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Xie AX, Tansey W, Reznik E. UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.24.24307903. [PMID: 38826234 PMCID: PMC11142294 DOI: 10.1101/2024.05.24.24307903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Comprehensively studying metabolism requires the measurement of metabolite levels. However, in contrast to the broad availability of gene expression data, metabolites are rarely measured in large molecularly-defined cohorts of tissue samples. To address this basic barrier to metabolic discovery, we propose a Bayesian framework ("UnitedMet") which leverages the empirical strength of RNA-metabolite covariation to impute otherwise unmeasured metabolite levels from widely available transcriptomic data. We demonstrate that UnitedMet is equally capable of imputing whole pool sizes as well as the outcomes of isotope tracing experiments. We apply UnitedMet to investigate the metabolic impact of driver mutations in kidney cancer, identifying a novel association between BAP1 and a highly oxidative tumor phenotype. We similarly apply UnitedMet to determine that advanced kidney cancers upregulate oxidative phosphorylation relative to early-stage disease, that oxidative metabolism in kidney cancer is associated with inferior outcomes to combination therapy, and that kidney cancer metastases themselves demonstrate elevated oxidative phosphorylation relative to primary tumors. UnitedMet therefore enables the assessment of metabolic phenotypes in contexts where metabolite measurements were not taken or are otherwise infeasible, opening new avenues for the generation and evaluation of metabolite-centered hypotheses. UnitedMet is open source and publicly available ( https://github.com/reznik-lab/UnitedMet ).
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2
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Zhou Z, Dong D, Yuan Y, Luo J, Liu XD, Chen LY, Wang G, Yin Y. Single cell atlas reveals multilayered metabolic heterogeneity across tumour types. EBioMedicine 2024; 109:105389. [PMID: 39393173 DOI: 10.1016/j.ebiom.2024.105389] [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: 04/26/2024] [Revised: 09/23/2024] [Accepted: 09/24/2024] [Indexed: 10/13/2024] Open
Abstract
BACKGROUND Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. METHODS We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. FINDINGS Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. INTERPRETATION This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. FUNDING National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.
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Affiliation(s)
- Zhe Zhou
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China
| | - Di Dong
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China
| | - Yuyao Yuan
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China
| | - Juan Luo
- Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen 518036, China
| | - Xiao-Ding Liu
- Research Centre for Molecular Pathology, Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100032, China
| | - Long-Yun Chen
- Research Centre for Molecular Pathology, Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100032, China
| | - Guangxi Wang
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China
| | - Yuxin Yin
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China.
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3
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Abecunas C, Kidd AD, Jiang Y, Zong H, Fallahi-Sichani M. Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations. Cell Rep 2024; 43:114775. [PMID: 39305483 PMCID: PMC11511630 DOI: 10.1016/j.celrep.2024.114775] [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: 11/28/2023] [Revised: 07/10/2024] [Accepted: 09/04/2024] [Indexed: 09/25/2024] Open
Abstract
Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges to identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens and by performing genetic and pharmacological experiments. Our analysis uncovers synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.
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Affiliation(s)
- Cara Abecunas
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Audrey D Kidd
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Ying Jiang
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22908, USA
| | - Hui Zong
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22908, USA; UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908, USA
| | - Mohammad Fallahi-Sichani
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908, USA.
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4
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Badawy AAB. The role of nonesterified fatty acids in cancer biology: Focus on tryptophan and related metabolism. Biochim Biophys Acta Mol Cell Biol Lipids 2024; 1869:159531. [PMID: 38986804 DOI: 10.1016/j.bbalip.2024.159531] [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: 02/15/2024] [Revised: 05/26/2024] [Accepted: 07/04/2024] [Indexed: 07/12/2024]
Abstract
Plasma nonesterified fatty acids (NEFA) are elevated in cancer, because of decreased albumin levels and of fatty acid oxidation, and increased fatty acid synthesis and lipolysis. Albumin depletion and NEFA elevation maximally release albumin-bound tryptophan (Trp) and increase its flux down the kynurenine pathway, leading to increased production of proinflammatory kynurenine metabolites, which tumors use to undermine T-cell function and achieve immune escape. Activation of the aryl hydrocarbon receptor by kynurenic acid promotes extrahepatic Trp degradation by indoleamine 2,3-dioxygenase and leads to upregulation of poly (ADP-ribose) polymerase, activation of which and also of SIRT1 (silent mating type information regulation 2 homolog 1) could lead to depletion of NAD+ and ATP, resulting in cell death. NEFA also modulate heme synthesis and degradation, changes in which impact homocysteine metabolism and production of reduced glutathione and hydrogen sulphide. The significance of the interactions between heme and homocysteine metabolism in cancer biology has received little attention. Targeting Trp disposition in cancer to prevent the NEFA effects is suggested.
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Affiliation(s)
- Abdulla A-B Badawy
- Formerly School of Health Sciences, Cardiff Metropolitan University, Western Avenue, Cardiff CF5 2YB, Wales, UK.
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5
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Long D, Chan M, Han M, Kamdar Z, Ma RK, Tsai PY, Francisco AB, Barrow J, Shackelford DB, Yarchoan M, McBride MJ, Orre LM, Vacanti NM, Gujral TS, Sethupathy P. Proteo-metabolomics and patient tumor slice experiments point to amino acid centrality for rewired mitochondria in fibrolamellar carcinoma. Cell Rep Med 2024; 5:101699. [PMID: 39208801 PMCID: PMC11528240 DOI: 10.1016/j.xcrm.2024.101699] [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: 02/26/2024] [Revised: 06/12/2024] [Accepted: 08/03/2024] [Indexed: 09/04/2024]
Abstract
Fibrolamellar carcinoma (FLC) is a rare, lethal, early-onset liver cancer with a critical need for new therapeutics. The primary driver in FLC is the fusion oncoprotein, DNAJ-PKAc, which remains challenging to target therapeutically. It is critical, therefore, to expand understanding of the FLC molecular landscape to identify druggable pathways/targets. Here, we perform the most comprehensive integrative proteo-metabolomic analysis of FLC. We also conduct nutrient manipulation, respirometry analyses, as well as key loss-of-function assays in FLC tumor tissue slices from patients. We propose a model of cellular energetics in FLC pointing to proline anabolism being mediated by ornithine aminotransferase hyperactivity and ornithine transcarbamylase hypoactivity with serine and glutamine catabolism fueling the process. We highlight FLC's potential dependency on voltage-dependent anion channel (VDAC), a mitochondrial gatekeeper for anions including pyruvate. The metabolic rewiring in FLC that we propose in our model, with an emphasis on mitochondria, can be exploited for therapeutic vulnerabilities.
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Affiliation(s)
- Donald Long
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
| | - Marina Chan
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Mingqi Han
- Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA
| | - Zeal Kamdar
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rosanna K Ma
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Pei-Yin Tsai
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA
| | - Adam B Francisco
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Joeva Barrow
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA
| | | | - Mark Yarchoan
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthew J McBride
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA
| | - Lukas M Orre
- Department of Oncology and Pathology, Karolinska Institute, SciLifeLab, Solna, Sweden
| | | | - Taranjit S Gujral
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Praveen Sethupathy
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
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Menegollo M, Bentham RB, Henriques T, Ng SQ, Ren Z, Esculier C, Agarwal S, Tong ETY, Lo C, Ilangovan S, Szabadkai Z, Suman M, Patani N, Ghanate A, Bryson K, Stein RC, Yuneva M, Szabadkai G. Multistate Gene Cluster Switches Determine the Adaptive Mitochondrial and Metabolic Landscape of Breast Cancer. Cancer Res 2024; 84:2911-2925. [PMID: 38924467 PMCID: PMC11372374 DOI: 10.1158/0008-5472.can-23-3172] [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: 10/11/2023] [Revised: 04/17/2024] [Accepted: 06/20/2024] [Indexed: 06/28/2024]
Abstract
Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labeled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome.
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Affiliation(s)
- Michela Menegollo
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Robert B Bentham
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Tiago Henriques
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Seow Q Ng
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Ziyu Ren
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Clarinde Esculier
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Sia Agarwal
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Emily T Y Tong
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Clement Lo
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Sanjana Ilangovan
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Zorka Szabadkai
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
| | - Matteo Suman
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Neill Patani
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
| | | | - Kevin Bryson
- Department of Computer Sciences, University College London, London, United Kingdom
| | - Robert C Stein
- Department of Oncology, University College London Hospitals, London, United Kingdom
- UCL Cancer Institute, University College London, London, United Kingdom
| | | | - Gyorgy Szabadkai
- Department of Biomedical Sciences, University of Padova, Padova, Italy
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
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7
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Zhu J. New Metabolomic Insights Into Cancer. Cancer J 2024; 30:301-306. [PMID: 39312449 PMCID: PMC11424019 DOI: 10.1097/ppo.0000000000000740] [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] [Indexed: 09/25/2024]
Abstract
ABSTRACT Cancer has been marked by metabolic irregularities that fuel various aggressive activities such as rapid cell proliferation, evasion of the immune system, and spread to distant organs. Therefore, exploiting cancer metabolism for diagnosis, monitoring, or treatment has been extensively studied in the past couple of decades with various molecular and cellular techniques. More recently, investigating cancer diagnostics and treatments through advanced metabolomics has emerged, and these comprehensive approaches provide a holistic understanding of cancer metabolism, which supported the discovery of metabolic targets relevant across multiple cancer types and the development of more effective treatments. This study offers highlights of new knowledge on cancer metabolism enabled by recent metabolomics studies and their potential applications in aiding cancer research and predicting cancer treatment outcomes. Specifically, we discussed the use of advanced metabolomics in cancer metabolism, tumor microenvironment, and cancer immunotherapy studies to provide valuable insights that can shape future research efforts in the dynamic field of cancer metabolism research.
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8
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Gaspary JFP, Edgar L, Lopes LFD, Rosa CB, Siluk JCM. Translational insights into the hormetic potential of carbon dioxide: from physiological mechanisms to innovative adjunct therapeutic potential for cancer. Front Physiol 2024; 15:1415037. [PMID: 39086932 PMCID: PMC11288912 DOI: 10.3389/fphys.2024.1415037] [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: 04/09/2024] [Accepted: 06/18/2024] [Indexed: 08/02/2024] Open
Abstract
Background Carbon dioxide (CO2), traditionally viewed as a mere byproduct of cellular respiration, plays a multifaceted role in human physiology beyond simple elimination through respiration. CO2 may regulate the tumor microenvironment by significantly affecting the release of oxygen (O2) to tissues through the Bohr effect and by modulating blood pH and vasodilation. Previous studies suggest hypercapnia (elevated CO2 levels) might trigger optimized cellular mechanisms with potential therapeutic benefits. The role of CO2 in cellular stress conditions within tumor environments and its impact on O2 utilization offers a new investigative area in oncology. Objectives This study aims to explore CO2's role in the tumor environment, particularly how its physiological properties and adaptive responses can influence therapeutic strategies. Methods By applying a structured translational approach using the Work Breakdown Structure method, the study divided the analysis into six interconnected work packages to comprehensively analyze the interactions between carbon dioxide and the tumor microenvironment. Methods included systematic literature reviews, data analyses, data integration for identifying critical success factors and exploring extracellular environment modulation. The research used SMART criteria for assessing innovation and the applicability of results. Results The research revealed that the human body's adaptability to hypercapnic conditions could potentially inform innovative strategies for manipulating the tumor microenvironment. This could enhance O2 utilization efficiency and manage adaptive responses to cellular stress. The study proposed that carbon dioxide's hormetic potential could induce beneficial responses in the tumor microenvironment, prompting clinical protocols for experimental validation. The research underscored the importance of pH regulation, emphasizing CO2 and carbonic acid's role in modulating metabolic and signaling pathways related to cancer. Conclusion The study underscores CO2 as vital to our physiology and suggests potential therapeutic uses within the tumor microenvironment. pH modulation and cellular oxygenation optimization via CO2 manipulation could offer innovative strategies to enhance existing cancer therapies. These findings encourage further exploration of CO2's therapeutic potential. Future research should focus on experimental validation and exploration of clinical applications, emphasizing the need for interdisciplinary and collaborative approaches to tackle current challenges in cancer treatment.
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Affiliation(s)
| | - Lee Edgar
- Elastro Crete, LLC. Research and Development Department, Veyo, UT, United States
| | - Luis Felipe Dias Lopes
- Department of Administrative Sciences, Federal University of Santa Maria, Santa Maria, Brazil
| | - Carmen Brum Rosa
- Production Engineering Department, Federal University of Santa Maria, Santa Maria, Brazil
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Abecunas C, Kidd AD, Jiang Y, Zong H, Fallahi-Sichani M. Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.28.569098. [PMID: 38076921 PMCID: PMC10705426 DOI: 10.1101/2023.11.28.569098] [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: 12/21/2023]
Abstract
Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens, and by performing new genetic and pharmacological experiments. Our analysis uncovers new synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.
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Affiliation(s)
- Cara Abecunas
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109
- Present address: Novartis Institutes for BioMedical Research, Cambridge, MA 02139
| | - Audrey D. Kidd
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908
| | - Ying Jiang
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22908
| | - Hui Zong
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA 22908
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908
| | - Mohammad Fallahi-Sichani
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908
- Lead contact
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Gondal MN, Shah SUR, Chinnaiyan AM, Cieslik M. A systematic overview of single-cell transcriptomics databases, their use cases, and limitations. FRONTIERS IN BIOINFORMATICS 2024; 4:1417428. [PMID: 39040140 PMCID: PMC11260681 DOI: 10.3389/fbinf.2024.1417428] [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: 04/14/2024] [Accepted: 06/11/2024] [Indexed: 07/24/2024] Open
Abstract
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
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Affiliation(s)
- Mahnoor N. Gondal
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Saad Ur Rehman Shah
- Gies College of Business, University of Illinois Business College, Champaign, MI, United States
| | - Arul M. Chinnaiyan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Urology, University of Michigan, Ann Arbor, MI, United States
- Howard Hughes Medical Institute, Ann Arbor, MI, United States
- University of Michigan Rogel Cancer Center, Ann Arbor, MI, United States
| | - Marcin Cieslik
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
- University of Michigan Rogel Cancer Center, Ann Arbor, MI, United States
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11
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Balonov I, Mattis M, Jarmusch S, Koletzko B, Heinrich K, Neumann J, Werner J, Angele MK, Heiliger C, Jacob S. Metabolomic profiling of upper GI malignancies in blood and tissue: a systematic review and meta-analysis. J Cancer Res Clin Oncol 2024; 150:331. [PMID: 38951269 PMCID: PMC11217139 DOI: 10.1007/s00432-024-05857-5] [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: 05/10/2024] [Accepted: 06/17/2024] [Indexed: 07/03/2024]
Abstract
OBJECTIVE To conduct a systematic review and meta-analysis of case-control and cohort human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on esophageal cancer (EC), cancer of the gastroesophageal junction (GEJ), and gastric cancer (GC) in blood and tissue. BACKGROUND Upper gastrointestinal cancers (UGC), predominantly EC, GEJ, and GC, are malignant tumour types with high morbidity and mortality rates. Numerous studies have focused on metabolomic profiling of UGC in recent years. In this systematic review and meta-analysis, we have provided a collective summary of previous findings on metabolites and metabolomic profiling associated with EC, GEJ and GC. METHODS Following the PRISMA procedure, a systematic search of four databases (Embase, PubMed, MEDLINE, and Web of Science) for molecular epidemiologic studies on the metabolomic profiles of EC, GEJ and GC was conducted and registered at PROSPERO (CRD42023486631). The Newcastle-Ottawa Scale (NOS) was used to benchmark the risk of bias for case-controlled and cohort studies. QUADOMICS, an adaptation of the QUADAS-2 (Quality Assessment of Diagnostic Accuracy) tool, was used to rate diagnostic accuracy studies. Original articles comparing metabolite patterns between patients with and without UGC were included. Two investigators independently completed title and abstract screening, data extraction, and quality evaluation. Meta-analysis was conducted whenever possible. We used a random effects model to investigate the association between metabolite levels and UGC. RESULTS A total of 66 original studies involving 7267 patients that met the required criteria were included for review. 169 metabolites were differentially distributed in patients with UGC compared to healthy patients among 44 GC, 9 GEJ, and 25 EC studies including metabolites involved in glycolysis, anaerobic respiration, tricarboxylic acid cycle, and lipid metabolism. Phosphatidylcholines, eicosanoids, and adenosine triphosphate were among the most frequently reported lipids and metabolites of cellular respiration, while BCAA, lysine, and asparagine were among the most commonly reported amino acids. Previously identified lipid metabolites included saturated and unsaturated free fatty acids and ketones. However, the key findings across studies have been inconsistent, possibly due to limited sample sizes and the majority being hospital-based case-control analyses lacking an independent replication group. CONCLUSION Thus far, metabolomic studies have provided new opportunities for screening, etiological factors, and biomarkers for UGC, supporting the potential of applying metabolomic profiling in early cancer diagnosis. According to the results of our meta-analysis especially BCAA and TMAO as well as certain phosphatidylcholines should be implicated into the diagnostic procedure of patients with UGC. We envision that metabolomics will significantly enhance our understanding of the carcinogenesis and progression process of UGC and may eventually facilitate precise oncological and patient-tailored management of UGC.
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Affiliation(s)
- Ilja Balonov
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Ludwig-Maximilians-University (LMU) Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Minca Mattis
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Ludwig-Maximilians-University (LMU) Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Stefanie Jarmusch
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Ludwig-Maximilians-University (LMU) Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Dr. Von Hauner Children's Hospital, Ludwig-Maximilians-University Munich Medical Center, Lindwurmstraße 4, 80337, Munich, Germany
| | - Kathrin Heinrich
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Jens Neumann
- Institute of Pathology, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Jens Werner
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Ludwig-Maximilians-University (LMU) Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Martin K Angele
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Ludwig-Maximilians-University (LMU) Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Christian Heiliger
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Ludwig-Maximilians-University (LMU) Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Sven Jacob
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Ludwig-Maximilians-University (LMU) Munich, Marchioninistr. 15, 81377, Munich, Germany.
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12
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Tharp KM, Kersten K, Maller O, Timblin GA, Stashko C, Canale FP, Menjivar RE, Hayward MK, Berestjuk I, Ten Hoeve J, Samad B, Ironside AJ, di Magliano MP, Muir A, Geiger R, Combes AJ, Weaver VM. Tumor-associated macrophages restrict CD8 + T cell function through collagen deposition and metabolic reprogramming of the breast cancer microenvironment. NATURE CANCER 2024; 5:1045-1062. [PMID: 38831058 DOI: 10.1038/s43018-024-00775-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 04/26/2024] [Indexed: 06/05/2024]
Abstract
Tumor progression is accompanied by fibrosis, a condition of excessive extracellular matrix accumulation, which is associated with diminished antitumor immune infiltration. Here we demonstrate that tumor-associated macrophages (TAMs) respond to the stiffened fibrotic tumor microenvironment (TME) by initiating a collagen biosynthesis program directed by transforming growth factor-β. A collateral effect of this programming is an untenable metabolic milieu for productive CD8+ T cell antitumor responses, as collagen-synthesizing macrophages consume environmental arginine, synthesize proline and secrete ornithine that compromises CD8+ T cell function in female breast cancer. Thus, a stiff and fibrotic TME may impede antitumor immunity not only by direct physical exclusion of CD8+ T cells but also through secondary effects of a mechano-metabolic programming of TAMs, which creates an inhospitable metabolic milieu for CD8+ T cells to respond to anticancer immunotherapies.
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Affiliation(s)
- Kevin M Tharp
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Kelly Kersten
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
- ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Ori Maller
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Greg A Timblin
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Connor Stashko
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Fernando P Canale
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Rosa E Menjivar
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Mary-Kate Hayward
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Ilona Berestjuk
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Johanna Ten Hoeve
- UCLA Metabolomics Center, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bushra Samad
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
- ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA
- UCSF CoLabs, University of California San Francisco, San Francisco, CA, USA
| | | | - Marina Pasca di Magliano
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
- Department of Cell and Developmental Biology, Cancer Biology Program, University of Michigan, Ann Arbor, MI, USA
| | - Alexander Muir
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | - Roger Geiger
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Alexis J Combes
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
- ImmunoX Initiative, University of California San Francisco, San Francisco, CA, USA
- UCSF CoLabs, University of California San Francisco, San Francisco, CA, USA
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Valerie M Weaver
- Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences and Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, and The Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.
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13
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Das C, Bhattacharya A, Adhikari S, Mondal A, Mondal P, Adhikary S, Roy S, Ramos K, Yadav KK, Tainer JA, Pandita TK. A prismatic view of the epigenetic-metabolic regulatory axis in breast cancer therapy resistance. Oncogene 2024; 43:1727-1741. [PMID: 38719949 PMCID: PMC11161412 DOI: 10.1038/s41388-024-03054-9] [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: 12/15/2023] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Abstract
Epigenetic regulation established during development to maintain patterns of transcriptional expression and silencing for metabolism and other fundamental cell processes can be reprogrammed in cancer, providing a molecular mechanism for persistent alterations in phenotype. Metabolic deregulation and reprogramming are thus an emerging hallmark of cancer with opportunities for molecular classification as a critical preliminary step for precision therapeutic intervention. Yet, acquisition of therapy resistance against most conventional treatment regimens coupled with tumor relapse, continue to pose unsolved problems for precision healthcare, as exemplified in breast cancer where existing data informs both cancer genotype and phenotype. Furthermore, epigenetic reprograming of the metabolic milieu of cancer cells is among the most crucial determinants of therapeutic resistance and cancer relapse. Importantly, subtype-specific epigenetic-metabolic interplay profoundly affects malignant transformation, resistance to chemotherapy, and response to targeted therapies. In this review, we therefore prismatically dissect interconnected epigenetic and metabolic regulatory pathways and then integrate them into an observable cancer metabolism-therapy-resistance axis that may inform clinical intervention. Optimally coupling genome-wide analysis with an understanding of metabolic elements, epigenetic reprogramming, and their integration by metabolic profiling may decode missing molecular mechanisms at the level of individual tumors. The proposed approach of linking metabolic biochemistry back to genotype, epigenetics, and phenotype for specific tumors and their microenvironment may thus enable successful mechanistic targeting of epigenetic modifiers and oncometabolites despite tumor metabolic heterogeneity.
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Affiliation(s)
- Chandrima Das
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India.
- Homi Bhabha National Institute, Mumbai, 400094, India.
| | - Apoorva Bhattacharya
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India
| | - Swagata Adhikari
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India
- Homi Bhabha National Institute, Mumbai, 400094, India
| | - Atanu Mondal
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India
- Homi Bhabha National Institute, Mumbai, 400094, India
| | - Payel Mondal
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India
- Homi Bhabha National Institute, Mumbai, 400094, India
| | - Santanu Adhikary
- Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India
- Structural Biology and Bioinformatics Division, Council of Scientific and Industrial Research (CSIR)-Indian Institute of Chemical Biology, Kolkata, 700032, India
| | - Siddhartha Roy
- Structural Biology and Bioinformatics Division, Council of Scientific and Industrial Research (CSIR)-Indian Institute of Chemical Biology, Kolkata, 700032, India
| | - Kenneth Ramos
- Center for Genomics and Precision Medicine, Texas A&M University, School of Medicine, Houston, TX, 77030, USA
| | - Kamlesh K Yadav
- Center for Genomics and Precision Medicine, Texas A&M University, School of Medicine, Houston, TX, 77030, USA
- School of Engineering Medicine, Texas A&M University, School of Medicine, Houston, TX, 77030, USA
| | - John A Tainer
- The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Tej K Pandita
- Center for Genomics and Precision Medicine, Texas A&M University, School of Medicine, Houston, TX, 77030, USA.
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14
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Kim M, Gorelick AN, Vàzquez-García I, Williams MJ, Salehi S, Shi H, Weiner AC, Ceglia N, Funnell T, Park T, Boscenco S, O'Flanagan CH, Jiang H, Grewal D, Tang C, Rusk N, Gammage PA, McPherson A, Aparicio S, Shah SP, Reznik E. Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes. Nat Genet 2024; 56:889-899. [PMID: 38741018 PMCID: PMC11096122 DOI: 10.1038/s41588-024-01724-8] [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: 06/21/2022] [Accepted: 03/20/2024] [Indexed: 05/16/2024]
Abstract
The extent of cell-to-cell variation in tumor mitochondrial DNA (mtDNA) copy number and genotype, and the phenotypic and evolutionary consequences of such variation, are poorly characterized. Here we use amplification-free single-cell whole-genome sequencing (Direct Library Prep (DLP+)) to simultaneously assay mtDNA copy number and nuclear DNA (nuDNA) in 72,275 single cells derived from immortalized cell lines, patient-derived xenografts and primary human tumors. Cells typically contained thousands of mtDNA copies, but variation in mtDNA copy number was extensive and strongly associated with cell size. Pervasive whole-genome doubling events in nuDNA associated with stoichiometrically balanced adaptations in mtDNA copy number, implying that mtDNA-to-nuDNA ratio, rather than mtDNA copy number itself, mediated downstream phenotypes. Finally, multimodal analysis of DLP+ and single-cell RNA sequencing identified both somatic loss-of-function and germline noncoding variants in mtDNA linked to heteroplasmy-dependent changes in mtDNA copy number and mitochondrial transcription, revealing phenotypic adaptations to disrupted nuclear/mitochondrial balance.
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Affiliation(s)
- Minsoo Kim
- Tri-Institutional PhD Program in Computational Biology & Medicine, Weill Cornell Medicine, New York City, NY, USA
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Alexander N Gorelick
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Ignacio Vàzquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Sohrab Salehi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Hongyu Shi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Adam C Weiner
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Nick Ceglia
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Tyler Funnell
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Tricia Park
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Sonia Boscenco
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Ciara H O'Flanagan
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Hui Jiang
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Diljot Grewal
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Cerise Tang
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Payam A Gammage
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
- CRUK Beatson Institute, Glasgow, UK
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Sam Aparicio
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.
| | - Ed Reznik
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.
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15
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Li Z, Peng W, Zhou J, Shui S, Liu Y, Li T, Zhan X, Chen Y, Lan F, Ying B, Wu Y. Multidimensional Interactive Cascading Nanochips for Detection of Multiple Liver Diseases via Precise Metabolite Profiling. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312799. [PMID: 38263756 DOI: 10.1002/adma.202312799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/11/2024] [Indexed: 01/25/2024]
Abstract
It is challenging to detect and differentiate multiple diseases with high complexity/similarity from the same organ. Metabolic analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is a promising platform for disease diagnosis, while the enhanced property of its core nanomatrix materials has plenty of room for improvement. Herein, a multidimensional interactive cascade nanochip composed of iron oxide nanoparticles (FeNPs)/MXene/gold nanoparticles (AuNPs), IMG, is reported for serum metabolic profiling to achieve high-throughput detection of multiple liver diseases. MXene serves as a multi-binding site and an electron-hole source for ionization during NMALDI-MS analysis. Introduction of AuNPs with surface plasmon resonance (SPR) properties facilitates surface charge accumulation and rapid energy conversion. FeNPs are integrated into the MXene/Au nanocomposite to sharply reduce the thermal conductivity of the nanochip with negligible heat loss for strong thermally-driven desorption, and construct a multi-interaction proton transport pathway with MXene and AuNPs for strong ionization. Analysis of these enhanced serum fingerprint signals detected from the IMG nanochip through a neural network model results in differentiation of multiple liver diseases via a single pass and revelation of potential metabolic biomarkers. The promising method can rapidly and accurately screen various liver diseases, thus allowing timely treatment of liver diseases.
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Affiliation(s)
- Zhiyu Li
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Weili Peng
- Machine Intelligence Lab, College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Juan Zhou
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Shaoxuan Shui
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Yicheng Liu
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Tan Li
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Xiaohui Zhan
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Yuanyuan Chen
- Machine Intelligence Lab, College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Fang Lan
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610064, China
| | - Yao Wu
- National Engineering Research Center for Biomaterials, School of Biomedical Engineering, Sichuan University, Chengdu, 610064, China
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16
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Gondal MN, Shah SUR, Chinnaiyan AM, Cieslik M. A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations. ARXIV 2024:arXiv:2404.10545v1. [PMID: 38699169 PMCID: PMC11065044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of genomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Furthermore, we propose that bridging the gap between computational and wet lab scientists through user-friendly web-based platforms is needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
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Affiliation(s)
- Mahnoor N. Gondal
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
| | - Saad Ur Rehman Shah
- Gies College of Business, University of Illinois Business College, Champaign, IL USA
| | - Arul M. Chinnaiyan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Urology, University of Michigan, Ann Arbor, MI USA
- Howard Hughes Medical Institute, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
| | - Marcin Cieslik
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
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17
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Jackson BT, Finley LWS. Metabolic regulation of the hallmarks of stem cell biology. Cell Stem Cell 2024; 31:161-180. [PMID: 38306993 PMCID: PMC10842269 DOI: 10.1016/j.stem.2024.01.003] [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: 12/03/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Stem cells perform many different functions, each of which requires specific metabolic adaptations. Over the past decades, studies of pluripotent and tissue stem cells have uncovered a range of metabolic preferences and strategies that correlate with or exert control over specific cell states. This review aims to describe the common themes that emerge from the study of stem cell metabolism: (1) metabolic pathways supporting stem cell proliferation, (2) metabolic pathways maintaining stem cell quiescence, (3) metabolic control of cellular stress responses and cell death, (4) metabolic regulation of stem cell identity, and (5) metabolic requirements of the stem cell niche.
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Affiliation(s)
- Benjamin T Jackson
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, New York, NY, USA
| | - Lydia W S Finley
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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18
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Ahluwalia P, Ballur K, Leeman T, Vashisht A, Singh H, Omar N, Mondal AK, Vaibhav K, Baban B, Kolhe R. Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine. Cancers (Basel) 2024; 16:480. [PMID: 38339232 PMCID: PMC10854941 DOI: 10.3390/cancers16030480] [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: 10/31/2023] [Revised: 01/10/2024] [Accepted: 01/13/2024] [Indexed: 02/12/2024] Open
Abstract
Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.
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Affiliation(s)
- Pankaj Ahluwalia
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA; (P.A.); (K.B.); (T.L.); (A.V.); (H.S.); (N.O.); (A.K.M.)
| | - Kalyani Ballur
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA; (P.A.); (K.B.); (T.L.); (A.V.); (H.S.); (N.O.); (A.K.M.)
| | - Tiffanie Leeman
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA; (P.A.); (K.B.); (T.L.); (A.V.); (H.S.); (N.O.); (A.K.M.)
| | - Ashutosh Vashisht
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA; (P.A.); (K.B.); (T.L.); (A.V.); (H.S.); (N.O.); (A.K.M.)
| | - Harmanpreet Singh
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA; (P.A.); (K.B.); (T.L.); (A.V.); (H.S.); (N.O.); (A.K.M.)
| | - Nivin Omar
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA; (P.A.); (K.B.); (T.L.); (A.V.); (H.S.); (N.O.); (A.K.M.)
| | - Ashis K. Mondal
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA; (P.A.); (K.B.); (T.L.); (A.V.); (H.S.); (N.O.); (A.K.M.)
| | - Kumar Vaibhav
- Department of Neurosurgery, Augusta University, Augusta, GA 30912, USA;
| | - Babak Baban
- Departments of Neurology and Surgery, Augusta University, Augusta, GA 30912, USA;
| | - Ravindra Kolhe
- Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA 30912, USA; (P.A.); (K.B.); (T.L.); (A.V.); (H.S.); (N.O.); (A.K.M.)
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