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Zhu E, Xie Q, Huang X, Zhang Z. Application of spatial omics in gastric cancer. Pathol Res Pract 2024; 262:155503. [PMID: 39128411 DOI: 10.1016/j.prp.2024.155503] [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: 04/27/2024] [Revised: 07/25/2024] [Accepted: 07/27/2024] [Indexed: 08/13/2024]
Abstract
Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.
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Affiliation(s)
- Erran Zhu
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Qi Xie
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Xinqi Huang
- Excellent Class, Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Zhiwei Zhang
- Cancer Research Institute of Hengyang Medical College, University of South China; Key Laboratory of Cancer Cellular and Molecular Pathology of Hunan; Department of Pathology, Department of Pathology of Hengyang Medical College, University of South China; The First Affiliated Hospital of University of South China, China.
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Vizza P, Aracri F, Guzzi PH, Gaspari M, Veltri P, Tradigo G. Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case. BMC Med Inform Decis Mak 2024; 24:93. [PMID: 38584282 PMCID: PMC11000316 DOI: 10.1186/s12911-024-02491-6] [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: 01/07/2024] [Accepted: 03/25/2024] [Indexed: 04/09/2024] Open
Abstract
Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists in studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed at studying and classifying proteomics data enriched using clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training, and (iii) models ensembling. We report the experience of applying such a pipeline to prostate-related diseases. Models have been trained on several biological datasets. We report experimental results about two datasets that result from the integration of clinical and mass spectrometry-based data in the contexts of serum and urine analysis. The pipeline receives input data from blood analytes, tissue samples, proteomic analysis, and urine biomarkers. It then trains different models for feature selection, classification and voting. The presented pipeline has been applied on two datasets obtained in a 2 years research project which aimed to extract hidden information from mass spectrometry, serum, and urine samples from hundreds of patients. We report results on analyzing prostate datasets serum with 143 samples, including 79 PCa and 84 BPH patients, and an urine dataset with 121 samples, including 67 PCa and 54 BPH patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both serum (AUC=0.87, Accuracy=0.83, F1=0.81, Sensitivity=0.84, Specificity=0.81) and urine (AUC=0.88, Accuracy=0.83, F1=0.83, Sensitivity=0.85, Specificity=0.80) datasets showed good predictive performances. We made the pipeline code available on GitHub and we are confident that it will be successfully adopted in similar clinical setups.
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Affiliation(s)
- Patrizia Vizza
- Department of Surgical and Medical Sciences, Magna Græcia University, 88100, Catanzaro, Italy
| | - Federica Aracri
- Department of Surgical and Medical Sciences, Magna Græcia University, 88100, Catanzaro, Italy.
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Græcia University, 88100, Catanzaro, Italy
| | - Marco Gaspari
- Department of Experimental and Clinical Medicine, Magna Græcia University, 88100, Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Computers, Modeling, Electronics and Systems Engineering, University of Calabria, 87036, Rende, Italy
| | - Giuseppe Tradigo
- Department of Theoretical and Applied Sciences, eCampus University, 22060, Novedrate, CO, Italy
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Banerjee S, Hatimuria M, Sarkar K, Das J, Pabbathi A, Sil PC. Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer. Chem Res Toxicol 2024; 37:137-180. [PMID: 38011513 DOI: 10.1021/acs.chemrestox.3c00223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".
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Affiliation(s)
- Subhrajit Banerjee
- Department of Physiology, Surendranath College, University of Calcutta, Kolkata 700009, India
- Department of Microbiology, St. Xavier's College, Kolkata 700016, India
| | - Madushmita Hatimuria
- Department of Industrial Chemistry, School of Physical Sciences, Mizoram University, Aizawl 796004, Mizoram India
| | - Kasturi Sarkar
- Department of Microbiology, St. Xavier's College, Kolkata 700016, India
| | - Joydeep Das
- Department of Chemistry, School of Physical Sciences, Mizoram University, Aizawl 796004, Mizoram, India
| | - Ashok Pabbathi
- Department of Industrial Chemistry, School of Physical Sciences, Mizoram University, Aizawl 796004, Mizoram India
| | - Parames C Sil
- Department of Molecular Medicine Bose Institute, Kolkata 700054, India
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Demicco M, Liu XZ, Leithner K, Fendt SM. Metabolic heterogeneity in cancer. Nat Metab 2024; 6:18-38. [PMID: 38267631 DOI: 10.1038/s42255-023-00963-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/06/2023] [Indexed: 01/26/2024]
Abstract
Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.
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Affiliation(s)
- Margherita Demicco
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Xiao-Zheng Liu
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Katharina Leithner
- Division of Pulmonology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium.
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
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Pierantoni L, Reis RL, Silva-Correia J, Oliveira JM, Heavey S. Spatial -omics technologies: the new enterprise in 3D breast cancer models. Trends Biotechnol 2023; 41:1488-1500. [PMID: 37544843 DOI: 10.1016/j.tibtech.2023.07.003] [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: 01/30/2023] [Revised: 06/28/2023] [Accepted: 07/06/2023] [Indexed: 08/08/2023]
Abstract
The fields of tissue bioengineering, -omics, and spatial biology are advancing rapidly, each offering the opportunity for a paradigm shift in breast cancer research. However, to date, collaboration between these fields has not reached its full potential. In this review, we describe the most recently generated 3D breast cancer models regarding the biomaterials and technological platforms employed. Additionally, their biological evaluation is reported, highlighting their advantages and limitations. Specifically, we focus on the most up-to-date -omics and spatial biology techniques, which can generate a deeper understanding of the biological relevance of bioengineered 3D breast cancer in vitro models, thus paving the way towards truly clinically relevant microphysiological systems, improved drug development success rates, and personalised medicine approaches.
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Affiliation(s)
- Lara Pierantoni
- 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics of University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Zona Industrial da Gandra, Barco, Guimarães 4805-017, Portugal; ICVS/3B's - PT Government Associated Laboratory, Braga/Guimarães, Portugal.
| | - Rui L Reis
- 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics of University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Zona Industrial da Gandra, Barco, Guimarães 4805-017, Portugal; ICVS/3B's - PT Government Associated Laboratory, Braga/Guimarães, Portugal
| | - Joana Silva-Correia
- 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics of University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Zona Industrial da Gandra, Barco, Guimarães 4805-017, Portugal; ICVS/3B's - PT Government Associated Laboratory, Braga/Guimarães, Portugal
| | - Joaquim M Oliveira
- 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics of University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Zona Industrial da Gandra, Barco, Guimarães 4805-017, Portugal; ICVS/3B's - PT Government Associated Laboratory, Braga/Guimarães, Portugal
| | - Susan Heavey
- Division of Surgery & Interventional Science, University College London, London, UK
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Chung HH, Huang P, Chen CL, Lee C, Hsu CC. Next-generation pathology practices with mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2023; 42:2446-2465. [PMID: 35815718 DOI: 10.1002/mas.21795] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.
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Affiliation(s)
- Hsin-Hsiang Chung
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Penghsuan Huang
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chih-Lin Chen
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chuping Lee
- Department of Chemistry, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Cheng-Chih Hsu
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
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Planque M, Igelmann S, Ferreira Campos AM, Fendt SM. Spatial metabolomics principles and application to cancer research. Curr Opin Chem Biol 2023; 76:102362. [PMID: 37413787 DOI: 10.1016/j.cbpa.2023.102362] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 05/07/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023]
Abstract
Mass spectrometry imaging (MSI) is an emerging technology in cancer metabolomics. Desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI) MSI are complementary techniques to identify hundreds of metabolites in space with close to single-cell resolution. This technology leap enables research focusing on tumor heterogeneity, cancer cell plasticity, and the communication signals between cancer and stromal cells in the tumor microenvironment (TME). Currently, unprecedented knowledge is generated using spatial metabolomics in fundamental cancer research. Yet, also translational applications are emerging, including the assessment of spatial drug distribution in organs and tumors. Moreover, clinical research investigates the use of spatial metabolomics as a rapid pathology tool during cancer surgeries. Here, we summarize MSI applications, the knowledge gained by this technology in space, future directions, and developments needed.
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Affiliation(s)
- Mélanie Planque
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Sebastian Igelmann
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Ana Margarida Ferreira Campos
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
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Genetics, Treatment, and New Technologies of Hormone Receptor-Positive Breast Cancer. Cancers (Basel) 2023; 15:cancers15041303. [PMID: 36831644 PMCID: PMC9954687 DOI: 10.3390/cancers15041303] [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: 06/17/2022] [Revised: 02/12/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
The current molecular classification divides breast cancer into four major subtypes, including luminal A, luminal B, HER2-positive, and basal-like, based on receptor gene expression profiling. Luminal A and luminal B are hormone receptor (HR, estrogen, and/or progesterone receptor)-positive and are the most common subtypes, accounting for around 50-60% and 15-20% of the total breast cancer cases, respectively. The drug treatment for HR-positive breast cancer includes endocrine therapy, HER2-targeted therapy (depending on the HER2 status), and chemotherapy (depending on the risk of recurrence). In this review, in addition to classification, we focused on discussing the important aspects of HR-positive breast cancer, including HR structure and signaling, genetics, including epigenetics and gene mutations, gene expression-based assays, the traditional and new drugs for treatment, and novel or new uses of technology in diagnosis and treatment. Particularly, we have summarized the commonly mutated genes and abnormally methylated genes in HR-positive breast cancer and compared four common gene expression-based assays that are used in breast cancer as prognostic and/or predictive tools in detail, including their clinical use, the factors being evaluated, patient demographics, and the scoring systems. All these topic discussions have not been fully described and summarized within other research or review articles.
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Santilli AML, Ren K, Oleschuk R, Kaufmann M, Rudan J, Fichtinger G, Mousavi P. Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review. IEEE Trans Biomed Eng 2022; 69:2220-2232. [PMID: 34982670 DOI: 10.1109/tbme.2021.3139992] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines. METHODS We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies. RESULTS Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively. CONCLUSION Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible. SIGNIFICANCE By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.
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