Advertisement

TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data*

  • Author Footnotes
    ** The authors contributed equally to this work.
    Mei-Ju May Chen
    Footnotes
    ** The authors contributed equally to this work.
    Affiliations
    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
    Search for articles by this author
  • Author Footnotes
    ** The authors contributed equally to this work.
    Jun Li
    Footnotes
    ** The authors contributed equally to this work.
    Affiliations
    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
    Search for articles by this author
  • Yumeng Wang
    Affiliations
    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
    Search for articles by this author
  • Rehan Akbani
    Affiliations
    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
    Search for articles by this author
  • Yiling Lu
    Affiliations
    Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas
    Search for articles by this author
  • Gordon B. Mills
    Affiliations
    Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas

    Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
    Search for articles by this author
  • Han Liang
    Correspondence
    To whom correspondence should be addressed:The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, Tel:713-745-9815; Fax:713-563-4242;
    Affiliations
    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas

    Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas
    Search for articles by this author
  • Author Footnotes
    * This study was supported by the National Institutes of Health (CA098258, CA217842, and CA210950 to G.B.M.; CA175486 to H.L.; CA209851 to H.L. and G.B.M.; and CCSG grant CA016672), the Lorraine Dell Program in Bioinformatics for Personalization of Cancer Medicine, and the Adelson Medical Research Foundation (to G.B.M.). G.B.M. has sponsored research support from AstraZeneca, Critical Outcomes Technology, Karus, Illumina, Immunomet, Nanostring, Tarveda, and Immunomet and is on the Scientific Advisory Board for AstraZeneca, Critical Outcomes Technology, ImmunoMet, Ionis, Nuevolution, Symphogen, and Tarveda. H.L. is a shareholder and scientific advisor of Precision Scientific Ltd., (Beijing, China) and Eagle Nebula Inc.
    ** The authors contributed equally to this work.
Open AccessPublished:June 14, 2019DOI:https://doi.org/10.1074/mcp.RA118.001260
      Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called “TCGA Pan-Cancer Analysis,” which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.

      Graphical Abstract

      Functional proteomics is a powerful approach to characterizing cell signaling pathways and understanding their phenotypic effects on cancer development. Reverse-phase protein arrays (RPPAs)
      The abbreviations used are: RPPA, reverse-phase protein arrays; TCGA, The Cancer Genome Atlas; TCPA, The Cancer Proteome Atlas; FDR, false discovery rate; SCNAs, somatic copy-number alterations; SMGs, significantly mutated genes.
      1The abbreviations used are: RPPA, reverse-phase protein arrays; TCGA, The Cancer Genome Atlas; TCPA, The Cancer Proteome Atlas; FDR, false discovery rate; SCNAs, somatic copy-number alterations; SMGs, significantly mutated genes.
      represent a cutting-edge proteomics technology that can quantitatively assess a large number of protein markers in thousands of samples in a cost-effective, sensitive, and high-throughput manner (
      • Sheehan K.M.
      • Calvert V.S.
      • Kay E.W.
      • Lu Y.
      • Fishman D.
      • Espina V.
      • Aquino J.
      • Speer R.
      • Araujo R.
      • Mills G.B.
      • Liotta L.A.
      • Petricoin 3rd, E.F.
      • Wulfkuhle J.D.
      Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma.
      ,
      • Spurrier B.
      • Ramalingam S.
      • Nishizuka S.
      Reverse-phase protein lysate microarrays for cell signaling analysis.
      ,
      • Lu Y.
      • Ling S.
      • Hegde A.M.
      • Byers L.A.
      • Coombes K.
      • Mills G.B.
      • Akbani R.
      Using reverse-phase protein arrays as pharmacodynamic assays for functional proteomics, biomarker discovery, and drug development in cancer.
      ). Using the RPPA platform, we have characterized ∼8,000 patient samples across 32 cancer types through The Cancer Genome Atlas (TCGA) project and >650 independent cancer cell lines of 19 cell lineages (
      • Li J.
      • Lu Y.
      • Akbani R.
      • Ju Z.
      • Roebuck P.L.
      • Liu W.
      • Yang J.Y.
      • Broom B.M.
      • Verhaak R.G.
      • Kane D.W.
      • Wakefield C.
      • Weinstein J.N.
      • Mills G.B.
      • Liang H.
      TCPA: A resource for cancer functional proteomics data.
      ,
      • Li J.
      • Zhao W.
      • Akbani R.
      • Liu W.
      • Ju Z.
      • Ling S.
      • Vellano C.P.
      • Roebuck P.
      • Yu Q.
      • Eterovic A.K.
      • Byers L.A.
      • Davies M.A.
      • Deng W.
      • Gopal Y.N.
      • Chen G.
      • von Euw E.M.
      • Slamon D.
      • Conklin D.
      • Heymach J.V.
      • Gazdar A.F.
      • Minna J.D.
      • Myers J.N.
      • Lu Y.
      • Mills G.B.
      • Liang H.
      Characterization of human cancer cell lines by reverse-phase protein arrays.
      ,
      • Hoadley K.A.
      • Yau C.
      • Hinoue T.
      • Wolf D.M.
      • Lazar A.J.
      • Drill E.
      • Shen R.
      • Taylor A.M.
      • Cherniack A.D.
      • Thorsson V.
      • Akbani R.
      • Bowlby R.
      • Wong C.K.
      • Wiznerowicz M.
      • Sanchez-Vega F.
      • Robertson A.G.
      • Schneider B.G.
      • Lawrence M.S.
      • Noushmehr H.
      • Malta T.M.
      • Cancer Genome Atlas N.
      • Stuart J.M.
      • Benz C.C.
      • Laird P.W.
      Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer.
      ). To better utilize the RPPA data and serve a broad biomedical research community, we developed an open-access bioinformatics resource, The Cancer Proteome Atlas (TCPA). This web-based platform not only releases the most updated data that are generated by our RPPA platform but also provides a user-friendly interface allowing users to analyze and visualize RPPA data in a rich context (
      • Li J.
      • Akbani R.
      • Zhao W.
      • Lu Y.
      • Weinstein J.N.
      • Mills G.B.
      • Liang H.
      Explore, visualize, and analyze functional cancer proteomic data using the Cancer Proteome Atlas.
      ), which has substantially reduced the computational barriers to analyzing complex RPPA data in large-scale sample sets.
      However, analytic modules in the previous TCPA versions have focused on the protein-based analyses in individual cancer types only and do not provide integrative analyses of RPPA data with other types of molecular data. Using those modules, it is difficult to explore the similarities and differences among cancer types, which limits the full potential of TCPA. To maximize its utility, we have updated TCPA by adding a newly developed module called “TCGA Pan-Cancer Analysis.” This module provides comprehensive protein-centric analyses that integrate association studies between protein data and other types of molecular and clinical data from TCGA. These data include somatic mutations, somatic copy-number alterations (SCNAs), DNA methylation, mRNA expression and miRNA expression, as well as patient survival, tumor subtype, and disease stage. With the new module, users can easily identify protein markers that show interesting patterns across cancer types. Overall, the current TCPA (v3.0) represents a comprehensive, cutting-edge, protein-centric pan-cancer analytic platform that is freely available at http://tcpaportal.org.

      DISCUSSION

      Pan-cancer analyses using multi-omic TCGA data have demonstrated tremendous potentials to identify biologically and clinically meaningful patterns. Here we present TCPA v3.0 as a user-friendly, interactive platform to explore and analyze TCGA pan-cancer RPPA-based protein expression data. The new pan-cancer analytic module provides a unique opportunity for biomedical researchers to test their protein-driven multi-omic hypotheses across a broad range of cancer types. Based on the analytic results obtained from this new module, we have identified many molecular and clinical features that show significant associations with protein markers in diverse cancer contexts. These findings further demonstrate the utility of the pan-cancer analytic module by helping users confirm known mechanisms, reveal novel biological insights, and test/refine specific hypotheses. We recognize one major limitation that the current pan-cancer RPPA data only cover ∼260 protein markers, which limits the scope of using functional proteomics to elucidate cancer-related mechanism. For example, when searching for cis-regulators, only a small number of known miRNA targets were identified. We are now in the process of expanding the list to 500 proteins covering all major cancer pathways. We expect this new version of TCPA to be a valuable bioinformatic resource for the cancer research community.

      Data Availability

      All the raw data are available at TCGA Pan-Cancer Atlas website (https://gdc.cancer.gov/about-data/publications/pancanatlas). The data and results are also available at TCPA website (http://tcpaportal.org).

      Acknowledgments

      We gratefully acknowledge contributions from the TCGA Research Network.

      REFERENCES

        • Sheehan K.M.
        • Calvert V.S.
        • Kay E.W.
        • Lu Y.
        • Fishman D.
        • Espina V.
        • Aquino J.
        • Speer R.
        • Araujo R.
        • Mills G.B.
        • Liotta L.A.
        • Petricoin 3rd, E.F.
        • Wulfkuhle J.D.
        Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma.
        Mol. Cell. Proteomics. 2005; 4: 346-355
        • Spurrier B.
        • Ramalingam S.
        • Nishizuka S.
        Reverse-phase protein lysate microarrays for cell signaling analysis.
        Nat. Protoc. 2008; 3: 1796-1808
        • Lu Y.
        • Ling S.
        • Hegde A.M.
        • Byers L.A.
        • Coombes K.
        • Mills G.B.
        • Akbani R.
        Using reverse-phase protein arrays as pharmacodynamic assays for functional proteomics, biomarker discovery, and drug development in cancer.
        Semin. Oncol. 2016; 43: 476-483
        • Li J.
        • Lu Y.
        • Akbani R.
        • Ju Z.
        • Roebuck P.L.
        • Liu W.
        • Yang J.Y.
        • Broom B.M.
        • Verhaak R.G.
        • Kane D.W.
        • Wakefield C.
        • Weinstein J.N.
        • Mills G.B.
        • Liang H.
        TCPA: A resource for cancer functional proteomics data.
        Nat. Methods. 2013; 10: 1046-1047
        • Li J.
        • Zhao W.
        • Akbani R.
        • Liu W.
        • Ju Z.
        • Ling S.
        • Vellano C.P.
        • Roebuck P.
        • Yu Q.
        • Eterovic A.K.
        • Byers L.A.
        • Davies M.A.
        • Deng W.
        • Gopal Y.N.
        • Chen G.
        • von Euw E.M.
        • Slamon D.
        • Conklin D.
        • Heymach J.V.
        • Gazdar A.F.
        • Minna J.D.
        • Myers J.N.
        • Lu Y.
        • Mills G.B.
        • Liang H.
        Characterization of human cancer cell lines by reverse-phase protein arrays.
        Cancer Cell. 2017; 31: 225-239
        • Hoadley K.A.
        • Yau C.
        • Hinoue T.
        • Wolf D.M.
        • Lazar A.J.
        • Drill E.
        • Shen R.
        • Taylor A.M.
        • Cherniack A.D.
        • Thorsson V.
        • Akbani R.
        • Bowlby R.
        • Wong C.K.
        • Wiznerowicz M.
        • Sanchez-Vega F.
        • Robertson A.G.
        • Schneider B.G.
        • Lawrence M.S.
        • Noushmehr H.
        • Malta T.M.
        • Cancer Genome Atlas N.
        • Stuart J.M.
        • Benz C.C.
        • Laird P.W.
        Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer.
        Cell. 2018; 173: 291-304.e296
        • Li J.
        • Akbani R.
        • Zhao W.
        • Lu Y.
        • Weinstein J.N.
        • Mills G.B.
        • Liang H.
        Explore, visualize, and analyze functional cancer proteomic data using the Cancer Proteome Atlas.
        Cancer Res. 2017; 77: e51-e54
        • Tibes R.
        • Qiu Y.
        • Lu Y.
        • Hennessy B.
        • Andreeff M.
        • Mills G.B.
        • Kornblau S.M.
        Reverse phase protein array: Validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells.
        Mol. Cancer Ther. 2006; 5: 2512-2521
        • Hennessy B.T.
        • Lu Y.
        • Gonzalez-Angulo A.M.
        • Carey M.S.
        • Myhre S.
        • Ju Z.
        • Davies M.A.
        • Liu W.
        • Coombes K.
        • Meric-Bernstam F.
        • Bedrosian I.
        • McGahren M.
        • Agarwal R.
        • Zhang F.
        • Overgaard J.
        • Alsner J.
        • Neve R.M.
        • Kuo W.L.
        • Gray J.W.
        • Borresen-Dale A.L.
        • Mills G.B.
        A technical assessment of the utility of reverse phase protein arrays for the study of the functional proteome in non-microdissected human breast cancers.
        Clin. Proteomics. 2010; 6: 129-151
        • Akbani R.
        • Ng P.K.
        • Werner H.M.
        • Shahmoradgoli M.
        • Zhang F.
        • Ju Z.
        • Liu W.
        • Yang J.Y.
        • Yoshihara K.
        • Li J.
        • Ling S.
        • Seviour E.G.
        • Ram P.T.
        • Minna J.D.
        • Diao L.
        • Tong P.
        • Heymach J.V.
        • Hill S.M.
        • Dondelinger F.
        • Städler N.
        • Byers L.A.
        • Meric-Bernstam F.
        • Weinstein J.N.
        • Broom B.M.
        • Verhaak R.G.
        • Liang H.
        • Mukherjee S.
        • Lu Y.
        • Mills G.B.
        A pan-cancer proteomic perspective on The Cancer Genome Atlas.
        Nat. Commun. 2014; 5: 3887
        • Ju Z.
        • Liu W.
        • Roebuck P.L.
        • Siwak D.R.
        • Zhang N.
        • Lu Y.
        • Davies M.A.
        • Akbani R.
        • Weinstein J.N.
        • Mills G.B.
        • Coombes K.R.
        Development of a robust classifier for quality control of reverse-phase protein arrays.
        Bioinformatics. 2015; 31: 912-918
        • Li J.
        • Han L.
        • Roebuck P.
        • Diao L.
        • Liu L.
        • Yuan Y.
        • Weinstein J.N.
        • Liang H.
        TANRIC: An interactive open platform to explore the function of lncRNAs in cancer.
        Cancer Res. 2015; 75: 3728-3737
        • Wang Y.
        • Xu X.
        • Maglic D.
        • Dill M.T.
        • Mojumdar K.
        • Ng P.K.
        • Jeong K.J.
        • Tsang Y.H.
        • Moreno D.
        • Bhavana V.H.
        • Peng X.
        • Ge Z.
        • Chen H.
        • Li J.
        • Chen Z.
        • Zhang H.
        • Han L.
        • Du D.
        • Creighton C.J.
        • Mills G.B.
        • Cancer Genome Atlas Research Network
        • Camargo F.
        • Liang H.
        Comprehensive molecular characterization of the Hippo signaling pathway in cancer.
        Cell Rep. 2018; 25: 1304-1317.e5
        • Bailey M.H.
        • Tokheim C.
        • Porta-Pardo E.
        • Sengupta S.
        • Bertrand D.
        • Weerasinghe A.
        • Colaprico A.
        • Wendl M.C.
        • Kim J.
        • Reardon B.
        • Kwok-Shing Ng P.
        • Jeong K.J.
        • Cao S.
        • Wang Z.
        • Gao J.
        • Gao Q.
        • Wang F.
        • Liu E.M.
        • Mularoni L.
        • Rubio-Perez C.
        • Nagarajan N.
        • Cortés-Ciriano I.
        • Zhou D.C.
        • Liang W.W.
        • Hess J.M.
        • Yellapantula V.D.
        • Tamborero D.
        • Gonzalez-Perez A.
        • Suphavilai C.
        • Ko J.Y.
        • Khurana E.
        • Park P.J.
        • Van Allen E.M.
        • Liang H.
        • MC3 Working Group, Cancer Genome Atlas Research Network
        • Lawrence M.S.
        • Godzik A.
        • Lopez-Bigas N.
        • Stuart J.
        • Wheeler D.
        • Getz G.
        • Chen K.
        • Lazar A.J.
        • Mills G.B.
        • Karchin R.
        • Ding L.
        Comprehensive characterization of cancer driver genes and mutations.
        Cell. 2018; 174: 1034-1035
        • Agarwal V.
        • Bell G.W.
        • Nam J.W.
        • Bartel D.P.
        Predicting effective microRNA target sites in mammalian mRNAs.
        Elife. 2015; 4: e05005
        • Yang C.
        • Asthagiri A.R.
        • Iyer R.R.
        • Lu J.
        • Xu D.S.
        • Ksendzovsky A.
        • Brady R.O.
        • Zhuang Z.
        • Lonser R.R.
        Missense mutations in the NF2 gene result in the quantitative loss of merlin protein and minimally affect protein intrinsic function.
        Proc. Natl. Acad. Sci. U.S.A. 2011; 108: 4980-4985
        • Zhou L.
        • Li Z.W.
        • Pan X.
        • Lai Y.
        • Quan J.
        • Zhao L.
        • Xu J.
        • Xu W.
        • Guan X.
        • Li H.
        • Yang S.
        • Gui Y.
        • Lai Y.
        Identification of miR-18a-5p as an oncogene and prognostic biomarker in RCC.
        Am. J. Transl. Res. 2018; 10: 1874-1886
        • Liang C.
        • Zhang X.
        • Wang H.M.
        • Liu X.M.
        • Zhang X.J.
        • Zheng B.
        • Qian G.R.
        • Ma Z.L.
        MicroRNA-18a-5p functions as an oncogene by directly targeting IRF2 in lung cancer.
        Cell Death Dis. 2017; 8: e2764
        • Han J.
        • Wang F.
        • Lan Y.
        • Wang J.
        • Nie C.
        • Liang Y.
        • Song R.
        • Zheng T.
        • Pan S.
        • Pei T.
        • Xie C.
        • Yang G.
        • Liu X.
        • Zhu M.
        • Wang Y.
        • Liu Y.
        • Meng F.
        • Cui Y.
        • Zhang B.
        • Liu Y.
        • Meng X.
        • Zhang J.
        • Liu L.
        KIFC1 regulated by miR-532–3p promotes epithelial-to-mesenchymal transition and metastasis of hepatocellular carcinoma via gankyrin/AKT signaling.
        Oncogene. 2019; 38: 406-420
        • Bartel D.P.
        MicroRNAs: Target recognition and regulatory functions.
        Cell. 2009; 136: 215-233
        • Fernandez-Garcia B.
        • Eiró N.
        • Marín L.
        • Gonzalez-Reyes S.
        • González L.O.
        • Lamelas M.L.
        • Vizoso F.J.
        Expression and prognostic significance of fibronectin and matrix metalloproteases in breast cancer metastasis.
        Histopathology. 2014; 64: 512-522
        • Gopal S.
        • Veracini L.
        • Grall D.
        • Butori C.
        • Schaub S.
        • Audebert S.
        • Camoin L.
        • Baudelet E.
        • Radwanska A.
        • Beghelli-de la Forest Divonne S.
        • Violette S.M.
        • Weinreb P.H.
        • Rekima S.
        • Ilie M.
        • Sudaka A.
        • Hofman P.
        • Van Obberghen-Schilling E.
        Fibronectin-guided migration of carcinoma collectives.
        Nat. Commun. 2017; 8: 14105
        • Yi W.
        • Xiao E.
        • Ding R.
        • Luo P.
        • Yang Y.
        High expression of fibronectin is associated with poor prognosis, cell proliferation and malignancy via the NF-kappaB/p53-apoptosis signaling pathway in colorectal cancer.
        Oncol. Rep. 2016; 36: 3145-3153