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Zhang L, Sagan A, Qin B, Kim E, Hu B, Osmanbeyoglu HU. STAN, a computational framework for inferring spatially informed transcription factor activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600782. [PMID: 38979296 PMCID: PMC11230390 DOI: 10.1101/2024.06.26.600782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN ( S patially informed T ranscription factor A ctivity N etwork), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand‒receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.
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Katebi A, Chen X, Ramirez D, Li S, Lu M. Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML. NPJ Syst Biol Appl 2024; 10:38. [PMID: 38594351 PMCID: PMC11003984 DOI: 10.1038/s41540-024-00366-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.
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Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Xiaowen Chen
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Daniel Ramirez
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Sheng Li
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
- Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA.
- The Jackson Laboratory Cancer Center, Bar Harbor, ME, USA.
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA, USA.
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA.
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Tang S, Wang Q, Sun K, Song Y, Liu R, Tan X, Li H, Lv Y, Yang F, Zhao J, Li S, Bi P, Yang J, Zhu Z, Chen D, Chuan Z, Luo X, Hu Z, Liu Y, Li Z, Ke T, Jiang D, Zheng K, Yang R, Chen K, Guo R. Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer. Apoptosis 2024:10.1007/s10495-024-01952-7. [PMID: 38578322 DOI: 10.1007/s10495-024-01952-7] [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] [Accepted: 03/02/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated. METHODS We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out. RESULTS According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells. CONCLUSIONS Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity.
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Affiliation(s)
- Shicong Tang
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China.
| | - Qing Wang
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Ke Sun
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People's Republic of China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan, 650500, People's Republic of China
| | - Ying Song
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Rui Liu
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Xin Tan
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Huimeng Li
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Yafeng Lv
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Fuying Yang
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Jiawen Zhao
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Sijia Li
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Pingping Bi
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Jiali Yang
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Zhengna Zhu
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People's Republic of China
| | - Dong Chen
- Department of Ultrasound, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Zhirui Chuan
- Department of Ultrasound, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Xiaomao Luo
- Department of Ultrasound, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Zaoxiu Hu
- Department of Pathology, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Ying Liu
- Department of Pathology, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Zhenhui Li
- Department of Radiology, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Tengfei Ke
- Department of Radiology, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Dewei Jiang
- Key Laboratory of Animal Models and Human, Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China
- Kunming College of Life Sciences, University of Chinese Academy Sciences, Kunming, Yunnan, China
| | - Kai Zheng
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China
| | - Rirong Yang
- Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, 530021, People's Republic of China.
- Department of Immunology, School of Basic Medical Sciences, Guangxi Medical University, Guangxi, 530021, People's Republic of China.
| | - Kai Chen
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People's Republic of China.
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan, 650500, People's Republic of China.
| | - Rong Guo
- Department of Breast Surgery, Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, Yunnan, People's Republic of China.
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Habib M, Lalagkas PN, Melamed RD. Mapping drug biology to disease genetics to discover drug impacts on the human phenome. BIOINFORMATICS ADVANCES 2024; 4:vbae038. [PMID: 38736684 PMCID: PMC11087821 DOI: 10.1093/bioadv/vbae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/18/2024] [Accepted: 03/07/2024] [Indexed: 05/14/2024]
Abstract
Motivation Medications can have unexpected effects on disease, including not only harmful drug side effects, but also beneficial drug repurposing. These effects on disease may result from hidden influences of drugs on disease gene networks. Then, discovering how biological effects of drugs relate to disease biology can both provide insight into the mechanism of latent drug effects, and can help predict new effects. Results Here, we develop Draphnet, a model that integrates molecular data on 429 drugs and gene associations of nearly 200 common phenotypes to learn a network that explains drug effects on disease in terms of these molecular signals. We present evidence that our method can both predict drug effects, and can provide insight into the biology of unexpected drug effects on disease. Using Draphnet to map a drug's known molecular effects to downstream effects on the disease genome, we put forward disease genes impacted by drugs, and we suggest a new grouping of drugs based on shared effects on the disease genome. Our approach has multiple applications, including predicting drug uses and learning drug biology, with implications for personalized medicine. Availability and implementation Code to reproduce the analysis is available at https://github.com/RDMelamed/drug-phenome.
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Affiliation(s)
- Mamoon Habib
- Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854, United States
| | | | - Rachel D Melamed
- Department of Biological Science, University of Massachusetts Lowell, Lowell, MA 01854, United States
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Katebi A, Chen X, Li S, Lu M. Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.29.551111. [PMID: 37577526 PMCID: PMC10418072 DOI: 10.1101/2023.07.29.551111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.
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Affiliation(s)
- Ataur Katebi
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
| | - Xiaowen Chen
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Sheng Li
- Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA
| | - Mingyang Lu
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
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Ramjattun K, Xiaojun M, Shou-Jiang G, Singh H, Osmanbeyoglu HU. COVID-19db linkage maps of cell surface proteins and transcription factors in immune cells. J Med Virol 2023; 95:e28887. [PMID: 37341527 PMCID: PMC10478683 DOI: 10.1002/jmv.28887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/25/2023] [Accepted: 06/08/2023] [Indexed: 06/22/2023]
Abstract
The highly contagious SARS-CoV-2 and its associated disease (COVID-19) are a threat to global public health and economies. To develop effective treatments for COVID-19, we must understand the host cell types, cell states and regulators associated with infection and pathogenesis such as dysregulated transcription factors (TFs) and surface proteins, including signaling receptors. To link cell surface proteins with TFs, we recently developed SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network) by integrating parallel single-cell proteomic and transcriptomic data based on Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and gene cis-regulatory information. We apply SPaRTAN to CITE-seq data sets from patients with varying degrees of COVID-19 severity and healthy controls to identify the associations between surface proteins and TFs in host immune cells. Here, we present COVID-19db of Immune Cell States (https://covid19db.streamlit.app/), a web server containing cell surface protein expression, SPaRTAN-inferred TF activities, and their associations with major host immune cell types. The data include four high-quality COVID-19 CITE-seq data sets with a toolset for user-friendly data analysis and visualization. We provide interactive surface protein and TF visualizations across major immune cell types for each data set, allowing comparison between various patient severity groups for the discovery of potential therapeutic targets and diagnostic biomarkers.
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Affiliation(s)
- Koushul Ramjattun
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Ma Xiaojun
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Gao Shou-Jiang
- UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Harinder Singh
- Center for Systems Immunology and Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hatice Ulku Osmanbeyoglu
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh School of Engineering, Pittsburgh, USA
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA
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7
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Sagan A, Ma X, Ramjattun K, Osmanbeyoglu HU. Linking Expression of Cell-Surface Receptors with Transcription Factors by Computational Analysis of Paired Single-Cell Proteomes and Transcriptomes. Methods Mol Biol 2023; 2660:149-169. [PMID: 37191796 DOI: 10.1007/978-1-0716-3163-8_11] [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: 05/17/2023]
Abstract
Complex signaling and transcriptional programs control the development and physiology of specialized cell types. Genetic perturbations in these programs cause human cancers to arise from a diverse set of specialized cell types and developmental states. Understanding these complex systems and their potential to drive cancer is critical for the development of immunotherapies and druggable targets. Pioneering single-cell multi-omics technologies that analyze transcriptional states have been coupled with the expression of cell-surface receptors. This chapter describes SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network), a computational framework, to link transcription factors with cell-surface protein expression. SPaRTAN uses CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) data and cis-regulatory sites to model the effect of interactions between transcription factors and cell-surface receptors on gene expression. We demonstrate the pipeline for SPaRTAN using CITE-seq data from peripheral blood mononuclear cells.
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Affiliation(s)
- April Sagan
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xiaojun Ma
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Koushul Ramjattun
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hatice Ulku Osmanbeyoglu
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
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Li Y, Azmi AS, Mohammad RM. Deregulated transcription factors and poor clinical outcomes in cancer patients. Semin Cancer Biol 2022; 86:122-134. [PMID: 35940398 DOI: 10.1016/j.semcancer.2022.08.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/22/2022] [Accepted: 08/04/2022] [Indexed: 01/27/2023]
Abstract
Transcription factors are a group of proteins, which possess DNA-binding domains, bind to DNA strands of promoters or enhancers, and initiate transcription of genes with cooperation of RNA polymerase and other co-factors. They play crucial roles in regulating transcription during embryogenesis and development. Their physiological status in different cell types is also important to maintain cellular homeostasis. Therefore, any deregulation of transcription factors will lead to the development of cancer cells and tumor progression. Based on their functions in cancer cells, transcription factors could be either oncogenic or tumor suppressive. Furthermore, transcription factors have been shown to modulate cancer stem cells, epithelial-mesenchymal transition (EMT) and drug response; therefore, measuring deregulated transcription factors is hypothesized to predict treatment outcomes of patients with cancers and targeting deregulated transcription factors could be an encouraging strategy for cancer therapy. Here, we summarize the current knowledge of major deregulated transcription factors and their effects on causing poor clinical outcome of patients with cancer. The information presented here will help to predict the prognosis and drug response and to design novel drugs and therapeutic strategies for the treatment of cancers by targeting deregulated transcription factors.
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Affiliation(s)
- Yiwei Li
- Karmanos Cancer Institute and Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Asfar S Azmi
- Karmanos Cancer Institute and Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Ramzi M Mohammad
- Karmanos Cancer Institute and Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA.
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Tao Y, Ma X, Palmer D, Schwartz R, Lu X, Osmanbeyoglu H. Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers. Nucleic Acids Res 2022; 50:10869-10881. [PMID: 36243974 PMCID: PMC9638905 DOI: 10.1093/nar/gkac881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 09/23/2022] [Accepted: 09/29/2022] [Indexed: 11/14/2022] Open
Abstract
Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.
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Affiliation(s)
| | | | - Drake Palmer
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Russell Schwartz
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA,Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA,Department of Pharmaceutical Science, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
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Zhen Z, Li M, Zhong M, Liu J, Huang W, Ye L. Expression and prognostic potential of TMEM204: a pan-cancer analysis. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2022; 15:258-271. [PMID: 35949807 PMCID: PMC9360586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
TMEM204 (Transmembrane Protein 204) is a member of the TMEM family that regulates cell function and angiogenesis. Previous studies showed that TMEM204 is related to pancreatic cancer, but its roles in other cancers remain unknown. To reveal this relationship, we conducted a pan-cancer analysis by several online databases. The expression of TMEM204 was analyzed by Oncomine and Tumor Immune Estimation Resource2.0 (TIMER2.0). The prognostic potential of TMEM204 was evaluated by the GEPIA2, UALCAN, and Oncolnc. The methylation level of gene expression was analyzed by UALCAN, and the relationship between cancer and immune invasion was displayed by TIMER2.0. The Protein-Protein Interactions Network and functional analysis of TMEM204 and its related genes were conducted by STRING and Webgestalt. We found that TMEM204 expression was up-regulated and correlated with prognosis in multiple cancers. In liver hepatocellular carcinoma (LIHC), high TMEM204 expression was associated with a good prognosis, and with high infiltrating levels of CD8+ T and CD4+ T cells, macrophages, neutrophils, and myeloid dendritic cells. In addition, the methylation level in LIHC was higher than in normal tissues. p53 signaling pathway and Fanconi anemia pathway were implicated by KEGG pathway analysis. These results indicate that TMEM204 is associated with the prognosis, methylation, and immune invasion of cancers, especially LIHC. TMEM204 may act as a prognostic marker of LIHC and its role in other cancers should be studied.
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Affiliation(s)
- Zicheng Zhen
- Zhongshan School of Medicine, Sun Yat-sen UniversityGuangzhou 510060, Guangdong, China
| | - Minghao Li
- The Second Clinical Medical School, Guangdong Medical UniversityDongguan 523808, Guangdong, China
| | - Muyan Zhong
- The Second Clinical Medical School, Guangdong Medical UniversityDongguan 523808, Guangdong, China
| | - Jiaqi Liu
- Zhongshan School of Medicine, Sun Yat-sen UniversityGuangzhou 510060, Guangdong, China
| | - Wendu Huang
- The First School of Clinical Medicine, Southern Medical UniversityGuangzhou 510515, Guangdong, China
| | - Liqun Ye
- Endoscopic Center, The Six Affiliated Hospital, South China University of Technology (People’s Hospital of Nanhai District)Foshan 528200, Guangdong, China
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11
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Rogers JD, Aguado BA, Watts KM, Anseth KS, Richardson WJ. Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera. Proc Natl Acad Sci U S A 2022; 119:e2117323119. [PMID: 35181609 PMCID: PMC8872767 DOI: 10.1073/pnas.2117323119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 01/12/2022] [Indexed: 02/08/2023] Open
Abstract
Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan-a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.
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Affiliation(s)
- Jesse D Rogers
- Bioengineering Department, Clemson University, Clemson, SC 29634
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830
| | - Brian A Aguado
- Chemical and Biological Engineering Department, BioFrontiers Institute, University of Colorado, Boulder, CO 80309
- Bioengineering Department, University of California San Diego, La Jolla, CA 92093
- Stem Cell Program, Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037
| | - Kelsey M Watts
- Bioengineering Department, Clemson University, Clemson, SC 29634
| | - Kristi S Anseth
- Chemical and Biological Engineering Department, BioFrontiers Institute, University of Colorado, Boulder, CO 80309;
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12
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Xinxin L, Zhang Q. LncRNA RP11-214F16.8 drives breast cancer tumorigenesis via a post-translational repression on NISCH expression. Cell Signal 2022; 92:110271. [DOI: 10.1016/j.cellsig.2022.110271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/27/2021] [Accepted: 01/26/2022] [Indexed: 12/24/2022]
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13
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Ma X, Somasundaram A, Qi Z, Hartman D, Singh H, Osmanbeyoglu H. SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators. Nucleic Acids Res 2021; 49:9633-9647. [PMID: 34500467 PMCID: PMC8464045 DOI: 10.1093/nar/gkab745] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 08/09/2021] [Accepted: 09/06/2021] [Indexed: 12/22/2022] Open
Abstract
The identity and functions of specialized cell types are dependent on the complex interplay between signaling and transcriptional networks. Recently single-cell technologies have been developed that enable simultaneous quantitative analysis of cell-surface receptor expression with transcriptional states. To date, these datasets have not been used to systematically develop cell-context-specific maps of the interface between signaling and transcriptional regulators orchestrating cellular identity and function. We present SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network), a computational method to link cell-surface receptors to transcription factors (TFs) by exploiting cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) datasets with cis-regulatory information. SPaRTAN is applied to immune cell types in the blood to predict the coupling of signaling receptors with cell context-specific TFs. Selected predictions are validated by prior knowledge and flow cytometry analyses. SPaRTAN is then used to predict the signaling coupled TF states of tumor infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas. SPaRTAN enhances the utility of CITE-seq datasets to uncover TF and cell-surface receptor relationships in diverse cellular states.
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Affiliation(s)
- Xiaojun Ma
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
| | - Ashwin Somasundaram
- Department of Medicine, Division of Hematology/Oncology, University of Pittsburgh, Pittsburgh, PA 15213, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
| | - Zengbiao Qi
- UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
| | - Douglas J Hartman
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
| | - Harinder Singh
- Center for Systems Immunology and Department of Immunology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Hatice Ulku Osmanbeyoglu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA 15213, USA
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14
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Li K, Du Y, Li L, Wei DQ. Bioinformatics Approaches for Anti-cancer Drug Discovery. Curr Drug Targets 2021; 21:3-17. [PMID: 31549592 DOI: 10.2174/1389450120666190923162203] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 07/17/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.
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Affiliation(s)
- Kening Li
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuxin Du
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lu Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing 211166, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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15
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Abstract
BACKGROUND Though accounts for 2.5% of all cancers in female, the death rate of ovarian cancer is high, which is the fifth leading cause of cancer death (5% of all cancer death) in female. The 5-year survival rate of ovarian cancer is less than 50%. The oncogenic molecular signaling of ovarian cancer are complicated and remain unclear, and there is a lack of effective targeted therapies for ovarian cancer treatment. METHODS In this study, we propose to investigate activated signaling pathways of individual ovarian cancer patients and sub-groups; and identify potential targets and drugs that are able to disrupt the activated signaling pathways. Specifically, we first identify the up-regulated genes of individual cancer patients using Markov chain Monte Carlo (MCMC), and then identify the potential activated transcription factors. After dividing ovarian cancer patients into several sub-groups sharing common transcription factors using K-modes method, we uncover the up-stream signaling pathways of activated transcription factors in each sub-group. Finally, we mapped all FDA approved drugs targeting on the upstream signaling. RESULTS The 427 ovarian cancer samples were divided into 3 sub-groups (with 100, 172, 155 samples respectively) based on the activated TFs (with 14, 25, 26 activated TFs respectively). Multiple up-stream signaling pathways, e.g., MYC, WNT, PDGFRA (RTK), PI3K, AKT TP53, and MTOR, are uncovered to activate the discovered TFs. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. The signaling diversity and heterogeneity can be potential therapeutic targets for drug combination discovery. CONCLUSIONS The proposed integrative network analysis could uncover potential core signaling pathways, targets and drugs for ovarian cancer treatment.
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Affiliation(s)
- Tianyu Zhang
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, 63130, USA
- Dalian University of Technology, Dalian, 116024, China
| | - Liwei Zhang
- Dalian University of Technology, Dalian, 116024, China
| | - Fuhai Li
- Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, 63130, USA.
- Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, 63130, USA.
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16
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Fortelny N, Bock C. Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data. Genome Biol 2020; 21:190. [PMID: 32746932 PMCID: PMC7397672 DOI: 10.1186/s13059-020-02100-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 07/10/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Deep learning has emerged as a versatile approach for predicting complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the biological mechanisms that underlie a successful prediction. Here we demonstrate deep learning on biological networks, where every node has a molecular equivalent, such as a protein or gene, and every edge has a mechanistic interpretation, such as a regulatory interaction along a signaling pathway. RESULTS With knowledge-primed neural networks (KPNNs), we exploit the ability of deep learning algorithms to assign meaningful weights in multi-layered networks, resulting in a widely applicable approach for interpretable deep learning. We present a learning method that enhances the interpretability of trained KPNNs by stabilizing node weights in the presence of redundancy, enhancing the quantitative interpretability of node weights, and controlling for uneven connectivity in biological networks. We validate KPNNs on simulated data with known ground truth and demonstrate their practical use and utility in five biological applications with single-cell RNA-seq data for cancer and immune cells. CONCLUSIONS We introduce KPNNs as a method that combines the predictive power of deep learning with the interpretability of biological networks. While demonstrated here on single-cell sequencing data, this method is broadly relevant to other research areas where prior domain knowledge can be represented as networks.
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Affiliation(s)
- Nikolaus Fortelny
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria.
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17
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Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks. mSystems 2020; 5:5/3/e00057-20. [PMID: 32487739 PMCID: PMC8534726 DOI: 10.1128/msystems.00057-20] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species. IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.
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18
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Jo K, Santos-Buitrago B, Kim M, Rhee S, Talcott C, Kim S. Logic-based analysis of gene expression data predicts association between TNF, TGFB1 and EGF pathways in basal-like breast cancer. Methods 2020; 179:89-100. [PMID: 32445696 DOI: 10.1016/j.ymeth.2020.05.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/30/2020] [Accepted: 05/13/2020] [Indexed: 12/16/2022] Open
Abstract
For breast cancer, clinically important subtypes are well characterized at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterize biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences. We designed a logic-based computational framework to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Our method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential association between pathways via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes. Codes and results are available at http://epigenomics.snu.ac.kr/PL/.
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Affiliation(s)
- Kyuri Jo
- Department of Computer Engineering, Chungbuk National University, Cheongju, Republic of Korea
| | - Beatriz Santos-Buitrago
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Minsu Kim
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Sungmin Rhee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | | | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea; Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea; Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.
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19
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Feng C, Song C, Liu Y, Qian F, Gao Y, Ning Z, Wang Q, Jiang Y, Li Y, Li M, Chen J, Zhang J, Li C. KnockTF: a comprehensive human gene expression profile database with knockdown/knockout of transcription factors. Nucleic Acids Res 2020; 48:D93-D100. [PMID: 31598675 PMCID: PMC6943067 DOI: 10.1093/nar/gkz881] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 09/25/2019] [Accepted: 10/05/2019] [Indexed: 12/26/2022] Open
Abstract
Transcription factors (TFs) and their target genes have important functions in human diseases and biological processes. Gene expression profile analysis before and after knockdown or knockout is one of the most important strategies for obtaining target genes of TFs and exploring TF functions. Human gene expression profile datasets with TF knockdown and knockout are accumulating rapidly. Based on the urgent need to comprehensively and effectively collect and process these data, we developed KnockTF (http://www.licpathway.net/KnockTF/index.html), a comprehensive human gene expression profile database of TF knockdown and knockout. KnockTF provides a number of resources for human gene expression profile datasets associated with TF knockdown and knockout and annotates TFs and their target genes in a tissue/cell type-specific manner. The current version of KnockTF has 570 manually curated RNA-seq and microarray datasets associated with 308 TFs disrupted by different knockdown and knockout techniques and across multiple tissue/cell types. KnockTF collects upstream pathway information of TFs and functional annotation results of downstream target genes. It provides details about TFs binding to promoters, super-enhancers and typical enhancers of target genes. KnockTF constructs a TF-differentially expressed gene network and performs network analyses for genes of interest. KnockTF will help elucidate TF-related functions and potential biological effects.
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Affiliation(s)
- Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chao Song
- Department of Pharmacology, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yuejuan Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Fengcui Qian
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yu Gao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Ziyu Ning
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qiuyu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yong Jiang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yanyu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Meng Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jiaxin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
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20
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Lederer S, Heskes T, van Heeringen SJ, Albers CA. Investigating the effect of dependence between conditions with Bayesian Linear Mixed Models for motif activity analysis. PLoS One 2020; 15:e0231824. [PMID: 32357166 PMCID: PMC7194367 DOI: 10.1371/journal.pone.0231824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 04/01/2020] [Indexed: 11/19/2022] Open
Abstract
MOTIVATION Cellular identity and behavior is controlled by complex gene regulatory networks. Transcription factors (TFs) bind to specific DNA sequences to regulate the transcription of their target genes. On the basis of these TF motifs in cis-regulatory elements we can model the influence of TFs on gene expression. In such models of TF motif activity the data is usually modeled assuming a linear relationship between the motif activity and the gene expression level. A commonly used method to model motif influence is based on Ridge Regression. One important assumption of linear regression is the independence between samples. However, if samples are generated from the same cell line, tissue, or other biological source, this assumption may be invalid. This same assumption of independence is also applied to different yet similar experimental conditions, which may also be inappropriate. In theory, the independence assumption between samples could lead to loss in signal detection. Here we investigate whether a Bayesian model that allows for correlations results in more accurate inference of motif activities. RESULTS We extend the Ridge Regression to a Bayesian Linear Mixed Model, which allows us to model dependence between different samples. In a simulation study, we investigate the differences between the two model assumptions. We show that our Bayesian Linear Mixed Model implementation outperforms Ridge Regression in a simulation scenario where the noise, which is the signal that can not be explained by TF motifs, is uncorrelated. However, we demonstrate that there is no such gain in performance if the noise has a similar covariance structure over samples as the signal that can be explained by motifs. We give a mathematical explanation to why this is the case. Using four representative real datasets we show that at most ∼​40% of the signal is explained by motifs using the linear model. With these data there is no advantage to using the Bayesian Linear Mixed Model, due to the similarity of the covariance structure. AVAILABILITY & IMPLEMENTATION The project implementation is available at https://github.com/Sim19/SimGEXPwMotifs.
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Affiliation(s)
- Simone Lederer
- Data Science, Radboud University, Institute for Computing and Information Sciences, Nijmegen, The Netherlands
- Molecular Developmental Biology, Radboud University, Research Institute for Molecular Life Sciences, Nijmegen, The Netherlands
- * E-mail: (LS); (VHS)
| | - Tom Heskes
- Data Science, Radboud University, Institute for Computing and Information Sciences, Nijmegen, The Netherlands
| | - Simon J. van Heeringen
- Molecular Developmental Biology, Radboud University, Research Institute for Molecular Life Sciences, Nijmegen, The Netherlands
- * E-mail: (LS); (VHS)
| | - Cornelis A. Albers
- Molecular Developmental Biology, Radboud University, Research Institute for Molecular Life Sciences, Nijmegen, The Netherlands
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21
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Zaffaroni G, Okawa S, Morales-Ruiz M, del Sol A. An integrative method to predict signalling perturbations for cellular transitions. Nucleic Acids Res 2020; 47:e72. [PMID: 30949696 PMCID: PMC6614844 DOI: 10.1093/nar/gkz232] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 02/22/2019] [Accepted: 03/22/2019] [Indexed: 12/19/2022] Open
Abstract
Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material. Here we present the first general computational method that systematically predicts signalling molecules, whose perturbations induce desired cellular transitions. This probabilistic method integrates gene regulatory networks (GRNs) with manually-curated signalling pathways obtained from MetaCore from Clarivate Analytics, to model how signalling cues are received and processed in the GRN. The method was applied to 219 cellular transition examples, including cell type transitions, and overall correctly predicted experimentally validated signalling molecules, consistently outperforming other well-established approaches, such as differential gene expression and pathway enrichment analyses. Further, we validated our method predictions in the case of rat cirrhotic liver, and identified the activation of angiopoietins receptor Tie2 as a potential target for reverting the disease phenotype. Experimental results indicated that this perturbation induced desired changes in the gene expression of key TFs involved in fibrosis and angiogenesis. Importantly, this method only requires gene expression data of the initial and desired cell states, and therefore is suited for the discovery of signalling interventions for disease treatments and cellular therapies.
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Affiliation(s)
- Gaia Zaffaroni
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg
| | - Satoshi Okawa
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg
- Integrated BioBank of Luxembourg, Dudelange L-3555, Luxembourg
| | - Manuel Morales-Ruiz
- Biochemistry and Molecular Genetics Department-Hospital Clínic of Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Barcelona 08036, Spain
- Working group for the biochemical assessment of hepatic disease-SEQC, Barcelona 08036, Spain
- Department of Biomedicine-Biochemistry Unit, School of Medicine-University of Barcelona, Barcelona 08036, Spain
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg
- CIC bioGUNE, Bizkaia Technology Park, Derio 48160, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao 48013, Spain
- To whom correspondence should be addressed. Tel: +352 46 66 44 6982; Fax: +352 46 66 44 6949;
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22
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Chiang S, Shinohara H, Huang JH, Tsai HK, Okada M. Inferring the transcriptional regulatory mechanism of signal-dependent gene expression via an integrative computational approach. FEBS Lett 2020; 594:1477-1496. [PMID: 32052437 DOI: 10.1002/1873-3468.13757] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 12/26/2019] [Accepted: 01/20/2020] [Indexed: 11/10/2022]
Abstract
Eukaryotic transcription factors (TFs) coordinate different upstream signals to regulate the expression of their target genes. To unveil this regulatory network in B-cell receptor signaling, we developed a computational pipeline to systematically analyze the extracellular signal-regulated kinase (ERK)- and IκB kinase (IKK)-dependent transcriptome responses. We combined a bilinear regression method and kinetic modeling to identify the signal-to-TF and TF-to-gene dynamics, respectively. We input a set of time-course experimental data for B cells and concentrated on transcriptional activators. The results show that the combination of TFs differentially controlled by ERK and IKK could contribute divergent expression dynamics in orchestrating the B-cell response. Our findings provide insights into the regulatory mechanisms underlying signal-dependent gene expression in eukaryotic cells.
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Affiliation(s)
- Sufeng Chiang
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | | | - Jia-Hsin Huang
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Huai-Kuang Tsai
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Mariko Okada
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory of Cell Systems, Institute for Protein Research, Osaka University, Suita, Japan
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23
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Osmanbeyoglu HU, Shimizu F, Rynne-Vidal A, Alonso-Curbelo D, Chen HA, Wen HY, Yeung TL, Jelinic P, Razavi P, Lowe SW, Mok SC, Chiosis G, Levine DA, Leslie CS. Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers. Nat Commun 2019; 10:4369. [PMID: 31554806 PMCID: PMC6761109 DOI: 10.1038/s41467-019-12291-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 09/02/2019] [Indexed: 02/08/2023] Open
Abstract
Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory differences between and within tumor types. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Many identified TFs are significantly associated with survival outcome. To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis.
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Affiliation(s)
- Hatice U Osmanbeyoglu
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Fumiko Shimizu
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Angela Rynne-Vidal
- Department of Gynecologic Oncology and Reproductive Medicine-Research, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Direna Alonso-Curbelo
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hsuan-An Chen
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hannah Y Wen
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tsz-Lun Yeung
- Department of Gynecologic Oncology and Reproductive Medicine-Research, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Petar Jelinic
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Medical Center, New York, NY, USA
| | - Pedram Razavi
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Scott W Lowe
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samuel C Mok
- Department of Gynecologic Oncology and Reproductive Medicine-Research, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Douglas A Levine
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Medical Center, New York, NY, USA
| | - Christina S Leslie
- Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Zhao Y, Zhao Q, Kaboli PJ, Shen J, Li M, Wu X, Yin J, Zhang H, Wu Y, Lin L, Zhang L, Wan L, Wen Q, Li X, Cho CH, Yi T, Li J, Xiao Z. m1A Regulated Genes Modulate PI3K/AKT/mTOR and ErbB Pathways in Gastrointestinal Cancer. Transl Oncol 2019; 12:1323-1333. [PMID: 31352195 PMCID: PMC6661385 DOI: 10.1016/j.tranon.2019.06.007] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 06/25/2019] [Accepted: 06/26/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND: Gene expression can be posttranscriptionally regulated by a complex network of proteins. N1-methyladenosine (m1A) is a newly validated RNA modification. However, little is known about both its influence and biogenesis in tumor development. METHODS: This study analyzed TCGA data of patients with five kinds of gastrointestinal (GI) cancers. Using data from cBioPortal, molecular features of the nine known m1A-related enzymes in GI cancers were investigated. Using a variety of bioinformatics approach, the impact of m1A regulators on its downstream signaling pathway was studied. To further confirm this regulation, the effect of m1A writer ALKBH3 knockdown was studied using RNA-seq data from published database. RESULTS: Dysregulation and multiple types of genetic alteration of putative m1A-related enzymes in tumor samples were observed. The ErbB and mTOR pathways with ErbB2, mTOR, and AKT1S1 hub genes were identified as being regulated by m1A-related enzymes. The expression of both ErbB2 and AKT1S1 was decreased after m1A writer ALKBH3 knockdown. Furthermore, Gene Ontology analysis revealed that m1A downstream genes were associated with cell proliferation, and the results showed that m1A genes are reliably linked to mTOR. CONCLUSION: This study demonstrated for the first time the dysregulation of m1A regulators in GI cancer and its signaling pathways and will contribute to the understanding of RNA modification in cancer.
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Affiliation(s)
- Yueshui Zhao
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Qijie Zhao
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Parham Jabbarzadeh Kaboli
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Jing Shen
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Mingxing Li
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Xu Wu
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Jianhua Yin
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Hanyu Zhang
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Yuanlin Wu
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Ling Lin
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Lingling Zhang
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Lin Wan
- Department of Hematology and Oncology, The Children's Hospital of Soochow, Jiangsu, China
| | - Qinglian Wen
- Department of Oncology, The Affiliated Hospital of Luzhou Medical College, Luzhou, Sichuan 646000, PR China
| | - Xiang Li
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China
| | - Chi Hin Cho
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China
| | - Tao Yi
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Jing Li
- Department of Oncology and Hematology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, Sichuan, China.
| | - Zhangang Xiao
- Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, 646000, Sichuan, PR China; South Sichuan Institution for Translational Medicine, Luzhou, 646000, Sichuan, PR China.
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Chen Y, Liu P, Sun P, Jiang J, Zhu Y, Dong T, Cui Y, Tian Y, An T, Zhang J, Li Z, Yang X. Oncogenic MSH6-CXCR4-TGFB1 Feedback Loop: A Novel Therapeutic Target of Photothermal Therapy in Glioblastoma Multiforme. Am J Cancer Res 2019; 9:1453-1473. [PMID: 30867843 PMCID: PMC6401508 DOI: 10.7150/thno.29987] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Accepted: 01/20/2019] [Indexed: 12/28/2022] Open
Abstract
Glioblastoma multiforme (GBM) has been considered the most aggressive glioma type. Temozolomide (TMZ) is the main first-line chemotherapeutic agent for GBM. Decreased mutS homolog 6 (MSH6) expression is clinically recognized as one of the principal reasons for GBM resistance to TMZ. However, the specific functions of MSH6 in GBM, in addition to its role in mismatch repair, remain unknown. Methods: Bioinformatics were employed to analyze MSH6 mRNA and protein levels in GBM clinical samples and to predict the potential cancer-promoting functions and mechanisms of MSH6. MSH6 levels were silenced or overexpressed in GBM cells to assess its functional effects in vitro and in vivo. Western blot, qRT-PCR, and immunofluorescence assays were used to explore the relevant molecular mechanisms. Cu2(OH)PO4@PAA nanoparticles were fabricated through a hydrothermal method. Their MRI and photothermal effects as well as their effect on restraining the MSH6-CXCR4-TGFB1 feedback loop were investigated in vitro and in vivo. Results: We demonstrated that MSH6 is an overexpressed oncogene in human GBM tissues. MSH6, CXCR4 and TGFB1 formed a triangular MSH6-CXCR4-TGFB1 feedback loop that accelerated gliomagenesis, proliferation (G1 phase), migration and invasion (epithelial-to-mesenchymal transition; EMT), stemness, angiogenesis and antiapoptotic effects by regulating the p-STAT3/Slug and p-Smad2/3/ZEB2 signaling pathways in GBM. In addition, the MSH6-CXCR4-TGFB1 feedback loop was a vital marker of GBM, making it a promising therapeutic target. Notably, photothermal therapy (PTT) mediated by Cu2(OH)PO4@PAA + near infrared (NIR) irradiation showed outstanding therapeutic effects, which might be associated with a repressed MSH6-CXCR4-TGFB1 feedback loop and its downstream factors in GBM. Simultaneously, the prominent MR imaging (T1WI) ability of Cu2(OH)PO4@PAA could provide visual guidance for PTT. Conclusions: Our findings indicate that the oncogenic MSH6-CXCR4-TGFB1 feedback loop is a novel therapeutic target for GBM and that PTT is associated with the inhibition of the MSH6-CXCR4-TGFB1 loop.
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26
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Alexandrou S, George SM, Ormandy CJ, Lim E, Oakes SR, Caldon CE. The Proliferative and Apoptotic Landscape of Basal-like Breast Cancer. Int J Mol Sci 2019; 20:ijms20030667. [PMID: 30720718 PMCID: PMC6387372 DOI: 10.3390/ijms20030667] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 01/21/2019] [Accepted: 01/28/2019] [Indexed: 02/07/2023] Open
Abstract
Basal-like breast cancer (BLBC) is an aggressive molecular subtype that represents up to 15% of breast cancers. It occurs in younger patients, and typically shows rapid development of locoregional and distant metastasis, resulting in a relatively high mortality rate. Its defining features are that it is positive for basal cytokeratins and, epidermal growth factor receptor and/or c-Kit. Problematically, it is typically negative for the estrogen receptor and human epidermal growth factor receptor 2 (HER2), which means that it is unsuitable for either hormone therapy or targeted HER2 therapy. As a result, there are few therapeutic options for BLBC, and a major priority is to define molecular subgroups of BLBC that could be targeted therapeutically. In this review, we focus on the highly proliferative and anti-apoptotic phenotype of BLBC with the goal of defining potential therapeutic avenues, which could take advantage of these aspects of tumor development.
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Affiliation(s)
- Sarah Alexandrou
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 2010 Sydney, Australia.
| | - Sandra Marie George
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 2010 Sydney, Australia.
| | - Christopher John Ormandy
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 2010 Sydney, Australia.
- St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, 2052 Sydney, Australia.
| | - Elgene Lim
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 2010 Sydney, Australia.
- St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, 2052 Sydney, Australia.
| | - Samantha Richelle Oakes
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 2010 Sydney, Australia.
- St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, 2052 Sydney, Australia.
| | - C Elizabeth Caldon
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 2010 Sydney, Australia.
- St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, 2052 Sydney, Australia.
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27
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Ohe K, Miyajima S, Abe I, Tanaka T, Hamaguchi Y, Harada Y, Horita Y, Beppu Y, Ito F, Yamasaki T, Terai H, Mori M, Murata Y, Tanabe M, Ashida K, Kobayashi K, Enjoji M, Yanase T, Harada N, Utsumi T, Mayeda A. HMGA1a induces alternative splicing of estrogen receptor alpha in MCF-7 human breast cancer cells. J Steroid Biochem Mol Biol 2018; 182:21-26. [PMID: 29678492 DOI: 10.1016/j.jsbmb.2018.04.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 12/23/2017] [Accepted: 04/13/2018] [Indexed: 12/11/2022]
Abstract
The high-mobility group A protein 1a (HMGA1a) protein is known as an oncogene whose expression level in cancer tissue correlates with the malignant potential, and known as a component of senescence-related structures connecting it to tumor suppressor networks in fibroblasts. HMGA1 protein binds to DNA, but recent studies have shown it exerts novel functions through RNA-binding. Our previous studies have shown that sequence-specific RNA-binding of HMGA1a induces exon-skipping of Presenilin-2 exon 5 in sporadic Alzheimer disease. Here we show that HMGA1a induced exon-skipping of the estrogen receptor alpha (ERα) gene and increased ERα46 mRNA expression in MCF-7 breast cancer cells. An RNA-decoy of HMGA1a efficiently blocked this event and reduced ERα46 protein expression. Blockage of HMGA1a RNA-binding property consequently induced cell growth through reduced ERα46 expression in MCF-7 cells and increased sensitivity to tamoxifen in the tamoxifen-resistant cell line, MCF-7/TAMR1. Stable expression of an HMGA1a RNA-decoy in MCF-7 cells exhibited decreased ERα46 protein expression and increased estrogen-dependent tumor growth when these cells were implanted in nude mice. These results show HMGA1a is involved in alternative splicing of the ERα gene and related to estrogen-related growth as well as tamoxifen sensitivity in MCF-7 breast cancer cells.
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Affiliation(s)
- Kenji Ohe
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan.
| | - Shinsuke Miyajima
- Department of Breast Surgery, School of Medicine, Fujita Health University, Toyoake, Aichi 470-1192, Japan
| | - Ichiro Abe
- Department of Endocrinology and Diabetes Mellitus, Fukuoka University Chikushi Hospital, Chikushino city, 818-8502, Japan
| | - Tomoko Tanaka
- Department of Endocrinology and Diabetes Mellitus, Faculty of Medicine, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Yuriko Hamaguchi
- Department of Endocrinology and Diabetes Mellitus, Faculty of Medicine, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Yoshihiro Harada
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Yuta Horita
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Yuki Beppu
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Fumiaki Ito
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Takafumi Yamasaki
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Hiroki Terai
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Masayoshi Mori
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Yusuke Murata
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Makito Tanabe
- Department of Endocrinology and Diabetes Mellitus, Faculty of Medicine, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Kenji Ashida
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kunihisa Kobayashi
- Department of Endocrinology and Diabetes Mellitus, Fukuoka University Chikushi Hospital, Chikushino city, 818-8502, Japan
| | - Munechika Enjoji
- Department of Pharmacotherapeutics, Faculty of Pharmaceutical Sciences, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Toshihiko Yanase
- Department of Endocrinology and Diabetes Mellitus, Faculty of Medicine, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka, 814-180, Japan
| | - Nobuhiro Harada
- Department of Biochemistry, School of Medicine, Fujita Health University, Toyoake, Aichi 470-1192, Japan
| | - Toshiaki Utsumi
- Department of Breast Surgery, School of Medicine, Fujita Health University, Toyoake, Aichi 470-1192, Japan
| | - Akila Mayeda
- Division of Gene Expression Mechanism, Institute for Comprehensive Medical Science (ICMS), Fujita Health University, Aichi, Toyoake, 470-1192, Japan
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28
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Zhang T, Xu J, Deng S, Zhou F, Li J, Zhang L, Li L, Wang QE, Li F. Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data. PLoS One 2018; 13:e0196351. [PMID: 29723215 PMCID: PMC5933740 DOI: 10.1371/journal.pone.0196351] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 04/11/2018] [Indexed: 12/12/2022] Open
Abstract
Tumor recurrence occurs in more than 70% of ovarian cancer patients, and the majority eventually becomes refractory to treatments. Ovarian Cancer Stem Cells (OCSCs) are believed to be responsible for the tumor relapse and drug resistance. Therefore, eliminating ovarian CSCs is important to improve the prognosis of ovarian cancer patients. However, there is a lack of effective drugs to eliminate OCSCs because the core signaling pathways regulating OCSCs remain unclear. Also it is often hard for biologists to identify a few testable targets and infer driver signaling pathways regulating CSCs from a large number of differentially expression genes in an unbiased manner. In this study, we propose a straightforward and integrative analysis to identify potential core signaling pathways of OCSCs by integrating transcriptome data of OCSCs isolated based on two distinctive markers, ALDH and side population, with regulatory network (Transcription Factor (TF) and Target Interactome) and signaling pathways. We first identify the common activated TFs in two OCSC populations integrating the gene expression and TF-target Interactome; and then uncover up-stream signaling cascades regulating the activated TFs. In specific, 22 activated TFs are identified. Through literature search validation, 15 of them have been reported in association with cancer stem cells. Additionally, 10 TFs are found in the KEGG signaling pathways, and their up-stream signaling cascades are extracted, which also provide potential treatment targets. Moreover, 40 FDA approved drugs are identified to target on the up-stream signaling cascades, and 15 of them have been reported in literatures in cancer stem cell treatment. In conclusion, the proposed approach can uncover the activated up-stream signaling, activated TFs and up-regulated target genes that constitute the potential core signaling pathways of ovarian CSC. Also drugs and drug combinations targeting on the core signaling pathways might be able to eliminate OCSCs. The proposed approach can also be applied for identifying potential activated signaling pathways of other types of cancers.
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Affiliation(s)
- Tianyu Zhang
- Department of BioMedical Informatics (BMI), The Ohio State University, Columbus, Ohio, United States of America
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Jielin Xu
- Department of BioMedical Informatics (BMI), The Ohio State University, Columbus, Ohio, United States of America
| | - Siyuan Deng
- Department of BioMedical Informatics (BMI), The Ohio State University, Columbus, Ohio, United States of America
| | - Fengqi Zhou
- Department of BioMedical Informatics (BMI), The Ohio State University, Columbus, Ohio, United States of America
| | - Jin Li
- Department of BioMedical Informatics (BMI), The Ohio State University, Columbus, Ohio, United States of America
| | - Liwei Zhang
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Lang Li
- Department of BioMedical Informatics (BMI), The Ohio State University, Columbus, Ohio, United States of America
| | - Qi-En Wang
- Department of Radiology, The Ohio State University, Columbus, Ohio, United States of America
| | - Fuhai Li
- Department of BioMedical Informatics (BMI), The Ohio State University, Columbus, Ohio, United States of America
- * E-mail:
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29
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Kim S, Yang JW, Kim C, Kim MG. Impact of suppression of tumorigenicity 14 (ST14)/serine protease 14 (Prss14) expression analysis on the prognosis and management of estrogen receptor negative breast cancer. Oncotarget 2017; 7:34643-63. [PMID: 27167193 PMCID: PMC5085182 DOI: 10.18632/oncotarget.9155] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 04/16/2016] [Indexed: 01/06/2023] Open
Abstract
To elucidate the role of a type II transmembrane serine protease, ST14/Prss14, during breast cancer progression, we utilized publically accessible databases including TCGA, GEO, NCI-60, and CCLE. Survival of breast cancer patients with high ST14/Prss14 expression is significantly poor in estrogen receptor (ER) negative populations regardless of the ratios of ST14/Prss14 to its inhibitors, SPINT1 or SPINT2. In a clustering of 1085 selected EMT signature genes, ST14/Prss14 is located in the same cluster with CDH3, and closer to post-EMT markers, CDH2, VIM, and FN1 than to the pre-EMT marker, CDH1. Coexpression analyses of known ST14/Prss14 substrates and transcription factors revealed context dependent action. In cell lines, paradoxically, ST14/Prss14 expression is higher in the ER positive group and located closer to CDH1 in clustering. This apparent contradiction is not likely due to ST14/Prss14 expression in a cancer microenvironment, nor due to negative regulation by ER. Genes consistently coexpressed with ST14/Prss14 include transcription factors, ELF5, GRHL1, VGLL1, suggesting currently unknown mechanisms for regulation. Here, we report that ST14/Prss14 is an emerging therapeutic target for breast cancer where HER2 is not applicable. In addition we suggest that careful conclusions should be drawn not exclusively from the cell line studies for target development.
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Affiliation(s)
- Sauryang Kim
- Inha University, Department of Biological Sciences, Incheon, Republic of Korea
| | - Jae Woong Yang
- Inha University, Department of Biological Sciences, Incheon, Republic of Korea
| | - Chungho Kim
- Department of Life Sciences, Korea University, Seoul, Republic of Korea
| | - Moon Gyo Kim
- Inha University, Department of Biological Sciences, Incheon, Republic of Korea.,Convergent Research Institute for Metabolism and Immunoregulation, Incheon, Republic of Korea
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30
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Yan G, Chen V, Lu X, Lu S. A signal-based method for finding driver modules of breast cancer metastasis to the lung. Sci Rep 2017; 7:10023. [PMID: 28855549 PMCID: PMC5577160 DOI: 10.1038/s41598-017-09951-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 07/31/2017] [Indexed: 12/25/2022] Open
Abstract
Tumor metastasis is mainly caused by somatic genomic alterations (SGAs) that perturb pathways regulating metastasis-relevant activities and thus help the primary tumor to adapt to the new microenvironment. Identifying drivers of metastasis, i.e. SGAs, sheds light on the metastasis mechanism and provides guidance for targeted therapy. In this paper, we introduce a novel method to search for SGAs driving breast cancer metastasis to the lung. First, we search for transcriptomic modules with genes that are differentially expressed in breast cell lines with strong metastatic activities to the lung and co-expressed in a large number of breast tumors. Then, for each transcriptomic module, we search for a set of SGA genes (driver modules) such that genes in each driver module carry a common signal regulating the transcriptomic module. Evaluations indicate that many genes in driver modules are indeed related to metastasis, and our methods have identified many new driver candidates. We further choose two novel metastatic driver genes, BCL2L11 and CDH9, for in vitro verification. The wound healing assay reveals that inhibiting either BCL2L11 or CDH9 will enhance the migration of cell lines, which provides evidence that these two genes are suppressors of tumor metastasis.
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Affiliation(s)
- Gaibo Yan
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vicky Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Songjian Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. .,Center for Causal Discovery, University of Pittsburgh, Pittsburgh, PA, USA.
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31
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Thangavelu PU, Lin CY, Vaidyanathan S, Nguyen THM, Dray E, Duijf PHG. Overexpression of the E2F target gene CENPI promotes chromosome instability and predicts poor prognosis in estrogen receptor-positive breast cancer. Oncotarget 2017; 8:62167-62182. [PMID: 28977935 PMCID: PMC5617495 DOI: 10.18632/oncotarget.19131] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 06/03/2017] [Indexed: 12/21/2022] Open
Abstract
During cell division, chromosome segregation is facilitated by the mitotic checkpoint, or spindle assembly checkpoint (SAC), which ensures correct kinetochore-microtubule attachments and prevents premature sister-chromatid separation. It is well established that misexpression of SAC components on the outer kinetochores promotes chromosome instability (CIN) and tumorigenesis. Here, we study the expression of CENP-I, a key component of the HIKM complex at the inner kinetochores, in breast cancer, including ductal, lobular, medullary and male breast carcinomas. CENPI mRNA and protein levels are significantly elevated in estrogen receptor-positive (ER+) but not in estrogen receptor-negative (ER-) breast carcinoma. Well-established prognostic tests indicate that CENPI overexpression constitutes a powerful independent marker for poor patient prognosis and survival in ER+ breast cancer. We further demonstrate that CENPI is an E2F target gene. Consistently, it is overexpressed in RB1-deficient breast cancers. However, CENP-I overexpression is not purely due to cell cycle-associated expression. In ER+ breast cancer cells, CENP-I overexpression promotes CIN, especially chromosome gains. In addition, in ER+ breast carcinomas the degree of CENPI overexpression is proportional to the level of aneuploidy and CENPI overexpression is one of the strongest markers for CIN identified to date. Our results indicate that overexpression of the inner kinetochore protein CENP-I promotes CIN and forecasts poor prognosis for ER+ breast cancer patients. These observations provide novel mechanistic insights and have important implications for breast cancer diagnostics and potentially therapeutic targeting.
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Affiliation(s)
- Pulari U Thangavelu
- University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Cheng-Yu Lin
- University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Srividya Vaidyanathan
- University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Thu H M Nguyen
- University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Eloise Dray
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Translational Research Institute, Brisbane, QLD, Australia
| | - Pascal H G Duijf
- University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
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32
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Jaiswal A, Peddinti G, Akimov Y, Wennerberg K, Kuznetsov S, Tang J, Aittokallio T. Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells. Genome Med 2017; 9:51. [PMID: 28569207 PMCID: PMC5452371 DOI: 10.1186/s13073-017-0440-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 05/15/2017] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Genome-wide loss-of-function profiling is widely used for systematic identification of genetic dependencies in cancer cells; however, the poor reproducibility of RNA interference (RNAi) screens has been a major concern due to frequent off-target effects. Currently, a detailed understanding of the key factors contributing to the sub-optimal consistency is still a lacking, especially on how to improve the reliability of future RNAi screens by controlling for factors that determine their off-target propensity. METHODS We performed a systematic, quantitative analysis of the consistency between two genome-wide shRNA screens conducted on a compendium of cancer cell lines, and also compared several gene summarization methods for inferring gene essentiality from shRNA level data. We then devised novel concepts of seed essentiality and shRNA family, based on seed region sequences of shRNAs, to study in-depth the contribution of seed-mediated off-target effects to the consistency of the two screens. We further investigated two seed-sequence properties, seed pairing stability, and target abundance in terms of their capability to minimize the off-target effects in post-screening data analysis. Finally, we applied this novel methodology to identify genetic interactions and synthetic lethal partners of cancer drivers, and confirmed differential essentiality phenotypes by detailed CRISPR/Cas9 experiments. RESULTS Using the novel concepts of seed essentiality and shRNA family, we demonstrate how genome-wide loss-of-function profiling of a common set of cancer cell lines can be actually made fairly reproducible when considering seed-mediated off-target effects. Importantly, by excluding shRNAs having higher propensity for off-target effects, based on their seed-sequence properties, one can remove noise from the genome-wide shRNA datasets. As a translational application case, we demonstrate enhanced reproducibility of genetic interaction partners of common cancer drivers, as well as identify novel synthetic lethal partners of a major oncogenic driver, PIK3CA, supported by a complementary CRISPR/Cas9 experiment. CONCLUSIONS We provide practical guidelines for improved design and analysis of genome-wide loss-of-function profiling and demonstrate how this novel strategy can be applied towards improved mapping of genetic dependencies of cancer cells to aid development of targeted anticancer treatments.
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Affiliation(s)
- Alok Jaiswal
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Gopal Peddinti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Yevhen Akimov
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Sergey Kuznetsov
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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33
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Bussemaker HJ, Causton HC, Fazlollahi M, Lee E, Muroff I. Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression. ACTA ACUST UNITED AC 2017; 2:98-102. [PMID: 28691107 DOI: 10.1016/j.coisb.2017.04.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Over the past decade, a number of methods have emerged for inferring protein-level transcription factor activities in individual samples based on prior information about the structure of the gene regulatory network. We discuss how this has enabled new methods for dissecting trans-acting mechanisms that underpin genetic variation in gene expression.
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Affiliation(s)
- Harmen J Bussemaker
- Department of Biological Sciences, Columbia University, New York, NY 10027.,Department of Systems Biology, Columbia University, New York, NY 10032
| | - Helen C Causton
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032
| | - Mina Fazlollahi
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029
| | - Eunjee Lee
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029
| | - Ivor Muroff
- Department of Biological Sciences, Columbia University, New York, NY 10027
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34
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Sychev ZE, Hu A, DiMaio TA, Gitter A, Camp ND, Noble WS, Wolf-Yadlin A, Lagunoff M. Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism. PLoS Pathog 2017; 13:e1006256. [PMID: 28257516 PMCID: PMC5352148 DOI: 10.1371/journal.ppat.1006256] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 03/15/2017] [Accepted: 02/22/2017] [Indexed: 12/22/2022] Open
Abstract
Kaposi’s Sarcoma associated Herpesvirus (KSHV), an oncogenic, human gamma-herpesvirus, is the etiological agent of Kaposi’s Sarcoma the most common tumor of AIDS patients world-wide. KSHV is predominantly latent in the main KS tumor cell, the spindle cell, a cell of endothelial origin. KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism. To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation, we identified changes in the host proteome, phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells. A Steiner forest algorithm was used to integrate the global data sets and, together with transcriptome based predicted transcription factor activity, cellular networks altered by latent KSHV were predicted. Several interesting pathways were identified, including peroxisome biogenesis. To validate the predictions, we showed that KSHV latent infection increases the number of peroxisomes per cell. Additionally, proteins involved in peroxisomal lipid metabolism of very long chain fatty acids, including ABCD3 and ACOX1, are required for the survival of latently infected cells. In summary, novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform, were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells. Kaposi’s Sarcoma herpesvirus (KSHV) is the etiologic agent of Kaposi’s Sarcoma, the most common tumor of AIDS patients. KSHV modulates host cell signaling and metabolism to maintain a life-long latent infection. To unravel the underlying cellular mechanisms modulated by KSHV, we used multiple global systems biology platforms to identify and integrate changes in both cellular protein expression and transcription following KSHV infection of endothelial cells, the relevant cell type for KS tumors. The analysis identified several interesting pathways including peroxisome biogenesis. Peroxisomes are small cytoplasmic organelles involved in redox reactions and lipid metabolism. KSHV latent infection increases the number of peroxisomes per cell and proteins involved in peroxisomal lipid metabolism are required for the survival of latently infected cells. In summary, through integration of multiple global systems biology analyses we were able to identify novel pathways that could not be predicted by one platform alone and found that lipid metabolism in a small cytoplasmic organelle is necessary for the survival of latent infection with a herpesvirus.
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Affiliation(s)
- Zoi E. Sychev
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, United States of America
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Alex Hu
- Department of Genome Science, University of Washington, Seattle, Washington, United States of America
| | - Terri A. DiMaio
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison and Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Nathan D. Camp
- Department of Genome Science, University of Washington, Seattle, Washington, United States of America
| | - William S. Noble
- Department of Genome Science, University of Washington, Seattle, Washington, United States of America
| | - Alejandro Wolf-Yadlin
- Department of Genome Science, University of Washington, Seattle, Washington, United States of America
- * E-mail: (ML); (AWY)
| | - Michael Lagunoff
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
- * E-mail: (ML); (AWY)
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35
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Liu Y, Yin X, Zhong J, Guan N, Luo Z, Min L, Yao X, Bo X, Dai L, Bai H. Systematic Identification and Assessment of Therapeutic Targets for Breast Cancer Based on Genome-Wide RNA Interference Transcriptomes. Genes (Basel) 2017; 8:genes8030086. [PMID: 28245581 PMCID: PMC5368690 DOI: 10.3390/genes8030086] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 01/24/2017] [Accepted: 02/13/2017] [Indexed: 12/15/2022] Open
Abstract
With accumulating public omics data, great efforts have been made to characterize the genetic heterogeneity of breast cancer. However, identifying novel targets and selecting the best from the sizeable lists of candidate targets is still a key challenge for targeted therapy, largely owing to the lack of economical, efficient and systematic discovery and assessment to prioritize potential therapeutic targets. Here, we describe an approach that combines the computational evaluation and objective, multifaceted assessment to systematically identify and prioritize targets for biological validation and therapeutic exploration. We first establish the reference gene expression profiles from breast cancer cell line MCF7 upon genome-wide RNA interference (RNAi) of a total of 3689 genes, and the breast cancer query signatures using RNA-seq data generated from tissue samples of clinical breast cancer patients in the Cancer Genome Atlas (TCGA). Based on gene set enrichment analysis, we identified a set of 510 genes that when knocked down could significantly reverse the transcriptome of breast cancer state. We then perform multifaceted assessment to analyze the gene set to prioritize potential targets for gene therapy. We also propose drug repurposing opportunities and identify potentially druggable proteins that have been poorly explored with regard to the discovery of small-molecule modulators. Finally, we obtained a small list of candidate therapeutic targets for four major breast cancer subtypes, i.e., luminal A, luminal B, HER2+ and triple negative breast cancer. This RNAi transcriptome-based approach can be a helpful paradigm for relevant researches to identify and prioritize candidate targets for experimental validation.
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Affiliation(s)
- Yang Liu
- Research Center for Clinical & Translational Medicine, Beijing 302 Hospital, Beijing 100039, China.
| | - Xiaoyao Yin
- Science and technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410073, China.
| | - Jing Zhong
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou 313000, China.
| | - Naiyang Guan
- Science and technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410073, China.
| | - Zhigang Luo
- Science and technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410073, China.
| | - Lishan Min
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou 313000, China.
| | - Xing Yao
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou 313000, China.
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing 100850, China.
| | - Licheng Dai
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou 313000, China.
| | - Hui Bai
- Beijing Institute of Radiation Medicine, Beijing 100850, China.
- No. 451 Hospital of PLA, Xi'an 710054, China.
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Osmanbeyoglu HU, Toska E, Chan C, Baselga J, Leslie CS. Pancancer modelling predicts the context-specific impact of somatic mutations on transcriptional programs. Nat Commun 2017; 8:14249. [PMID: 28139702 PMCID: PMC5290314 DOI: 10.1038/ncomms14249] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 12/12/2016] [Indexed: 12/15/2022] Open
Abstract
Pancancer studies have identified many genes that are frequently somatically altered across multiple tumour types, suggesting that pathway-targeted therapies can be deployed across diverse cancers. However, the same ‘actionable mutation' impacts distinct context-specific gene regulatory programs and signalling networks—and interacts with different genetic backgrounds of co-occurring alterations—in different cancers. Here we apply a computational strategy for integrating parallel (phospho)proteomic and mRNA sequencing data across 12 TCGA tumour data sets to interpret the context-specific impact of somatic alterations in terms of functional signatures such as (phospho)protein and transcription factor (TF) activities. Our analysis predicts distinct dysregulated transcriptional regulators downstream of somatic alterations in different cancers, and we validate the context-specific differential activity of TFs associated to mutant PIK3CA in isogenic cancer cell line models. These results have implications for the pancancer use of targeted drugs and potentially for the design of combination therapies. Cancer genomic data sets contain a wealth of data that can be used to predict prognosis and further understand disease. Here, the authors integrate multiple genomics data types to identify transcriptional dysregulation in response to somatic mutations.
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Affiliation(s)
- Hatice U Osmanbeyoglu
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Box No. 460, New York, New York 10065, USA
| | - Eneda Toska
- Human Oncogenesis and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Carmen Chan
- Human Oncogenesis and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - José Baselga
- Human Oncogenesis and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Christina S Leslie
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Box No. 460, New York, New York 10065, USA
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Cheng F, Zhao J, Hanker AB, Brewer MR, Arteaga CL, Zhao Z. Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer. Breast Cancer Res Treat 2016; 160:457-474. [PMID: 27771839 PMCID: PMC10183099 DOI: 10.1007/s10549-016-4011-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/05/2016] [Indexed: 01/25/2023]
Abstract
PURPOSE Phosphatidylinositol 3-kinase (PI3K)/AKT pathway aberrations are common in human breast cancer. Furthermore, PIK3CA mutations are commonly associated with resistance to anti-epidermal growth factor receptor 2 (HER2) or anti-estrogen receptor (ER) agents in HER2 or ER positive (HER2+/ER+) breast cancer. Hence, deciphering the underlying mechanisms of PIK3CA mutations in HER2+/ER+ breast cancer would provide novel insights into elucidating resistance to anti-HER2/ER therapies. METHODS In this study, we systematically investigated the biological consequences of PIK3CA H1047R in HER2+/ER+ breast cancer by uniquely incorporating mRNA transcriptomic data from The Cancer Genome Atlas and proteomic data from reverse-phase protein arrays. RESULTS Our integrative bioinformatics analyses revealed that several important pathways such as STAT3 and VEGF/hypoxia were selectively altered by PIK3CA H1047R in HER2+/ER+ breast cancer. Protein differential expression analysis indicated that an elevated eIF4G might promote tumor angiogenesis and growth via regulation of the hypoxia-activated switch in HER2+ PIK3CA H1047R breast cancer. We observed hypo-phosphorylation of EGFR in HER2+ PIK3CA H1047R breast cancer versus HER2+PIK3CAwild-type (PIK3CA WT). In addition, ER and PIK3CA H1047R might cooperate to activate STAT3, MAPK, AKT, and Hippo pathways in ER+ PIK3CA H1047R breast cancer. A higher YAPpS127 level was observed in ER+ PIK3CA H1047R patients than that in an ER+ PIK3CA WT subgroup. By examining breast cancer cell lines having both microarray gene expression and drug treatment data from the Genomics of Drug Sensitivity in Cancer and the Stand Up to Cancer datasets, we found that the elevated YAP1 mRNA expression was associated with the resistance of BCL-2 family inhibitors, but with the sensitivity to MEK/MAPK inhibitors in breast cancer cells. CONCLUSIONS In summary, these findings shed light on the functional consequences of PIK3CA H1047R-driven breast tumorigenesis and resistance to the existing therapeutic agents in HER2+/ER+ breast cancer.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA.,Center for Complex Networks Research, Northeastern University, Boston, MA, 02115, USA
| | - Junfei Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Ariella B Hanker
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Monica Red Brewer
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Carlos L Arteaga
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA. .,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
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38
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Sonnenblick A, Brohée S, Fumagalli D, Rothé F, Vincent D, Ignatiadis M, Desmedt C, Salgado R, Sirtaine N, Loi S, Neven P, Loibl S, Denkert C, Joensuu H, Piccart M, Sotiriou C. Integrative proteomic and gene expression analysis identify potential biomarkers for adjuvant trastuzumab resistance: analysis from the Fin-her phase III randomized trial. Oncotarget 2016; 6:30306-16. [PMID: 26358523 PMCID: PMC4745800 DOI: 10.18632/oncotarget.5080] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 08/21/2015] [Indexed: 01/03/2023] Open
Abstract
Trastuzumab is a remarkably effective therapy for patients with human epidermal growth factor receptor 2 (HER2) - positive breast cancer (BC). However, not all women with high levels of HER2 benefit from trastuzumab. By integrating mRNA and protein expression data from Reverse-Phase Protein Array Analysis (RPPA) in HER2-positive BC, we developed gene expression metagenes that reflect pathway activation levels. Next we assessed the ability of these metagenes to predict resistance to adjuvant trastuzumab using gene expression data from two independent datasets. 10 metagenes passed external validation (false discovery rate [fdr] < 0.05) and showed biological relevance with their pathway of origin. These metagenes were further screened for their association with trastuzumab resistance. An association with trastuzumab resistance was observed and validated only for the AnnexinA1 metagene (ANXA1). In the randomised phase III Fin-her study, tumours with low levels of the ANXA1 metagene showed a benefit from trastuzumab (multivariate: hazard ratio [HR] for distant recurrence = 0.16[95%CI 0.05–0.5]; p = 0.002; fdr = 0.03), while high expression levels of the ANXA1 metagene were associated with a lack of benefit to trastuzmab (HR = 1.29[95%CI 0.55–3.02]; p = 0.56). The association of ANXA1 with trastuzumab resistance was successfully validated in an independent series of subjects who had received trastuzumab with chemotherapy (Log Rank; p = 0.01). In conclusion, in HER2-positive BC, some proteins are associated with distinct gene expression profiles. Our findings identify the ANXA1metagene as a novel biomarker for trastuzumab resistance.
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Affiliation(s)
- Amir Sonnenblick
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Sylvain Brohée
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Debora Fumagalli
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Françoise Rothé
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Delphine Vincent
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Michael Ignatiadis
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Christine Desmedt
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Roberto Salgado
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Nicolas Sirtaine
- Pathology Dept, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Sherene Loi
- Division of Cancer Medicine and Research, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
| | - Patrick Neven
- Multidisciplinary Breast Center, KULeuven, University Hospitals, Leuven, Belgium
| | - Sibylle Loibl
- German Breast Group, Neu-Isenburg and Sana-Klinikum, Offenbach, Germany
| | - Carsten Denkert
- Institute of Pathology, Charité Hospital, Campus Mitte, German Cancer Consortium (DKTK), Berlin, Germany
| | - Heikki Joensuu
- Department of Oncology, Helsinki University Central Hospital and Helsinki University, Helsinki, Finland
| | - Martine Piccart
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
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A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research. Sci Rep 2016; 6:30159. [PMID: 27444576 PMCID: PMC4957118 DOI: 10.1038/srep30159] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 06/28/2016] [Indexed: 02/07/2023] Open
Abstract
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.
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Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens. PLoS Comput Biol 2016; 12:e1005013. [PMID: 27403523 PMCID: PMC4942116 DOI: 10.1371/journal.pcbi.1005013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 06/06/2016] [Indexed: 12/17/2022] Open
Abstract
Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. An important challenge in infectious disease research is to understand how the human immune system responds to different types of pathogenic infections. An important component of mounting proper response is the transcriptional regulatory network that specifies the context-specific gene expression program in the host cell. However, our understanding of this regulatory network and how it drives context-specific transcriptional programs is incomplete. To address this gap, we performed a network-based analysis of host response to influenza viruses that integrated high-throughput mRNA- and protein measurements and protein-protein interaction networks to identify virus and pathogenicity-specific modules and their upstream physical regulatory programs. We inferred regulatory networks for human cell line and mouse host systems, which recapitulated several known regulators and pathways of the immune response and viral life cycle. We used the networks to study time point and strain-specific subnetworks and to prioritize important regulators of host response. We predicted several novel regulators, both at the mRNA and protein levels, and experimentally verified their role in the virus life cycle based on their ability to significantly impact viral replication.
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Chasman D, Fotuhi Siahpirani A, Roy S. Network-based approaches for analysis of complex biological systems. Curr Opin Biotechnol 2016; 39:157-166. [PMID: 27115495 DOI: 10.1016/j.copbio.2016.04.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2015] [Revised: 04/04/2016] [Accepted: 04/05/2016] [Indexed: 12/22/2022]
Abstract
Cells function and respond to changes in their environment by the coordinated activity of their molecular components, including mRNAs, proteins and metabolites. At the heart of proper cellular function are molecular networks connecting these components to process extra-cellular environmental signals and drive dynamic, context-specific cellular responses. Network-based computational approaches aim to systematically integrate measurements from high-throughput experiments to gain a global understanding of cellular function under changing environmental conditions. We provide an overview of recent methodological developments toward solving two major computational problems within this field in the past two years (2013-2015): network reconstruction and network-based interpretation. Looking forward, we envision development of methods that can predict phenotypes with high accuracy as well as provide biologically plausible mechanistic hypotheses.
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Affiliation(s)
- Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, United States
| | - Alireza Fotuhi Siahpirani
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, United States; Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, United States; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, United States
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, United States; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, United States; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, United States.
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Nishizuka SS, Mills GB. New era of integrated cancer biomarker discovery using reverse-phase protein arrays. Drug Metab Pharmacokinet 2016; 31:35-45. [DOI: 10.1016/j.dmpk.2015.11.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 11/26/2015] [Accepted: 11/29/2015] [Indexed: 12/11/2022]
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Park S, Kim SJ, Yu D, Peña-Llopis S, Gao J, Park JS, Chen B, Norris J, Wang X, Chen M, Kim M, Yong J, Wardak Z, Choe K, Story M, Starr T, Cheong JH, Hwang TH. An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types. Bioinformatics 2015; 32:1643-51. [PMID: 26635139 DOI: 10.1093/bioinformatics/btv692] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2015] [Accepted: 11/09/2015] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. Precise identification and understanding of these altered pathways may provide novel insights into patient stratification, therapeutic strategies and the development of new drugs. However, a challenge remains in accurately identifying pathways altered by somatic mutations across human cancers, due to the diverse mutation spectrum. We developed an innovative approach to integrate somatic mutation data with gene networks and pathways, in order to identify pathways altered by somatic mutations across cancers. RESULTS We applied our approach to The Cancer Genome Atlas (TCGA) dataset of somatic mutations in 4790 cancer patients with 19 different types of tumors. Our analysis identified cancer-type-specific altered pathways enriched with known cancer-relevant genes and targets of currently available drugs. To investigate the clinical significance of these altered pathways, we performed consensus clustering for patient stratification using member genes in the altered pathways coupled with gene expression datasets from 4870 patients from TCGA, and multiple independent cohorts confirmed that the altered pathways could be used to stratify patients into subgroups with significantly different clinical outcomes. Of particular significance, certain patient subpopulations with poor prognosis were identified because they had specific altered pathways for which there are available targeted therapies. These findings could be used to tailor and intensify therapy in these patients, for whom current therapy is suboptimal. AVAILABILITY AND IMPLEMENTATION The code is available at: http://www.taehyunlab.org CONTACT jhcheong@yuhs.ac or taehyun.hwang@utsouthwestern.edu or taehyun.cs@gmail.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sunho Park
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Seung-Jun Kim
- Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, USA
| | - Donghyeon Yu
- Department of Statistics, Keimyung University, Daegu, South Korea
| | - Samuel Peña-Llopis
- Internal Medicine and Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jianjiong Gao
- Center for Molecular Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jin Suk Park
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Beibei Chen
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jessie Norris
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xinlei Wang
- Department of Statistical Science, Southern Methodist University, Dallas, TX, USA
| | - Min Chen
- Department of Mathematical Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Minsoo Kim
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jeongsik Yong
- Department of Biochemistry, Molecular Biology and Biophysics, Obstetrics, Gynecology & Women's Health, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Zabi Wardak
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Kevin Choe
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael Story
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Timothy Starr
- Genetics, Cell Biology, University of Minnesota Twin Cities, Minneapolis, MN, USA, Masonic Cancer Center, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Jae-Ho Cheong
- Department of Surgery, Yonsei University College of Medicine, Seoul, South Korea and Open NBI Convergence Technology Research Laboratory, Yonsei University College of Medicine, Seoul, South Korea
| | - Tae Hyun Hwang
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Affinity regression predicts the recognition code of nucleic acid-binding proteins. Nat Biotechnol 2015; 33:1242-1249. [PMID: 26571099 PMCID: PMC4871164 DOI: 10.1038/nbt.3343] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Accepted: 07/30/2015] [Indexed: 12/26/2022]
Abstract
Predicting the affinity profiles of nucleic acid-binding proteins directly from the protein sequence is a major unsolved problem. We present a statistical approach for learning the recognition code of a family of transcription factors (TFs) or RNA-binding proteins (RBPs) from high-throughput binding assays. Our method, called affinity regression, trains on protein binding microarray (PBM) or RNA compete experiments to learn an interaction model between proteins and nucleic acids, using only protein domain and probe sequences as inputs. By training on mouse homeodomain PBM profiles, our model correctly identifies residues that confer DNA-binding specificity and accurately predicts binding motifs for an independent set of divergent homeodomains. Similarly, learning from RNA compete profiles for diverse RBPs, our model can predict the binding affinities of held-out proteins and identify key RNA-binding residues. More broadly, we envision applying our method to model and predict biological interactions in any setting where there is a high-throughput ‘affinity’ readout.
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Kalimutho M, Parsons K, Mittal D, López JA, Srihari S, Khanna KK. Targeted Therapies for Triple-Negative Breast Cancer: Combating a Stubborn Disease. Trends Pharmacol Sci 2015; 36:822-846. [PMID: 26538316 DOI: 10.1016/j.tips.2015.08.009] [Citation(s) in RCA: 207] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Revised: 08/14/2015] [Accepted: 08/17/2015] [Indexed: 11/17/2022]
Abstract
Triple-negative breast cancers (TNBCs) constitute a heterogeneous subtype of breast cancers that have a poor clinical outcome. Although no approved targeted therapy is available for TNBCs, molecular-profiling efforts have revealed promising molecular targets, with several candidate compounds having now entered clinical trials for TNBC patients. However, initial results remain modest, thereby highlighting challenges potentially involving intra- and intertumoral heterogeneity and acquisition of therapy resistance. We present a comprehensive review on emerging targeted therapies for treating TNBCs, including the promising approach of immunotherapy and the prognostic value of tumor-infiltrating lymphocytes. We discuss the impact of pathway rewiring in the acquisition of drug resistance, and the prospect of employing combination therapy strategies to overcome challenges towards identifying clinically-viable targeted treatment options for TNBC.
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Affiliation(s)
- Murugan Kalimutho
- Signal Transduction Laboratory, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia.
| | - Kate Parsons
- Signal Transduction Laboratory, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia; School of Natural Sciences, Griffith University, Nathan, QLD 411, Australia
| | - Deepak Mittal
- Immunology in Cancer and Infection Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia
| | - J Alejandro López
- School of Natural Sciences, Griffith University, Nathan, QLD 411, Australia; Oncogenomics Laboratory, QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia
| | - Sriganesh Srihari
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Kum Kum Khanna
- Signal Transduction Laboratory, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia; School of Natural Sciences, Griffith University, Nathan, QLD 411, Australia.
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Dai X, Li T, Bai Z, Yang Y, Liu X, Zhan J, Shi B. Breast cancer intrinsic subtype classification, clinical use and future trends. Am J Cancer Res 2015; 5:2929-2943. [PMID: 26693050 PMCID: PMC4656721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2015] [Accepted: 08/19/2015] [Indexed: 06/05/2023] Open
Abstract
Breast cancer is composed of multiple subtypes with distinct morphologies and clinical implications. The advent of microarrays has led to a new paradigm in deciphering breast cancer heterogeneity, based on which the intrinsic subtyping system using prognostic multigene classifiers was developed. Subtypes identified using different gene panels, though overlap to a great extent, do not completely converge, and the avail of new information and perspectives has led to the emergence of novel subtypes, which complicate our understanding towards breast tumor heterogeneity. This review explores and summarizes the existing intrinsic subtypes, patient clinical features and management, commercial signature panels, as well as various information used for tumor classification. Two trends are pointed out in the end on breast cancer subtyping, i.e., either diverging to more refined groups or converging to the major subtypes. This review improves our understandings towards breast cancer intrinsic classification, current status on clinical application, and future trends.
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Affiliation(s)
- Xiaofeng Dai
- National Engineering Laboratory for Cereal Fermentation Technology, School of Biotechnology, Jiangnan UniversityWuxi 214122, Jiangsu, China
| | - Ting Li
- National Engineering Laboratory for Cereal Fermentation Technology, School of Biotechnology, Jiangnan UniversityWuxi 214122, Jiangsu, China
| | - Zhonghu Bai
- National Engineering Laboratory for Cereal Fermentation Technology, School of Biotechnology, Jiangnan UniversityWuxi 214122, Jiangsu, China
| | - Yankun Yang
- National Engineering Laboratory for Cereal Fermentation Technology, School of Biotechnology, Jiangnan UniversityWuxi 214122, Jiangsu, China
| | - Xiuxia Liu
- National Engineering Laboratory for Cereal Fermentation Technology, School of Biotechnology, Jiangnan UniversityWuxi 214122, Jiangsu, China
| | - Jinling Zhan
- National Engineering Laboratory for Cereal Fermentation Technology, School of Biotechnology, Jiangnan UniversityWuxi 214122, Jiangsu, China
| | - Bozhi Shi
- Bioengineering College, Chongqing UniversityChongqing, 404100, China
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Sonnenblick A, Brohée S, Fumagalli D, Vincent D, Venet D, Ignatiadis M, Salgado R, Van den Eynden G, Rothé F, Desmedt C, Neven P, Loibl S, Denkert C, Joensuu H, Loi S, Sirtaine N, Kellokumpu-Lehtinen PL, Piccart M, Sotiriou C. Constitutive phosphorylated STAT3-associated gene signature is predictive for trastuzumab resistance in primary HER2-positive breast cancer. BMC Med 2015; 13:177. [PMID: 26234940 PMCID: PMC4522972 DOI: 10.1186/s12916-015-0416-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 07/01/2015] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The likelihood of recurrence in patients with breast cancer who have HER2-positive tumors is relatively high, although trastuzumab is a remarkably effective drug in this setting. Signal transducer and activator of transcription 3 protein (STAT3), a transcription factor that is persistently tyrosine-705 phosphorylated (pSTAT3) in response to numerous oncogenic signaling pathways, activates downstream proliferative and anti-apoptotic pathways. We hypothesized that pSTAT3 expression in HER2-positive breast cancer will confer trastuzumab resistance. METHODS We integrated reverse phase protein array (RPPA) and gene expression data from patients with HER2-positive breast cancer treated with trastuzumab in the adjuvant setting. RESULTS We show that a pSTAT3-associated gene signature (pSTAT3-GS) is able to predict pSTAT3 status in an independent dataset (TCGA; AUC = 0.77, P = 0.02). This suggests that STAT3 induces a characteristic set of gene expression changes in HER2-positive cancers. Tumors characterized as high pSTAT3-GS were associated with trastuzumab resistance (log rank P = 0.049). These results were confirmed using data from the prospective, randomized controlled FinHer study, where the effect was especially prominent in HER2-positive estrogen receptor (ER)-negative tumors (interaction test P = 0.02). Of interest, constitutively activated pSTAT3 tumors were associated with loss of PTEN, elevated IL6, and stromal reactivation. CONCLUSIONS This study provides compelling evidence for a link between pSTAT3 and trastuzumab resistance in HER2-positive primary breast cancers. Our results suggest that it may be valuable to add agents targeting the STAT3 pathway to trastuzumab for treatment of HER2-positive breast cancer.
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Affiliation(s)
- Amir Sonnenblick
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - Sylvain Brohée
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - Debora Fumagalli
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - Delphine Vincent
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - David Venet
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - Michail Ignatiadis
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
- Medical Oncology Unit, Institut Jules Bordet, Université Libre de Bruxelles, Bld de Waterloo, 1000, Brussels, Belgium
| | - Roberto Salgado
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - Gert Van den Eynden
- Molecular Immunology Lab, Institut Jules Bordet, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - Françoise Rothé
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - Christine Desmedt
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium
| | - Patrick Neven
- Multidisciplinary Breast Center, KULeuven, University Hospitals, Leuven, Belgium
| | - Sibylle Loibl
- German Breast Group, Neu-Isenburg and Sana-Klinikum, Offenbach, Germany
| | - Carsten Denkert
- Institute of Pathology, Charité Hospital Campus Mitte, and German Cancer Consortium (DKTK), Berlin, Germany
| | - Heikki Joensuu
- Department of Oncology, Helsinki University, Hospital and Helsinki University, Helsinki, Finland
| | - Sherene Loi
- Division of Cancer Medicine and Research, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Nicolas Sirtaine
- Pathology Department, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Martine Piccart
- Medical Oncology Unit, Institut Jules Bordet, Université Libre de Bruxelles, Bld de Waterloo, 1000, Brussels, Belgium
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory J-C Heuson, Institut Jules Bordet, Bld de Waterloo, Université Libre de Bruxelles, 1000, Brussels, Belgium.
- Medical Oncology Unit, Institut Jules Bordet, Université Libre de Bruxelles, Bld de Waterloo, 1000, Brussels, Belgium.
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Randhawa V, Acharya V. Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma. BMC Med Genomics 2015; 8:39. [PMID: 26179909 PMCID: PMC4502639 DOI: 10.1186/s12920-015-0114-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 07/06/2015] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity but, OSCC can be difficult to detect at its earliest stage due to its molecular complexity and clinical behavior. Therefore, identification of key gene signatures at an early stage will be highly helpful. METHODS The aim of this study was to identify key genes associated with progression of OSCC stages. Gene expression profiles were classified into cancer stage-related modules, i.e., groups of genes that are significantly related to a clinical stage. For prioritizing the candidate genes, analysis was further restricted to genes with high connectivity and a significant association with a stage. To assess predictive power of these genes, a classification model was also developed and tested by 5-fold cross validation and on an independent dataset. RESULTS The identified genes were enriched for significant processes and functional pathways, and various genes were found to be directly implicated in OSCC. Forward and stepwise, multivariate logistic regression analyses identified 13 key genes whose expression discriminated early- and late-stage OSCC with predictive accuracy (area under curve; AUC) of ~0.81 in a 5-fold cross-validation strategy. CONCLUSIONS The proposed network-driven integrative analytical approach can identify multiple genes significantly related to an OSCC stage; the classification model that is developed with these genes may help to distinguish cancer stages. The proposed genes and model hold promise for monitoring of OSCC stage progression, and our findings may facilitate cancer detection at an earlier stage, resulting in improved treatment outcomes.
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Affiliation(s)
- Vinay Randhawa
- Functional Genomics and Complex Systems Laboratory, Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Council of Scientific and Industrial Research, Palampur, Himachal Pradesh, India. .,Academy of Scientific and Innovative Research (AcSIR), New Delhi, India.
| | - Vishal Acharya
- Functional Genomics and Complex Systems Laboratory, Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Council of Scientific and Industrial Research, Palampur, Himachal Pradesh, India. .,Academy of Scientific and Innovative Research (AcSIR), New Delhi, India.
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