1
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Zhang Y, Wang C, Li JJ. Revisiting the role of mesenchymal stromal cells in cancer initiation, metastasis and immunosuppression. Exp Hematol Oncol 2024; 13:64. [PMID: 38951845 PMCID: PMC11218091 DOI: 10.1186/s40164-024-00532-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/26/2024] [Indexed: 07/03/2024] Open
Abstract
Immune checkpoint blockade (ICB) necessitates a thorough understanding of intricate cellular interactions within the tumor microenvironment (TME). Mesenchymal stromal cells (MSCs) play a pivotal role in cancer generation, progression, and immunosuppressive tumor microenvironment. Within the TME, MSCs encompass both resident and circulating counterparts that dynamically communicate and actively participate in TME immunosurveillance and response to ICB. This review aims to reevaluate various facets of MSCs, including their potential self-transformation to function as cancer-initiating cells and contributions to the creation of a conducive environment for tumor proliferation and metastasis. Additionally, we explore the immune regulatory functions of tumor-associated MSCs (TA-MSCs) and MSC-derived extracellular vesicles (MSC-EVs) with analysis of potential connections between circulating and tissue-resident MSCs. A comprehensive understanding of the dynamics of MSC-immune cell communication and the heterogeneous cargo of tumor-educated versus naïve MSCs may unveil a new MSC-mediated immunosuppressive pathway that can be targeted to enhance cancer control by ICB.
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Affiliation(s)
- Yanyan Zhang
- Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Radiation Oncology, School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Charles Wang
- Department of Radiation Oncology, School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Jian Jian Li
- Department of Radiation Oncology, School of Medicine, University of California Davis, Sacramento, CA, USA.
- NCI-Designated Comprehensive Cancer Center, University of California Davis, Sacramento, CA, 95817, USA.
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2
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Sarmah D, Meredith WO, Weber IK, Price MR, Birtwistle MR. Predicting anti-cancer drug combination responses with a temporal cell state network model. PLoS Comput Biol 2023; 19:e1011082. [PMID: 37126527 PMCID: PMC10174488 DOI: 10.1371/journal.pcbi.1011082] [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: 10/18/2022] [Revised: 05/11/2023] [Accepted: 04/06/2023] [Indexed: 05/02/2023] Open
Abstract
Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging, even for simple in vitro systems. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines in vitro. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. While further application to different cell lines, more drugs, additional cell state networks, and more complex co-culture or in vivo systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination response for single cell lines in vitro.
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Affiliation(s)
- Deepraj Sarmah
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
| | - Wesley O. Meredith
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
| | - Ian K. Weber
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
- The University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Madison R. Price
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
- College of Pharmacy, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Marc R. Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
- Department of Bioengineering, Clemson University, Clemson, South Carolina, United States of America
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3
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Manipur I, Giordano M, Piccirillo M, Parashuraman S, Maddalena L. Community Detection in Protein-Protein Interaction Networks and Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:217-237. [PMID: 34951849 DOI: 10.1109/tcbb.2021.3138142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.
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4
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Hu C, Zeng Z, Ma D, Yin Z, Zhao S, Chen T, Tang L, Zuo S. Discovery of novel IDH1-R132C inhibitors through structure-based virtual screening. Front Pharmacol 2022; 13:982375. [PMID: 36160383 PMCID: PMC9491111 DOI: 10.3389/fphar.2022.982375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/16/2022] [Indexed: 11/13/2022] Open
Abstract
Isocitrate dehydrogenase (IDH) belongs to a family of enzymes involved in glycometabolism. It is found in many living organisms and is one of the most mutated metabolic enzymes. In the current study, we identified novel IDH1-R132C inhibitors using docking-based virtual screening and cellular inhibition assays. A total of 100 molecules with high docking scores were obtained from docking-based virtual screening. The cellular inhibition assay demonstrated five compounds at a concentration of 10 μM could inhibit cancer cells harboring the IDH1-R132C mutation proliferation by > 50%. The compound (T001-0657) showed the most potent effect against cancer cells harboring the IDH1-R132C mutation with a half-maximal inhibitory concentration (IC50) value of 1.311 μM. It also showed a cytotoxic effect against cancer cells with wild-type IDH1 and normal cells with IC50 values of 49.041 μM and >50 μM, respectively. Molecular dynamics simulations were performed to investigate the stability of the kinase structure binding of allosteric inhibitor compound A and the identified compound T001-0657 binds to IDH1-R132C. Root-mean-square deviation, root-mean-square fluctuation, and binding free energy calculations showed that both compounds bind tightly to IDH1-R132C. In conclusion, the compound identified in this study had high selectivity for cancer cells harboring IDH1-R132C mutation and could be considered a promising hit compound for further development of IDH1-R132C inhibitors.
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Affiliation(s)
- Chujiao Hu
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R and D, Guiyang, China
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- Precision Medicine Research Institute of Guizhou, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Zhirui Zeng
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- Precision Medicine Research Institute of Guizhou, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Dan Ma
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R and D, Guiyang, China
- Department of Hematology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Zhixin Yin
- College of Pharmacy, Guizhou Medical University, Guiyang, China
| | - Shanshan Zhao
- College of Pharmacy, Guizhou Medical University, Guiyang, China
| | - Tengxiang Chen
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- Precision Medicine Research Institute of Guizhou, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- *Correspondence: Tengxiang Chen, ; Lei Tang, ; Shi Zuo,
| | - Lei Tang
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R and D, Guiyang, China
- *Correspondence: Tengxiang Chen, ; Lei Tang, ; Shi Zuo,
| | - Shi Zuo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Precision Medicine Research Institute of Guizhou, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- *Correspondence: Tengxiang Chen, ; Lei Tang, ; Shi Zuo,
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5
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Yousefi M, Boross G, Weiss C, Murray CW, Hebert JD, Cai H, Ashkin EL, Karmakar S, Andrejka L, Chen L, Wang M, Tsai MK, Lin WY, Li C, Yakhchalian P, Colón CI, Chew SK, Chu P, Swanton C, Kunder CA, Petrov DA, Winslow MM. Combinatorial Inactivation of Tumor Suppressors Efficiently Initiates Lung Adenocarcinoma with Therapeutic Vulnerabilities. Cancer Res 2022; 82:1589-1602. [PMID: 35425962 PMCID: PMC9022333 DOI: 10.1158/0008-5472.can-22-0059] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 11/16/2022]
Abstract
Lung cancer is the leading cause of cancer death worldwide, with lung adenocarcinoma being the most common subtype. Many oncogenes and tumor suppressor genes are altered in this cancer type, and the discovery of oncogene mutations has led to the development of targeted therapies that have improved clinical outcomes. However, a large fraction of lung adenocarcinomas lacks mutations in known oncogenes, and the genesis and treatment of these oncogene-negative tumors remain enigmatic. Here, we perform iterative in vivo functional screens using quantitative autochthonous mouse model systems to uncover the genetic and biochemical changes that enable efficient lung tumor initiation in the absence of oncogene alterations. Generation of hundreds of diverse combinations of tumor suppressor alterations demonstrates that inactivation of suppressors of the RAS and PI3K pathways drives the development of oncogene-negative lung adenocarcinoma. Human genomic data and histology identified RAS/MAPK and PI3K pathway activation as a common feature of an event in oncogene-negative human lung adenocarcinomas. These Onc-negativeRAS/PI3K tumors and related cell lines are vulnerable to pharmacologic inhibition of these signaling axes. These results transform our understanding of this prevalent yet understudied subtype of lung adenocarcinoma. SIGNIFICANCE To address the large fraction of lung adenocarcinomas lacking mutations in proto-oncogenes for which targeted therapies are unavailable, this work uncovers driver pathways of oncogene-negative lung adenocarcinomas and demonstrates their therapeutic vulnerabilities.
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Affiliation(s)
- Maryam Yousefi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally
| | - Gábor Boross
- Department of Biology, Stanford University, Stanford, CA, USA
- These authors contributed equally
| | - Carly Weiss
- Department of Biology, Stanford University, Stanford, CA, USA
| | | | - Jess D. Hebert
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Hongchen Cai
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Emily L. Ashkin
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Saswati Karmakar
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Laura Andrejka
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Leo Chen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Minwei Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Min K. Tsai
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Wen-Yang Lin
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Chuan Li
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Pegah Yakhchalian
- Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, USA
| | - Caterina I. Colón
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Su-Kit Chew
- Cancer Evolution and Genome Instability Laboratory, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Pauline Chu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Christian A. Kunder
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Dmitri A. Petrov
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Monte M. Winslow
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
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6
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Shen JP. Artificial intelligence, molecular subtyping, biomarkers, and precision oncology. Emerg Top Life Sci 2021; 5:747-756. [PMID: 34881776 PMCID: PMC8786277 DOI: 10.1042/etls20210212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 11/17/2022]
Abstract
A targeted cancer therapy is only useful if there is a way to accurately identify the tumors that are susceptible to that therapy. Thus rapid expansion in the number of available targeted cancer treatments has been accompanied by a robust effort to subdivide the traditional histological and anatomical tumor classifications into molecularly defined subtypes. This review highlights the history of the paired evolution of targeted therapies and biomarkers, reviews currently used methods for subtype identification, and discusses challenges to the implementation of precision oncology as well as possible solutions.
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Affiliation(s)
- John Paul Shen
- Department of Gastrointestinal Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, U.S.A
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7
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Swaney DL, Ramms DJ, Wang Z, Park J, Goto Y, Soucheray M, Bhola N, Kim K, Zheng F, Zeng Y, McGregor M, Herrington KA, O'Keefe R, Jin N, VanLandingham NK, Foussard H, Von Dollen J, Bouhaddou M, Jimenez-Morales D, Obernier K, Kreisberg JF, Kim M, Johnson DE, Jura N, Grandis JR, Gutkind JS, Ideker T, Krogan NJ. A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity. Science 2021; 374:eabf2911. [PMID: 34591642 DOI: 10.1126/science.abf2911] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Danielle L Swaney
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Dana J Ramms
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Department of Pharmacology, University of California San Diego, La Jolla, CA.,Moores Cancer Center, University of California San Diego, La Jolla, CA
| | - Zhiyong Wang
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Moores Cancer Center, University of California San Diego, La Jolla, CA
| | - Jisoo Park
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA
| | - Yusuke Goto
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Moores Cancer Center, University of California San Diego, La Jolla, CA
| | - Margaret Soucheray
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Neil Bhola
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Kyumin Kim
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Fan Zheng
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA
| | - Yan Zeng
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Michael McGregor
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Kari A Herrington
- Department of Biochemistry and Biophysics Center for Advanced Light Microscopy at UCSF, University of California San Francisco, San Francisco, CA, USA
| | - Rachel O'Keefe
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Nan Jin
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Nathan K VanLandingham
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Helene Foussard
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - John Von Dollen
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Mehdi Bouhaddou
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - David Jimenez-Morales
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Kirsten Obernier
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Jason F Kreisberg
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA
| | - Minkyu Kim
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
| | - Daniel E Johnson
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Natalia Jura
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA
| | - Jennifer R Grandis
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - J Silvio Gutkind
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Department of Pharmacology, University of California San Diego, La Jolla, CA.,Moores Cancer Center, University of California San Diego, La Jolla, CA
| | - Trey Ideker
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA.,Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA.,Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science, University of California San Diego, La Jolla, CA, USA
| | - Nevan J Krogan
- Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA.,J. David Gladstone Institutes, San Francisco, CA, USA.,Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA
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8
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Zheng F, Kelly MR, Ramms DJ, Heintschel ML, Tao K, Tutuncuoglu B, Lee JJ, Ono K, Foussard H, Chen M, Herrington KA, Silva E, Liu S, Chen J, Churas C, Wilson N, Kratz A, Pillich RT, Patel DN, Park J, Kuenzi B, Yu MK, Licon K, Pratt D, Kreisberg JF, Kim M, Swaney DL, Nan X, Fraley SI, Gutkind JS, Krogan NJ, Ideker T. Interpretation of cancer mutations using a multiscale map of protein systems. Science 2021; 374:eabf3067. [PMID: 34591613 PMCID: PMC9126298 DOI: 10.1126/science.abf3067] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.
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Affiliation(s)
- Fan Zheng
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Marcus R. Kelly
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Dana J. Ramms
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
| | - Marissa L. Heintschel
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Kai Tao
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
- Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, 97201, USA
| | - Beril Tutuncuoglu
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, CA 94158, USA
- The J. David Gladstone Institutes, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, 94158, USA
| | - John J. Lee
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Keiichiro Ono
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Helene Foussard
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, CA 94158, USA
- The J. David Gladstone Institutes, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Michael Chen
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Kari A. Herrington
- Department of Biochemistry and Biophysics Center for Advanced Light Microscopy at UCSF, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Erica Silva
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Sophie Liu
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Jing Chen
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Christopher Churas
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Nicholas Wilson
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Anton Kratz
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Rudolf T. Pillich
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Devin N. Patel
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Jisoo Park
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Brent Kuenzi
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Michael K. Yu
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Katherine Licon
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Dexter Pratt
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Jason F. Kreisberg
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Minkyu Kim
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, CA 94158, USA
- The J. David Gladstone Institutes, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Danielle L. Swaney
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, CA 94158, USA
- The J. David Gladstone Institutes, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Xiaolin Nan
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
- Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR, 97201, USA
- Knight Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, 97201, USA
| | - Stephanie I. Fraley
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - J. Silvio Gutkind
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
| | - Nevan J. Krogan
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, CA 94158, USA
- The J. David Gladstone Institutes, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
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9
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Kim M, Park J, Bouhaddou M, Kim K, Rojc A, Modak M, Soucheray M, McGregor MJ, O'Leary P, Wolf D, Stevenson E, Foo TK, Mitchell D, Herrington KA, Muñoz DP, Tutuncuoglu B, Chen KH, Zheng F, Kreisberg JF, Diolaiti ME, Gordan JD, Coppé JP, Swaney DL, Xia B, van 't Veer L, Ashworth A, Ideker T, Krogan NJ. A protein interaction landscape of breast cancer. Science 2021; 374:eabf3066. [PMID: 34591612 PMCID: PMC9040556 DOI: 10.1126/science.abf3066] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Minkyu Kim
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Jisoo Park
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, CA, USA
| | - Mehdi Bouhaddou
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Kyumin Kim
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Ajda Rojc
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Maya Modak
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Margaret Soucheray
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Michael J McGregor
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Patrick O'Leary
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Denise Wolf
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Erica Stevenson
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Tzeh Keong Foo
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Dominique Mitchell
- Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.,Division of Hematology/Oncology, University of California, San Francisco, CA, USA
| | - Kari A Herrington
- Department of Biochemistry and Biophysics, Center for Advanced Light Microscopy, University of California, San Francisco, CA, USA
| | - Denise P Muñoz
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Beril Tutuncuoglu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Kuei-Ho Chen
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Fan Zheng
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, CA, USA
| | - Jason F Kreisberg
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, CA, USA
| | - Morgan E Diolaiti
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - John D Gordan
- Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.,Division of Hematology/Oncology, University of California, San Francisco, CA, USA
| | - Jean-Philippe Coppé
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Danielle L Swaney
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Bing Xia
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Laura van 't Veer
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Alan Ashworth
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Trey Ideker
- The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.,Department of Medicine, University of California, San Diego, CA, USA.,Department of Bioengineering, University of California, San Diego, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.,The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.,Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.,The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
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10
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Gonzalez G, Gong S, Laponogov I, Bronstein M, Veselkov K. Predicting anticancer hyperfoods with graph convolutional networks. Hum Genomics 2021; 15:33. [PMID: 34099048 PMCID: PMC8182908 DOI: 10.1186/s40246-021-00333-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/13/2021] [Indexed: 11/10/2022] Open
Abstract
Background Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to learn the systemic-wide effect on the human interactome of 1962 FDA-approved drugs and a supervised algorithm to predict anticancer therapeutics using the learned representations. Then, a set of bioactive molecules within foods was fed into the model, which predicted molecules with cancer-beating potential.The employed methodology consisted of disjoint unsupervised feature generation and classification tasks, which can result in sub-optimal learned drug representations with respect to the classification task. Additionally, due to the disjoint nature of the tasks, the employed approach proved cumbersome to optimize, requiring testing of thousands of hyperparameter combinations and significant computational resources.To overcome the technical limitations highlighted above, we represent each drug as a graph (human interactome) with its targets as binary node features on the graph and formulate the problem as a graph classification task. To solve this task, inspired by the success of graph neural networks in graph classification problems, we use an end-to-end graph neural network model operating directly on the graphs, which learns drug representations to optimize model performance in the prediction of anticancer therapeutics. Results The proposed model outperforms the baseline approach in the anticancer therapeutic prediction task, achieving an F1 score of 67.99%±2.52% and an AUPR of 73.91%±3.49%. It is also shown that the model is able to capture knowledge of biological pathways to predict anticancer molecules based on the molecules’ effects on cancer-related pathways. Conclusions We introduce an end-to-end graph convolutional model to predict cancer-beating molecules within food. The introduced model outperforms the existing baseline approach, and shows interpretability, paving the way to the future of a personalized nutritional science approach allowing the development of nutrition strategies for cancer prevention and/or therapeutics. Supplementary Information The online version contains supplementary material available at (10.1186/s40246-021-00333-4).
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Affiliation(s)
| | - Shunwang Gong
- Department of Computing, Imperial College London, London, UK
| | - Ivan Laponogov
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Michael Bronstein
- Department of Computing, Imperial College London, London, UK.,Institute of Computational Science, University of Lugano (USI), Lugano, Switzerland.,Twitter, London, UK
| | - Kirill Veselkov
- Department of Surgery and Cancer, Imperial College London, London, UK. .,Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
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11
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Gumpinger AC, Rieck B, Grimm DG, Borgwardt K. Network-guided search for genetic heterogeneity between gene pairs. Bioinformatics 2021; 37:57-65. [PMID: 32573681 PMCID: PMC8034561 DOI: 10.1093/bioinformatics/btaa581] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 05/19/2020] [Accepted: 06/15/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Correlating genetic loci with a disease phenotype is a common approach to improve our understanding of the genetics underlying complex diseases. Standard analyses mostly ignore two aspects, namely genetic heterogeneity and interactions between loci. Genetic heterogeneity, the phenomenon that genetic variants at different loci lead to the same phenotype, promises to increase statistical power by aggregating low-signal variants. Incorporating interactions between loci results in a computational and statistical bottleneck due to the vast amount of candidate interactions. RESULTS We propose a novel method SiNIMin that addresses these two aspects by finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple Arabidopsis thaliana phenotypes, and, with an adapted variant of SiNIMin, for a study of rare variants in migraine patients. AVAILABILITY AND IMPLEMENTATION Code available at https://github.com/BorgwardtLab/SiNIMin. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Anja C Gumpinger
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Bastian Rieck
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Dominik G Grimm
- Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing 94315, Germany.,Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing 94315, Germany
| | | | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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12
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Murray D, Petrey D, Honig B. Integrating 3D structural information into systems biology. J Biol Chem 2021; 296:100562. [PMID: 33744294 PMCID: PMC8095114 DOI: 10.1016/j.jbc.2021.100562] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/18/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus–host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.
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Affiliation(s)
- Diana Murray
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Donald Petrey
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Barry Honig
- Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Department of Medicine, Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, New York, USA.
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13
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Broyde J, Simpson DR, Murray D, Paull EO, Chu BW, Tagore S, Jones SJ, Griffin AT, Giorgi FM, Lachmann A, Jackson P, Sweet-Cordero EA, Honig B, Califano A. Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses. Nat Biotechnol 2021; 39:215-224. [PMID: 32929263 PMCID: PMC7878435 DOI: 10.1038/s41587-020-0652-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 07/23/2020] [Indexed: 02/08/2023]
Abstract
Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.
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Affiliation(s)
- Joshua Broyde
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - David R Simpson
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, UCSF Benioff Children's Hospital, San Francisco, CA, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Evan O Paull
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Brennan W Chu
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Somnath Tagore
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sunny J Jones
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Federico M Giorgi
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Alexander Lachmann
- Mount Sinai Center for Bioinformatics; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Peter Jackson
- Baxter Laboratory, Department of Microbiology & Immunology, Stanford University, Palo Alto, CA, USA
- Department of Pathology, Stanford University, Palo Alto, CA, USA
| | - E Alejandro Sweet-Cordero
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, UCSF Benioff Children's Hospital, San Francisco, CA, USA.
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
- Department of Medicine, Columbia University, New York, NY, USA.
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
- Department of Medicine, Columbia University, New York, NY, USA.
- JP Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
- Institute for Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Motor Neuron Center and Columbia Initiative in Stem Cells, Columbia University, New York, NY, USA.
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14
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Richards AL, Eckhardt M, Krogan NJ. Mass spectrometry-based protein-protein interaction networks for the study of human diseases. Mol Syst Biol 2021; 17:e8792. [PMID: 33434350 PMCID: PMC7803364 DOI: 10.15252/msb.20188792] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 09/23/2020] [Accepted: 11/03/2020] [Indexed: 12/13/2022] Open
Abstract
A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein-protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)-based approaches have allowed unbiased mapping of these disease-mediated changes in protein-protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein-protein interactions at a system-level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS-based protein-protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.
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Affiliation(s)
- Alicia L Richards
- Quantitative Biosciences Institute (QBI)University of California San FranciscoSan FranciscoCAUSA
- J. David Gladstone InstitutesSan FranciscoCAUSA
- Department of Cellular and Molecular PharmacologyUniversity of California San FranciscoSan FranciscoCAUSA
| | - Manon Eckhardt
- Quantitative Biosciences Institute (QBI)University of California San FranciscoSan FranciscoCAUSA
- J. David Gladstone InstitutesSan FranciscoCAUSA
- Department of Cellular and Molecular PharmacologyUniversity of California San FranciscoSan FranciscoCAUSA
| | - Nevan J Krogan
- Quantitative Biosciences Institute (QBI)University of California San FranciscoSan FranciscoCAUSA
- J. David Gladstone InstitutesSan FranciscoCAUSA
- Department of Cellular and Molecular PharmacologyUniversity of California San FranciscoSan FranciscoCAUSA
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15
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Bharadwaj A, Wahi N, Saxena A. Occurrence of Inborn Errors of Metabolism in Newborns, Diagnosis and Prophylaxis. Endocr Metab Immune Disord Drug Targets 2020; 21:592-616. [PMID: 33357204 DOI: 10.2174/1871530321666201223110918] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/09/2020] [Accepted: 11/09/2020] [Indexed: 11/22/2022]
Abstract
Inborn errors of metabolism (IEM) are a heterogeneous group of rare genetic disorders that are generally transmitted as autosomal or X-linked recessive disorders. These defects arise due to mutations associated with specific gene(s), especially the ones associated with key metabolic enzymes. These enzymes or their product(s) are involved in various metabolic pathways, leading to the accumulation of intermediary metabolite(s), reflecting their toxic effects upon mutations. The diagnosis of these metabolic disorders is based on the biochemical analysis of the clinical manifestations produced and their molecular mechanism. Therefore, it is imperative to devise diagnostic tests with high sensitivity and specificity for early detection of IEM. Recent advances in biochemical and polymerase chain reaction-based genetic analysis along with pedigree and prenatal diagnosis can be life-saving in nature. The latest development in exome sequencing for rapid diagnosis and enzyme replacement therapy would facilitate the successful treatment of these metabolic disorders in the future. However, the longterm clinical implications of these genetic manipulations is still a matter of debate among intellectuals and requires further research.
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Affiliation(s)
- Alok Bharadwaj
- Department of Biotechnology, GLA University, Mathura, Uttar Pradesh, India
| | - Nitin Wahi
- Department of Bioinformatics, Pathfinder Research and Training Foundation, Greater Noida - 201308, Uttar Pradesh, India
| | - Aditya Saxena
- Department of Biotechnology, GLA University, Mathura, Uttar Pradesh, India
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16
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Aghakhani S, Zerrouk N, Niarakis A. Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches. Cancers (Basel) 2020; 13:E35. [PMID: 33374292 PMCID: PMC7795338 DOI: 10.3390/cancers13010035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 12/12/2020] [Accepted: 12/17/2020] [Indexed: 12/29/2022] Open
Abstract
Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
- Lifeware Group, Inria Saclay, 91120 Palaiseau, France
| | - Naouel Zerrouk
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
| | - Anna Niarakis
- GenHotel, University of Evry, University of Paris-Saclay, Genopole, 91000 Evry, France; (S.A.); (N.Z.)
- Lifeware Group, Inria Saclay, 91120 Palaiseau, France
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17
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Perlasca P, Frasca M, Ba CT, Gliozzo J, Notaro M, Pennacchioni M, Valentini G, Mesiti M. Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools. PLoS One 2020; 15:e0244241. [PMID: 33351828 PMCID: PMC7755227 DOI: 10.1371/journal.pone.0244241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 12/04/2020] [Indexed: 11/19/2022] Open
Abstract
The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.
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Affiliation(s)
- Paolo Perlasca
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Marco Frasca
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Cheick Tidiane Ba
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Jessica Gliozzo
- Neuroradiology Unit, IRCCS San Raffaele Hospital, Milan, Italy
| | - Marco Notaro
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Mario Pennacchioni
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
| | - Giorgio Valentini
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
- CINI National Laboratory in Artificial Intelligence and Intelligent Systems—AIIS, Rome, Italy
| | - Marco Mesiti
- AnacletoLab, Department of Computer Science, University of Milan, Milan, Italy
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18
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Leung JY, Chia K, Ong DST, Taneja R. Interweaving Tumor Heterogeneity into the Cancer Epigenetic/Metabolic Axis. Antioxid Redox Signal 2020; 33:946-965. [PMID: 31841357 DOI: 10.1089/ars.2019.7942] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Significance: The epigenomic/metabolic landscape in cancer has been studied extensively in the past decade and forms the basis of various drug targets. Yet, cancer treatment remains a challenge, with clinical trials exhibiting limited efficacy and high relapse rates. Patients respond differently to therapy, which is fundamentally attributed to tumor heterogeneity, both across and within tumors. This review focuses on the interactions between the heterogeneous tumor microenvironment (TME) and the epigenomic/metabolic axis in cancer, as well as the emerging technologies under development to aid heterogeneity studies. Recent Advances: Interlinks between epigenetics and metabolism in cancer have been reported. Emerging studies have unveiled interactions between the TME and cancer cells that play a critical role in regulating epigenetics and reprogramming cancer metabolism, suggesting a three-way cross talk. Critical Issues: This cross talk accentuates the multiplex nature of cancer, and the importance of considering tumor heterogeneity in various epigenomic/metabolic cancer studies. Future Directions: With the advancement in single-cell profiling, it may be possible to identify cancer subclones and their unique vulnerabilities to develop a multimodal therapy. Drugs targeting the TME are currently being studied, and a better understanding of the TME in regulating cancer epigenetics and metabolism may hold the key to identifying novel therapeutic targets.
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Affiliation(s)
- Jia Yu Leung
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kimberly Chia
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Derrick Sek Tong Ong
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Institute of Molecular Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Reshma Taneja
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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19
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Brademan DR, Miller IJ, Kwiecien NW, Pagliarini DJ, Westphall MS, Coon JJ, Shishkova E. Argonaut: A Web Platform for Collaborative Multi-omic Data Visualization and Exploration. PATTERNS (NEW YORK, N.Y.) 2020; 1:100122. [PMID: 33154995 PMCID: PMC7641515 DOI: 10.1016/j.patter.2020.100122] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/15/2020] [Accepted: 09/14/2020] [Indexed: 12/12/2022]
Abstract
Researchers now generate large multi-omic datasets using increasingly mature mass spectrometry techniques at an astounding pace, facing new challenges of "Big Data" dissemination, visualization, and exploration. Conveniently, web-based data portals accommodate the complexity of multi-omic experiments and the many experts involved. However, developing these tailored companion resources requires programming expertise and knowledge of web server architecture-a substantial burden for most. Here, we describe Argonaut, a simple, code-free, and user-friendly platform for creating customizable, interactive data-hosting websites. Argonaut carries out real-time statistical analyses of the data, which it organizes into easily sharable projects. Collaborating researchers worldwide can explore the results, visualized through popular plots, and modify them to streamline data interpretation. Increasing the pace and ease of access to multi-omic data, Argonaut aims to propel discovery of new biological insights. We showcase the capabilities of this tool using a published multi-omics dataset on the large mitochondrial protease deletion collection.
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Affiliation(s)
- Dain R. Brademan
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Ian J. Miller
- Department of Biomolecular Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
| | - Nicholas W. Kwiecien
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
| | - David J. Pagliarini
- Morgridge Institute for Research, Madison, WI 53715, USA
- Department of Biomolecular Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Michael S. Westphall
- Department of Biomolecular Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
| | - Joshua J. Coon
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
- Department of Biomolecular Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
| | - Evgenia Shishkova
- Department of Biomolecular Chemistry, University of Wisconsin–Madison, Madison, WI 53706, USA
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20
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Mast FD, Navare AT, van der Sloot AM, Coulombe-Huntington J, Rout MP, Baliga NS, Kaushansky A, Chait BT, Aderem A, Rice CM, Sali A, Tyers M, Aitchison JD. Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality. J Cell Biol 2020; 219:e202006159. [PMID: 32785687 PMCID: PMC7659715 DOI: 10.1083/jcb.202006159] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 07/28/2020] [Accepted: 07/28/2020] [Indexed: 02/07/2023] Open
Abstract
With the rapid global spread of SARS-CoV-2, we have become acutely aware of the inadequacies of our ability to respond to viral epidemics. Although disrupting the viral life cycle is critical for limiting viral spread and disease, it has proven challenging to develop targeted and selective therapeutics. Synthetic lethality offers a promising but largely unexploited strategy against infectious viral disease; as viruses infect cells, they abnormally alter the cell state, unwittingly exposing new vulnerabilities in the infected cell. Therefore, we propose that effective therapies can be developed to selectively target the virally reconfigured host cell networks that accompany altered cellular states to cripple the host cell that has been converted into a virus factory, thus disrupting the viral life cycle.
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Affiliation(s)
- Fred D. Mast
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA
| | - Arti T. Navare
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA
| | - Almer M. van der Sloot
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Canada
| | | | - Michael P. Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY
| | | | - Alexis Kaushansky
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA
- Department of Pediatrics, University of Washington, Seattle, WA
| | - Brian T. Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY
| | - Alan Aderem
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA
- Department of Pediatrics, University of Washington, Seattle, WA
| | - Charles M. Rice
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA
| | - Mike Tyers
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Canada
| | - John D. Aitchison
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, WA
- Department of Pediatrics, University of Washington, Seattle, WA
- Department of Biochemistry, University of Washington, Seattle, WA
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21
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Mullane K, Williams M. Alzheimer’s disease beyond amyloid: Can the repetitive failures of amyloid-targeted therapeutics inform future approaches to dementia drug discovery? Biochem Pharmacol 2020; 177:113945. [DOI: 10.1016/j.bcp.2020.113945] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
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22
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Yadav A, Vidal M, Luck K. Precision medicine - networks to the rescue. Curr Opin Biotechnol 2020; 63:177-189. [PMID: 32199228 PMCID: PMC7308189 DOI: 10.1016/j.copbio.2020.02.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 02/13/2020] [Indexed: 12/11/2022]
Abstract
Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.
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Affiliation(s)
- Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Current address: Institute of Molecular Biology, Mainz, Germany.
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23
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Yazaki J, Kawashima Y, Ogawa T, Kobayashi A, Okoshi M, Watanabe T, Yoshida S, Kii I, Egami S, Amagai M, Hosoya T, Shiroguchi K, Ohara O. HaloTag-based conjugation of proteins to barcoding-oligonucleotides. Nucleic Acids Res 2020; 48:e8. [PMID: 31752022 PMCID: PMC6954424 DOI: 10.1093/nar/gkz1086] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 10/29/2019] [Accepted: 11/18/2019] [Indexed: 11/12/2022] Open
Abstract
Highly sensitive protein quantification enables the detection of a small number of protein molecules that serve as markers/triggers for various biological phenomena, such as cancer. Here, we describe the development of a highly sensitive protein quantification system called HaloTag protein barcoding. The method involves covalent linking of a target protein to a unique molecule counting oligonucleotide at a 1:1 conjugation ratio based on an azido-cycloalkyne click reaction. The sensitivity of the HaloTag-based barcoding was remarkably higher than that of a conventional luciferase assay. The HaloTag system was successfully validated by analyzing a set of protein-protein interactions, with the identification rate of 44% protein interactions between positive reference pairs reported in the literature. Desmoglein 3, the target antigen of pemphigus vulgaris, an IgG-mediated autoimmune blistering disease, was used in a HaloTag protein barcode assay to detect the anti-DSG3 antibody. The dynamic range of the assay was over 104-times wider than that of a conventional enzyme-linked immunosorbent assay (ELISA). The technology was used to detect anti-DSG3 antibody in patient samples with much higher sensitivity compared to conventional ELISA. Our detection system, with its superior sensitivity, enables earlier detection of diseases possibly allowing the initiation of care/treatment at an early disease stage.
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Affiliation(s)
- Junshi Yazaki
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Yusuke Kawashima
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Taisaku Ogawa
- Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka 565-0874, Japan
| | - Atsuo Kobayashi
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Mayu Okoshi
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Takashi Watanabe
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Suguru Yoshida
- Laboratory of Chemical Bioscience, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
| | - Isao Kii
- Common Facilities Unit, Compass to Healthy Life Research Complex Program, RIKEN Cluster for Science, Technology and Innovation Hub, Kobe 650-0047, Japan
| | - Shohei Egami
- Laboratory for Skin Homeostasis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama 230-0045, Japan.,Department of Dermatology, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Masayuki Amagai
- Laboratory for Skin Homeostasis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama 230-0045, Japan.,Department of Dermatology, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Takamitsu Hosoya
- Laboratory of Chemical Bioscience, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan.,Laboratory for Chemical Biology, RIKEN Center for Biosystems Dynamics Research (BDR), Kobe 650-0047, Japan
| | - Katsuyuki Shiroguchi
- Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka 565-0874, Japan.,Laboratory for Immunogenetics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama 230-0045, Japan
| | - Osamu Ohara
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
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24
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Gauthier L, Stynen B, Serohijos AWR, Michnick SW. Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics. Bioessays 2019; 42:e1900169. [PMID: 31854021 DOI: 10.1002/bies.201900169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 11/15/2019] [Indexed: 11/07/2022]
Abstract
How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.
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Affiliation(s)
- Louis Gauthier
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Bram Stynen
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Adrian W R Serohijos
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Stephen W Michnick
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
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25
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Yu MC, Liu JX, Ma XL, Hu B, Fu PY, Sun HX, Tang WG, Yang ZF, Qiu SJ, Zhou J, Fan J, Xu Y. Differential network analysis depicts regulatory mechanisms for hepatocellular carcinoma from diverse backgrounds. Future Oncol 2019; 15:3917-3934. [PMID: 31729887 DOI: 10.2217/fon-2019-0275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: To elucidate the integrative combinational gene regulatory network landscape of hepatocellular carcinoma (HCC) molecular carcinogenesis from diverse background. Materials & methods: Modified gene regulatory network analysis was used to prioritize differentially regulated genes and links. Integrative comparisons using bioinformatics methods were applied to identify potential critical molecules and pathways in HCC with different backgrounds. Results: E2F1 with its surrounding regulatory links were identified to play different key roles in the HCC risk factor dysregulation mechanisms. Hsa-mir-19a was identified as showed different effects in the three HCC differential regulation networks, and showed vital regulatory role in HBV-related HCC. Conclusion: We describe in detail the regulatory networks involved in HCC with different backgrounds. E2F1 may serve as a universal target for HCC treatment.
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Affiliation(s)
- Min-Cheng Yu
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China
| | - Ji-Xiang Liu
- Shanghai Center for Bioinformation Technology & Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology Institute, 1278 Keyuan Road, Shanghai 201203, PR China
| | - Xiao-Lu Ma
- Department of Laboratory Medicine, Shanghai Cancer Center, Fudan University, Shanghai 200032, PR China
| | - Bo Hu
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China
| | - Pei-Yao Fu
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China
| | - Hai-Xiang Sun
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China
| | - Wei-Guo Tang
- Institute of Fudan-Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai 201199, PR China
| | - Zhang-Fu Yang
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China
| | - Shuang-Jian Qiu
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China
| | - Jian Zhou
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China.,State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200032, PR China.,Institute of Biomedical Sciences, Fudan University, Shanghai 200032, PR China
| | - Jia Fan
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China.,State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200032, PR China.,Institute of Biomedical Sciences, Fudan University, Shanghai 200032, PR China
| | - Yang Xu
- Department of Liver Surgery & Transplantation, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis & Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, PR China
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26
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Tutuncuoglu B, Krogan NJ. Mapping genetic interactions in cancer: a road to rational combination therapies. Genome Med 2019; 11:62. [PMID: 31640753 PMCID: PMC6805649 DOI: 10.1186/s13073-019-0680-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 10/16/2019] [Indexed: 01/08/2023] Open
Abstract
The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies.
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Affiliation(s)
- Beril Tutuncuoglu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 16th Street, Mission Bay Campus, San Francisco, CA, 94158-2140, USA.,The J. David Gladstone Institutes, Owens Street, San Francisco, CA, 94158, USA.,Quantitative Biosciences Institute, University of California, San Francisco, 4th Street, San Francisco, CA, 94158, USA.,Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 16th Street, Mission Bay Campus, San Francisco, CA, 94158-2140, USA. .,The J. David Gladstone Institutes, Owens Street, San Francisco, CA, 94158, USA. .,Quantitative Biosciences Institute, University of California, San Francisco, 4th Street, San Francisco, CA, 94158, USA. .,Cancer Cell Map Initiative (CCMI), La Jolla and San Francisco, CA, USA.
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27
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Hjaltelin JX, Izarzugaza JMG, Jensen LJ, Russo F, Westergaard D, Brunak S. Identification of hyper-rewired genomic stress non-oncogene addiction genes across 15 cancer types. NPJ Syst Biol Appl 2019; 5:27. [PMID: 31396397 PMCID: PMC6685999 DOI: 10.1038/s41540-019-0104-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 06/26/2019] [Indexed: 12/24/2022] Open
Abstract
Non-oncogene addiction (NOA) genes are essential for supporting the stress-burdened phenotype of tumours and thus vital for their survival. Although NOA genes are acknowledged to be potential drug targets, there has been no large-scale attempt to identify and characterise them as a group across cancer types. Here we provide the first method for the identification of conditional NOA genes and their rewired neighbours using a systems approach. Using copy number data and expression profiles from The Cancer Genome Atlas (TCGA) we performed comparative analyses between high and low genomic stress tumours for 15 cancer types. We identified 101 condition-specific differential coexpression modules, mapped to a high-confidence human interactome, comprising 133 candidate NOA rewiring hub genes. We observe that most modules lose coexpression in the high-stress state and that activated stress modules and hubs take part in homoeostasis maintenance processes such as chromosome segregation, oxireductase activity, mitotic checkpoint (PLK1 signalling), DNA replication initiation and synaptic signalling. We furthermore show that candidate NOA rewiring hubs are unique for each cancer type, but that their respective rewired neighbour genes largely are shared across cancer types.
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Affiliation(s)
- Jessica Xin Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Jose M. G. Izarzugaza
- Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Francesco Russo
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
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28
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Bouhaddou M, Eckhardt M, Chi Naing ZZ, Kim M, Ideker T, Krogan NJ. Mapping the protein-protein and genetic interactions of cancer to guide precision medicine. Curr Opin Genet Dev 2019; 54:110-117. [PMID: 31288129 DOI: 10.1016/j.gde.2019.04.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 04/06/2019] [Accepted: 04/09/2019] [Indexed: 01/05/2023]
Abstract
Massive efforts to sequence cancer genomes have compiled an impressive catalogue of cancer mutations, revealing the recurrent exploitation of a handful of 'hallmark cancer pathways'. However, unraveling how sets of mutated proteins in these and other pathways hijack pro-proliferative signaling networks and dictate therapeutic responsiveness remains challenging. Here, we show that cancer driver protein-protein interactions are enriched for additional cancer drivers, highlighting the power of physical interaction maps to explain known, as well as uncover new, disease-promoting pathway interrelationships. We hypothesize that by systematically mapping the protein-protein and genetic interactions in cancer-thereby creating Cancer Cell Maps-we will create resources against which to contextualize a patient's mutations into perturbed pathways/complexes and thereby specify a matching targeted therapeutic cocktail.
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Affiliation(s)
- Mehdi Bouhaddou
- Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States
| | - Manon Eckhardt
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States
| | - Zun Zar Chi Naing
- Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States
| | - Minkyu Kim
- Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States.
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, California, United States.
| | - Nevan J Krogan
- Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States.
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29
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Coppé JP, Mori M, Pan B, Yau C, Wolf DM, Ruiz-Saenz A, Brunen D, Prahallad A, Cornelissen-Steijger P, Kemper K, Posch C, Wang C, Dreyer CA, Krijgsman O, Lee PRE, Chen Z, Peeper DS, Moasser MM, Bernards R, van 't Veer LJ. Mapping phospho-catalytic dependencies of therapy-resistant tumours reveals actionable vulnerabilities. Nat Cell Biol 2019; 21:778-790. [PMID: 31160710 DOI: 10.1038/s41556-019-0328-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 04/09/2019] [Indexed: 12/21/2022]
Abstract
Phosphorylation networks intimately regulate mechanisms of response to therapies. Mapping the phospho-catalytic profile of kinases in cells or tissues remains a challenge. Here, we introduce a practical high-throughput system to measure the enzymatic activity of kinases using biological peptide targets as phospho-sensors to reveal kinase dependencies in tumour biopsies and cell lines. A 228-peptide screen was developed to detect the activity of >60 kinases, including ABLs, AKTs, CDKs and MAPKs. Focusing on BRAFV600E tumours, we found mechanisms of intrinsic resistance to BRAFV600E-targeted therapy in colorectal cancer, including targetable parallel activation of PDPK1 and PRKCA. Furthermore, mapping the phospho-catalytic signatures of melanoma specimens identifies RPS6KB1 and PIM1 as emerging druggable vulnerabilities predictive of poor outcome in BRAFV600E patients. The results show that therapeutic resistance can be caused by the concerted upregulation of interdependent pathways. Our kinase activity-mapping system is a versatile strategy that innovates the exploration of actionable kinases for precision medicine.
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Affiliation(s)
- Jean-Philippe Coppé
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
| | - Miki Mori
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.,Department of Breast Surgical Oncology, Showa University, Tokyo, Japan
| | - Bo Pan
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.,Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Christina Yau
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Denise M Wolf
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Ana Ruiz-Saenz
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Diede Brunen
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Anirudh Prahallad
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | | | - Kristel Kemper
- Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Christian Posch
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.,Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany.,School of Medicine, Sigmund Freud University, Vienna, Austria
| | - Changjun Wang
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.,Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Courtney A Dreyer
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Oscar Krijgsman
- Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Pei Rong Evelyn Lee
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Zhongzhong Chen
- The State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.,Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.,Department of Urology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Daniel S Peeper
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Mark M Moasser
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - René Bernards
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Laura J van 't Veer
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
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30
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Ramón Y Cajal S, Hümmer S, Peg V, Guiu XM, De Torres I, Castellvi J, Martinez-Saez E, Hernandez-Losa J. Integrating clinical, molecular, proteomic and histopathological data within the tissue context: tissunomics. Histopathology 2019; 75:4-19. [PMID: 30667539 PMCID: PMC6851567 DOI: 10.1111/his.13828] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 01/19/2019] [Indexed: 12/14/2022]
Abstract
Malignant tumours show a marked degree of morphological, molecular and proteomic heterogeneity. This variability is closely related to microenvironmental factors and the location of the tumour. The activation of genetic alterations is very tissue‐dependent and only few tumours have distinct genetic alterations. Importantly, the activation state of proteins and signaling factors is heterogeneous in the primary tumour and in metastases and recurrences. The molecular diagnosis based only on genetic alterations can lead to treatments with unpredictable responses, depending on the tumour location, such as the tumour response in melanomas versus colon carcinomas with BRAF mutations. Therefore, we understand that the correct evaluation of tumours requires a system that integrates both morphological, molecular and protein information in a clinical and pathological context, where intratumoral heterogeneity can be assessed. Thus, we propose the term ‘tissunomics’, where the diagnosis will be contextualised in each tumour based on the complementation of the pathological, molecular, protein expression, environmental cells and clinical data.
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Affiliation(s)
- Santiago Ramón Y Cajal
- Translational Molecular Pathology, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
| | - Stefan Hümmer
- Translational Molecular Pathology, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
| | - Vicente Peg
- Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
| | - Xavier M Guiu
- Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain.,Department of Pathology, Bellvitge University Hospital, Barcelona, Spain
| | - Inés De Torres
- Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Josep Castellvi
- Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
| | - Elena Martinez-Saez
- Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Javier Hernandez-Losa
- Translational Molecular Pathology, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Department of Pathology, Vall d'Hebron University Hospital, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Barcelona, Spain
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31
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Abstract
Human cancers often harbor large numbers of somatic mutations. However, only a small proportion of these mutations are expected to contribute to tumor growth and progression. Therefore, determining causal driver mutations and the genes they target is becoming an important challenge in cancer genomics. Here we describe an approach for mapping somatic mutations onto 3D structures of human proteins in complex to identify "driver interfaces." Our strategy relies on identifying protein-interaction interfaces that are unexpectedly biased toward nonsynonymous mutations, which suggests that these interfaces are subject to positive selection during tumorigenesis, implicating the interacting proteins as candidate drivers.
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Affiliation(s)
- Kivilcim Ozturk
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics Program, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics Program, University of California San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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32
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Capriotti E, Ozturk K, Carter H. Integrating molecular networks with genetic variant interpretation for precision medicine. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2018; 11:e1443. [PMID: 30548534 PMCID: PMC6450710 DOI: 10.1002/wsbm.1443] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/23/2018] [Accepted: 10/30/2018] [Indexed: 02/01/2023]
Abstract
More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine. This article is categorized under:
Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods
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Affiliation(s)
- Emidio Capriotti
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Kivilcim Ozturk
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, California
| | - Hannah Carter
- Department of Medicine and Institute for Genomic Medicine, University of California, San Diego, La Jolla, California
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33
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Kondratova M, Sompairac N, Barillot E, Zinovyev A, Kuperstein I. Signalling maps in cancer research: construction and data analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4964960. [PMID: 29688383 PMCID: PMC5890450 DOI: 10.1093/database/bay036] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 03/19/2018] [Indexed: 12/22/2022]
Abstract
Generation and usage of high-quality molecular signalling network maps can be augmented by standardizing notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.
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Affiliation(s)
- Maria Kondratova
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Inna Kuperstein
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM, U900, F-75005 Paris, France.,MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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34
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Bennett L, Howell M, Memon D, Smowton C, Zhou C, Miller CJ. Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma. Sci Rep 2018; 8:14830. [PMID: 30287876 PMCID: PMC6172282 DOI: 10.1038/s41598-018-33276-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 09/26/2018] [Indexed: 12/12/2022] Open
Abstract
The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a 'mini-driver' model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.
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Affiliation(s)
- Laura Bennett
- RNA Biology Group, CRUK Manchester Institute, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK
| | - Matthew Howell
- RNA Biology Group, CRUK Manchester Institute, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK
- Cancer Research UK Lung Cancer Centre of Excellence, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK
| | - Danish Memon
- RNA Biology Group, CRUK Manchester Institute, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Chris Smowton
- Scientific Computing Team, CRUK Manchester Institute, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK
| | - Cong Zhou
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester Cancer Research Centre, University of Manchester, Wilmslow Road, Manchester, M20 4GJ, UK
| | - Crispin J Miller
- RNA Biology Group, CRUK Manchester Institute, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK.
- Cancer Research UK Lung Cancer Centre of Excellence, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK.
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35
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Li D, Chi G, Chen Z, Jin X. MicroRNA-1225-5p behaves as a tumor suppressor in human glioblastoma via targeting of IRS1. Onco Targets Ther 2018; 11:6339-6350. [PMID: 30319274 PMCID: PMC6167988 DOI: 10.2147/ott.s178001] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background MicroRNAs (miRNAs) play an important role in cancer initiation, progression, and metastasis by directly regulating their target genes. Materials and methods In this study, we observed that the miR-1225-5p expression level in glioblastoma tissues was significantly lower as compared with that in normal brain tissues, and its low expression was significantly associated with histopathological grade and poor patient prognosis. Results Through establishing a miR-1225-5p overexpression glioblastoma cell line, we found that ectopic overexpression of miR-1225-5p inhibited the proliferation, migration, and invasion of glioblastoma cells in vitro. Moreover, the growth of a glioblastoma xenograft tumor was attenuated by overexpression of miR-1225-5p. Further integrative studies suggested that the insulin receptor substrate 1 (IRS1) was a direct functional target of miR-1225-5p in glioblastoma, and the mRNA and protein levels of IRS1 in six human glioblastoma cell lines (A172, SW1783, U87, LN-229, SW1088, and T98G) were significantly higher as compared with normal human astrocytes. Conclusion These results suggest that miR-1225-5p may be a novel candidate for glioblastoma therapy.
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Affiliation(s)
- Dongyuan Li
- First Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, People's Republic of China,
| | - Guonan Chi
- First Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, People's Republic of China,
| | - Zhuo Chen
- First Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, People's Republic of China,
| | - Xingyi Jin
- First Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, People's Republic of China,
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36
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Shen JP, Ideker T. Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls. J Mol Biol 2018; 430:2900-2912. [PMID: 29932943 PMCID: PMC6097899 DOI: 10.1016/j.jmb.2018.06.026] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 06/10/2018] [Accepted: 06/13/2018] [Indexed: 12/22/2022]
Abstract
Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.
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Affiliation(s)
- John Paul Shen
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Cancer Cell Map Initiative, USA.
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Cancer Cell Map Initiative, USA
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37
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Lapointe S, Perry A, Butowski NA. Primary brain tumours in adults. Lancet 2018; 392:432-446. [PMID: 30060998 DOI: 10.1016/s0140-6736(18)30990-5] [Citation(s) in RCA: 808] [Impact Index Per Article: 134.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 04/05/2018] [Accepted: 04/23/2018] [Indexed: 12/11/2022]
Abstract
Primary CNS tumours refer to a heterogeneous group of tumours arising from cells within the CNS, and can be benign or malignant. Malignant primary brain tumours remain among the most difficult cancers to treat, with a 5 year overall survival no greater than 35%. The most common malignant primary brain tumours in adults are gliomas. Recent advances in molecular biology have improved understanding of glioma pathogenesis, and several clinically significant genetic alterations have been described. A number of these (IDH, 1p/19q codeletion, H3 Lys27Met, and RELA-fusion) are now combined with histology in the revised 2016 WHO classification of CNS tumours. It is likely that understanding such molecular alterations will contribute to the diagnosis, grading, and treatment of brain tumours. This progress in genomics, along with significant advances in cancer and CNS immunology, has defined a new era in neuro-oncology and holds promise for diagntic and therapeutic improvement. The challenge at present is to translate these advances into effective treatments. Current efforts are focused on developing molecular targeted therapies, immunotherapies, gene therapies, and novel drug-delivery technologies. Results with single-agent therapies have been disappointing so far, and combination therapies seem to be required to achieve a broad and durable antitumour response. Biomarker-targeted clinical trials could improve efficiencies of therapeutic development.
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Affiliation(s)
- Sarah Lapointe
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Arie Perry
- Division of Neuropathology, Department of Pathology, University of California, San Francisco, CA, USA
| | - Nicholas A Butowski
- Department of Neurological Surgery, University of California, San Francisco, CA, USA.
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38
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Willsey AJ, Morris MT, Wang S, Willsey HR, Sun N, Teerikorpi N, Baum TB, Cagney G, Bender KJ, Desai TA, Srivastava D, Davis GW, Doudna J, Chang E, Sohal V, Lowenstein DH, Li H, Agard D, Keiser MJ, Shoichet B, von Zastrow M, Mucke L, Finkbeiner S, Gan L, Sestan N, Ward ME, Huttenhain R, Nowakowski TJ, Bellen HJ, Frank LM, Khokha MK, Lifton RP, Kampmann M, Ideker T, State MW, Krogan NJ. The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders. Cell 2018; 174:505-520. [PMID: 30053424 PMCID: PMC6247911 DOI: 10.1016/j.cell.2018.06.016] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 05/07/2018] [Accepted: 06/08/2018] [Indexed: 12/11/2022]
Abstract
Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.
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Affiliation(s)
- A Jeremy Willsey
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA.
| | - Montana T Morris
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Sheng Wang
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Helen R Willsey
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nawei Sun
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nia Teerikorpi
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Tierney B Baum
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Gerard Cagney
- School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Kevin J Bender
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Tejal A Desai
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Deepak Srivastava
- Gladstone Institutes, San Francisco, CA 94158, USA; Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Graeme W Davis
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jennifer Doudna
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA, 94720, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA 94720, USA; MBIB Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Edward Chang
- Department of Neurological Surgery, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Vikaas Sohal
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Daniel H Lowenstein
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Hao Li
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - David Agard
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Michael J Keiser
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Brian Shoichet
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Mark von Zastrow
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Lennart Mucke
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA
| | - Steven Finkbeiner
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Li Gan
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA
| | - Nenad Sestan
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Michael E Ward
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA
| | - Ruth Huttenhain
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Tomasz J Nowakowski
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA; The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Hugo J Bellen
- Departments of Molecular and Human Genetics and Neuroscience, Neurological Research Institute at TCH, Baylor College of Medicine, Houston, TX 77030, USA; Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX 77030, USA
| | - Loren M Frank
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Physiology, University of California, San Francisco, San Francisco, CA 94143, USA; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Mustafa K Khokha
- Pediatric Genomics Discovery Program, Departments of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Richard P Lifton
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY 10065, USA
| | - Martin Kampmann
- Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Matthew W State
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nevan J Krogan
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA 94143, USA; Gladstone Institutes, San Francisco, CA 94158, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94143, USA; Helen Diller Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
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Monraz Gomez LC, Kondratova M, Ravel JM, Barillot E, Zinovyev A, Kuperstein I. Application of Atlas of Cancer Signalling Network in preclinical studies. Brief Bioinform 2018; 20:701-716. [DOI: 10.1093/bib/bby031] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 03/28/2018] [Indexed: 12/27/2022] Open
Affiliation(s)
- L Cristobal Monraz Gomez
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Maria Kondratova
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Jean-Marie Ravel
- Genetic Laboratory, Nancy's Regional University Hospital, Vandœuvre-lès-Nancy and INSERM UMR 954, Lorraine University, Vandœuvre-lès-Nancy
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
| | - Inna Kuperstein
- Institut Curie, PSL Research University, F-75005 Paris, France, INSERM, U900, F-75005 Paris, France and MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
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40
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Hasche D, Vinzón SE, Rösl F. Cutaneous Papillomaviruses and Non-melanoma Skin Cancer: Causal Agents or Innocent Bystanders? Front Microbiol 2018; 9:874. [PMID: 29770129 PMCID: PMC5942179 DOI: 10.3389/fmicb.2018.00874] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 04/16/2018] [Indexed: 12/12/2022] Open
Abstract
There is still controversy in the scientific field about whether certain types of cutaneous human papillomaviruses (HPVs) are causally involved in the development of non-melanoma skin cancer (NMSC). Deciphering the etiological role of cutaneous HPVs requires - besides tissue culture systems - appropriate preclinical models to match the obtained results with clinical data from affected patients. Clear scientific evidence about the etiology and underlying mechanisms involved in NMSC development is fundamental to provide reasonable arguments for public health institutions to classify at least certain cutaneous HPVs as group 1 carcinogens. This in turn would have implications on fundraising institutions and health care decision makers to force - similarly as for anogenital cancer - the implementation of a broad vaccination program against "high-risk" cutaneous HPVs to prevent NMSC as the most frequent cancer worldwide. Precise knowledge of the multi-step progression from normal cells to cancer is a prerequisite to understand the functional and clinical impact of cofactors that affect the individual outcome and the personalized treatment of a disease. This overview summarizes not only recent arguments that favor the acceptance of a viral etiology in NMSC development but also reflects aspects of causality in medicine, the use of empirically meaningful model systems and strategies for prevention.
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Affiliation(s)
- Daniel Hasche
- Division of Viral Transformation Mechanisms, Research Program "Infection, Inflammation and Cancer", German Cancer Research Center, Heidelberg, Germany
| | - Sabrina E Vinzón
- Laboratory of Molecular and Cellular Therapy, Fundación Instituto Leloir, IIBBA-CONICET, Buenos Aires, Argentina
| | - Frank Rösl
- Division of Viral Transformation Mechanisms, Research Program "Infection, Inflammation and Cancer", German Cancer Research Center, Heidelberg, Germany
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41
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Hastings JF, Skhinas JN, Fey D, Croucher DR, Cox TR. The extracellular matrix as a key regulator of intracellular signalling networks. Br J Pharmacol 2018; 176:82-92. [PMID: 29510460 DOI: 10.1111/bph.14195] [Citation(s) in RCA: 129] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 02/06/2018] [Accepted: 02/13/2018] [Indexed: 12/11/2022] Open
Abstract
The extracellular matrix (ECM) is a salient feature of all solid tissues within the body. This complex, acellular entity is composed of hundreds of individual molecules whose assembly, architecture and biomechanical properties are critical to controlling the behaviour and phenotype of the different cell types residing within tissues. Cells are the basic unit of life and the core building block of tissues and organs. At their simplest, they follow a set of rules, governed by their genetic code and effected through the complex protein signalling networks that these genes encode. These signalling networks assimilate and process the information received by the cell to control cellular decisions that govern cell fate. The ECM is the biggest provider of external stimuli to cells and as such is responsible for influencing intracellular signalling dynamics. In this review, we discuss the inclusion of ECM as a central regulatory signalling sub-network in computational models of cellular decision making, with a focus on its role in diseases such as cancer. LINKED ARTICLES: This article is part of a themed section on Translating the Matrix. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.1/issuetoc.
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Affiliation(s)
- Jordan F Hastings
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Darlinghurst, NSW, 2010, Australia
| | - Joanna N Skhinas
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Darlinghurst, NSW, 2010, Australia
| | - Dirk Fey
- Systems Biology Ireland, University College Dublin, Dublin 4, Ireland
| | - David R Croucher
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Darlinghurst, NSW, 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Kensington, NSW, 2010, Australia.,School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland
| | - Thomas R Cox
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Darlinghurst, NSW, 2010, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Kensington, NSW, 2010, Australia
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42
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Analysis of variability in high throughput screening data: applications to melanoma cell lines and drug responses. Oncotarget 2018; 8:27786-27799. [PMID: 28212541 PMCID: PMC5438608 DOI: 10.18632/oncotarget.15347] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 01/27/2017] [Indexed: 12/16/2022] Open
Abstract
High-throughput screening (HTS) strategies and protocols have undergone significant development in the last decade. It is now possible to screen hundreds of thousands of compounds, each exploring multiple biological phenotypes and parameters, against various cell lines or model systems in a single setting. However, given the vast amount of data such studies generate, the fact that they use multiple reagents, and are often technician-intensive, questions have been raised about the variability, reliability and reproducibility of HTS results. Assessments of the impact of the multiple factors in HTS studies could arguably lead to more compelling insights into the robustness of the results of a particular screen, as well as the overall quality of the study. We leveraged classical, yet highly flexible, analysis of variance (ANOVA)-based linear models to explore how different factors contribute to the variation observed in a screening study of four different melanoma cell lines and 120 drugs over nine dosages studied in two independent academic laboratories. We find that factors such as plate effects, appropriate dosing ranges, and to a lesser extent, the laboratory performing the screen, are significant predictors of variation in drug responses across the cell lines. Further, we show that when sources of variation are quantified and controlled for, they contextualize claims of inconsistencies and reveal the overall quality of the HTS studies performed at each participating laboratory. In the context of the broader screening study, we show that our analysis can also elucidate the robust effects of drugs, even those within specific cell lines.
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43
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Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 2018; 173:11-19. [PMID: 29570991 PMCID: PMC6014618 DOI: 10.1016/j.cell.2018.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 11/25/2017] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.
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Affiliation(s)
- Jitin Singla
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kyle M McClary
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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44
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De S, Ganesan S. Looking beyond drivers and passengers in cancer genome sequencing data. Ann Oncol 2018; 28:938-945. [PMID: 27998972 DOI: 10.1093/annonc/mdw677] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Cancer arises as a result of acquired changes in the DNA sequence of the genome of somatic cells. A subset of the genetic changes, dubbed driver mutations, propels tumor growth, and the remaining changes are passengers, apparently inconsequential for neoplastic transformation. Massive genome sequencing of thousands of tumors from all major cancer types has enabled cataloging of the so-called driver and passenger mutations, and facilitated molecular classification of cancer, guiding precision medicine approach for the patients. Nonetheless, innovative analyses of cancer genomics data has led to novel, sometimes serendipitous findings that have aided to our understanding of other aspects of the biology of the disease and opened up new frontiers. For instance, emerging findings show that mutational patterns in cancer genomes can help detect signatures of known and novel DNA damage and repair processes, provide a likely chronological account of genomic changes in cancer genomes, and allow revisiting the models of cancer evolution. These findings have stimulated original approaches to identify disease etiology, stratify patients, target the disease, and monitor patient responses, complementing driver-mutation centric approaches. In this review, we discuss these emerging approaches and unexpected breakthroughs, and their implications for basic cancer research and clinical practices.
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Affiliation(s)
- S De
- Center for Cancer Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, New Brunswick, USA
| | - S Ganesan
- Center for Cancer Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, New Brunswick, USA
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45
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Gonzalez VD, Samusik N, Chen TJ, Savig ES, Aghaeepour N, Quigley DA, Huang YW, Giangarrà V, Borowsky AD, Hubbard NE, Chen SY, Han G, Ashworth A, Kipps TJ, Berek JS, Nolan GP, Fantl WJ. Commonly Occurring Cell Subsets in High-Grade Serous Ovarian Tumors Identified by Single-Cell Mass Cytometry. Cell Rep 2018; 22:1875-1888. [PMID: 29444438 PMCID: PMC8556706 DOI: 10.1016/j.celrep.2018.01.053] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 12/18/2017] [Accepted: 01/17/2018] [Indexed: 01/16/2023] Open
Abstract
We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.
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Affiliation(s)
- Veronica D Gonzalez
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Nikolay Samusik
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tiffany J Chen
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Erica S Savig
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Nima Aghaeepour
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David A Quigley
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, 1450 Third Street, San Francisco, CA 94158, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, 1450 Third Street, San Francisco, CA 94158, USA
| | - Ying-Wen Huang
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Valeria Giangarrà
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexander D Borowsky
- Center for Comparative Medicine, University of California, Davis, Davis, CA 95616, USA; Department of Pathology and Laboratory Medicine, Comprehensive Cancer Center, University of California, Davis School of Medicine, Sacramento, CA 95817, USA
| | - Neil E Hubbard
- Center for Comparative Medicine, University of California, Davis, Davis, CA 95616, USA; Department of Pathology and Laboratory Medicine, Comprehensive Cancer Center, University of California, Davis School of Medicine, Sacramento, CA 95817, USA
| | - Shih-Yu Chen
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Guojun Han
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, 1450 Third Street, San Francisco, CA 94158, USA; Department of Medicine, University of California, San Francisco, 1450 Third Street, San Francisco, CA 94158, USA
| | - Thomas J Kipps
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jonathan S Berek
- Stanford Comprehensive Cancer Institute and Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Garry P Nolan
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Wendy J Fantl
- Stanford Comprehensive Cancer Institute and Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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Abstract
Comprehensive identification of direct, physical interactions between biological macromolecules, such as protein-protein, protein-DNA, and protein-RNA interactions, is critical for our understanding of the function of gene products as well as the global organization and interworkings of various molecular machines within the cell. The accurate and comprehensive detection of direct interactions, however, remains a huge challenge due to the inherent structural complexity arising from various post-transcriptional and translational modifications coupled with huge heterogeneity in concentration, affinity, and subcellular location differences existing for any interacting molecules. This has created a need for developing multiple orthogonal and complementary assays for detecting various types of biological interactions. In this introduction, we discuss the methods developed for measuring different types of molecular interactions with an emphasis on direct protein-protein interactions, critical issues for generating high-quality interactome datasets, and the insights into biological networks and human diseases that current interaction mapping efforts provide. Further, we will discuss what future might lie ahead for the continued evolution of two-hybrid methods and the role of interactomics for expanding the advancement of biomedical science.
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Affiliation(s)
- Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Aaron Richardson
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
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47
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Case Studies. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1069:135-209. [DOI: 10.1007/978-3-319-89354-9_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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48
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Singh V, Ostaszewski M, Kalliolias GD, Chiocchia G, Olaso R, Petit-Teixeira E, Helikar T, Niarakis A. Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model. GENOMICS AND COMPUTATIONAL BIOLOGY 2017; 4:e100050. [PMID: 29951575 PMCID: PMC6016388 DOI: 10.18547/gcb.2018.vol4.iss1.e100050] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts' advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts' behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts' activation.
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Affiliation(s)
- Vidisha Singh
- GenHotel EA3886, Univ Evry, Université Paris-Saclay, 91025, Evry, France
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - George D. Kalliolias
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, USA; Department of Medicine, Weill Cornell Medical College, New York City, USA
| | - Gilles Chiocchia
- Faculty of Health Sciences Simone Veil, INSERM U1173, University of Versailles Saint-Quentin-en-Yvelines, Montigny-le-Bretonneux, France
| | - Robert Olaso
- Centre National de Recherche en Génomique Humaine (CNRGH), CEA, Evry, France
| | | | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Anna Niarakis
- GenHotel EA3886, Univ Evry, Université Paris-Saclay, 91025, Evry, France
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49
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Pratt D, Chen J, Pillich R, Rynkov V, Gary A, Demchak B, Ideker T. NDEx 2.0: A Clearinghouse for Research on Cancer Pathways. Cancer Res 2017; 77:e58-e61. [PMID: 29092941 DOI: 10.1158/0008-5472.can-17-0606] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 05/26/2017] [Accepted: 07/21/2017] [Indexed: 12/31/2022]
Abstract
We present NDEx 2.0, the latest release of the Network Data Exchange (NDEx) online data commons (www.ndexbio.org) and the ways in which it can be used to (i) improve the quality and abundance of biological networks relevant to the cancer research community; (ii) provide a medium for collaboration involving networks; and (iii) facilitate the review and dissemination of networks. We describe innovations addressing the challenges of an online data commons: scalability, data integration, data standardization, control of content and format by authors, and decentralized mechanisms for review. The practical use of NDEx is presented in the context of a novel strategy to foster network-oriented communities of interest in cancer research by adapting methods from academic publishing and social media. Cancer Res; 77(21); e58-61. ©2017 AACR.
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Affiliation(s)
- Dexter Pratt
- Department of Medicine, University of California San Diego, La Jolla, California.
| | - Jing Chen
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Rudolf Pillich
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Vladimir Rynkov
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Aaron Gary
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Barry Demchak
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, California.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California
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Mezlini AM, Goldenberg A. Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases. PLoS Comput Biol 2017; 13:e1005580. [PMID: 29023450 PMCID: PMC5638204 DOI: 10.1371/journal.pcbi.1005580] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Accepted: 05/09/2017] [Indexed: 12/12/2022] Open
Abstract
Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios. Networks and pathway-based methods are commonly used to improve the power of gene detection in associations with complex human diseases. Network diffusion approaches have shown their effectiveness and superior performance in cancer studies. Still, there are many problems such as noise and missingness with currently available human networks that bias the results of gene detection. We propose a novel graphical model-based method Conflux that overcomes several of the pitfalls of the existing state-of-the-art approaches while building on their successes. Conflux integrates genotype data with networks directly, using diffusion-like methods, but only as part of a structure in a probabilistic model to reduce the negative effect of the noise in the networks. This Bayesian framework allows Conflux to keep track of the uncertainty in the gene list that is being associated with the disease and consequently rank the genes with respect to our confidence in the association. It also allows for the discovery of gene sets that are not fully supported by the network if they have enough support in the data. These improvements result in a flexible approach that improves the power in many gene-disease association scenarios while reducing the number of false positives reported.
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Affiliation(s)
- Aziz M Mezlini
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
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