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

doi:10.1007/s40484-019-0185-6

Quantitative Biology

2020, Vol. 8

Issue (2): 130-142 https://doi.org/10.1007/s40484-019-0185-6

本期目录

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

Henry Linder, Yuping Zhang(

)

Department of Statistics, University of Connecticut, Storrs, CT 06269, USA

全文: PDF(698 KB) HTML

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.

Key words： multi-platform data integration pathway analysis imputation cancer genomics data visualization

收稿日期: 2019-07-04 出版日期: 2020-07-13

Corresponding Author(s): Yuping Zhang

引用本文:

. [J]. Quantitative Biology, 2020, 8(2): 130-142.
Henry Linder, Yuping Zhang. A pan-cancer integrative pathway analysis of multi-omics data. Quant. Biol., 2020, 8(2): 130-142.

链接本文:

https://academic.hep.com.cn/qb/CN/10.1007/s40484-019-0185-6
https://academic.hep.com.cn/qb/CN/Y2020/V8/I2/130

Tab.1

Fig.1

Tab.2

Fig.2

1	D.S., Chandrashekar, B. Bashel,, S. Akshaya,, H. Balasubramanya,, C.J. Creighton,, I. Ponce-Rodriguez,, B. Chakravarthi, and S. Varambally, (2017) UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia, 19, 649–658
2	Y. Zhang, , Z. Ouyang, and H. Zhao, (2017) A statistical framework for data integration through graphical models with application to cancer genomics. Ann. Appl. Stat., 11, 161–184 https://doi.org/10.1214/16-AOAS998. pmid: 30956747
3	Cancer Genome Atlas Research Network (2017) Integrated genomic and molecular characterization of cervical cancer. Nature, 543, 378–384 https://doi.org/10.1038/nature21386. pmid: 28112728
4	R. Shen, , A. B. Olshen, and M. Ladanyi, (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 https://doi.org/10.1093/bioinformatics/btp543. pmid: 19759197
5	A. Subramanian, , P. Tamayo, , V. K. Mootha,, S. Mukherjee,, B. L. Ebert,, M. A. Gillette,, A. Paulovich,, S. L. Pomeroy,, T. R. Golub,, E. S. Lander, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci., 102,15545–15550
6	J. Yan, , S. L. Risacher,, L. Shen, and A. J. Saykin, (2017) Network approaches to systems biology analysis of complex disease: integrative methods for multi -omics data. Brief. Bioinform., 19, 1370–1381 pmid: 28679163
7	Z. Ge, , J. S. Leighton, , Y. Wang, , X. Peng, , Z. Chen, , H. Chen, , Y. Sun, , F. Yao, , J. Li, , H. Zhang, , et al. (2018) Integrated genomic analysis of the ubiquitin pathway across cancer types. Cell Reports, 23, 213–226.e3 https://doi.org/10.1016/j.celrep.2018.03.047. pmid: 29617661
8	J. K. Huang,, D.E. Carlin,, M. K. Yu,, W. Zhang,, J. F. Kreisberg,, P. Tamayo, and T. Ideker, (2018) Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst., 6, 484–495
9	A. Baryshnikova, (2016) Systematic functional annotation and visualization of biological networks. Cell Syst., 2, 412–421 https://doi.org/10.1016/j.cels.2016.04.014. pmid: 27237738
10	C. J. Vaske, , S. C. Benz, , J. Z. Sanborn, , D. Earl, , C. Szeto, , J. Zhu, , D. Haussler, and J. M. Stuart, (2010) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using paradigm. Bioinformatics, 26, i237–i245 https://doi.org/10.1093/bioinformatics/btq182
11	J. D. Campbell, , C. Yau, , R. Bowlby, , Y. Liu, , K. Brennan, , H. Fan, , A. M. Taylor, , C. Wang, , V. Walter, , R. Akbani, , et al., (2018) Genomic, pathway network, and immunologic features distinguishing squamous carcinomas. Cell Reports, 23, 194–212.e6 https://doi.org/10.1016/j.celrep.2018.03.063. pmid: 29617660
12	J. Ma, , A. Shojaie, and G. Michailidis, (2016) Network-based pathway enrichment analysis with incomplete network information. Bioinformatics, 32, 3165–3174 https://doi.org/10.1093/bioinformatics/btw410. pmid: 27357170
13	D. Robinson, , E. M. Van Allen, , Y. M. Wu, , N. Schultz, , R. J. Lonigro, , J. M. Mosquera, , B. Montgomery, , M. E. Taplin, , C. C. Pritchard, , G. Attard, , et al. (2015) Integrative clinical genomics of advanced prostate cancer. Cell, 161, 1215–1228 https://doi.org/10.1016/j.cell.2015.05.001. pmid: 26000489
14	F. Sanchez-Vega, , M. Mina, , J. Armenia, , W. K. Chatila, , A. Luna, , K. C. La, , S. Dimitriadoy, , D. L. Liu, , H. S. Kantheti, , S. Saghafinia, , et al. (2018) Oncogenic signaling pathways in the cancer genome atlas. Cell, 173, 321–337.e10 https://doi.org/10.1016/j.cell.2018.03.035. pmid: 29625050
15	E. Bonnet, , L. Calzone, and T. Michoel, (2015) Integrative multi -omics module network inference with Lemon-Tree. PLOS Comput. Biol., 11, e1003983 https://doi.org/10.1371/journal.pcbi.1003983. pmid: 25679508
16	J. Hadfield, , N. J. Croucher, , R. J. Goater, , K. Abudahab, , D. M. Aanensen, and S. R. Harris, (2017) Phandango: an interactive viewer for bacterial population genomics. Bioinformatics, 34, 292–293 https://doi.org/10.1093/bioinformatics/btx610.\
17	, H. Wickham (2016) ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag
18	T. Yin, , D. Cook, and M. Lawrence, (2012) ggbio: an R package for extending the grammar of graphics for genomic data. Genome Biol., 13, R77 https://doi.org/10.1186/gb-2012-13-8-r77. pmid: 22937822
19	P. Stempor, and J. Ahringer, (2016) SeqPlots–Interactive software for exploratory data analyses, pattern discovery and visualization in genomics. Wellcome Open Res., 1, 14 https://doi.org/10.12688/wellcomeopenres.10004.1. pmid: 27918597
20	H. Linder, and Y. Zhang, (2019) Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes. Comm. Statis. Appl. Meth., 26, 411–430 https://doi.org/10.29220/CSAM.2019.26.4.411
21	Y. Zhang, ,H. M. Linder, Shojaie, A. Ouyang,, Z. Shen,, R. Baggerly,, K.A. Baladandayuthapani, and V. H. Zhao, (2017) Dissecting pathway disturbances using network topology and multi-platform genomics data. Stat. Biosci., 10, 1–21
22	K. Tomczak, , P. Czerwińska, and M. Wiznerowicz, (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. (Pozn.), 19, A68–A77 https://doi.org/10.5114/wo.2014.47136. pmid: 25691825
23	C.F. Schaefer, , K. Anthony,, S. Krupa, , J. Buchoff, , M. Day, , T. Hannay, and K. H. Buetow, (2008) Pid: the pathway interaction database. Nucleic acids research, 37 (suppl), D674–D679
24	T. Cai, , T. T. Cai, and A. Zhang, (2016) Structured matrix completion with applications to genomic data integration. J. Am. Stat. Assoc., 111, 621–633 https://doi.org/10.1080/01621459.2015.1021005. pmid: 28042188
25	A. Shojaie, and G. Michailidis, (2009) Analysis of gene sets based on the underlying regulatory network. J. Comput. Biol., 16, 407–426 https://doi.org/10.1089/cmb.2008.0081. pmid: 19254181
26	Y. Benjamini, and Y. Hochberg, (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B, 57, 289–300 https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
27	N. Fowler, and E. Davis, (2013) Targeting B-cell receptor signaling: changing the paradigm. Hematology, 553–560 https://doi.org/10.1182/asheducation-2013.1.553. pmid: 24319231
28	J. A. Burger, and A. Wiestner, (2018) Targeting B cell receptor signalling in cancer: preclinical and clinical advances. Nat. Rev. Cancer, 18, 148–167 https://doi.org/10.1038/nrc.2017.121. pmid: 29348577
29	R. Roskoski, Jr. (2014) The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacol. Res., 79, 34–74 https://doi.org/10.1016/j.phrs.2013.11.002. pmid: 24269963
30	S. B. Jakowlew, (2006) Transforming growth factor-β in cancer and metastasis. Cancer Metastasis Rev., 25, 435–457 https://doi.org/10.1007/s10555-006-9006-2. pmid: 16951986
31	J. Massagué, (2008) TGFbeta in Cancer. Cell, 134, 215–230 https://doi.org/10.1016/j.cell.2008.07.001. pmid: 18662538
32	I. Fabregat, , J. Fernando, , J. Mainez, and P. Sancho, (2014) TGF-beta signaling in cancer treatment. Curr. Pharm. Des., 20, 2934–2947 https://doi.org/10.2174/13816128113199990591. pmid: 23944366
33	P. Iengar, (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	C. Barletta, , D. Lazzaro, , R. Prosperi Porta, , U. Testa, , F. Grignani, , R. M. Ragusa, , R. Leone, , A. Patella, , L. Carenza, and C. Peschle, (1992) C-MYB activation and the pathogenesis of ovarian cancer. Eur. J. Gynaecol. Oncol., 13, 53–59 pmid: 1547794
36	Y. Jin, , H. Zhu, , W. Cai, , X. Fan, , Y. Wang, , Y. Niu, , F. Song, and Y. Bu, (2017) B-myb is up-regulated and promotes cell growth and motility in non-small cell lung cancer. Int. J. Mol. Sci., 18, 860 https://doi.org/10.3390/ijms18060860. pmid: 28555007
37	S. Lawn, , N. Krishna, , A. Pisklakova, , X. Qu, , D. A. Fenstermacher, , M. Fournier, , F. D. Vrionis, , N. Tran, , J. A. Chan, , R. S. Kenchappa, , et al. (2015) Neurotrophin signaling via TrkB and TrkC receptors promotes the growth of brain tumor-initiating cells. J. Biol. Chem., 290, 3814–3824 https://doi.org/10.1074/jbc.M114.599373. pmid: 25538243
38	L. Meng, , B. Liu, , R. Ji, , X. Jiang, , X. Yan, and Y. Xin, (2019) Targeting the BDNF/TrkB pathway for the treatment of tumors. Oncol Lett, 17, 2031–2039 pmid: 30675270
39	A. Drilon, , S. Siena, , S. I. Ou, , M. Patel, , M. J. Ahn, , J. Lee, , T. M. Bauer, , A. F. Farago, , J. J. Wheler, , S. V. Liu, , et al. (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 https://doi.org/10.1158/2159-8290.CD-16-1237. pmid: 28183697
40	T. E. Heinen, , R. P. Dos Santos, , A. da Rocha, , M. P. Dos Santos, , P. L. Lopez, , M. A. Silva Filho, , B. K. Souza, , L. F. Rivero, , R. G. Becker, , L. J. Gregianin, , et al. (2016) Trk inhibition reduces cell proliferation and potentiates the effects of chemotherapeutic agents in Ewing sarcoma. Oncotarget, 7, 34860–34880 https://doi.org/10.18632/oncotarget.8992. pmid: 27145455
41	B. C. Perry, , S. Wang, and M. D. Basson, (2010) Extracellular pressure stimulates adhesion of sarcoma cells via activation of focal adhesion kinase and akt. Am. J. Surg., 200, 610–614 https://doi.org/10.1016/j.amjsurg.2010.07.013. pmid: 21056138
42	B. D. Crompton, , A. L. Carlton, , A. R. Thorner, , A. L. Christie, , J. Du, , M. L. Calicchio, , M. N. Rivera, , M. D. Fleming, , N. E. Kohl, , A. L. Kung, , et al. (2013) High-throughput tyrosine kinase activity profiling identifies FAK as a candidate therapeutic target in Ewing sarcoma. Cancer Res., 73, 2873–2883 https://doi.org/10.1158/0008-5472.CAN-12-1944. pmid: 23536552
43	S. Wang, , E. E. Hwang,, R. Guha,, A. F. O’Neill,, N. Melong,, C. J. Veinotte,, A.S. Conway,, K. Wuerthele,, M. Shen,, C. McKnight, et al. (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	P. Pihlajamaa, , B. Sahu, , L. Lyly, , V. Aittomäki, , S. Hautaniemi, and O. A. Jänne, (2014) Tissue-specific pioneer factors associate with androgen receptor cistromes and transcription programs. EMBO J., 33, 312–326 https://doi.org/10.1002/embj.201385895. pmid: 24451200
45	S. Foersch, , M. Schindeldecker, , M. Keith, , K. E. Tagscherer, , A. Fernandez, , P. J. Stenzel, , S. Pahernik, , M. Hohenfellner, , P. Schirmacher, , W. Roth, , et al. (2017) Prognostic relevance of androgen receptor expression in renal cell carcinomas. Oncotarget, 8, 78545–78555 https://doi.org/10.18632/oncotarget.20827. pmid: 29108248
46	H. Zhao, , J. T. Leppert, and D. M. Peehl, (2016) A protective role for androgen receptor in clear cell renal cell carcinoma based on mining tcga data. PLoS One, 11, e0146505 https://doi.org/10.1371/journal.pone.0146505. pmid: 26814892
47	R. L. Grossman,, A. P. Heath,, V. Ferretti,, H. E. Varmus,, D.R. Lowy,, W. A. Kibbe, and L. M Staudt,. (2016) Toward a shared vision for cancer genomic data. N. Engl. J. Med., 375, 1109–1112
48	Y. Zhu, , P. Qiu, and Y. Ji, (2014) TCGA-assembler: open-source software for retrieving and processing TCGA data. Nat. Methods, 11, 599–600 https://doi.org/10.1038/nmeth.2956. pmid: 24874569
49	L. Wei, , Z. Jin, , S. Yang, , Y. Xu, , Y. Zhu, and Y. Ji, (2018) TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data. Bioinformatics, 34, 1615–1617 https://doi.org/10.1093/bioinformatics/btx812. pmid: 29272348
50	G. Sales, , E. Calura, and C. Romualdi, (2018) graphite: GRAPH Interaction from pathway Topological Environment. R package version 1.26.1
51	N. Krämer, , J. Schäfer, and A.-L. Boulesteix, (2009) Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics, 10, 384 https://doi.org/10.1186/1471-2105-10-384. pmid: 19930695
52	S. Kim, (2015) ppcor: an r package for a fast calculation to semi-partial correlation coefficients. Commun. Stat. Appl. Methods, 22, 665–674 https://doi.org/10.5351/CSAM.2015.22.6.665. pmid: 26688802
53	A. Shojaie, and G. Michailidis, (2010) Network enrichment analysis in complex experiments. Stat. Appl. Genet. Mol. Biol., 9, e22 https://doi.org/10.2202/1544-6115.1483. pmid: 20597848
54	W. Chang, , J. Cheng,, J. J. Allaire,, Y. H. Xie, and J. McPherson, (2018) shiny: Web Application Framework for R. R package version 1.2.0
55	G. Csardi, and T. Nepusz, (2006) The igraph software package for complex network research. InterJournal, Complex Syst., 1695
56	B. V. Almende, B. Thieurmel, and T. Robert, (2018) visNetwork: Network Visualization using vis.js Library. R package version 2.0.4

Viewed

Full text

Abstract

Cited

Shared

Discussed