Evaluation of reverse phase protein array (RPPA)-based pathway-activation profiling in 84 non-small cell lung cancer (NSCLC) cell lines as platform for cancer proteomics and biomarker discovery.

The reverse phase protein array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant proteins and phosphoproteins in 84 non-small cell lung cancer (NSCLC) cell lines and by monitoring the activation state of selected receptor tyrosine kinases, PI3K/AKT and MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT- and RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, and PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA were identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

[1]  Erika Avila-Tang,et al.  Lung Cancer Occurrence in Never-Smokers: An Analysis of 13 Cohorts and 22 Cancer Registry Studies , 2008, PLoS medicine.

[2]  E. Petricoin,et al.  Clinical Proteomics: From Biomarker Discovery and Cell Signaling Profiles to Individualized Personal Therapy , 2005, Bioscience reports.

[3]  M. Selbach,et al.  Global quantification of mammalian gene expression control , 2011, Nature.

[4]  Emmanuel Barillot,et al.  NormaCurve: A SuperCurve-Based Method That Simultaneously Quantifies and Normalizes Reverse Phase Protein Array Data , 2012, PloS one.

[5]  J. Shih,et al.  Predictive Factors of Gefitinib Antitumor Activity in East Asian Advanced Non-small Cell Lung Cancer Patients , 2006, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[6]  Heiko A. Mannsperger,et al.  RPPanalyzer: Analysis of reverse-phase protein array data , 2010, Bioinform..

[7]  William Pao,et al.  Identifying genotype-dependent efficacy of single and combined PI3K- and MAPK-pathway inhibition in cancer , 2009, Proceedings of the National Academy of Sciences.

[8]  Stefan Wiemann,et al.  Infrared‐based protein detection arrays for quantitative proteomics , 2007, Proteomics.

[9]  Stefan Wiemann,et al.  RNAi-based validation of antibodies for reverse phase protein arrays , 2010, Proteome Science.

[10]  Julian Downward,et al.  RAS Interaction with PI3K: More Than Just Another Effector Pathway. , 2011, Genes & cancer.

[11]  G. Mills,et al.  Reverse phase protein array: validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells , 2006, Molecular Cancer Therapeutics.

[12]  F. Shepherd,et al.  Use of the Epidermal Growth Factor Receptor Inhibitors Gefitinib and Erlotinib in the Treatment of Non-small Cell Lung Cancer: A Systematic Review , 2006, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[13]  Li Zhang,et al.  Serial dilution curve: a new method for analysis of reverse phase protein array data , 2009, Bioinform..

[14]  Yiling Lu,et al.  Exploiting the PI3K/AKT Pathway for Cancer Drug Discovery , 2005, Nature Reviews Drug Discovery.

[15]  D. Haber,et al.  Molecular predictors of response to epidermal growth factor receptor antagonists in non-small-cell lung cancer. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  Elisa Rossi,et al.  Epidermal growth factor receptor gene and protein and gefitinib sensitivity in non-small-cell lung cancer. , 2005, Journal of the National Cancer Institute.

[17]  Douglas Lauffenburger,et al.  HER2-mediated effects on EGFR endosomal sorting: analysis of biophysical mechanisms. , 2003, Biophysical journal.

[18]  Laura Tolosi,et al.  Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions. , 2009, The Journal of clinical investigation.

[19]  G. Scagliotti,et al.  The role of the insulin-like growth factor signaling pathway in non-small cell lung cancer and other solid tumors. , 2012, Cancer treatment reviews.

[20]  M. Meyerson,et al.  Genomic Approaches to Lung Cancer , 2006, Clinical Cancer Research.

[21]  P. Dennis,et al.  Akt/protein kinase B is constitutively active in non-small cell lung cancer cells and promotes cellular survival and resistance to chemotherapy and radiation. , 2001, Cancer research.

[22]  J. Demoulin,et al.  Tyrosine kinase gene fusions in cancer: translating mechanisms into targeted therapies , 2012, Journal of cellular and molecular medicine.

[23]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[24]  Achim Tresch,et al.  Quantitative protein microarrays for time‐resolved measurements of protein phosphorylation , 2008, Proteomics.

[25]  Shuhang Wang,et al.  Phosphorylated EGFR expression may predict outcome of EGFR-TKIs therapy for the advanced NSCLC patients with wild-type EGFR , 2012, Journal of experimental & clinical cancer research : CR.

[26]  Feimeng Zheng,et al.  Aurora kinase inhibitory VX-680 increases Bax/Bcl-2 ratio and induces apoptosis in Aurora-A-high acute myeloid leukemia. , 2008, Blood.

[27]  M. Chan-yeung,et al.  Lung cancer epidemiology and risk factors in Asia and Africa. , 2004, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[28]  Jun Yu,et al.  Loss of beta-catenin expression in metastatic gastric cancer. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[29]  K. Coombes,et al.  A Technical Assessment of the Utility of Reverse Phase Protein Arrays for the Study of the Functional Proteome in Non-microdissected Human Breast Cancers , 2010, Clinical Proteomics.

[30]  S. Gabriel,et al.  EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy , 2004, Science.

[31]  Patricia L. Harris,et al.  Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. , 2004, The New England journal of medicine.

[32]  S. Nishizuka,et al.  Reverse-phase protein lysate microarrays for cell signaling analysis , 2008, Nature Protocols.

[33]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[34]  M. Oren,et al.  Deregulated β‐catenin induces a p53‐ and ARF‐dependent growth arrest and cooperates with Ras in transformation , 2001, The EMBO journal.

[35]  S. Lippman,et al.  Lung cancer. , 2008, The New England journal of medicine.

[36]  Ester Piek,et al.  Role of Transforming Growth Factor-β Signaling in Cancer , 2000 .

[37]  P. Jänne,et al.  Epidermal growth factor receptor mutations in non-small-cell lung cancer: implications for treatment and tumor biology. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[38]  F. Hirsch,et al.  Epidermal growth factor receptor inhibitors in lung cancer: smaller or larger molecules, selected or unselected populations? , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  Roman K. Thomas,et al.  Systematically linking drug susceptibility to cancer genome aberrations , 2009, Cell cycle.

[40]  Holger Fröhlich,et al.  Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance , 2009, BMC Systems Biology.

[41]  William C Reinhold,et al.  Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[42]  R. Salgia,et al.  Molecular abnormalities in lung cancer. , 1998, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[43]  B. Park,et al.  Mutation of the PIK3CA oncogene in human cancers , 2006, British Journal of Cancer.

[44]  Quentin Liu,et al.  Inhibition of mitotic kinase Aurora suppresses Akt-1 activation and induces apoptotic cell death in all-trans retinoid acid-resistant acute promyelocytic leukemia cells , 2011, Journal of Translational Medicine.

[45]  Kohei Miyazono,et al.  TGF-β signalling from cell membrane to nucleus through SMAD proteins , 1997, Nature.

[46]  E. Petricoin,et al.  Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front , 2001, Oncogene.

[47]  Zhi Hu,et al.  An integrative genomic and proteomic analysis of PIK3CA, PTEN, and AKT mutations in breast cancer. , 2008, Cancer research.

[48]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[49]  H. Woo,et al.  Integrative Analysis of Proteomic Signatures, Mutations, and Drug Responsiveness in the NCI 60 Cancer Cell Line Set , 2010, Molecular Cancer Therapeutics.

[50]  Brian H. Dunford-Shore,et al.  Somatic mutations affect key pathways in lung adenocarcinoma , 2008, Nature.

[51]  Emanuel F Petricoin,et al.  Signal pathway profiling of ovarian cancer from human tissue specimens using reverse‐phase protein microarrays , 2003, Proteomics.

[52]  M. A. Bopp,et al.  Zeptosens' protein microarrays: A novel high performance microarray platform for low abundance protein analysis , 2002, Proteomics.