and webtool to reliably subtype HPV+ HNSCC tumors using the IMU/KRT paradigm and highlights the importance of subtype in HPV+ HNSCC. Objective To develop a robust, accurate machine learning-based classification tool that standardizes the process of subtyping HPV+ HNSCC, and to investigate the clinical, demographic, and molecular features associated with subtype in a meta-analysis of four patient cohorts. Data Sources We conducted RNA-seq on 67 HNSCC FFPE blocks from University of Michigan hospital. Combining this with three publicly available datasets, we utilized a total of 229 HPV+ HNSCC RNA-seq samples. All participants were HPV+ according to RNA expression. An ensemble machine learning approach with five algorithms and three different input training gene sets were developed, with final subtype determined by majority vote. Several additional steps were taken to ensure rigor and reproducibility throughout. Study Selection The classifier was trained and tested using 84 subtype-labeled HPV+ RNA-seq samples from two cohorts: University of Michigan (UM; n=18) and TCGA-HNC (n=66). The classifier robustness was validated with two independent cohorts: 83 samples from the HPV Virome Consortium and 62 additional samples from UM. We revealed 24 of 39 tested clinicodemographic and molecular variables significantly associated with subtype. Results The classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigating the relationship between subtype and 39 molecular and clinicodemographic variables revealed IMU is associated with epithelial-mesenchymal transition (p=2.25×10-4), various immune cell types, and lower radiation resistance (p=0.0050), while KRT is more highly keratinized (p=2.53×10-8), and more likely female than IMU (p=0.0082). Conclusions and Relevance This study provides a reliable classifier for subtyping HPV+ HNSCC tumors as either IMU or KRT based on bulk RNA-seq data, and additionally, improves our understanding of the HPV+ HNSCC subtypes.
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Affiliation(s)
- Shiting Li
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Bailey F. Garb
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Tingting Qin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | | | - Elizabeth Lopez
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Snehal Patil
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Nisha J. D’Silva
- Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan, USA
| | - Laura S. Rozek
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Maureen A. Sartor
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
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Hablase R, Kyrou I, Randeva H, Karteris E, Chatterjee J. The "Road" to Malignant Transformation from Endometriosis to Endometriosis-Associated Ovarian Cancers (EAOCs): An mTOR-Centred Review. Cancers (Basel) 2024; 16:2160. [PMID: 38893278 PMCID: PMC11172073 DOI: 10.3390/cancers16112160] [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: 04/19/2024] [Revised: 05/29/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
Ovarian cancer is an umbrella term covering a number of distinct subtypes. Endometrioid and clear-cell ovarian carcinoma are endometriosis-associated ovarian cancers (EAOCs) frequently arising from ectopic endometrium in the ovary. The mechanistic target of rapamycin (mTOR) is a crucial regulator of cellular homeostasis and is dysregulated in both endometriosis and endometriosis-associated ovarian cancer, potentially favouring carcinogenesis across a spectrum from benign disease with cancer-like characteristics, through an atypical phase, to frank malignancy. In this review, we focus on mTOR dysregulation in endometriosis and EAOCs, investigating cancer driver gene mutations and their potential interaction with the mTOR pathway. Additionally, we explore the complex pathogenesis of transformation, considering environmental, hormonal, and epigenetic factors. We then discuss postmenopausal endometriosis pathogenesis and propensity for malignant transformation. Finally, we summarize the current advancements in mTOR-targeted therapeutics for endometriosis and EAOCs.
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Affiliation(s)
- Radwa Hablase
- College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB83PH, UK; (R.H.); (E.K.)
- Academic Department of Gynaecological Oncology, Royal Surrey NHS Foundation Trust Hospital, Guildford GU2 7XX, UK
| | - Ioannis Kyrou
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK (H.R.)
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- Institute for Cardiometabolic Medicine, University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK
- Centre for Sport, Exercise and Life Sciences, Research Institute for Health & Wellbeing, Coventry University, Coventry CV1 5FB, UK
- Aston Medical School, College of Health and Life Sciences, Aston University, Birmingham B4 7ET, UK
- College of Health, Psychology and Social Care, University of Derby, Derby DE22 1GB, UK
- Laboratory of Dietetics and Quality of Life, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece
| | - Harpal Randeva
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK (H.R.)
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- Institute for Cardiometabolic Medicine, University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK
- Centre for Sport, Exercise and Life Sciences, Research Institute for Health & Wellbeing, Coventry University, Coventry CV1 5FB, UK
| | - Emmanouil Karteris
- College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB83PH, UK; (R.H.); (E.K.)
| | - Jayanta Chatterjee
- College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB83PH, UK; (R.H.); (E.K.)
- Academic Department of Gynaecological Oncology, Royal Surrey NHS Foundation Trust Hospital, Guildford GU2 7XX, UK
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6
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Xie M, Kuang Y, Song M, Bao E. Subtype-MGTP: a cancer subtype identification framework based on multi-omics translation. Bioinformatics 2024; 40:btae360. [PMID: 38857453 PMCID: PMC11194476 DOI: 10.1093/bioinformatics/btae360] [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: 12/20/2023] [Revised: 04/30/2024] [Accepted: 06/07/2024] [Indexed: 06/12/2024] Open
Abstract
MOTIVATION The identification of cancer subtypes plays a crucial role in cancer research and treatment. With the rapid development of high-throughput sequencing technologies, there has been an exponential accumulation of cancer multi-omics data. Integrating multi-omics data has emerged as a cost-effective and efficient strategy for cancer subtyping. While current methods primarily rely on genomics data, protein expression data offers a closer representation of phenotype. Therefore, integrating protein expression data holds promise for enhancing subtyping accuracy. However, the scarcity of protein expression data compared to genomics data presents a challenge in its direct incorporation into existing methods. Moreover, striking a balance between omics-specific learning and cross-omics learning remains a prevalent challenge in current multi-omics integration methods. RESULTS We introduce Subtype-MGTP, a novel cancer subtyping framework based on the translation of Multiple Genomics To Proteomics. Subtype-MGTP comprises two modules: a translation module, which leverages available protein data to translate multi-type genomics data into predicted protein expression data, and an improved deep subspace clustering module, which integrates contrastive learning to cluster the predicted protein data, yielding refined subtyping results. Extensive experiments conducted on benchmark datasets demonstrate that Subtype-MGTP outperforms nine state-of-the-art cancer subtyping methods. The interpretability of clustering results is further supported by the clinical and survival analysis. Subtype-MGTP also exhibits strong robustness against varying rates of missing protein data and demonstrates distinct advantages in integrating multi-omics data with imbalanced multi-omics data. AVAILABILITY AND IMPLEMENTATION The code and results are available at https://github.com/kybinn/Subtype-MGTP.
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Affiliation(s)
- Minzhu Xie
- College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
- Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education), Changsha 410081, China
- College of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China
| | - Yabin Kuang
- College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
| | - Mengyun Song
- College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
| | - Ergude Bao
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
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Zhou Z, Lin T, Chen S, Zhang G, Xu Y, Zou H, Zhou A, Zhang Y, Weng S, Han X, Liu Z. Omics-based molecular classifications empowering in precision oncology. Cell Oncol (Dordr) 2024; 47:759-777. [PMID: 38294647 DOI: 10.1007/s13402-023-00912-8] [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] [Accepted: 12/23/2023] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine. CONCLUSION This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.
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Affiliation(s)
- Zhaokai Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ting Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Shuang Chen
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yudi Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Haijiao Zou
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Aoyang Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan, 450052, China.
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan, 450052, China.
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
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8
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Xi Y, Zheng K, Deng F, Liu Y, Sun H, Zheng Y, Tong HHY, Ji Y, Zhang Y, Chen W, Zhang Y, Zou X, Hao J. Themis: advancing precision oncology through comprehensive molecular subtyping and optimization. Brief Bioinform 2024; 25:bbae261. [PMID: 38833322 PMCID: PMC11149663 DOI: 10.1093/bib/bbae261] [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: 01/30/2024] [Revised: 04/30/2024] [Accepted: 05/21/2024] [Indexed: 06/06/2024] Open
Abstract
Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.
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Affiliation(s)
- Yue Xi
- Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
- Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Kun Zheng
- Department of Urology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Fulan Deng
- School of Materials Science and Engineering, Shanghai Institute of Technology, Shanghai 201418, China
| | - Yujun Liu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Hourong Sun
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yingxia Zheng
- Department of Laboratory Medicine, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Henry H Y Tong
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China
| | - Yuan Ji
- Molecular Pathology center, Dept. Pathology, Zhongshan Hospital, Fudan University
| | - Yingchun Zhang
- Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Wantao Chen
- Ninth People's Hospital, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, National Clinical Research Center of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Yiming Zhang
- Department of Reproductive Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Xin Zou
- National Engineering Center for Biochip at Shanghai, China
| | - Jie Hao
- Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai, China
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9
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Liu X, Wu L, Wang L, Li Y. Identification and classification of glioma subtypes based on RNA-binding proteins. Comput Biol Med 2024; 174:108404. [PMID: 38582000 DOI: 10.1016/j.compbiomed.2024.108404] [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: 12/04/2023] [Revised: 03/23/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Glioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy. METHODS RNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers. RESULTS Six tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways. CONCLUSIONS The study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.
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Affiliation(s)
- Xudong Liu
- School of Medicine, Chongqing University, Chongqing, 400044, China; Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Lei Wu
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Lei Wang
- College of Life Sciences, Xinyang Normal University, Xinyang, 464000, China.
| | - Yongsheng Li
- Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China.
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10
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Liu S, Meng Y, Zhang Y, Qiu L, Wan X, Yang X, Zhang Y, Liu X, Wen L, Lei X, Zhang B, Han J. Integrative analysis of senescence-related genes identifies robust prognostic clusters with distinct features in hepatocellular carcinoma. J Adv Res 2024:S2090-1232(24)00150-4. [PMID: 38614215 DOI: 10.1016/j.jare.2024.04.007] [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: 11/15/2023] [Revised: 04/09/2024] [Accepted: 04/09/2024] [Indexed: 04/15/2024] Open
Abstract
INTRODUCTION Senescence refers to a state of permanent cell growth arrest and is regarded as a tumor suppressive mechanism, whereas accumulative evidence demonstrate that senescent cells play an adverse role during cancer progression. The scarcity of specific and reliable markers reflecting senescence level in cancer impede our understanding of this biological basis. OBJECTIVES Senescence-related genes (SRGs) were collected for integrative analysis to reveal the role of senescence in hepatocellular carcinoma (HCC). METHODS Consensus clustering was used to subtype HCC based on SRGs. Several computational methods, including single sample gene set enrichment analysis (ssGSEA), fuzzy c-means algorithm, were performed. Data of drug sensitivities were utilized to screen potential therapeutic agents for different senescence patients. Additionally, we developed a method called signature-related gene analysis (SRGA) for identification of markers relevant to phenotype of interest. Experimental strategies consisting quantitative real-time PCR (qRT-PCR), β-galactosidase assay, western blot, and tumor-T cell co-culture system were used to validate the findings in vitro. RESULTS We identified three robust prognostic clusters of HCC patients with distinct survival outcome, mutational landscape, and immune features. We further extracted signature genes of senescence clusters to construct the senescence scoring system and profile senescence level in HCC at bulk and single-cell resolution. Senescence-induced stemness reprogramming was confirmed both in silico and in vitro. HCC patients with high senescence were immune suppressed and sensitive to Tozasertib and other drugs. We suggested that MAFG, PLIN3, and 4 other genes were pertinent to HCC senescence, and MAFG potentially mediated immune suppression, senescence, and stemness. CONCLUSION Our findings provide insights into the role of SRGs in patients stratification and precision medicine.
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Affiliation(s)
- Sicheng Liu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yang Meng
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yaguang Zhang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Lei Qiu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaowen Wan
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xuyang Yang
- Research Laboratory of Cancer Epigenetics and Genomics, Department of General Surgery, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yang Zhang
- Research Laboratory of Cancer Epigenetics and Genomics, Department of General Surgery, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xueqin Liu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Linda Wen
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xue Lei
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bo Zhang
- Research Laboratory of Cancer Epigenetics and Genomics, Department of General Surgery, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Junhong Han
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
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11
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Zhou Q, Ye W, Yu X, Bao YJ. A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108077. [PMID: 38382307 DOI: 10.1016/j.cmpb.2024.108077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/14/2024] [Accepted: 02/10/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance. OBJECTIVE The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers. METHODS Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC). RESULTS The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance. CONCLUSIONS The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.
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Affiliation(s)
- Qi Zhou
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, China
| | - Weicai Ye
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, and National Engineering Laboratory for Big Data Analysis and Application, Sun Yat-sen University, Guangzhou, China
| | - Xiaolan Yu
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, China; Hubei Jiangxia Laboratory, Wuhan, China
| | - Yun-Juan Bao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, China.
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12
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Ma T, Wang J. GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network. Bioinformatics 2024; 40:btae165. [PMID: 38530778 PMCID: PMC11007237 DOI: 10.1093/bioinformatics/btae165] [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: 09/22/2023] [Revised: 02/22/2024] [Accepted: 03/22/2024] [Indexed: 03/28/2024] Open
Abstract
MOTIVATION Studying the molecular heterogeneity of cancer is essential for achieving personalized therapy. At the same time, understanding the biological processes that drive cancer development can lead to the identification of valuable therapeutic targets. Therefore, achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. RESULTS Here, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling. Experiment results show that our method outperforms P-NET and other baseline methods. Besides, two external cohorts are used to validate that the model can be generalized to unseen samples with adequate predictive performance. We reduce the dimensionality of latent pathway embeddings and visualize corresponding classes to further demonstrate the optimal performance of the model. Additionally, since GraphPath's predictions are interpretable, we identify target cancer-associated pathways that significantly contribute to the model's predictions. Such a robust and interpretable model has the potential to greatly enhance our understanding of cancer's biological mechanisms and accelerate the development of targeted therapies. AVAILABILITY AND IMPLEMENTATION https://github.com/amazingma/GraphPath.
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Affiliation(s)
- Teng Ma
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 41083, Hunan, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 41083, Hunan, China
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13
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Unger M, Kather JN. Deep learning in cancer genomics and histopathology. Genome Med 2024; 16:44. [PMID: 38539231 PMCID: PMC10976780 DOI: 10.1186/s13073-024-01315-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/13/2024] [Indexed: 07/08/2024] Open
Abstract
Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other hand, is evaluated by engineered computational pipelines. In both applications, the advent of modern artificial intelligence methods, specifically machine learning (ML) and deep learning (DL), have opened up a fundamentally new way of extracting actionable insights from raw data, which could augment and potentially replace some aspects of traditional evaluation workflows. In this review, we summarize current and emerging applications of DL in histopathology and genomics, including basic diagnostic as well as advanced prognostic tasks. Based on a growing body of evidence, we suggest that DL could be the groundwork for a new kind of workflow in oncology and cancer research. However, we also point out that DL models can have biases and other flaws that users in healthcare and research need to know about, and we propose ways to address them.
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Affiliation(s)
- Michaela Unger
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine I, University Hospital Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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14
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Li A, Wang S, Nie J, Xiao S, Xie X, Zhang Y, Tong W, Yao G, Liu N, Dan F, Shu Z, Liu J, Liu Z, Yang F. USP3 promotes osteosarcoma progression via deubiquitinating EPHA2 and activating the PI3K/AKT signaling pathway. Cell Death Dis 2024; 15:235. [PMID: 38531846 DOI: 10.1038/s41419-024-06624-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 03/15/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024]
Abstract
Ubiquitin-specific protease 3 (USP3) plays an important role in the progression of various tumors. However, the role of USP3 in osteosarcoma (OS) remains poorly understood. The aim of this study was to explore the biological function of USP3 in OS and the underlying molecular mechanism. We found that OS had higher USP3 expression compared with that of normal bone tissue, and high expression of USP3 was associated with poor prognosis in patients with OS. Overexpression of USP3 significantly increased OS cell proliferation, migration, and invasion. Mechanistically, USP3 led to the activation of the PI3K/AKT signaling pathway in OS by binding to EPHA2 and then reducing its protein degradation. Notably, the truncation mutant USP3-F2 (159-520) interacted with EPHA2, and amino acid 203 was found to play an important role in this process. And knockdown of EPHA2 expression reversed the pro-tumour effects of USP3-upregulating. Thus, our study indicates the USP3/EPHA2 axis may be a novel potential target for OS treatment.
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Affiliation(s)
- Anan Li
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Shijiang Wang
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Jiangbo Nie
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Shining Xiao
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xinsheng Xie
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Yu Zhang
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Weilai Tong
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Geliang Yao
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Ning Liu
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Fan Dan
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Zhiguo Shu
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Jiaming Liu
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Zhili Liu
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
| | - Feng Yang
- Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
- Medical Innovation Center, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
- Institute of Spine and Spinal Cord, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
- Postdoctoral Innovation Practice Base, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
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15
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Altorki NK, Bhinder B, Borczuk AC, Elemento O, Mittal V, McGraw TE. A signature of enhanced proliferation associated with response and survival to anti-PD-L1 therapy in early-stage non-small cell lung cancer. Cell Rep Med 2024; 5:101438. [PMID: 38401548 PMCID: PMC10982989 DOI: 10.1016/j.xcrm.2024.101438] [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: 11/02/2022] [Revised: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 02/26/2024]
Abstract
In early-stage non-small cell lung cancer, the combination of neoadjuvant anti-PD-L1 and subablative stereotactic body radiation therapy (SBRT) is associated with higher rates of major pathologic response compared to anti-PD-L1 alone. Here, we identify a 140-gene set, enriched in genes characteristic of highly proliferating cells, associated with response to the dual therapy. Analysis of on-treatment transcriptome data indicate roles for T and B cells in response. The 140-gene set is associated with disease-free survival when applied to the combined trial arms. This 140-gene set identifies a subclass of tumors in all 7 of The Cancer Genome Atlas tumor types examined. Worse survival is associated with the 140-gene signature in 5 of these tumor types. Collectively, our data support that this 140-gene set, discovered in association with response to combined anti-PD-L1 and SBRT, identifies a clinically aggressive subclass of solid tumors that may be more likely to respond to immunotherapies.
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Affiliation(s)
- Nasser K Altorki
- Meyer Cancer Center, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY 10065, USA; Department of Cardiothoracic Surgery, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY 10065, USA.
| | - Bhavneet Bhinder
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alain C Borczuk
- Department of Pathology and Laboratory Medicine, Northwell Health Cancer Institute, Northwell Health, Greenvale, NY 10042, USA
| | - Olivier Elemento
- Meyer Cancer Center, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY 10065, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Vivek Mittal
- Meyer Cancer Center, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY 10065, USA; Department of Cardiothoracic Surgery, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY 10065, USA
| | - Timothy E McGraw
- Meyer Cancer Center, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY 10065, USA; Department of Cardiothoracic Surgery, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY 10065, USA; Department of Biochemistry, Weill Cornell Medicine, New York, NY 10065, USA.
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16
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Lenoci D, Resteghini C, Serafini MS, Pistore F, Canevari S, Ma B, Cavalieri S, Alfieri S, Trama A, Licitra L, De Cecco L. Tumor molecular landscape of Epstein-Barr virus (EBV) related nasopharyngeal carcinoma in EBV-endemic and non-endemic areas: Implications for improving treatment modalities. Transl Res 2024; 265:1-16. [PMID: 37949350 DOI: 10.1016/j.trsl.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 10/14/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023]
Abstract
Epstein-Barr virus (EBV) related- nasopharyngeal carcinoma (NPC) is a squamous carcinoma of the nasopharyngeal mucosal lining. Endemic areas (EA) are east and Southeast Asia, were NPC was recorded with higher incidence and longer estimated survival than in non-endemic area (NEA) such as Europe, We analyzed the gene expression and microenvironment properties of NPC in both areas to identify molecular subtypes and assess biological and clinical correlates that might explain the differences in incidence and outcome between EA- and NEA-NPCs. Six EA-NPC transcriptomic datasets, including tumor and normal samples, were integrated in a meta-analysis to identify molecular subtypes using a ConsensusClusterPlus bioinformatic approach. Based on the biological/functional characterization of four identified clusters were identified: Cl1, Immune-active; Cl2, defense-response; Cl3, proliferation; Cl4, perineural-interaction/EBV-exhaustion. Kaplan-Meier survival analysis, applied to the single dataset with available disease-free survival indicated Cl3 as the cluster with the worst prognosis (P = 0.0476), confirmed when applying four previously published prognostic signatures. A Cl3 classifier signature was generated and its prognostic performance was confirmed (P = 0.0368) on a validation dataset. Prediction of treatment response suggested better responses to: radiotherapy and immune checkpoint inhibitors immune-active and defense-response clusters; chemotherapy proliferation cluster; cisplatin perineural-interaction/EBV-exhaustion cluster. RNA sequencing for gene expression profiling was performed on 50 NEA-NPC Italian samples. In the NEA cohort, Cl1, Cl2 and Cl3 were represented, while perineural-interaction/EBV-exhaustion was almost absent. The immune/biological characterization and treatment-response prediction analyses of NEA-NPC partially replicated the EA-NPC results. Well characterized EA- and NEA-NPC retrospective and prospective cohorts are needed to validate the obtained results and can help designing future clinical studies.
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Affiliation(s)
- Deborah Lenoci
- Molecular Mechanisms Unit, Experimental Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Via GA. Amadeo, 42-20133 Milano, Italy
| | - Carlo Resteghini
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Mara S Serafini
- Molecular Mechanisms Unit, Experimental Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Via GA. Amadeo, 42-20133 Milano, Italy
| | - Federico Pistore
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Silvana Canevari
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Brigette Ma
- Department of Clinical Oncology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Salvatore Alfieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Annalisa Trama
- Evaluative Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Loris De Cecco
- Molecular Mechanisms Unit, Experimental Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Via GA. Amadeo, 42-20133 Milano, Italy.
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17
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Angeloni M, van Doeveren T, Lindner S, Volland P, Schmelmer J, Foersch S, Matek C, Stoehr R, Geppert CI, Heers H, Wach S, Taubert H, Sikic D, Wullich B, van Leenders GJLH, Zaburdaev V, Eckstein M, Hartmann A, Boormans JL, Ferrazzi F, Bahlinger V. A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing. J Pathol Clin Res 2024; 10:e12369. [PMID: 38504364 PMCID: PMC10951050 DOI: 10.1002/2056-4538.12369] [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: 11/24/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024]
Abstract
Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.
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Affiliation(s)
- Miriam Angeloni
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Thomas van Doeveren
- Department of UrologyErasmus MC Urothelial Cancer Research GroupRotterdamThe Netherlands
| | - Sebastian Lindner
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Patrick Volland
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Jorina Schmelmer
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | | | - Christian Matek
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Robert Stoehr
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Hendrik Heers
- Department of UrologyPhilipps‐Universität MarburgMarburgGermany
| | - Sven Wach
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Helge Taubert
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Danijel Sikic
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Bernd Wullich
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Geert JLH van Leenders
- Department of PathologyErasmus MC Cancer Institute, University Medical CentreRotterdamthe Netherlands
| | - Vasily Zaburdaev
- Department of BiologyFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Max‐Planck‐Zentrum für Physik und MedizinErlangenGermany
| | - Markus Eckstein
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Joost L Boormans
- Department of UrologyErasmus MC Urothelial Cancer Research GroupRotterdamThe Netherlands
| | - Fulvia Ferrazzi
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of NephropathologyInstitute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Veronika Bahlinger
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Pathology and NeuropathologyUniversity Hospital and Comprehensive Cancer Center TübingenTübingenGermany
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18
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Bouwman L, Sepodes B, Leufkens H, Torre C. Trends in orphan medicinal products approvals in the European Union between 2010-2022. Orphanet J Rare Dis 2024; 19:91. [PMID: 38413985 PMCID: PMC10900541 DOI: 10.1186/s13023-024-03095-z] [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: 11/13/2023] [Accepted: 02/21/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Over the last twenty years of orphan drug regulation in Europe, the regulatory framework has increased its complexity, with different regulatory paths and tools engineered to facilitate the innovation and accelerate approvals. Recently, the proposal of the new Pharmaceutical Legislation for the European Union, which will replace at least three Regulations and one Directive, was released and its new framework is raising many questions. The aim of this study was to present a characterisation of the Orphan Medicinal Products (OMPs) authorised by the European Commission (EC), between 2010 and 2022, looking into eighteen variables, contributing to the ongoing discussion on the proposal and implementation of the new Pharmaceutical Legislation proposed. METHODS Data of the OMPs identified and approved between 2010 and 2022 were extracted from the European Public Assessment Reports (EPARs) produced by the European Medicines Agency. Information regarding legal basis of the application, applicant, protocol assistance received, type of authorization, registration status, type of molecule, ATC code, therapeutic area, target age, disease prevalence, number of pivotal clinical trials supporting the application, clinical trial designs, respective efficacy endpoints and number of patients enrolled in the pivotal clinical trials were extracted. A descriptive statistical analysis was applied. RESULTS We identified 192 OMPs approved in the period between 2010 and 2022. 89% of the OMPs have legal basis of "full application". 86% of the sponsors received protocol assistance whereas 64% of the MAA benefited from the accelerated assessment. 53% of the active substances are small molecules; about 1 in 5 molecules are repurposed. 40% of the OMPs have oncological therapeutic indications and 56% of the OMPs are intended to treat only adults. 71% of the products were approved based on a single pivotal trial. CONCLUSIONS This analysis of OMPs approved between 2010 and 2022 shows that a shift has occurred in the rare disease medicine development space. Through the period studied we observe an increase of non-small molecules approved, accelerated assessment received and non-standard MA's granted.
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Affiliation(s)
- Luísa Bouwman
- Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal.
- Laboratory of Systems Integration Pharmacology, Clinical and Regulatory Science, Research Institute for Medicines of the University of Lisbon (iMED.ULisboa), Lisbon, Portugal.
| | - Bruno Sepodes
- Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal
- Laboratory of Systems Integration Pharmacology, Clinical and Regulatory Science, Research Institute for Medicines of the University of Lisbon (iMED.ULisboa), Lisbon, Portugal
| | - Hubert Leufkens
- Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands
| | - Carla Torre
- Faculdade de Farmácia, Universidade de Lisboa, Lisbon, Portugal
- Laboratory of Systems Integration Pharmacology, Clinical and Regulatory Science, Research Institute for Medicines of the University of Lisbon (iMED.ULisboa), Lisbon, Portugal
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19
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Essouma M. Autoimmune inflammatory myopathy biomarkers. Clin Chim Acta 2024; 553:117742. [PMID: 38176522 DOI: 10.1016/j.cca.2023.117742] [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: 07/18/2023] [Revised: 12/21/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024]
Abstract
The autoimmune inflammatory myopathy disease spectrum, commonly known as myositis, is a group of systemic diseases that mainly affect the muscles, skin and lungs. Biomarker assessment helps in understanding disease mechanisms, allowing for the implementation of precise strategies in the classification, diagnosis, and management of these diseases. This review examines the pathogenic mechanisms and highlights current data on blood and tissue biomarkers of autoimmune inflammatory myopathies.
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Affiliation(s)
- Mickael Essouma
- Network of Immunity in Infections, Malignancy and Autoimmunity, Universal Scientific Education and Research Network, Cameroon
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20
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Hasan MN, Rahman MM, Husna AA, Nozaki N, Yamato O, Miura N. YRNA and tRNA fragments can differentiate benign from malignant canine mammary gland tumors. Biochem Biophys Res Commun 2024; 691:149336. [PMID: 38039834 DOI: 10.1016/j.bbrc.2023.149336] [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: 09/25/2023] [Accepted: 11/23/2023] [Indexed: 12/03/2023]
Abstract
Mammary gland tumors (MGT) are the most common tumors in sexually intact female dogs. The functional regulation of miRNAs, a type of noncoding RNAs (ncRNAs), in canine MGT has been extensively investigated. However, the expression of other ncRNAs, such as YRNAs and transfer RNA-derived fragments (tRFs) in canine MGT is unknown. We investigated ncRNAs other than miRNAs from our small RNA project (PRJNA716131) in different canine MGT histologic subtypes. This study included benign tumors (benign mixed tumor, complex adenoma) and malignant tumors (carcinoma in benign tumor and carcinoma with metastasis) samples. Aberrantly expressed ncRNAs were examined by comparisons among MGT subtypes. The relative expression trends were validated in canine MGT tissues, plasma, extracellular vesicles, and MGT cell lines using quantitative reverse transcription PCR. Three aberrantly expressed ncRNAs were identified by comparisons among MGT subtypes. YRNA and tRNA-Gly-GCC distinguished benign mixed tumor from other MGT histologic subtypes, while tRNA-Val differentiated complex adenoma, carcinoma in benign tumors, and carcinoma with metastasis. The ROC curve of the three ncRNAs showed they might be potential biomarkers to discriminate malignant from benign MGT. YRNA and tRFs expression levels were decreased in metastatic compared with primary canine MGT cell lines. To the best of our knowledge, this is the first investigation of YRNA and tRFs in canine MGT. The three identified ncRNAs may be biomarkers for differentiating MGT histologic subtypes. Suggested Reviewers: Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporatio.
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Affiliation(s)
- Md Nazmul Hasan
- Joint Graduate School of Veterinary Medicine, Kagoshima University, 1-21-24, Korimoto, Kagoshima, 890-0065, Japan; Veterinary Teaching Hospital, Joint Faculty of Veterinary Medicine, Kagoshima University, 1-21-24, Korimoto, Kagoshima, 890-0065, Japan.
| | - Md Mahfuzur Rahman
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Al Asmaul Husna
- Veterinary Teaching Hospital, Joint Faculty of Veterinary Medicine, Kagoshima University, 1-21-24, Korimoto, Kagoshima, 890-0065, Japan.
| | - Nobuhiro Nozaki
- Joint Graduate School of Veterinary Medicine, Kagoshima University, 1-21-24, Korimoto, Kagoshima, 890-0065, Japan.
| | - Osamu Yamato
- Joint Graduate School of Veterinary Medicine, Kagoshima University, 1-21-24, Korimoto, Kagoshima, 890-0065, Japan; Veterinary Teaching Hospital, Joint Faculty of Veterinary Medicine, Kagoshima University, 1-21-24, Korimoto, Kagoshima, 890-0065, Japan.
| | - Naoki Miura
- Joint Graduate School of Veterinary Medicine, Kagoshima University, 1-21-24, Korimoto, Kagoshima, 890-0065, Japan; Veterinary Teaching Hospital, Joint Faculty of Veterinary Medicine, Kagoshima University, 1-21-24, Korimoto, Kagoshima, 890-0065, Japan.
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21
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Zhang JZ, Wang C. A comparative study of clustering methods on gene expression data for lung cancer prognosis. BMC Res Notes 2023; 16:319. [PMID: 37941025 PMCID: PMC10630994 DOI: 10.1186/s13104-023-06604-8] [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: 05/07/2023] [Accepted: 10/27/2023] [Indexed: 11/10/2023] Open
Abstract
Lung cancer subtyping based on gene expression data is important for identifying patient subgroups with differing survival prognosis to facilitate customized treatment strategies for each subtype of patients. Unsupervised clustering methods are the traditional approach for clustering patients into subtypes. However, since those methods cluster patients based only on gene expression data, the resulting clusters may not always be relevant to the survival outcome of interest. In recent years, semi-supervised and supervised methods have been proposed, which leverage the survival outcome data to identify clusters more relevant to survival prognosis. This paper aims to compare the performance of different clustering methods for identifying clinically prognostic lung cancer subtypes based on two lung adenocarcinoma datasets. For each method, we clustered patients into two clusters and assessed the difference in patient survival time between clusters. Unsupervised methods were found to have large logrank p-values and no significant results in most cases. Semi-supervised and supervised methods had improved performance over unsupervised methods and very significant p-values. These results indicate that unsupervised methods are not capable of identifying clusters with significant differences in survival prognosis in most cases, while supervised and semi-supervised methods can better cluster patients into clinically useful subtypes.
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Affiliation(s)
- Jason Z Zhang
- Wake Forest University, Winston-Salem, NC, United States of America
| | - Chi Wang
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA.
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22
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Li K, Sun L, Wang Y, Cen Y, Zhao J, Liao Q, Wu W, Sun J, Zhou M. Single-cell characterization of macrophages in uveal melanoma uncovers transcriptionally heterogeneous subsets conferring poor prognosis and aggressive behavior. Exp Mol Med 2023; 55:2433-2444. [PMID: 37907747 PMCID: PMC10689813 DOI: 10.1038/s12276-023-01115-9] [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: 09/09/2022] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 11/02/2023] Open
Abstract
Uveal melanoma (UM) is the most frequent primary intraocular malignancy with high metastatic potential and poor prognosis. Macrophages represent one of the most abundant infiltrating immune cells with diverse functions in cancers. However, the cellular heterogeneity and functional diversity of macrophages in UM remain largely unexplored. In this study, we analyzed 63,264 single-cell transcriptomes from 11 UM patients and identified four transcriptionally distinct macrophage subsets (termed MΦ-C1 to MΦ-C4). Among them, we found that MΦ-C4 exhibited relatively low expression of both M1 and M2 signature genes, loss of inflammatory pathways and antigen presentation, instead demonstrating enhanced signaling for proliferation, mitochondrial functions and metabolism. We quantified the infiltration abundance of MΦ-C4 from single-cell and bulk transcriptomes across five cohorts and found that increased MΦ-C4 infiltration was relevant to aggressive behaviors and may serve as an independent prognostic indicator for poor outcomes. We propose a novel subtyping scheme based on macrophages by integrating the transcriptional signatures of MΦ-C4 and machine learning to stratify patients into MΦ-C4-enriched or MΦ-C4-depleted subtypes. These two subtypes showed significantly different clinical outcomes and were validated through bulk RNA sequencing and immunofluorescence assays in both public multicenter cohorts and our in-house cohort. Following further translational investigation, our findings highlight a potential therapeutic strategy of targeting macrophage subsets to control metastatic disease and consistently improve the outcome of patients with UM.
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Affiliation(s)
- Ke Li
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
| | - Lanfang Sun
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
| | - Yanan Wang
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
| | - Yixin Cen
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
| | - Jingting Zhao
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
| | - Qianling Liao
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China
| | - Wencan Wu
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China.
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China.
| | - Jie Sun
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China.
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China.
| | - Meng Zhou
- State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China.
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China.
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23
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Bao J, Yu Y. Identification of a prognostic evaluator from glutamine metabolic heterogeneity studies within and between tissues in hepatocellular carcinoma. Front Pharmacol 2023; 14:1241677. [PMID: 37954858 PMCID: PMC10637396 DOI: 10.3389/fphar.2023.1241677] [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: 06/17/2023] [Accepted: 09/21/2023] [Indexed: 11/14/2023] Open
Abstract
Background: The liver is the major metabolic organ of the human body, and abnormal metabolism is the main factor influencing hepatocellular carcinoma (HCC). This study was designed to determine the effect of glutamine metabolism on HCC heterogeneity and to develop a prognostic evaluator based on the heterogeneity study of glutamine metabolism within HCC tumors and between tissues. Methods: Single-cell transcriptome data were extracted from the GSE149614 dataset and processed using the Seurat package in R for quality control of these data. HCC subtypes in the Cancer Genome Atlas and the GSE14520 dataset were identified via consensus clustering based on glutamine family amino acid metabolism (GFAAM) process genes. The machine learning algorithms gradient boosting machine, support vector machine, random forest, eXtreme gradient boosting, decision trees, and least absolute shrinkage and selection operator were utilized to develop the prognosis model of differentially expressed genes among the molecular gene subtypes. Results: The samples in the GSE149614 dataset included 10 cell types, and there was no significant difference in the GFAAM pathway. HCC was classified into three molecular subtypes according to GFAAM process genes, showing molecular heterogeneity in prognosis, clinicopathological features, and immune cell infiltration. C1 showed the worst survival rate and the highest immune score and immune cell infiltration. A six-gene model for prognostic and immunotherapy responses was constructed among subtypes, and the calculated high-risk score was significantly correlated with poor prognosis, high immune abundance, and a low response rate of immunotherapy in HCC. Conclusion: Our discovery of GFAAM-associated marker genes may help to further decipher the role in HCC occurrence and progression. In particular, this six-gene prognostic model may serve as a predictor of treatment and prognosis in HCC patients.
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Affiliation(s)
- Jie Bao
- Digestive System Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Yu
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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24
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Wei L, Niraula D, Gates EDH, Fu J, Luo Y, Nyflot MJ, Bowen SR, El Naqa IM, Cui S. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br J Radiol 2023; 96:20230211. [PMID: 37660402 PMCID: PMC10546458 DOI: 10.1259/bjr.20230211] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/15/2023] [Accepted: 06/27/2023] [Indexed: 09/05/2023] Open
Abstract
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Michigan, United States
| | - Dipesh Niraula
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Evan D. H. Gates
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Jie Fu
- Department of Radiation Oncology, Stanford University, Stanford, California, United States
| | - Yi Luo
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington, Washington, United States
| | - Issam M El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, United States
| | - Sunan Cui
- Department of Radiation Oncology, University of Washington, Washington, United States
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25
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Bond A, Mccay K, Lal S. Artificial intelligence & clinical nutrition: What the future might have in store. Clin Nutr ESPEN 2023; 57:542-549. [PMID: 37739704 DOI: 10.1016/j.clnesp.2023.07.082] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/02/2023] [Accepted: 07/17/2023] [Indexed: 09/24/2023]
Abstract
Artificial Intelligence (AI) is a rapidly emerging technology in healthcare that has the potential to revolutionise clinical nutrition. AI can assist in analysing complex data, interpreting medical images, and providing personalised nutrition interventions for patients. Clinical nutrition is a critical aspect of patient care, and AI can help clinicians make more informed decisions regarding patients' nutritional requirements, disease prevention, and management. AI algorithms can analyse large datasets to identify novel associations between diet and disease outcomes, enabling clinicians to make evidence-based nutritional recommendations. AI-powered devices and applications can also assist in tracking dietary intake, providing feedback, and motivating patients towards healthier food choices. However, the adoption of AI in clinical nutrition raises several ethical and regulatory concerns, such as data privacy and bias. Further research is needed to assess the clinical effectiveness and safety of AI-powered nutrition interventions. In conclusion, AI has the potential to transform clinical nutrition, but its integration into clinical practice should be carefully monitored to ensure patient safety and benefit. This article discusses the current and future applications of AI in clinical nutrition and highlights its potential benefits.
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Affiliation(s)
- Ashley Bond
- Intestinal Failure Unit, Salford Royal Foundation Trust, UK; University of Manchester, Manchester, UK.
| | - Kevin Mccay
- Manchester Metropolitan University, Manchester, UK; Northern Care Alliance NHS Foundation Trust, Salford Royal Hospital, Salford, UK
| | - Simon Lal
- Intestinal Failure Unit, Salford Royal Foundation Trust, UK; University of Manchester, Manchester, UK
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26
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Zhang J, Liu L, Wei X, Zhao C, Li S, Li J, Le TD. Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers. PLoS Comput Biol 2023; 19:e1011308. [PMID: 37812646 PMCID: PMC10586676 DOI: 10.1371/journal.pcbi.1011308] [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: 06/29/2023] [Revised: 10/19/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
Non-coding RNAs (ncRNAs) act as important modulators of gene expression and they have been confirmed to play critical roles in the physiology and development of malignant tumors. Understanding the synergism of multiple ncRNAs in competing endogenous RNA (ceRNA) regulation can provide important insights into the mechanisms of malignant tumors caused by ncRNA regulation. In this work, we present a framework, SCOM, for identifying ncRNA synergistic competition. We systematically construct the landscape of ncRNA synergistic competition across 31 malignant tumors, and reveal that malignant tumors tend to share hub ncRNAs rather than the ncRNA interactions involved in the synergistic competition. In addition, the synergistic competition ncRNAs (i.e. ncRNAs involved in the synergistic competition) are likely to be involved in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. Furthermore, SCOM can help to infer ncRNA synergistic competition across malignant tumors and uncover potential diagnostic and prognostic biomarkers of malignant tumors. Altogether, the SCOM framework (https://github.com/zhangjunpeng411/SCOM/) and the resulting web-based database SCOMdb (https://comblab.cn/SCOMdb/) serve as a useful resource for exploring ncRNA regulation and to accelerate the identification of carcinogenic biomarkers.
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Affiliation(s)
- Junpeng Zhang
- School of Engineering, Dali University, Dali, Yunnan, People’s Republic of China
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Xuemei Wei
- School of Engineering, Dali University, Dali, Yunnan, People’s Republic of China
| | - Chunwen Zhao
- School of Engineering, Dali University, Dali, Yunnan, People’s Republic of China
| | - Sijing Li
- School of Engineering, Dali University, Dali, Yunnan, People’s Republic of China
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Thuc Duy Le
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
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27
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Chen W, Wang H, Liang C. Deep multi-view contrastive learning for cancer subtype identification. Brief Bioinform 2023; 24:bbad282. [PMID: 37539822 DOI: 10.1093/bib/bbad282] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/29/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
Cancer heterogeneity has posed great challenges in exploring precise therapeutic strategies for cancer treatment. The identification of cancer subtypes aims to detect patients with distinct molecular profiles and thus could provide new clues on effective clinical therapies. While great efforts have been made, it remains challenging to develop powerful computational methods that can efficiently integrate multi-omics datasets for the task. In this paper, we propose a novel self-supervised learning model called Deep Multi-view Contrastive Learning (DMCL) for cancer subtype identification. Specifically, by incorporating the reconstruction loss, contrastive loss and clustering loss into a unified framework, our model simultaneously encodes the sample discriminative information into the extracted feature representations and well preserves the sample cluster structures in the embedded space. Moreover, DMCL is an end-to-end framework where the cancer subtypes could be directly obtained from the model outputs. We compare DMCL with eight alternatives ranging from classic cancer subtype identification methods to recently developed state-of-the-art systems on 10 widely used cancer multi-omics datasets as well as an integrated dataset, and the experimental results validate the superior performance of our method. We further conduct a case study on liver cancer and the analysis results indicate that different subtypes might have different responses to the selected chemotherapeutic drugs.
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Affiliation(s)
- Wenlan Chen
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Hong Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
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Nourbakhsh M, Saksager A, Tom N, Chen XS, Colaprico A, Olsen C, Tiberti M, Papaleo E. A workflow to study mechanistic indicators for driver gene prediction with Moonlight. Brief Bioinform 2023; 24:bbad274. [PMID: 37551622 PMCID: PMC10516357 DOI: 10.1093/bib/bbad274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 08/09/2023] Open
Abstract
Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
| | - Nikola Tom
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
| | - Xi Steven Chen
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Catharina Olsen
- Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Clinical Sciences, Reproduction and Genetics
- Brussels Interuniversity Genomics High Throughput core (BRIGHTcore), VUB-ULB, Brussels 1090, Belgium
- Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels 1050, Belgium
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, Copenhagen, Denmark
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Ji Y, Dutta P, Davuluri R. Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes. BIOINFORMATICS ADVANCES 2023; 3:vbad075. [PMID: 37424943 PMCID: PMC10328436 DOI: 10.1093/bioadv/vbad075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/08/2023] [Indexed: 07/11/2023]
Abstract
Motivation Molecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches. Results We developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks. Availability and implementation Source codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Yanrong Ji
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Pratik Dutta
- Department of Biomedical Informatics, Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ramana Davuluri
- Department of Biomedical Informatics, Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY 11794, USA
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Shetty KS, Jose A, Bani M, Vinod PK. Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of papillary renal cell carcinoma. Mol Genet Genomics 2023; 298:871-882. [PMID: 37093328 DOI: 10.1007/s00438-023-02022-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 04/12/2023] [Indexed: 04/25/2023]
Abstract
Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between subtypes showed that genes related to immune, extracellular matrix organization, and genomic instability are upregulated in the poor survival group. Overall, the network-based approach revealed the molecular subtypes of KIRP and captured the relationship between gene expression and CNVs. This framework can be further expanded to integrate other omics data.
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Affiliation(s)
- Keerthi S Shetty
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, 500032, India
| | - Aswin Jose
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, 500032, India
| | - Mihir Bani
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, 500032, India
| | - P K Vinod
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, 500032, India.
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Yang M, Matan-Lithwick S, Wang Y, De Jager PL, Bennett DA, Felsky D. Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing. Brain Commun 2023; 5:fcad110. [PMID: 37082508 PMCID: PMC10110975 DOI: 10.1093/braincomms/fcad110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/17/2023] [Accepted: 04/03/2023] [Indexed: 04/07/2023] Open
Abstract
Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (n S1 = 53, n S2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions (p Bonf = 5.9 × 10-3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain.
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Affiliation(s)
- Mu Yang
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Stuart Matan-Lithwick
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Yanling Wang
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL 60612, USA
| | - Philip L De Jager
- The Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY 10033, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL 60612, USA
| | - Daniel Felsky
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
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Rather AA, Chachoo MA. Robust correlation estimation and UMAP assisted topological analysis of omics data for disease subtyping. Comput Biol Med 2023; 155:106640. [PMID: 36774889 DOI: 10.1016/j.compbiomed.2023.106640] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/08/2023] [Accepted: 02/05/2023] [Indexed: 02/10/2023]
Abstract
Deciphering information hidden in the gene expression assays for identifying disease subtypes has significant importance in precision medicine. However, computational limitations thwart this process due to the intricacy of the biological networks and the curse of dimensionality of gene expression data. Therefore, clustering in such scenarios often becomes the first choice of exploratory data analysis to identify natural structures and intrinsic patterns in the data. However, sparse and high dimensional nature of omics data prevents conventional clustering algorithms to discover subtypes that are clinically relevant and statistically significant. Hence, non-linear dimensionality reduction techniques coupled with clustering in such scenarios often becomes imperative to improve the clustering results. In this study, we present a robust pipeline to discover disease subtypes with clinical relevance. Specifically, we focus on discovering patient sub-groups that have a residual life patterns remarkably different from other sub-groups. This is significant because by refining prognosis, subtyping can reduce uncertainty in approximating patients expected outcome. The methodology present is based on robust correlation estimation, UMAP- a non-linear dimensionality reduction method and mapper- a tool from topology. Notably, we suggest a method for improving the robustness of the correlation matrix of gene expression data for improving the clustering results. The performance of the model is evaluated by applying to five cancer datasets obtained through TCGA and comparisons are performed with some state of the art methods of NEMO, RSC-OTRI and SNF with regard to log-rank test and Restricted Life Expectancy Difference. For example in GBM dataset, the minimum separation for any two discovered subtypes is 221 days which is significantly higher than the other methodologies. We also compared the results without using the robust correlation based estimate and observed that robust correlation improves separability between survival curves significantly. From the results we infer that our methodology performs better compared to other methodologies with regard to separating survival curves of patient sub-groups despite using single omics profiles of patients compared to multiple omics profiles of SNF and NEMO. Pathway over-representation analysis is performed on the final clustering results to investigate the biological underpinnings characterizing each subtype.
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Affiliation(s)
- Arif Ahmad Rather
- Department of Computer Sciences, University of Kashmir, Srinagar, JK, India.
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Zhao L, Cai Y, Cho WC. Editorial: Genomics of immunoregulation and inflammatory responses in the tumor microenvironment. Front Genet 2023; 14:1136342. [PMID: 36845401 PMCID: PMC9950731 DOI: 10.3389/fgene.2023.1136342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023] Open
Affiliation(s)
- Lan Zhao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States,*Correspondence: Lan Zhao,
| | - Yunpeng Cai
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - William C. Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
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Xie C, Hu J, Hu Q, Jiang L, Chen W. Classification of the mitochondrial ribosomal protein-associated molecular subtypes and identified a serological diagnostic biomarker in hepatocellular carcinoma. Front Surg 2023; 9:1062659. [PMID: 36684217 PMCID: PMC9853988 DOI: 10.3389/fsurg.2022.1062659] [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: 10/06/2022] [Accepted: 12/12/2022] [Indexed: 01/07/2023] Open
Abstract
Purpose The objective of this study was to sort out innovative molecular subtypes associated with mitochondrial ribosomal proteins (MRPs) to predict clinical therapy response and determine the presence of circulating markers in hepatocellular carcinoma (HCC) patients. Methods Using an unsupervised clustering method, we categorized the relative molecular subtypes of MRPs in HCC patients. The prognosis, biological properties, immune checkpoint inhibitor and chemotherapy response of the patients were clarified. A signature and nomogram were developed to evaluate the prognosis. Enzyme-linked immunosorbent assay (ELISA) measured serum mitochondrial ribosomal protein L9 (MRPL9) levels in liver disease patients and normal individuals. Receiver operating characteristic (ROC) curves were conducted to calculate the diagnostic effect. The Cell Counting Kit 8 was carried out to examine cell proliferation, and flow cytometry was used to investigate the cell cycle. Transwell assay was applied to investigate the potential of cell migration and invasion. Western blot detected corresponding changes of biological markers. Results Participants were classified into two subtypes according to MRPs expression levels, which were characterized by different prognoses, biological features, and marked differences in response to chemotherapy and immune checkpoint inhibitors. Serum MRPL9 was significantly higher in HCC patients than in normal individuals and the benign liver disease group. ROC curve analysis showed that MRPL9 was superior to AFP and Ferritin in differentiating HCC from healthy and benign patients, or alone. Overexpressed MRPL9 could enhance aggressiveness and facilitate the G1/S progression in HCC cells. Conclusion We constructed novel molecular subtypes based on MRPs expression in HCC patients, which provided valuable strategies for the prediction of prognosis and clinical personalized treatment. MRPL9 might act as a reliable circulating diagnostic biomarker and therapeutic target for HCC patients.
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Shi X, Liang C, Wang H. Multiview Robust Graph-Based Clustering for Cancer Subtype Identification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:544-556. [PMID: 35044919 DOI: 10.1109/tcbb.2022.3143897] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cancer subtype identification is to classify cancer into groups according to their molecular characteristics and clinical manifestations and is the basis for more personalized diagnosis and therapy. Public datasets such as The Cancer Genome Atlas (TCGA) have collected a massive number of multi-omics data. The accumulation of these datasets provides unprecedented opportunities to study the mechanism of cancers and further identify cancer subtypes at a comprehensive level. In this paper, we propose a multi-view robust graph-based clustering (MRGC) method to effectively identify cancer subtypes. Our method first learns robust latent representations from the raw omics data to alleviate the influences of the noise, where a set of similarity matrices are then adaptively learned based on these new representations. Finally, a global similarity graph is obtained by exploiting the consensus structure from the graphs. As a result, the three parts in our method can reinforce each other in a mutual iterative manner. We conduct extensive experiments on both generic machine learning datasets and cancer datasets. The experimental results confirm that our model can achieve satisfactory clustering performance compared to several state-of-the-art approaches. Moreover, we convey the practicability of MRGC by carrying out a case study on hepatocellular carcinoma.
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36
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Fan Y, S Chan A, Zhu J, Yi Leung S, Fan X. A Bayesian model for identifying cancer subtypes from paired methylation profiles. Brief Bioinform 2022; 24:6961790. [PMID: 36575828 PMCID: PMC9851340 DOI: 10.1093/bib/bbac568] [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: 07/29/2022] [Revised: 10/19/2022] [Accepted: 11/22/2022] [Indexed: 12/29/2022] Open
Abstract
Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.
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Affiliation(s)
- Yetian Fan
- School of Mathematics and Statistics, Liaoning University, Shenyang, 110036, China,Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong SAR, China
| | - April S Chan
- Department of Pathology, School of Clinical Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jun Zhu
- Sema4, Stamford, CT, 06902, USA,Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suet Yi Leung
- Department of Pathology, School of Clinical Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xiaodan Fan
- Corresponding author: Xiaodan Fan, Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong SAR, China. E-mail:
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Shojaee Z, Shahzadeh Fazeli SA, Abbasi E, Adibnia F, Masuli F, Rovetta S. A Mutual Information Based on Ant Colony Optimization Method to Feature Selection for Categorical Data Clustering. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY, TRANSACTIONS A: SCIENCE 2022. [DOI: 10.1007/s40995-022-01395-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Sarode P, Pullamsetti SS, Savai R. New Insights into the Epigenomic Landscape of Small-Cell Lung Cancer: A Game Changer? Am J Respir Crit Care Med 2022; 206:1441-1443. [PMID: 35947642 PMCID: PMC9757098 DOI: 10.1164/rccm.202208-1471ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Poonam Sarode
- Max Planck Institute for Heart and Lung ResearchBad Nauheim, Germany
| | - Soni Savai Pullamsetti
- Max Planck Institute for Heart and Lung ResearchBad Nauheim, Germany,Department of Internal MedicineGerman Center for Lung Research (DZL)Cardio-Pulmonary Institute (CPI),Institute for Lung HealthJustus Liebig UniversityGiessen, Germany
| | - Rajkumar Savai
- Max Planck Institute for Heart and Lung ResearchBad Nauheim, Germany,Department of Internal MedicineGerman Center for Lung Research (DZL)Cardio-Pulmonary Institute (CPI),Institute for Lung HealthJustus Liebig UniversityGiessen, Germany,Frankfurt Cancer InstituteGoethe University FrankfurtFrankfurt, Germany
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Mendeville M, Roemer MGM, Los-de Vries GT, Chamuleau MED, de Jong D, Ylstra B. The path towards consensus genome classification of diffuse large B-cell lymphoma for use in clinical practice. Front Oncol 2022; 12:970063. [DOI: 10.3389/fonc.2022.970063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/17/2022] [Indexed: 11/13/2022] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a widely heterogeneous disease in presentation, treatment response and outcome that results from a broad biological heterogeneity. Various stratification approaches have been proposed over time but failed to sufficiently capture the heterogeneous biology and behavior of the disease in a clinically relevant manner. The most recent DNA-based genomic subtyping studies are a major step forward by offering a level of refinement that could serve as a basis for exploration of personalized and targeted treatment for the years to come. To enable consistent trial designs and allow meaningful comparisons between studies, harmonization of the currently available knowledge into a single genomic classification widely applicable in daily practice is pivotal. In this review, we investigate potential avenues for harmonization of the presently available genomic subtypes of DLBCL inspired by consensus molecular classifications achieved for other malignancies. Finally, suggestions for laboratory techniques and infrastructure required for successful clinical implementation are described.
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Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection. Cancers (Basel) 2022; 14:cancers14174120. [PMID: 36077657 PMCID: PMC9454699 DOI: 10.3390/cancers14174120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 11/26/2022] Open
Abstract
Simple Summary Patient stratification is clinically important because it allows us to understand the characteristics and establish treatment strategies for a group. Transcriptomic data play an important role in determining molecular subtypes and predicting survival. In the case of breast cancer, although the order of prognosis according to molecular subtypes is well known, there is heterogeneity even within a subtype. Therefore, patient stratification considering both molecular subtypes and survival outcomes is required. In this study, a methodology to handle this problem is presented. A genetic algorithm is used to select a set of genes, and a risk score is assigned to each patient using their expression level. According to the risk score, patients are ordered and stratified considering molecular subtypes and survival outcomes. Consequently, informative genes for patient stratification with respect to both aspects could be nominated, and the usefulness of the risk score was shown through comparison with other indicators. Abstract Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.
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Wei X, Liu J, Hong Z, Chen X, Wang K, Cai J. Identification of novel tumor microenvironment-associated genes in gastric cancer based on single-cell RNA-sequencing datasets. Front Genet 2022; 13:896064. [PMID: 36046240 PMCID: PMC9421061 DOI: 10.3389/fgene.2022.896064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Tumor microenvironment and heterogeneity play vital roles in the development and progression of gastric cancer (GC). In the past decade, a considerable amount of single-cell RNA-sequencing (scRNA-seq) studies have been published in the fields of oncology and immunology, which improve our knowledge of the GC immune microenvironment. However, much uncertainty still exists about the relationship between the macroscopic and microscopic data in transcriptomics. In the current study, we made full use of scRNA-seq data from the Gene Expression Omnibus database (GSE134520) to identify 25 cell subsets, including 11 microenvironment-related cell types. The MIF signaling pathway network was obtained upon analysis of receptor–ligand pairs and cell–cell interactions. By comparing the gene expression in a wide variety of cells between intestinal metaplasia and early gastric cancer, we identified 64 differentially expressed genes annotated as immune response and cellular communication. Subsequently, we screened these genes for prognostic clinical value based on the patients’ follow-up data from The Cancer Genome Atlas. TMPRSS15, VIM, APOA1, and RNASE1 were then selected for the construction of LASSO risk scores, and a nomogram model incorporating another five clinical risk factors was successfully created. The effectiveness of least absolute shrinkage and selection operator risk scores was validated using gene set enrichment analysis and levels of immune cell infiltration. These findings will drive the development of prognostic evaluations affected by the immune tumor microenvironment in GC.
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Affiliation(s)
- Xujin Wei
- The Graduate School of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
| | - Jie Liu
- The Graduate School of Fujian Medical University, Fuzhou, China
| | - Zhijun Hong
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
| | - Xin Chen
- The Graduate School of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
| | - Kang Wang
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
| | - Jianchun Cai
- The Graduate School of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Institute of Gastrointestinal Oncology, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Municipal Key Laboratory of Gastrointestinal Oncology, Xiamen, China
- *Correspondence: Jianchun Cai,
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Talaat IM, Kim B. A brief glimpse of a tangled web in a small world: Tumor microenvironment. Front Med (Lausanne) 2022; 9:1002715. [PMID: 36045917 PMCID: PMC9421133 DOI: 10.3389/fmed.2022.1002715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 07/28/2022] [Indexed: 12/20/2022] Open
Abstract
A tumor is a result of stepwise accumulation of genetic and epigenetic alterations. This notion has deepened the understanding of cancer biology and has introduced the era of targeted therapies. On the other hand, there have been a series of attempts of using the immune system to treat tumors, dating back to ancient history, to sporadic reports of inflamed tumors undergoing spontaneous regression. This was succeeded by modern immunotherapies and immune checkpoint inhibitors. The recent breakthrough has broadened the sight to other players within tumor tissue. Tumor microenvironment is a niche or a system orchestrating reciprocal and dynamic interaction of various types of cells including tumor cells and non-cellular components. The output of this complex communication dictates the functions of the constituent elements present within it. More complicated factors are biochemical and biophysical settings unique to TME. This mini review provides a brief guide on a range of factors to consider in the TME research.
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Affiliation(s)
- Iman M. Talaat
- Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Byoungkwon Kim
- Department of Pathology, H.H. Sheikh Khalifa Specialty Hospital, Ras Al Khaimah, United Arab Emirates
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43
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A bioinformatics perspective on molecular classification of diffuse large B-cell lymphoma. Leukemia 2022; 36:2177-2179. [PMID: 35933522 PMCID: PMC9417979 DOI: 10.1038/s41375-022-01670-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 07/21/2022] [Accepted: 07/25/2022] [Indexed: 12/21/2022]
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44
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Wong ANN, He Z, Leung KL, To CCK, Wong CY, Wong SCC, Yoo JS, Chan CKR, Chan AZ, Lacambra MD, Yeung MHY. Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 14:3780. [PMID: 35954443 PMCID: PMC9367360 DOI: 10.3390/cancers14153780] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/27/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.
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Affiliation(s)
- Alex Ngai Nick Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Zebang He
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Ka Long Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Curtis Chun Kit To
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Chun Yin Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Sze Chuen Cesar Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Jung Sun Yoo
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Cheong Kin Ronald Chan
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Angela Zaneta Chan
- Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, Shatin, Hong Kong SAR, China;
| | - Maribel D. Lacambra
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
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Loeffler-Wirth H, Kreuz M, Schmidt M, Ott G, Siebert R, Binder H. Classifying Germinal Center Derived Lymphomas-Navigate a Complex Transcriptional Landscape. Cancers (Basel) 2022; 14:3434. [PMID: 35884496 PMCID: PMC9321060 DOI: 10.3390/cancers14143434] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/07/2022] [Accepted: 07/11/2022] [Indexed: 11/16/2022] Open
Abstract
Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 "double hit lymphomas" (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.
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Affiliation(s)
- Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics, University Leipzig (IZBI), 04107 Leipzig, Germany; (H.L.-W.); (M.S.)
| | - Markus Kreuz
- Fraunhofer Institute for Cell Therapy and Immunology (IZI), 04103 Leipzig, Germany;
| | - Maria Schmidt
- Interdisciplinary Centre for Bioinformatics, University Leipzig (IZBI), 04107 Leipzig, Germany; (H.L.-W.); (M.S.)
| | - German Ott
- Department of Clinical Pathology, Robert-Bosch-Krankenhaus, Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, 70376 Stuttgart, Germany;
| | - Reiner Siebert
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, 89073 Ulm, Germany;
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics, University Leipzig (IZBI), 04107 Leipzig, Germany; (H.L.-W.); (M.S.)
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46
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Qiu Y, Kingsford C. The effect of genome graph expressiveness on the discrepancy between genome graph distance and string set distance. Bioinformatics 2022; 38:i404-i412. [PMID: 35758819 PMCID: PMC9235494 DOI: 10.1093/bioinformatics/btac264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Motivation Intra-sample heterogeneity describes the phenomenon where a genomic sample contains a diverse set of genomic sequences. In practice, the true string sets in a sample are often unknown due to limitations in sequencing technology. In order to compare heterogeneous samples, genome graphs can be used to represent such sets of strings. However, a genome graph is generally able to represent a string set universe that contains multiple sets of strings in addition to the true string set. This difference between genome graphs and string sets is not well characterized. As a result, a distance metric between genome graphs may not match the distance between true string sets. Results We extend a genome graph distance metric, Graph Traversal Edit Distance (GTED) proposed by Ebrahimpour Boroojeny et al., to FGTED to model the distance between heterogeneous string sets and show that GTED and FGTED always underestimate the Earth Mover’s Edit Distance (EMED) between string sets. We introduce the notion of string set universe diameter of a genome graph. Using the diameter, we are able to upper-bound the deviation of FGTED from EMED and to improve FGTED so that it reduces the average error in empirically estimating the similarity between true string sets. On simulated T-cell receptor sequences and actual Hepatitis B virus genomes, we show that the diameter-corrected FGTED reduces the average deviation of the estimated distance from the true string set distances by more than 250%. Availability and implementation Data and source code for reproducing the experiments are available at: https://github.com/Kingsford-Group/gtedemedtest/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yutong Qiu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15232, USA
| | - Carl Kingsford
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15232, USA
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Yang Y, Yu J, Xiong Y, Xiao J, Dai D, Zhang F. Prognostic Analysis of Differentially Expressed DNA Damage Repair Genes in Bladder Cancer. Pathol Oncol Res 2022; 28:1610267. [PMID: 35685866 PMCID: PMC9172279 DOI: 10.3389/pore.2022.1610267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/21/2022] [Indexed: 11/13/2022]
Abstract
Bladder cancer (BCa) is the tenth most common tumor in humans. DNA damage repair genes (DDRGs) play important roles in many malignant tumors; thus, their functions in BCa should also be explored. We performed a comprehensive analysis of the expression profiles of DDRGs in 410 BCa tumors and 19 normal tissues from The Cancer Genome Atlas database. We identified 123 DDRGs differentially expressed between BCa tumors and normal tissues, including 95 upregulated and 28 downregulated genes. We detected 22 DDRGs associated with overall survival (OS) of patients with BCa by performing univariate Cox regression analysis. To explore the interactions between OS-associated DDRGs, we constructed a PPI network, which showed that the top six DDRGs (CDCA2, FOXM1, PBK, RRM2, ORC1, and HDAC4) with the highest scores in the PPI network might play significant roles in OS of BCa. Moreover, to investigate the latent regulatory mechanism of these OS-associated DDRGs, we analyzed the transcription factors (TFs)-DDRGs regulatory network. The core seven TFs (NCAPG, DNMT1, LMNB1, BRCA1, E2H2, CENPA, and E2F7) were shown to be critical regulators of the OS-related DDRGs. The 22 DDRGs were incorporated into a stepwise multivariable Cox analysis. Then, we built the index of risk score based on the expression of 8 DDRGs (CAD, HDAC10, JDP2, LDLR, PDGFRA, POLA2, SREBF1, and STAT1). The p-value < 0.0001 in the Kaplan–Meier survival plot and an area under the ROC curve (AUC) of 0.771 in TCGA-BLCA training dataset suggested the high specificity and sensitivity of the prognostic index. Furthermore, we validated the risk score in the internal TCGA-BLCA and an independent GSE32894 dataset, with AUC of 0.743 and 0.827, respectively. More importantly, the multivariate Cox regression and stratification analysis demonstrated that the predictor was independent of various clinical parameters, including age, tumor stage, grade, and number of positive tumor lymph nodes. In summary, a panel of 8 DNA damage repair genes associated with overall survival in bladder cancer may be a useful prognostic tool.
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Affiliation(s)
- Yong Yang
- Department of Otolaryngology Head and Neck Surgery, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China.,Department of Otolaryngology Head and Neck Surgery, Jiangxi Provincial People's Hospital Affiliated to Nanchang Medical College, Nanchang, China
| | - Jieqing Yu
- Otorhinolaryngology Head and Neck Surgery Institute, Department of Otorhinolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yuanping Xiong
- Otorhinolaryngology Head and Neck Surgery Institute, Department of Otorhinolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jiansheng Xiao
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Daofeng Dai
- Otorhinolaryngology Head and Neck Surgery Institute, Department of Otorhinolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Feng Zhang
- The National Engineering Research Center for Bioengineering Drugs and the Technologies, The Institute of Translational Medicine, Nanchang University, Nanchang, China
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Zhang G, Peng Z, Yan C, Wang J, Luo J, Luo H. MultiGATAE: A Novel Cancer Subtype Identification Method Based on Multi-Omics and Attention Mechanism. Front Genet 2022; 13:855629. [PMID: 35391797 PMCID: PMC8979770 DOI: 10.3389/fgene.2022.855629] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Cancer is one of the leading causes of death worldwide, which brings an urgent need for its effective treatment. However, cancer is highly heterogeneous, meaning that one cancer can be divided into several subtypes with distinct pathogenesis and outcomes. This is considered as the main problem which limits the precision treatment of cancer. Thus, cancer subtypes identification is of great importance for cancer diagnosis and treatment. In this work, we propose a deep learning method which is based on multi-omics and attention mechanism to effectively identify cancer subtypes. We first used similarity network fusion to integrate multi-omics data to construct a similarity graph. Then, the similarity graph and the feature matrix of the patient are input into a graph autoencoder composed of a graph attention network and omics-level attention mechanism to learn embedding representation. The K-means clustering method is applied to the embedding representation to identify cancer subtypes. The experiment on eight TCGA datasets confirmed that our proposed method performs better for cancer subtypes identification when compared with the other state-of-the-art methods. The source codes of our method are available at https://github.com/kataomoi7/multiGATAE.
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Affiliation(s)
- Ge Zhang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Zhen Peng
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Chaokun Yan
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Jianlin Wang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Junwei Luo
- College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
| | - Huimin Luo
- School of Computer and Information Engineering, Henan University, Kaifeng, China
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Beinecke JM, Anders P, Schurrat T, Heider D, Luster M, Librizzi D, Hauschild AC. Evaluation of machine learning strategies for imaging confirmed prostate cancer recurrence prediction on electronic health records. Comput Biol Med 2022; 143:105263. [PMID: 35131608 DOI: 10.1016/j.compbiomed.2022.105263] [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: 09/21/2021] [Revised: 01/21/2022] [Accepted: 01/21/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND The main screening parameter to monitor prostate cancer recurrence (PCR) after primary treatment is the serum concentration of prostate-specific antigen (PSA). In recent years, Ga-68-PSMA PET/CT has become an important method for additional diagnostics in patients with biochemical recurrence. PURPOSE While Ga-68-PSMA PET/CT performs better, it is an expensive, invasive, and time-consuming examination. Therefore, in this study, we aim to employ modern multivariate Machine Learning (ML) methods on electronic health records (EHR) of prostate cancer patients to improve the prediction of imaging confirmed PCR (IPCR). METHODS We retrospectively analyzed the clinical information of 272 patients, who were examined using Ga-68-PSMA PET/CT. The PSA values ranged from 0 ng/mL to 2270.38 ng/mL with a median PSA level at 1.79 ng/mL. We performed a descriptive analysis using Logistic Regression. Additionally, we evaluated the predictive performance of Logistic Regression, Support Vector Machine, Gradient Boosting, and Random Forest. Finally, we assessed the importance of all features using Ensemble Feature Selection (EFS). RESULTS The descriptive analysis found significant associations between IPCR and logarithmic PSA values as well as between IPCR and performed hormonal therapy. Our models were able to predict IPCR with an AUC score of 0.78 ± 0.13 (mean ± standard deviation) and a sensitivity of 0.997 ± 0.01. Features such as PSA, PSA doubling time, PSA velocity, hormonal therapy, radiation treatment, and injected activity show high importance for IPCR prediction using EFS. CONCLUSION This study demonstrates the potential of employing a multitude of parameters into multivariate ML models to improve identification of non-recurring patients compared to the current focus on the main screening parameter (PSA). We showed that ML models are able to predict IPCR, detectable by Ga-68-PSMA PET/CT, and thereby pave the way for optimized early imaging and treatment.
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Affiliation(s)
- Jacqueline Michelle Beinecke
- Department of Mathematics and Computer Science at the Philipps University Marburg, Germany; Institute for Medical Informatics at the University Medical Center Göttingen, Göttingen, Germany.
| | - Patrick Anders
- Department of Nuclear Medicine, University Hospital Marburg, Germany
| | - Tino Schurrat
- Department of Nuclear Medicine, University Hospital Marburg, Germany
| | - Dominik Heider
- Department of Mathematics and Computer Science at the Philipps University Marburg, Germany
| | - Markus Luster
- Department of Nuclear Medicine, University Hospital Marburg, Germany
| | - Damiano Librizzi
- Department of Nuclear Medicine, University Hospital Marburg, Germany
| | - Anne-Christin Hauschild
- Department of Mathematics and Computer Science at the Philipps University Marburg, Germany; Institute for Medical Informatics at the University Medical Center Göttingen, Göttingen, Germany
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50
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Zambonin C, Aresta A. MALDI-TOF/MS Analysis of Non-Invasive Human Urine and Saliva Samples for the Identification of New Cancer Biomarkers. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27061925. [PMID: 35335287 PMCID: PMC8951187 DOI: 10.3390/molecules27061925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/06/2022] [Accepted: 03/14/2022] [Indexed: 01/22/2023]
Abstract
Cancer represents a group of heterogeneous diseases that are a leading global cause of death. Even though mortality has decreased in the past thirty years for different reasons, most patients are still diagnosed at the advanced stage, with limited therapeutic choices and poor outcomes. Moreover, the majority of cancers are detected using invasive painful methods, such as endoscopic biopsy, making the development of non-invasive or minimally invasive methods for the discovery and fast detection of specific biomarkers a crucial need. Among body fluids, a valuable non-invasive alternative to tissue biopsy, the most accessible and least invasive are undoubtedly urine and saliva. They are easily retrievable complex fluids containing a large variety of endogenous compounds that may provide information on the physiological condition of the body. The combined analysis of these fluids with matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF/MS), a reliable and easy-to-use instrumentation that provides information with relatively simple sample pretreatments, could represent the ideal option to rapidly achieve fast early stage diagnosis of tumors and their real-time monitoring. On this basis, the present review summarizes the recently reported applications relevant to the MALDI analysis of human urine and saliva samples.
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