1
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Silva AAR, Cardoso MR, de Oliveira DC, Godoy P, Talarico MCR, Gutiérrez JM, Rodrigues Peres RM, de Carvalho LM, Miyaguti NADS, Sarian LO, Tata A, Derchain SFM, Porcari AM. Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2024; 16:2473. [PMID: 39001535 PMCID: PMC11240312 DOI: 10.3390/cancers16132473] [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: 06/18/2024] [Revised: 06/27/2024] [Accepted: 07/04/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data. METHODS Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively. RESULTS The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance. CONCLUSION We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.
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Affiliation(s)
- Alex Ap. Rosini Silva
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Marcella R. Cardoso
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP—Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Danilo Cardoso de Oliveira
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Pedro Godoy
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Maria Cecília R. Talarico
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP—Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil
| | - Junier Marrero Gutiérrez
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Raquel M. Rodrigues Peres
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Lucas M. de Carvalho
- Post Graduate Program in Health Sciences, São Francisco University, Bragança Paulista 12916900, São Paulo, Brazil
| | - Natália Angelo da Silva Miyaguti
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
| | - Luis O. Sarian
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP—Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil
| | - Alessandra Tata
- Laboratory of Experimental Chemistry, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Viale Fiume 78, 36100 Vicenza, Italy;
| | - Sophie F. M. Derchain
- Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP—Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil
| | - Andreia M. Porcari
- MSLife Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil; (A.A.R.S.); (D.C.d.O.)
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Leontyev D, Pulliam AN, Ma X, Gaul DA, LaPlaca MC, Fernández FM. Spatial lipidomics maps brain alterations associated with mild traumatic brain injury. Front Chem 2024; 12:1394064. [PMID: 38873407 PMCID: PMC11169706 DOI: 10.3389/fchem.2024.1394064] [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: 02/29/2024] [Accepted: 05/07/2024] [Indexed: 06/15/2024] Open
Abstract
Traumatic brain injury (TBI) is a global public health problem with 50-60 million incidents per year, most of which are considered mild (mTBI) and many of these repetitive (rmTBI). Despite their massive implications, the pathologies of mTBI and rmTBI are not fully understood, with a paucity of information on brain lipid dysregulation following mild injury event(s). To gain more insight on mTBI and rmTBI pathology, a non-targeted spatial lipidomics workflow utilizing high resolution mass spectrometry imaging was developed to map brain region-specific lipid alterations in rats following injury. Discriminant multivariate models were created for regions of interest including the hippocampus, cortex, and corpus callosum to pinpoint lipid species that differentiated between injured and sham animals. A multivariate model focused on the hippocampus region differentiated injured brain tissues with an area under the curve of 0.99 using only four lipid species. Lipid classes that were consistently discriminant included polyunsaturated fatty acid-containing phosphatidylcholines (PC), lysophosphatidylcholines (LPC), LPC-plasmalogens (LPC-P) and PC potassium adducts. Many of the polyunsaturated fatty acid-containing PC and LPC-P selected have never been previously reported as altered in mTBI. The observed lipid alterations indicate that neuroinflammation and oxidative stress are important pathologies that could serve to explain cognitive deficits associated with rmTBI. Therapeutics which target or attenuate these pathologies may be beneficial to limit persistent damage following a mild brain injury event.
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Affiliation(s)
- Dmitry Leontyev
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Alexis N. Pulliam
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA, United States
| | - Xin Ma
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
- Parker H. Petit Institute for Bioengineering and Bioscience, Atlanta, GA, United States
| | - Michelle C. LaPlaca
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA, United States
- Parker H. Petit Institute for Bioengineering and Bioscience, Atlanta, GA, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
- Parker H. Petit Institute for Bioengineering and Bioscience, Atlanta, GA, United States
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3
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Sah S, Bifarin OO, Moore SG, Gaul DA, Chung H, Kwon SY, Cho H, Cho CH, Kim JH, Kim J, Fernández FM. Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women. Cancer Epidemiol Biomarkers Prev 2024; 33:681-693. [PMID: 38412029 PMCID: PMC11061607 DOI: 10.1158/1055-9965.epi-23-1293] [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: 10/19/2023] [Revised: 01/11/2024] [Accepted: 02/23/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention. METHODS Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches. RESULTS We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set. CONCLUSIONS This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women. IMPACT Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.
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Affiliation(s)
- Samyukta Sah
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia
| | - Olatomiwa O. Bifarin
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
| | - Samuel G. Moore
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia
| | - Hyewon Chung
- Department of Obstetrics and Gynecology, School of Medicine, Keimyung University, Daegu Republic of Korea
| | - Sun Young Kwon
- Department of Pathology, School of Medicine, Keimyung University, Daegu, Republic of Korea
| | - Hanbyoul Cho
- Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chi-Heum Cho
- Department of Obstetrics and Gynecology, School of Medicine, Keimyung University, Daegu Republic of Korea
| | - Jae-Hoon Kim
- Department of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jaeyeon Kim
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia
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4
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Leontyev D, Pulliam AN, Ma X, Gaul DA, LaPlaca MC, Fernandez FM. Spatial Lipidomics Maps Brain Alterations Associated with Mild Traumatic Brain Injury. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.25.577203. [PMID: 38328252 PMCID: PMC10849710 DOI: 10.1101/2024.01.25.577203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Traumatic brain injury (TBI) is a global public health problem with 50-60 million incidents per year, most of which are considered mild (mTBI) and many of these repetitive (rmTBI). Despite their massive implications, the pathologies of mTBI and rmTBI are not fully understood, with a paucity of information on brain lipid dysregulation following mild injury event(s). To gain more insight on mTBI and rmTBI pathology, a non-targeted spatial lipidomics workflow utilizing ultrahigh resolution mass spectrometry imaging was developed to map brain region-specific lipid alterations in rats following injury. Discriminant multivariate models were created for regions of interest including the hippocampus, cortex, and corpus callosum to pinpoint lipid species that differentiated between injured and sham animals. A multivariate model focused on the hippocampus region differentiated injured brain tissues with an area under the curve of 0.994 using only four lipid species. Lipid classes that were consistently discriminant included polyunsaturated fatty acid-containing phosphatidylcholines (PC), lysophosphatidylcholines (LPC), LPC-plasmalogens (LPC-P) and PC potassium adducts. Many of the polyunsaturated fatty acid-containing PC and LPC-P selected have never been previously reported as altered in mTBI. The observed lipid alterations indicate that neuroinflammation, oxidative stress and disrupted sodium-potassium pumps are important pathologies that could serve to explain cognitive deficits associated with rmTBI. Therapeutics which target or attenuate these pathologies may be beneficial to limit persistent damage following a mild brain injury event.
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5
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Ma X, Fernández FM. Advances in mass spectrometry imaging for spatial cancer metabolomics. MASS SPECTROMETRY REVIEWS 2024; 43:235-268. [PMID: 36065601 PMCID: PMC9986357 DOI: 10.1002/mas.21804] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 05/09/2023]
Abstract
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.
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Affiliation(s)
- Xin Ma
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
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6
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Ma X, Botros A, Yun SR, Park EY, Kim O, Park S, Pham TH, Chen R, Palaniappan M, Matzuk MM, Kim J, Fernández FM. Ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models. Front Chem 2024; 11:1332816. [PMID: 38260043 PMCID: PMC10800477 DOI: 10.3389/fchem.2023.1332816] [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: 11/03/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering that little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights into how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSOC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes were compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.
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Affiliation(s)
- Xin Ma
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
| | - Andro Botros
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, United States
| | - Sylvia R. Yun
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, United States
| | - Eun Young Park
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, United States
| | - Olga Kim
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, United States
| | - Soojin Park
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, United States
| | - Thu-Huyen Pham
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, United States
| | - Ruihong Chen
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, United States
| | - Murugesan Palaniappan
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, United States
- Center for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, United States
| | - Martin M. Matzuk
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, United States
- Center for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, United States
| | - Jaeyeon Kim
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, United States
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, United States
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Moore JL, Charkoftaki G. A Guide to MALDI Imaging Mass Spectrometry for Tissues. J Proteome Res 2023; 22:3401-3417. [PMID: 37877579 DOI: 10.1021/acs.jproteome.3c00167] [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] [Indexed: 10/26/2023]
Abstract
Imaging mass spectrometry is a well-established technology that can easily and succinctly communicate the spatial localization of molecules within samples. This review communicates the recent advances in the field, with a specific focus on matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) applied on tissues. The general sample preparation strategies for different analyte classes are explored, including special considerations for sample types (fresh frozen or formalin-fixed,) strategies for various analytes (lipids, metabolites, proteins, peptides, and glycans) and how multimodal imaging strategies can leverage the strengths of each approach is mentioned. This work explores appropriate experimental design approaches and standardization of processes needed for successful studies, as well as the various data analysis platforms available to analyze data and their strengths. The review concludes with applications of imaging mass spectrometry in various fields, with a focus on medical research, and some examples from plant biology and microbe metabolism are mentioned, to illustrate the breadth and depth of MALDI IMS.
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Affiliation(s)
- Jessica L Moore
- Department of Proteomics, Discovery Life Sciences, Huntsville, Alabama 35806, United States
| | - Georgia Charkoftaki
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, Connecticut 06520, United States
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8
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Ma X, Botros A, Yun SR, Park EY, Kim O, Chen R, Palaniappan M, Matzuk MM, Kim J, Fernández FM. Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564760. [PMID: 37961688 PMCID: PMC10634942 DOI: 10.1101/2023.10.30.564760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights on how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and a triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide a direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.
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Affiliation(s)
- Xin Ma
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Andro Botros
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, 46202, United States
| | - Sylvia R. Yun
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, 46202, United States
| | - Eun Young Park
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, 46202, United States
| | - Olga Kim
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, 46202, United States
| | - Ruihong Chen
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Murugesan Palaniappan
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
- Center for Drug Discovery, Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Martin M. Matzuk
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
- Center for Drug Discovery, Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Jaeyeon Kim
- Departments of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, 46202, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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9
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Jia W, Li M. Unveiling Insights into Ovarian Cancer Metabolism through Space- and Time-Resolved Analysis. Cancers (Basel) 2023; 15:4881. [PMID: 37835575 PMCID: PMC10572059 DOI: 10.3390/cancers15194881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 10/06/2023] [Indexed: 10/15/2023] Open
Abstract
High-grade serous carcinoma (HGSC) represents a formidable challenge in the realm of ovarian cancer, notorious for its elusive early detection, poor prognosis and limited understanding of the intricacies of its pathogenesis [...].
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Affiliation(s)
- Wei Jia
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong 999077, Hong Kong
- Center for Translational Medicine and Shanghai Key Laboratory of Diabetes Mellitus, The Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China;
| | - Mengci Li
- Center for Translational Medicine and Shanghai Key Laboratory of Diabetes Mellitus, The Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China;
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10
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Bifarin O, Sah S, Gaul DA, Moore SG, Chen R, Palaniappan M, Kim J, Matzuk MM, Fernández FM. Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer. J Proteome Res 2023; 22:2092-2108. [PMID: 37220064 PMCID: PMC10243112 DOI: 10.1021/acs.jproteome.3c00226] [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: 04/14/2023] [Indexed: 05/25/2023]
Abstract
Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipid alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.
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Affiliation(s)
- Olatomiwa
O. Bifarin
- School
of Chemistry and Biochemistry, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
| | - Samyukta Sah
- School
of Chemistry and Biochemistry, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
| | - David A. Gaul
- School
of Chemistry and Biochemistry, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
- Petit
Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Samuel G. Moore
- Petit
Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Ruihong Chen
- Department
of Pathology & Immunology, Baylor College
of Medicine, Houston, Texas 77030, United States
| | - Murugesan Palaniappan
- Department
of Pathology & Immunology, Baylor College
of Medicine, Houston, Texas 77030, United States
- Center
for Drug Discovery, Department of Pathology & Immunology, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Jaeyeon Kim
- Department
of Biochemistry and Molecular Biology, Indiana University School of
Medicine, Indiana University Melvin and
Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana 46202, United States
| | - Martin M. Matzuk
- Department
of Pathology & Immunology, Baylor College
of Medicine, Houston, Texas 77030, United States
- Center
for Drug Discovery, Department of Pathology & Immunology, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Facundo M. Fernández
- School
of Chemistry and Biochemistry, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States
- Petit
Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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Huang X, Huang Y, Li P. How do serum lipid levels change and influence progression-free survival in epithelial ovarian cancer patients receiving bevacizumab treatment? Front Oncol 2023; 13:1168996. [PMID: 37064140 PMCID: PMC10090393 DOI: 10.3389/fonc.2023.1168996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 03/10/2023] [Indexed: 03/30/2023] Open
Abstract
BackgroundThis study aimed to investigate how serum lipid levels affect epithelial ovarian cancer (EOC) patients receiving bevacizumab treatment and to develop a model for predicting the patients’ prognosis.MethodsA total of 139 EOC patients receiving bevacizumab treatment were involved in this study. Statistical analysis was used to compare the median and average values of serum lipid level variables between the baseline and final follow-up. Additionally, a method based on machine learning was proposed to identify independent risk factors for estimating progression-free survival (PFS) in EOC patients receiving bevacizumab treatment. A PFS nomogram dividing the patients into low- and high-risk categories was created based on these independent prognostic variables. Finally, Kaplan–Meier curves and log-rank tests were utilized to perform survival analysis.ResultsAmong EOC patients involved in this study, statistical analysis of serum lipid level variables revealed a substantial increase in total cholesterol, triglycerides, apolipoprotein A1, and free fatty acids, and a significant decrease in apolipoprotein B from baseline to final follow-up. Our method identified FIGO stage, combined chemotherapy regimen, activated partial thromboplastin time, globulin, direct bilirubin, free fatty acids, blood urea nitrogen, high-density lipoprotein cholesterol, and triglycerides as risk factors. These risk factors were then included in our nomogram as independent predictors for EOC patients. PFS was substantially different between the low-risk group (total score < 298) and the high-risk group (total score ≥ 298) according to Kaplan–Meier curves (P < 0.05).ConclusionSerum lipid levels changed variously in EOC patients receiving bevacizumab treatment. A prediction model for PFS of EOC patients receiving bevacizumab treatment was constructed, and it can be beneficial in determining the prognosis, selecting a treatment plan, and monitoring these patients’ long-term care.
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Affiliation(s)
- Xiaoyu Huang
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- First Clinical Medical College, Anhui Medical University, Hefei, China
| | - Yong Huang
- Department of Medical Oncology, The Second People’s Hospital of Hefei, Hefei, China
| | - Ping Li
- Department of Chinese Integrative Medicine Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Ping Li,
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12
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Sánchez-Vinces S, Garcia PHD, Silva AAR, Fernandes AMADP, Barreto JA, Duarte GHB, Antonio MA, Birbrair A, Porcari AM, Carvalho PDO. Mass-Spectrometry-Based Lipidomics Discriminates Specific Changes in Lipid Classes in Healthy and Dyslipidemic Adults. Metabolites 2023; 13:metabo13020222. [PMID: 36837840 PMCID: PMC9964724 DOI: 10.3390/metabo13020222] [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: 12/10/2022] [Revised: 12/31/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Triacylglycerols (TAGs) and cholesterol lipoprotein levels are widely used to predict cardiovascular risk and metabolic disorders. The aim of this study is to determine how the comprehensive lipidome (individual molecular lipid species) determined by mass spectrometry is correlated to the serum whole-lipidic profile of adults with different lipidemic conditions. The study included samples from 128 adults of both sexes, and they were separated into four groups according to their lipid profile: Group I-normolipidemic (TAG < 150 mg/dL, LDL-C < 160 mg/dL and HDL-c > 40 mg/dL); Group II-isolated hypertriglyceridemia (TAG ≥ 150 mg/dL); Group III-isolated hypercholesterolemia (LDL-C ≥ 160 mg/dL) and Group IV-mixed dyslipidemia. An untargeted mass spectrometry (MS)-based approach was applied to determine the lipidomic signature of 32 healthy and 96 dyslipidemic adults. Limma linear regression was used to predict the correlation of serum TAGs and cholesterol lipoprotein levels with the abundance of the identified MS-annotated lipids found in the subgroups of subjects. Serum TAG levels of dyslipidemic adults have a positive correlation with some of the MS-annotated specific TAGs and ceramides (Cer) and a negative correlation with sphingomyelins (SMs). High-density lipoprotein-cholesterol (HDL-C) levels are positively correlated with some groups of glycerophosphocholine, while low-density lipoprotein-cholesterol (LDL-C) has a positive correlation with SMs.
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Affiliation(s)
- Salvador Sánchez-Vinces
- Health Sciences Postgraduate Program, São Francisco University–USF, Bragança Paulista 12900-000, SP, Brazil
| | - Pedro Henrique Dias Garcia
- Health Sciences Postgraduate Program, São Francisco University–USF, Bragança Paulista 12900-000, SP, Brazil
| | - Alex Ap. Rosini Silva
- Health Sciences Postgraduate Program, São Francisco University–USF, Bragança Paulista 12900-000, SP, Brazil
| | | | - Joyce Aparecida Barreto
- Integrated Unit of Pharmacology and Gastroenterology (UNIFAG), São Francisco University–USF, Bragança Paulista 12900-000, SP, Brazil
| | | | - Marcia Aparecida Antonio
- Integrated Unit of Pharmacology and Gastroenterology (UNIFAG), São Francisco University–USF, Bragança Paulista 12900-000, SP, Brazil
| | - Alexander Birbrair
- Department of Pathology, Federal University of Minas Gerais, Belo Horizonte 31270-901, MG, Brazil
- Department of Dermatology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53715-1149, USA
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Andreia M. Porcari
- Health Sciences Postgraduate Program, São Francisco University–USF, Bragança Paulista 12900-000, SP, Brazil
| | - Patricia de Oliveira Carvalho
- Health Sciences Postgraduate Program, São Francisco University–USF, Bragança Paulista 12900-000, SP, Brazil
- Correspondence: ; Tel.: +55-11-24548298
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Khamidova N, Pergande MR, Pathmasiri KC, Khan R, Mohr JT, Cologna SM. DBDA matrix increases ion abundance of fatty acids and sulfatides in MALDI-TOF and mass spectrometry imaging studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.13.524013. [PMID: 36711800 PMCID: PMC9882223 DOI: 10.1101/2023.01.13.524013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
MALDI-TOF MS is a powerful tool to analyze biomolecules owing to its soft ionization nature and generally results in simple spectra of singly charged ions. Moreover, implementation of the technology in imaging mode provides a means to spatially map analytes in situ. Recently, a new matrix, DBDA (N1,N4-dibenzylidenebenzene-1,4-diamine) was reported to facilitate the ionization of free fatty acids in the negative ion mode. Building on this finding, we sought to implement DBDA for MALDI mass spectrometry imaging studies in brain tissue and successfully map oleic acid, palmitic acid, stearic acid, docosahexaenoic acid and arachidonic acid using mouse brain sections. Moreover, we hypothesized that DBDA would provide superior ionization for sulfatides, a class of sulfolipids, with multiple biological functions. Herein we also demonstrate that DBDA is ideal for MALDI mass spectrometry imaging of fatty acids and sulfatides in brain tissue sections. Additionally, we show enhanced ionization of sulfatides using DBDA compared to three different traditionally used MALDI matrices. Together these results provide new opportunities for studies to measure sulfatides by MALDI-TOF MS including in imaging modes.
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14
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Bifarin OO, Sah S, Gaul DA, Moore SG, Chen R, Palaniappan M, Kim J, Matzuk MM, Fernández FM. Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.04.520434. [PMID: 36711577 PMCID: PMC9881992 DOI: 10.1101/2023.01.04.520434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer. Teaser Time-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.
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Affiliation(s)
- Olatomiwa O. Bifarin
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Samyukta Sah
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Samuel G. Moore
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Ruihong Chen
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Murugesan Palaniappan
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
- Center for Drug Discovery, Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Jaeyeon Kim
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, 46202, United States
| | - Martin M. Matzuk
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
- Center for Drug Discovery, Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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Patil N, Howe O, Cahill P, Byrne HJ. Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives. Mol Metab 2022; 66:101635. [PMID: 36379354 PMCID: PMC9703637 DOI: 10.1016/j.molmet.2022.101635] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/28/2022] [Accepted: 11/06/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The dynamics of the cellular glycolysis pathway underpin cellular function and dysfunction, and therefore ultimately health, disease, diagnostic and therapeutic strategies. Evolving our understanding of this fundamental process and its dynamics remains critical. SCOPE OF REVIEW This paper reviews the medical relevance of glycolytic pathway in depth and explores the current state of the art for monitoring and modelling the dynamics of the process. The future perspectives of label free, vibrational microspectroscopic techniques to overcome the limitations of the current approaches are considered. MAJOR CONCLUSIONS Vibrational microspectroscopic techniques can potentially operate in the niche area of limitations of other omics technologies for non-destructive, real-time, in vivo label-free monitoring of glycolysis dynamics at a cellular and subcellular level.
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Affiliation(s)
- Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland; School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological and Health Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
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Bergsten TM, Levy SE, Zink KE, Lusk HJ, Pergande MR, Cologna SM, Burdette JE, Sanchez LM. Fallopian tube secreted protein affects ovarian metabolites in high grade serous ovarian cancer. Front Cell Dev Biol 2022; 10:1042734. [PMID: 36420136 PMCID: PMC9676663 DOI: 10.3389/fcell.2022.1042734] [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: 09/13/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
High grade serous ovarian cancer (HGSOC), the most lethal histotype of ovarian cancer, frequently arises from fallopian tube epithelial cells (FTE). Once transformed, tumorigenic FTE often migrate specifically to the ovary, completing the crucial primary metastatic step and allowing the formation of the ovarian tumors after which HGSOC was originally named. As only the fimbriated distal ends of the fallopian tube that reside in close proximity to the ovary develop precursor lesions such as serous tubal intraepithelial carcinomas, this suggests that the process of transformation and primary metastasis to the ovary is impacted by the local microenvironment. We hypothesize that chemical cues, including small molecules and proteins, may help stimulate the migration of tumorigenic FTE to the ovary. However, the specific mediators of this process are still poorly understood, despite a recent growth in interest in the tumor microenvironment. Our previous work utilized imaging mass spectrometry (IMS) to identify the release of norepinephrine (NE) from the ovary in co-cultures of tumorigenic FTE cells with an ovarian explant. We predicted that tumorigenic FTE cells secreted a biomolecule, not produced or produced with low expression by non-tumorigenic cells, that stimulated the ovary to release NE. As such, we utilized an IMS mass-guided bioassay, using NE release as our biological marker, and bottom-up proteomics to demonstrate that a secreted protein, SPARC, is a factor produced by tumorigenic FTE responsible for enhancing release of ovarian NE and influencing primary metastasis of HGSOC. This discovery highlights the bidirectional interplay between different types of biomolecules in the fallopian tube and ovarian microenvironment and their combined roles in primary metastasis and disease progression.
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Affiliation(s)
- Tova M. Bergsten
- Burdette Lab, College of Pharmacy, University of Illinois Chicago, Chicago, IL, United States
| | - Sarah E. Levy
- Sanchez Lab, University of California, Santa Cruz, Department of Chemistry and Biochemistry, Santa Cruz, CA, United States
| | - Katherine E. Zink
- Sanchez Lab, College of Pharmacy, University of Illinois Chicago, Chicago, IL, United States
| | - Hannah J. Lusk
- Sanchez Lab, University of California, Santa Cruz, Department of Chemistry and Biochemistry, Santa Cruz, CA, United States
| | - Melissa R. Pergande
- Cologna Lab, University of Illinois Chicago, Department of Chemistry, Chicago, IL, United States
| | - Stephanie M. Cologna
- Cologna Lab, University of Illinois Chicago, Department of Chemistry, Chicago, IL, United States
| | - Joanna E. Burdette
- Burdette Lab, College of Pharmacy, University of Illinois Chicago, Chicago, IL, United States,*Correspondence: Joanna E. Burdette, ; Laura M. Sanchez,
| | - Laura M. Sanchez
- Sanchez Lab, University of California, Santa Cruz, Department of Chemistry and Biochemistry, Santa Cruz, CA, United States,*Correspondence: Joanna E. Burdette, ; Laura M. Sanchez,
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17
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Identification of Novel Drugs Targeting Cell Cycle Regulators for the Treatment of High-Grade Serous Ovarian Cancer via Integrated Bioinformatics Analysis. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
High-grade serous ovarian carcinoma (HGSC), the most common and aggressive histological type of ovarian cancer, remains the leading cause of cancer-related deaths among females. It is important to develop novel drugs to improve the therapeutic outcomes of HGSC patients, thereby reducing their mortality. Symmetry is one of the most important properties of the biological network, which determines the stability of a biological system. As aberrant gene expression is a critical symmetry-breaking event that perturbs the stability of biological networks and triggers tumor progression, we aim in this study to discover new candidate drugs and predict their targets for HGSC therapy based on differentially expressed genes involved in HGSC pathogenesis. Firstly, 98 up-regulated genes and 108 down-regulated genes were identified from three independent transcriptome datasets. Then, the small-molecule compounds PHA-793887, pidorubicine and lestaurtinib, which target cell-cycle-related processes, were identified as novel candidate drugs for HGSC treatment by adopting the connectivity map (CMap)-based drug repositioning approach. Furthermore, through a topological analysis of the protein–protein interaction network, cell cycle regulators CDK1, TOP2A and AURKA were identified as bottleneck nodes, and their expression patterns were validated at the mRNA and protein expression levels. Moreover, the results of molecular docking analysis showed that PHA-793887, pidorubicine and lestaurtinib had a strong binding affinity for CDK1, TOP2A and AURKA, respectively. Therefore, our study repositioned PHA-793887, pidorubicine and lestaurtinib, which can inhibit cell cycle regulators, as novel agents for HGSC treatment, thereby helping to optimize the therapeutic strategy for HGSC.
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18
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Sah S, Yun SR, Gaul DA, Botros A, Park EY, Kim O, Kim J, Fernández FM. Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression. Metabolites 2022; 12:metabo12060532. [PMID: 35736465 PMCID: PMC9230880 DOI: 10.3390/metabo12060532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/04/2022] [Accepted: 06/07/2022] [Indexed: 12/04/2022] Open
Abstract
The lack of effective screening strategies for high-grade serous carcinoma (HGSC), a subtype of ovarian cancer (OC) responsible for 70–80% of OC related deaths, emphasizes the need for new diagnostic markers and a better understanding of disease pathogenesis. Capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) offers high selectivity and sensitivity for ionic compounds, thereby enhancing biomarker discovery. Recent advances in CE-MS include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI) to provide rapid, high-resolution analysis of biological specimens. Here, we describe the development of a targeted microchip (µ) CE-HRMS method, with an acquisition time of only 3 min and sample injection volume of 4nL, to analyze 40 target metabolites in serum samples from a triple-mutant (TKO) mouse model of HGSC. Extracted ion electropherograms showed sharp, baseline resolved peak shapes, even for structural isomers such as leucine and isoleucine. All calibration curves of the analytes maintained good linearity with an average R2 of 0.994, while detection limits were in the nM range. Thirty metabolites were detected in mouse serum with recoveries ranging from 78 to 120%, indicating minimal ionization suppression and good accuracy. We applied the µCE-HRMS method to biweekly-collected serum samples from TKO and TKO control mice. A time-resolved analysis revealed characteristic temporal trends for amino acids, nucleosides, and amino acid derivatives. These metabolic alterations are indicative of altered nucleotide biosynthesis and amino acid metabolism in HGSC development and progression. A comparison of the µCE-HRMS dataset with non-targeted ultra-high performance liquid chromatography (UHPLC)–MS results showed identical temporal trends for the five metabolites detected with both platforms, indicating the µCE-HRMS method performed satisfactorily in terms of capturing metabolic reprogramming due to HGSC progression while reducing the total data collection time three-fold.
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Affiliation(s)
- Samyukta Sah
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA; (S.S.); (D.A.G.)
| | - Sylvia R. Yun
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.R.Y.); (A.B.); (E.Y.P.); (O.K.)
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA; (S.S.); (D.A.G.)
| | - Andro Botros
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.R.Y.); (A.B.); (E.Y.P.); (O.K.)
| | - Eun Young Park
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.R.Y.); (A.B.); (E.Y.P.); (O.K.)
| | - Olga Kim
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.R.Y.); (A.B.); (E.Y.P.); (O.K.)
| | - Jaeyeon Kim
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.R.Y.); (A.B.); (E.Y.P.); (O.K.)
- Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN 46202, USA
- Correspondence: (J.K.); (F.M.F.); Tel.: +1-317-278-9740 (ext. 274-4648) (J.K.); +1-404-385-4432 (ext. 894-7452) (F.M.F.)
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA; (S.S.); (D.A.G.)
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence: (J.K.); (F.M.F.); Tel.: +1-317-278-9740 (ext. 274-4648) (J.K.); +1-404-385-4432 (ext. 894-7452) (F.M.F.)
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