TY - JOUR
T1 - Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer
AU - Udyavar, Akshata R.
AU - Hoeksema, Megan D.
AU - Clark, Jonathan E.
AU - Zou, Yong
AU - Tang, Zuojian
AU - Li, Zhiguo
AU - Li, Ming
AU - Chen, Heidi
AU - Statnikov, Alexander
AU - Shyr, Yu
AU - Liebler, Daniel C.
AU - Field, John
AU - Eisenberg, Rosana
AU - Estrada, Lourdes
AU - Massion, Pierre P.
AU - Quaranta, Vito
N1 - Funding Information:
We thank Dr. Steve Horvath (UCLA), Dr. Darren Tyson and Shawn Garbett for valuable input on statistical data analysis and visualization. We also thank the Vanderbilt Epithelial Biology Center and Vanderbilt Translational Pathology Shared Resource for imaging and staining of the patient TMA respectively, and the Vanderbilt Genome Science Resources for generating the RNAseq dataset. AS: Grant 1UL1 RR029893 from the National Center for Research Resources and 1 R01 LM011179-01A1 from the National Library of Medicine, National Institutes of Health. AU, VQ, LE: Grant 5 U54 CA113007-09 from the National Cancer Institute, Integrative Cancer Biology Program (NCI ICBP). PPM, AS: Grant 1I01CX000242 from the Department of Veterans Affairs and CA90949 from the NCI SPORE program.
PY - 2013/12/9
Y1 - 2013/12/9
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=84889655850&partnerID=8YFLogxK
U2 - 10.1186/1752-0509-7-S5-S1
DO - 10.1186/1752-0509-7-S5-S1
M3 - Article
C2 - 24564859
AN - SCOPUS:84889655850
VL - 7
JO - BMC Systems Biology
JF - BMC Systems Biology
SN - 1752-0509
IS - SUPPL 5
M1 - S1
ER -