Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer

BMC Systems Biology, Dec 2013

Background Oncogenic mechanisms in small-cell lung cancer remain poorly understood leaving this tumor with the worst prognosis among all lung cancers. Unlike other cancer types, sequencing genomic approaches have been of limited success in small-cell lung cancer, i.e., no mutated oncogenes with potential driver characteristics have emerged, as it is the case for activating mutations of epidermal growth factor receptor in non-small-cell lung cancer. Differential gene expression analysis has also produced SCLC signatures with limited application, since they are generally not robust across datasets. Nonetheless, additional genomic approaches are warranted, due to the increasing availability of suitable small-cell lung cancer datasets. Gene co-expression network approaches are a recent and promising avenue, since they have been successful in identifying gene modules that drive phenotypic traits in several biological systems, including other cancer types. Results We derived an SCLC-specific classifier from weighted gene co-expression network analysis (WGCNA) of a lung cancer dataset. The classifier, termed SCLC-specific hub network (SSHN), robustly separates SCLC from other lung cancer types across multiple datasets and multiple platforms, including RNA-seq and shotgun proteomics. The classifier was also conserved in SCLC cell lines. SSHN is enriched for co-expressed signaling network hubs strongly associated with the SCLC phenotype. Twenty of these hubs are actionable kinases with oncogenic potential, among which spleen tyrosine kinase (SYK) exhibits one of the highest overall statistical associations to SCLC. In patient tissue microarrays and cell lines, SCLC can be separated into SYK-positive and -negative. SYK siRNA decreases proliferation rate and increases cell death of SYK-positive SCLC cell lines, suggesting a role for SYK as an oncogenic driver in a subset of SCLC. Conclusions SCLC treatment has thus far been limited to chemotherapy and radiation. Our WGCNA analysis identifies SYK both as a candidate biomarker to stratify SCLC patients and as a potential therapeutic target. In summary, WGCNA represents an alternative strategy to large scale sequencing for the identification of potential oncogenic drivers, based on a systems view of signaling networks. This strategy is especially useful in cancer types where no actionable mutations have emerged.

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Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer

Akshata R Udyavar 0 9 Megan D Hoeksema 4 Jonathan E Clark 2 Yong Zou 4 Zuojian Tang 3 Zhiguo Li 3 Ming Li 1 Heidi Chen 1 Alexander Statnikov 3 10 Yu Shyr 1 5 Daniel C Liebler 2 7 John Field 11 Rosana Eisenberg 6 Lourdes Estrada 0 9 Pierre P Massion 0 4 8 Vito Quaranta 0 9 0 Department of Cancer Biology, Vanderbilt University , 2220 Pierce Avenue, Nashville, TN 37232 , USA 1 Department of Biostatistics, Vanderbilt University , 2220 Pierce Avenue, Nashville, TN 37232 , USA 2 Department of Biochemistry, Vanderbilt University , 2220 Pierce Avenue, Nashville, TN 37232 , USA 3 Center for Health Informatics and Bioinformatics, New York University Langone Medical Center , New York, NY 10016 , USA 4 Allergy/Pulmonary & Critical Care Medicine, Vanderbilt University , 2220 Pierce Avenue, Nashville, TN 37232 , USA 5 Divisions of Cancer Biostatistics, Vanderbilt University , 2220 Pierce Avenue, Nashville, TN 37232 , USA 6 Department of Pathology, Vanderbilt University , 2220 Pierce Avenue, Nashville, TN 37232 , USA 7 Department of Pharmacology, Vanderbilt University , 2220 Pierce Avenue, Nashville, TN 37232 , USA 8 Veterans Affairs, Tennessee Valley Healthcare System , Nashville Campus, TN 37232 , USA 9 Center for Cancer Systems Biology, Vanderbilt University , 2220 Pierce Avenue, Nashville, TN 37232 , USA 10 Department of Medicine, New York University School of Medicine , New York, NY 10016 , USA 11 Department of Molecular and Clinical Cancer Medicine, University of Liverpool , Liverpool, Merseyside L69 3BX , UK Background: Oncogenic mechanisms in small-cell lung cancer remain poorly understood leaving this tumor with the worst prognosis among all lung cancers. Unlike other cancer types, sequencing genomic approaches have been of limited success in small-cell lung cancer, i.e., no mutated oncogenes with potential driver characteristics have emerged, as it is the case for activating mutations of epidermal growth factor receptor in non-small-cell lung cancer. Differential gene expression analysis has also produced SCLC signatures with limited application, since they are generally not robust across datasets. Nonetheless, additional genomic approaches are warranted, due to the increasing availability of suitable small-cell lung cancer datasets. Gene co-expression network approaches are a recent and promising avenue, since they have been successful in identifying gene modules that drive phenotypic traits in several biological systems, including other cancer types. Results: We derived an SCLC-specific classifier from weighted gene co-expression network analysis (WGCNA) of a lung cancer dataset. The classifier, termed SCLC-specific hub network (SSHN), robustly separates SCLC from other lung cancer types across multiple datasets and multiple platforms, including RNA-seq and shotgun proteomics. The classifier was also conserved in SCLC cell lines. SSHN is enriched for co-expressed signaling network hubs strongly associated with the SCLC phenotype. Twenty of these hubs are actionable kinases with oncogenic potential, among which spleen tyrosine kinase (SYK) exhibits one of the highest overall statistical associations to SCLC. In patient tissue microarrays and cell lines, SCLC can be separated into SYK-positive and -negative. SYK siRNA decreases proliferation rate and increases cell death of SYK-positive SCLC cell lines, suggesting a role for SYK as an oncogenic driver in a subset of SCLC. Conclusions: SCLC treatment has thus far been limited to chemotherapy and radiation. Our WGCNA analysis identifies SYK both as a candidate biomarker to stratify SCLC patients and as a potential therapeutic target. In summary, WGCNA represents an alternative strategy to large scale sequencing for the identification of potential oncogenic drivers, based on a systems view of signaling networks. This strategy is especially useful in cancer types where no actionable mutations have emerged. - From The International Conference on Intelligent Biology and Medicine (ICIBM 2013) Nashville, TN, USA. 11-13 August 2013 Background Small-cell lung cancer (SCLC) represent up to 15 % of lung cancers and pose a major challenge as we are unable to diagnose it early, its most aggressive clinical behavior and the lack of lasting benefit from therapy. Patients presenting with this neuroendocrine tumor of the lung have a dismal 5% 5-year survival rate. Although SCLC is highly sensitive to chemotherapy and radiation, it invariably recurs with fatal widespread metastasis [1]. In contrast to non-small cell lung cancer (NSCLC), to date no specific genetic biomarkers or molecular subtypes have been identified in SCLC [2]. Gene expression profiling has had limited success in SCLC stratification for the purpose of personalized treatment. Although recent advances in genomic analysis of SCLC have identified potential driver mutations in SCLC [3-5], there remains an unmet need for approaches that can stratify SCLC patients and/or uncover viable molecular targets in SCLC. (...truncated)


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Akshata R Udyavar, Megan D Hoeksema, Jonathan E Clark, Yong Zou, Zuojian Tang, Zhiguo Li, Ming Li, Heidi Chen, Alexander Statnikov, Yu Shyr, Daniel C Liebler, John Field, Rosana Eisenberg, Lourdes Estrada, Pierre P Massion, Vito Quaranta. Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer, BMC Systems Biology, 2013, pp. S1, 7, DOI: 10.1186/1752-0509-7-S5-S1