A pan-cancer integrative pathway analysis of multi-omics data

Abstract

Background: Multi-view -omics datasets offer rich opportunities for integrative analysis across genomic, transcriptomic, and epigenetic data platforms. Statistical methods are needed to rigorously implement current research on functional biology, matching the complex dynamics of systems genomic datasets.

Methods: We apply imputation for missing data and a structural, graph-theoretic pathway model to a dataset of 22 cancers across 173 signaling pathways. Our pathway model integrates multiple data platforms, and we test for differential activation between cancerous tumor and healthy tissue populations.

Results: Our pathway analysis reveals significant disturbance in signaling pathways that are known to relate to oncogenesis. We identify several pathways that suggest new research directions, including the Trk signaling and focal adhesion kinase activation pathways in sarcoma.

Conclusions: Our integrative analysis confirms contemporary research findings, which supports the validity of our findings. We implement an interactive data visualization for exploration of the pathway analyses, which is available online for public access.

Graphical abstract

Keywords

multi-platform data integration / pathway analysis / imputation / cancer genomics / data visualization

References

[1] Chandrashekar, D.S., Bashel, B., Akshaya, S., Balasubramanya, H., Creighton, C.J., Ponce-Rodriguez, I., Chakravarthi, B. and Varambally, S. (2017) UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia, 19, 649–658

[2] Zhang, Y., Ouyang, Z. and Zhao, H. (2017) A statistical framework for data integration through graphical models with application to cancer genomics. Ann. Appl. Stat., 11, 161–184

[3] Cancer Genome Atlas Research Network (2017) Integrated genomic and molecular characterization of cervical cancer. Nature, 543, 378–384

[4] Shen, R., Olshen, A. B. and Ladanyi, M. (2009) Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics, 25, 2906–2912

[5] Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci., 102,15545–15550

[6] Yan, J., Risacher, S. L., Shen, L. and Saykin, A. J. (2017) Network approaches to systems biology analysis of complex disease: integrative methods for multi -omics data. Brief. Bioinform., 19, 1370–1381

[7] Ge, Z., Leighton, J. S., Wang, Y., Peng, X., Chen, Z., Chen, H., Sun, Y., Yao, F., Li, J., Zhang, H., (2018) Integrated genomic analysis of the ubiquitin pathway across cancer types. Cell Reports, 23, 213–226.e3

[8] Huang, J. K., Carlin, D.E., Yu, M. K., Zhang, W., Kreisberg, J. F., Tamayo, P. and Ideker, T. (2018) Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst., 6, 484–495

[9] Baryshnikova, A. (2016) Systematic functional annotation and visualization of biological networks. Cell Syst., 2, 412–421

[10] Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D. and Stuart, J. M. (2010) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using paradigm. Bioinformatics, 26, i237–i245

[11] Campbell, J. D., Yau, C., Bowlby, R., Liu, Y., Brennan, K., Fan, H., Taylor, A. M., Wang, C., Walter, V., Akbani, R., , (2018) Genomic, pathway network, and immunologic features distinguishing squamous carcinomas. Cell Reports, 23, 194–212.e6

[12] Ma, J., Shojaie, A. and Michailidis, G. (2016) Network-based pathway enrichment analysis with incomplete network information. Bioinformatics, 32, 3165–3174

[13] Robinson, D., Van Allen, E. M., Wu, Y. M., Schultz, N., Lonigro, R. J., Mosquera, J. M., Montgomery, B., Taplin, M. E., Pritchard, C. C., Attard, G., (2015) Integrative clinical genomics of advanced prostate cancer. Cell, 161, 1215–1228

[14] Sanchez-Vega, F., Mina, M., Armenia, J., Chatila, W. K., Luna, A., La, K. C., Dimitriadoy, S., Liu, D. L., Kantheti, H. S., Saghafinia, S., (2018) Oncogenic signaling pathways in the cancer genome atlas. Cell, 173, 321–337.e10

[15] Bonnet, E., Calzone, L. and Michoel, T. (2015) Integrative multi -omics module network inference with Lemon-Tree. PLOS Comput. Biol., 11, e1003983

[16] Hadfield, J., Croucher, N. J., Goater, R. J., Abudahab, K., Aanensen, D. M. and Harris, S. R. (2017) Phandango: an interactive viewer for bacterial population genomics. Bioinformatics, 34, 292–293

[17] Wickham, H. (2016) ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag

[18] Yin, T., Cook, D. and Lawrence, M. (2012) ggbio: an R package for extending the grammar of graphics for genomic data. Genome Biol., 13, R77

[19] Stempor, P. and Ahringer, J. (2016) SeqPlots–Interactive software for exploratory data analyses, pattern discovery and visualization in genomics. Wellcome Open Res., 1, 14

[20] Linder, H. and Zhang, Y. (2019) Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes. Comm. Statis. Appl. Meth., 26, 411–430

[21] Zhang, Y., Linder, H. M.Shojaie, Ouyang, A., Shen, Z., Baggerly, R., Baladandayuthapani, K.A. and Zhao, V. H. (2017) Dissecting pathway disturbances using network topology and multi-platform genomics data. Stat. Biosci., 10, 1–21

[22] Tomczak, K., Czerwińska, P. and Wiznerowicz, M. (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. (Pozn.), 19, A68–A77

[23] Schaefer, C.F., Anthony, K., Krupa, S., Buchoff, J., Day, M., Hannay, T. and Buetow, K. H. (2008) Pid: the pathway interaction database. Nucleic acids research, 37 (suppl), D674–D679

[24] Cai, T., Cai, T. T. and Zhang, A. (2016) Structured matrix completion with applications to genomic data integration. J. Am. Stat. Assoc., 111, 621–633

[25] Shojaie, A. and Michailidis, G. (2009) Analysis of gene sets based on the underlying regulatory network. J. Comput. Biol., 16, 407–426

[26] Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B, 57, 289–300

[27] Fowler, N. and Davis, E. (2013) Targeting B-cell receptor signaling: changing the paradigm. Hematology, 553–560

[28] Burger, J. A. and Wiestner, A. (2018) Targeting B cell receptor signalling in cancer: preclinical and clinical advances. Nat. Rev. Cancer, 18, 148–167

[29] Roskoski, R. Jr. (2014) The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacol. Res., 79, 34–74

[30] Jakowlew, S. B. (2006) Transforming growth factor-β in cancer and metastasis. Cancer Metastasis Rev., 25, 435–457

[31] Massagué J. (2008) TGFbeta in Cancer. Cell, 134, 215–230

[32] Fabregat, I., Fernando, J., Mainez, J. and Sancho, P. (2014) TGF-beta signaling in cancer treatment. Curr. Pharm. Des., 20, 2934–2947

[33] Iengar, P. (2018) Identifying pathways affected by cancer mutations. Genomics, 110, 318–328

[34] Leiserson, M. D. M., Blokh, D., Sharan, R. and Raphael. B. J., (2013) Simultaneous identification of multiple driver pathways in cancer. PLOS Comput. Biol., 9, e1003054

[35] Barletta, C., Lazzaro, D., Prosperi Porta, R., Testa, U., Grignani, F., Ragusa, R. M., Leone, R., Patella, A., Carenza, L. and Peschle, C. (1992) C-MYB activation and the pathogenesis of ovarian cancer. Eur. J. Gynaecol. Oncol., 13, 53–59

[36] Jin, Y., Zhu, H., Cai, W., Fan, X., Wang, Y., Niu, Y., Song, F. and Bu, Y. (2017) B-myb is up-regulated and promotes cell growth and motility in non-small cell lung cancer. Int. J. Mol. Sci., 18, 860

[37] Lawn, S., Krishna, N., Pisklakova, A., Qu, X., Fenstermacher, D. A., Fournier, M., Vrionis, F. D., Tran, N., Chan, J. A., Kenchappa, R. S., (2015) Neurotrophin signaling via TrkB and TrkC receptors promotes the growth of brain tumor-initiating cells. J. Biol. Chem., 290, 3814–3824

[38] Meng, L., Liu, B., Ji, R., Jiang, X., Yan, X. and Xin, Y. (2019) Targeting the BDNF/TrkB pathway for the treatment of tumors. Oncol Lett, 17, 2031–2039

[39] Drilon, A., Siena, S., Ou, S. I., Patel, M., Ahn, M. J., Lee, J., Bauer, T. M., Farago, A. F., Wheler, J. J., Liu, S. V., (2017) Safety and antitumor activity of the multitargeted pan-trk, ros1, and alk inhibitor entrectinib: combined results from two phase i trials (alka-372-001 and startrk-1). Cancer Discov., 7, 400–409

[40] Heinen, T. E., Dos Santos, R. P., da Rocha, A., Dos Santos, M. P., Lopez, P. L., Silva Filho, M. A., Souza, B. K., Rivero, L. F., Becker, R. G., Gregianin, L. J., (2016) Trk inhibition reduces cell proliferation and potentiates the effects of chemotherapeutic agents in Ewing sarcoma. Oncotarget, 7, 34860–34880

[41] Perry, B. C., Wang, S. and Basson, M. D. (2010) Extracellular pressure stimulates adhesion of sarcoma cells via activation of focal adhesion kinase and akt. Am. J. Surg., 200, 610–614

[42] Crompton, B. D., Carlton, A. L., Thorner, A. R., Christie, A. L., Du, J., Calicchio, M. L., Rivera, M. N., Fleming, M. D., Kohl, N. E., Kung, A. L., (2013) High-throughput tyrosine kinase activity profiling identifies FAK as a candidate therapeutic target in Ewing sarcoma. Cancer Res., 73, 2873–2883

[43] Wang, S., Hwang, E. E., Guha, R., O’Neill, A. F., Melong, N., Veinotte, C. J., Conway, A.S., Wuerthele, K., Shen, M., McKnight, C. (2019) High-throughput chemical screening identifies focal adhesion kinase and aurora kinase B inhibition as a synergistic treatment combination in ewing sarcoma. Clin. Cancer Res.,77

[44] Pihlajamaa, P., Sahu, B., Lyly, L., Aittomäki, V., Hautaniemi, S. and Jänne, O. A. (2014) Tissue-specific pioneer factors associate with androgen receptor cistromes and transcription programs. EMBO J., 33, 312–326

[45] Foersch, S., Schindeldecker, M., Keith, M., Tagscherer, K. E., Fernandez, A., Stenzel, P. J., Pahernik, S., Hohenfellner, M., Schirmacher, P., Roth, W., (2017) Prognostic relevance of androgen receptor expression in renal cell carcinomas. Oncotarget, 8, 78545–78555

[46] Zhao, H., Leppert, J. T. and Peehl, D. M. (2016) A protective role for androgen receptor in clear cell renal cell carcinoma based on mining tcga data. PLoS One, 11, e0146505

[47] Grossman, R. L., Heath, A. P., Ferretti, V., Varmus, H. E., Lowy, D.R., Kibbe, W. A. and Staudt, L. M. (2016) Toward a shared vision for cancer genomic data. N. Engl. J. Med., 375, 1109–1112

[48] Zhu, Y., Qiu, P. and Ji, Y. (2014) TCGA-assembler: open-source software for retrieving and processing TCGA data. Nat. Methods, 11, 599–600

[49] Wei, L., Jin, Z., Yang, S., Xu, Y., Zhu, Y. and Ji, Y. (2018) TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data. Bioinformatics, 34, 1615–1617

[50] Sales, G., Calura, E. and Romualdi, C. (2018) graphite: GRAPH Interaction from pathway Topological Environment. R package version 1.26.1

[51] Krämer, N., Schäfer, J. and Boulesteix, A.-L. (2009) Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics, 10, 384

[52] Kim, S. (2015) ppcor: an r package for a fast calculation to semi-partial correlation coefficients. Commun. Stat. Appl. Methods, 22, 665–674

[53] Shojaie, A. and Michailidis, G. (2010) Network enrichment analysis in complex experiments. Stat. Appl. Genet. Mol. Biol., 9, e22

[54] Chang, W., Cheng, J., Allaire, J. J., Xie, Y. H. and McPherson, J. (2018) shiny: Web Application Framework for R. R package version 1.2.0

[55] Csardi, G. and Nepusz, T. (2006) The igraph software package for complex network research. InterJournal, Complex Syst., 1695

[56] Almende B. V., Thieurmel, B. and Robert, T. (2018) visNetwork: Network Visualization using vis.js Library. R package version 2.0.4

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