Molecules and Cells

Korean Society for Molecular and Cellular Biology (한국분자세포생물학회)
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Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

  • Heo, Yong Jin (School of Biosystem and Biomedical Science, College of Health Science, Korea University) ;
  • Hwa, Chanwoong (School of Biosystem and Biomedical Science, College of Health Science, Korea University) ;
  • Lee, Gang-Hee (School of Biosystem and Biomedical Science, College of Health Science, Korea University) ;
  • Park, Jae-Min (School of Biosystem and Biomedical Science, College of Health Science, Korea University) ;
  • An, Joon-Yong (School of Biosystem and Biomedical Science, College of Health Science, Korea University)
  • Received : 2021.02.20
  • Accepted : 2021.05.12
  • Published : 2021.07.31
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Abstract

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

Keywords

Acknowledgement

This work was supported by the Korean NRF Grant 2019M3E5D3073568 (to J.Y.A.) and a Korea University Grant.

References

  1. Aguirre, A.J., Nowak, J.A., Camarda, N.D., Moffitt, R.A., Ghazani, A.A., Hazar-Rethinam, M., Raghavan, S., Kim, J., Brais, L.K., Ragon, D., et al. (2018). Real-time genomic characterization of advanced pancreatic cancer to enable precision medicine. Cancer Discov. 8, 1096-1111. https://doi.org/10.1158/2159-8290.CD-18-0275
  2. Amit, I., Citri, A., Shay, T., Lu, Y., Katz, M., Zhang, F., Tarcic, G., Siwak, D., Lahad, J., Jacob-Hirsch, J., et al. (2007). A module of negative feedback regulators defines growth factor signaling. Nat. Genet. 39, 503-512.
  3. Basu, A., Bodycombe, N.E., Cheah, J.H., Price, E.V., Liu, K., Schaefer, G.I., Ebright, R.Y., Stewart, M.L., Ito, D., Wang, S., et al. (2013). An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151-1161. https://doi.org/10.1016/j.cell.2013.08.003
  4. Benson, J.D., Chen, Y.N., Cornell-Kennon, S.A., Dorsch, M., Kim, S., Leszczyniecka, M., Sellers, W.R., and Lengauer, C. (2006). Validating cancer drug targets. Nature 441, 451-456. https://doi.org/10.1038/nature04873
  5. Berns, K. and Bernards, R. (2012). Understanding resistance to targeted cancer drugs through loss of function genetic screens. Drug Resist. Updat. 15, 268-275. https://doi.org/10.1016/j.drup.2012.10.002
  6. Bodenmiller, B., Zunder, E.R., Finck, R., Chen, T.J., Savig, E.S., Bruggner, R.V., Simonds, E.F., Bendall, S.C., Sachs, K., Krutzik, P.O., et al. (2012). Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858-867. https://doi.org/10.1038/nbt.2317
  7. Bordbar, A., Mo, M.L., Nakayasu, E.S., Schrimpe-Rutledge, A.C., Kim, Y.M., Metz, T.O., Jones, M.B., Frank, B.C., Smith, R.D., Peterson, S.N., et al. (2012). Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol. Syst. Biol. 8, 558. https://doi.org/10.1038/msb.2012.21
  8. Bozic, I., Antal, T., Ohtsuki, H., Carter, H., Kim, D., Chen, S., Karchin, R., Kinzler, K.W., Vogelstein, B., and Nowak, M.A. (2010). Accumulation of driver and passenger mutations during tumor progression. Proc. Natl. Acad. Sci. U. S. A. 107, 18545-18550. https://doi.org/10.1073/pnas.1010978107
  9. Cancer Genome Atlas Network (2012a). Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330-337. https://doi.org/10.1038/nature11252
  10. Cancer Genome Atlas Network (2012b). Comprehensive molecular portraits of human breast tumours. Nature 490, 61-70. https://doi.org/10.1038/nature11412
  11. Cancer Genome Atlas Research Network (2011). Integrated genomic analyses of ovarian carcinoma. Nature 474, 609-615. https://doi.org/10.1038/nature10166
  12. Cancer Genome Atlas Research Network (2013). Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43-49. https://doi.org/10.1038/nature12222
  13. Cancer Genome Atlas Research Network (2014). Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202-209. https://doi.org/10.1038/nature13480
  14. Cancer Genome Atlas Research Network, Kandoth, C., Schultz, N., Cherniack, A.D., Akbani, R., Liu, Y., Shen, H., Robertson, A.G., Pashtan, I., Shen, R., et al. (2013a). Integrated genomic characterization of endometrial carcinoma. Nature 497, 67-73. https://doi.org/10.1038/nature12113
  15. Cancer Genome Atlas Research Network, Ley, T.J., Miller, C., Ding, L., Raphael, B.J., Mungall, A.J., Robertson, A., Hoadley, K., Triche, T.J., Jr., Laird, P.W., et al. (2013b). Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059-2074. https://doi.org/10.1056/NEJMoa1301689
  16. Cancer Genome Atlas Research Network (2017). Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell 32, 185-203.e13. https://doi.org/10.1016/j.ccell.2017.07.007
  17. Carro, M.S., Lim, W.K., Alvarez, M.J., Bollo, R.J., Zhao, X., Snyder, E.Y., Sulman, E.P., Anne, S.L., Doetsch, F., Colman, H., et al. (2010). The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318-325. https://doi.org/10.1038/nature08712
  18. Cavalli, F.M.G., Remke, M., Rampasek, L., Peacock, J., Shih, D.J.H., Luu, B., Garzia, L., Torchia, J., Nor, C., Morrissy, A.S., et al. (2017). Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 31, 737-754.e6. https://doi.org/10.1016/j.ccell.2017.05.005
  19. Chauvel, C., Novoloaca, A., Veyre, P., Reynier, F., and Becker, J. (2020). Evaluation of integrative clustering methods for the analysis of multiomics data. Brief. Bioinform. 21, 541-552. https://doi.org/10.1093/bib/bbz015
  20. Chen, Y., McGee, J., Chen, X., Doman, T.N., Gong, X., Zhang, Y., Hamm, N., Ma, X., Higgs, R.E., Bhagwat, S.V., et al. (2014). Identification of druggable cancer driver genes amplified across TCGA datasets. PLoS One 9, e98293. https://doi.org/10.1371/journal.pone.0098293
  21. Chen, Y.J., Roumeliotis, T.I., Chang, Y.H., Chen, C.T., Han, C.L., Lin, M.H., Chen, H.W., Chang, G.C., Chang, Y.L., Wu, C.T., et al. (2020). Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression. Cell 182, 226-244.e17. https://doi.org/10.1016/j.cell.2020.06.012
  22. Chin, K., DeVries, S., Fridlyand, J., Spellman, P.T., Roydasgupta, R., Kuo, W.L., Lapuk, A., Neve, R.M., Qian, Z., Ryder, T., et al. (2006). Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10, 529-541. https://doi.org/10.1016/j.ccr.2006.10.009
  23. Cibulskis, K., Lawrence, M.S., Carter, S.L., Sivachenko, A., Jaffe, D., Sougnez, C., Gabriel, S., Meyerson, M., Lander, E.S., and Getz, G. (2013). Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213-219. https://doi.org/10.1038/nbt.2514
  24. Csibi, A., Fendt, S.M., Li, C., Poulogiannis, G., Choo, A.Y., Chapski, D.J., Jeong, S.M., Dempsey, J.M., Parkhitko, A., Morrison, T., et al. (2013). The mTORC1 pathway stimulates glutamine metabolism and cell proliferation by repressing SIRT4. Cell 153, 840-854. https://doi.org/10.1016/j.cell.2013.04.023
  25. Cui, H., Kong, H., Peng, F., Wang, C., Zhang, D., Tian, J., and Zhang, L. (2020). Inferences of individual drug response-related long non-coding RNAs based on integrating multi-omics data in breast cancer. Mol. Ther. Nucleic Acids 20, 128-139. https://doi.org/10.1016/j.omtn.2020.01.038
  26. Curtis, C., Shah, S.P., Chin, S.F., Turashvili, G., Rueda, O.M., Dunning, M.J., Speed, D., Lynch, A.G., Samarajiwa, S., Yuan, Y., et al. (2012). The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346-352. https://doi.org/10.1038/nature10983
  27. Dekker, L.J.M., Kannegieter, N.M., Haerkens, F., Toth, E., Kros, J.M., Steenhoff Hov, D.A., Fillebeen, J., Verschuren, L., Leenstra, S., Ressa, A., et al. (2020). Multiomics profiling of paired primary and recurrent glioblastoma patient tissues. Neurooncol. Adv. 2, vdaa083.
  28. Gentles, A.J. and Gallahan, D. (2011). Systems biology: confronting the complexity of cancer. Cancer Res. 71, 5961-5964. https://doi.org/10.1158/0008-5472.CAN-11-1569
  29. Gillette, M.A., Satpathy, S., Cao, S., Dhanasekaran, S.M., Vasaikar, S.V., Krug, K., Petralia, F., Li, Y., Liang, W.W., Reva, B., et al. (2020). Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200-225.e35. https://doi.org/10.1016/j.cell.2020.06.013
  30. Greenbaum, D., Colangelo, C., Williams, K., and Gerstein, M. (2003). Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 117. https://doi.org/10.1186/gb-2003-4-9-117
  31. Greenman, C., Stephens, P., Smith, R., Dalgliesh, G.L., Hunter, C., Bignell, G., Davies, H., Teague, J., Butler, A., Stevens, C., et al. (2007). Patterns of somatic mutation in human cancer genomes. Nature 446, 153-158. https://doi.org/10.1038/nature05610
  32. Hegde, P.S., White, I.R., and Debouck, C. (2003). Interplay of transcriptomics and proteomics. Curr. Opin. Biotechnol. 14, 647-651. https://doi.org/10.1016/j.copbio.2003.10.006
  33. 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., et al. (2010). 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 6, 129-151. https://doi.org/10.1007/s12014-010-9055-y
  34. Hill, S.M., Lu, Y., Molina, J., Heiser, L.M., Spellman, P.T., Speed, T.P., Gray, J.W., Mills, G.B., and Mukherjee, S. (2012). Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics 28, 2804-2810. https://doi.org/10.1093/bioinformatics/bts514
  35. Hong, S., Choi, S., Kim, R., and Koh, J. (2020). Mechanisms of macromolecular interactions mediated by protein intrinsic disorder. Mol. Cells 43, 899-908. https://doi.org/10.14348/molcells.2020.0186
  36. Huang, S.S., Clarke, D.C., Gosline, S.J., Labadorf, A., Chouinard, C.R., Gordon, W., Lauffenburger, D.A., and Fraenkel, E. (2013). Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling. PLoS Comput. Biol. 9, e1002887. https://doi.org/10.1371/journal.pcbi.1002887
  37. Huang, Z., Zhan, X., Xiang, S., Johnson, T.S., Helm, B., Yu, C.Y., Zhang, J., Salama, P., Rizkalla, M., Han, Z., et al. (2019). SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on breast cancer. Front. Genet. 10, 166. https://doi.org/10.3389/fgene.2019.00166
  38. Iadevaia, S., Lu, Y., Morales, F.C., Mills, G.B., and Ram, P.T. (2010). Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res. 70, 6704-6714. https://doi.org/10.1158/0008-5472.CAN-10-0460
  39. Jain, M., Nilsson, R., Sharma, S., Madhusudhan, N., Kitami, T., Souza, A.L., Kafri, R., Kirschner, M.W., Clish, C.B., and Mootha, V.K. (2012). Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336, 1040-1044. https://doi.org/10.1126/science.1218595
  40. Kirk, P., Griffin, J.E., Savage, R.S., Ghahramani, Z., and Wild, D.L. (2012). Bayesian correlated clustering to integrate multiple datasets. Bioinformatics 28, 3290-3297. https://doi.org/10.1093/bioinformatics/bts595
  41. Koh, H.W.L., Fermin, D., Vogel, C., Choi, K.P., Ewing, R.M., and Choi, H. (2019). iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery. NPJ Syst. Biol. Appl. 5, 22. https://doi.org/10.1038/s41540-019-0099-y
  42. Komurov, K., Tseng, J.T., Muller, M., Seviour, E.G., Moss, T.J., Yang, L., Nagrath, D., and Ram, P.T. (2012). The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells. Mol. Syst. Biol. 8, 596. https://doi.org/10.1038/msb.2012.25
  43. Krug, K., Jaehnig, E.J., Satpathy, S., Blumenberg, L., Karpova, A., Anurag, M., Miles, G., Mertins, P., Geffen, Y., Tang, L.C., et al. (2020). Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell 183, 1436-1456.e31. https://doi.org/10.1016/j.cell.2020.10.036
  44. Lai, Z., Tsugawa, H., Wohlgemuth, G., Mehta, S., Mueller, M., Zheng, Y., Ogiwara, A., Meissen, J., Showalter, M., Takeuchi, K., et al. (2018). Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nat. Methods 15, 53-56. https://doi.org/10.1038/nmeth.4512
  45. Langfelder, P. and Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559. https://doi.org/10.1186/1471-2105-9-559
  46. Lee, S., Kim, J., and Park, J.E. (2021). Single-cell toolkits opening a new era for cell engineering. Mol. Cells 44, 127-135. https://doi.org/10.14348/molcells.2021.0002
  47. Li, T., Kung, H.J., Mack, P.C., and Gandara, D.R. (2013). Genotyping and genomic profiling of non-small-cell lung cancer: implications for current and future therapies. J. Clin. Oncol. 31, 1039-1049. https://doi.org/10.1200/JCO.2012.45.3753
  48. Lindsay, M.A. (2003). Target discovery. Nat. Rev. Drug Discov. 2, 831-838. https://doi.org/10.1038/nrd1202
  49. Lock, E.F. and Dunson, D.B. (2013). Bayesian consensus clustering. Bioinformatics 29, 2610-2616. https://doi.org/10.1093/bioinformatics/btt425
  50. Marx, V. (2019). A dream of single-cell proteomics. Nat. Methods 16, 809-812. https://doi.org/10.1038/s41592-019-0540-6
  51. Mertins, P., Mani, D.R., Ruggles, K.V., Gillette, M.A., Clauser, K.R., Wang, P., Wang, X., Qiao, J.W., Cao, S., Petralia, F., et al. (2016). Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 55-62. https://doi.org/10.1038/nature18003
  52. Mo, Q., Wang, S., Seshan, V.E., Olshen, A.B., Schultz, N., Sander, C., Powers, R.S., Ladanyi, M., and Shen, R. (2013). Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl. Acad. Sci. U. S. A. 110, 4245-4250. https://doi.org/10.1073/pnas.1208949110
  53. Mun, D.G., Bhin, J., Kim, S., Kim, H., Jung, J.H., Jung, Y., Jang, Y.E., Park, J.M., Kim, H., Jung, Y., et al. (2019). Proteogenomic characterization of human early-onset gastric cancer. Cancer Cell 35, 111-124.e10. https://doi.org/10.1016/j.ccell.2018.12.003
  54. Neve, R.M., Chin, K., Fridlyand, J., Yeh, J., Baehner, F.L., Fevr, T., Clark, L., Bayani, N., Coppe, J.P., Tong, F., et al. (2006). A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515-527. https://doi.org/10.1016/j.ccr.2006.10.008
  55. Nguyen, H., Shrestha, S., Draghici, S., and Nguyen, T. (2019). PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics 35, 2843-2846. https://doi.org/10.1093/bioinformatics/bty1049
  56. Nguyen, N.D. and Wang, D. (2020). Multiview learning for understanding functional multiomics. PLoS Comput. Biol. 16, e1007677. https://doi.org/10.1371/journal.pcbi.1007677
  57. Paananen, J. and Fortino, V. (2020). An omics perspective on drug target discovery platforms. Brief. Bioinform. 21, 1937-1953. https://doi.org/10.1093/bib/bbz122
  58. Palmer, A., Phapale, P., Chernyavsky, I., Lavigne, R., Fay, D., Tarasov, A., Kovalev, V., Fuchser, J., Nikolenko, S., Pineau, C., et al. (2017). FDRcontrolled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods 14, 57-60. https://doi.org/10.1038/nmeth.4072
  59. Pascal, J., Bearer, E.L., Wang, Z., Koay, E.J., Curley, S.A., and Cristini, V. (2013). Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements. Proc. Natl. Acad. Sci. U. S. A. 110, 14266-14271. https://doi.org/10.1073/pnas.1300619110
  60. Pauli, C., Hopkins, B.D., Prandi, D., Shaw, R., Fedrizzi, T., Sboner, A., Sailer, V., Augello, M., Puca, L., Rosati, R., et al. (2017). Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7, 462-477. https://doi.org/10.1158/2159-8290.CD-16-1154
  61. Pritchard, J.R., Bruno, P.M., Gilbert, L.A., Capron, K.L., Lauffenburger, D.A., and Hemann, M.T. (2013). Defining principles of combination drug mechanisms of action. Proc. Natl. Acad. Sci. U. S. A. 110, E170-E179.
  62. Qiu, P., Simonds, E.F., Bendall, S.C., Gibbs, K.D., Jr., Bruggner, R.V., Linderman, M.D., Sachs, K., Nolan, G.P., and Plevritis, S.K. (2011). Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886-891. https://doi.org/10.1038/nbt.1991
  63. Ramaswami, G., Won, H., Gandal, M.J., Haney, J., Wang, J.C., Wong, C.C.Y., Sun, W., Prabhakar, S., Mill, J., and Geschwind, D.H. (2020). Integrative genomics identifies a convergent molecular subtype that links epigenomic with transcriptomic differences in autism. Nat. Commun. 11, 4873. https://doi.org/10.1038/s41467-020-18526-1
  64. Rappoport, N., Safra, R., and Shamir, R. (2020). MONET: multi-omic module discovery by omic selection. PLoS Comput. Biol. 16, e1008182. https://doi.org/10.1371/journal.pcbi.1008182
  65. Rappoport, N. and Shamir, R. (2019). NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics 35, 3348-3356. https://doi.org/10.1093/bioinformatics/btz058
  66. Schubert, O.T., Rost, H.L., Collins, B.C., Rosenberger, G., and Aebersold, R. (2017). Quantitative proteomics: challenges and opportunities in basic and applied research. Nat. Protoc. 12, 1289-1294. https://doi.org/10.1038/nprot.2017.040
  67. 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. https://doi.org/10.1093/bioinformatics/btp543
  68. Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W.M., 3rd, Hao, Y., Stoeckius, M., Smibert, P., and Satija, R. (2019). Comprehensive integration of single-cell data. Cell 177, 1888-1902.e21. https://doi.org/10.1016/j.cell.2019.05.031
  69. Stuart, T. and Satija, R. (2019). Integrative single-cell analysis. Nat. Rev. Genet. 20, 257-272. https://doi.org/10.1038/s41576-019-0093-7
  70. Sumazin, P., Yang, X., Chiu, H.S., Chung, W.J., Iyer, A., Llobet-Navas, D., Rajbhandari, P., Bansal, M., Guarnieri, P., Silva, J., et al. (2011). An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell 147, 370-381. https://doi.org/10.1016/j.cell.2011.09.041
  71. Swanson, K.R., Rockne, R.C., Claridge, J., Chaplain, M.A., Alvord, E.C., Jr., and Anderson, A.R. (2011). Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology. Cancer Res. 71, 7366-7375. https://doi.org/10.1158/0008-5472.CAN-11-1399
  72. Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P., et al. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607-D613. https://doi.org/10.1093/nar/gky1131
  73. Tentner, A.R., Lee, M.J., Ostheimer, G.J., Samson, L.D., Lauffenburger, D.A., and Yaffe, M.B. (2012). Combined experimental and computational analysis of DNA damage signaling reveals context-dependent roles for Erk in apoptosis and G1/S arrest after genotoxic stress. Mol. Syst. Biol. 8, 568. https://doi.org/10.1038/msb.2012.1
  74. Teves, J.M. and Won, K.J. (2020). Mapping cellular coordinates through advances in spatial transcriptomics technology. Mol. Cells 43, 591-599. https://doi.org/10.14348/molcells.2020.0020
  75. Tyers, M. and Mann, M. (2003). From genomics to proteomics. Nature 422, 193-197. https://doi.org/10.1038/nature01510
  76. 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. https://doi.org/10.1093/bioinformatics/btq182
  77. Vidova, V. and Spacil, Z. (2017). A review on mass spectrometry-based quantitative proteomics: targeted and data independent acquisition. Anal. Chim. Acta 964, 7-23. https://doi.org/10.1016/j.aca.2017.01.059
  78. Wang, B., Mezlini, A.M., Demir, F., Fiume, M., Tu, Z., Brudno, M., HaibeKains, B., and Goldenberg, A. (2014). Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333-337. https://doi.org/10.1038/nmeth.2810
  79. Whitehurst, A.W., Bodemann, B.O., Cardenas, J., Ferguson, D., Girard, L., Peyton, M., Minna, J.D., Michnoff, C., Hao, W., Roth, M.G., et al. (2007). Synthetic lethal screen identification of chemosensitizer loci in cancer cells. Nature 446, 815-819. https://doi.org/10.1038/nature05697
  80. Witten, D.M. and Tibshirani, R.J. (2009). Extensions of sparse canonical correlation analysis with applications to genomic data. Stat. Appl. Genet. Mol. Biol. 8, Article28.
  81. Wu, D., Wang, D., Zhang, M.Q., and Gu, J. (2015). Fast dimension reduction and integrative clustering of multi-omics data using lowrank approximation: application to cancer molecular classification. BMC Genomics 16, 1022. https://doi.org/10.1186/s12864-015-2223-8
  82. Yang, Z. and Michailidis, G. (2016). A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data. Bioinformatics 32, 1-8. https://doi.org/10.1093/bioinformatics/btv544
  83. Yuan, Y., Savage, R.S., and Markowetz, F. (2011). Patient-specific data fusion defines prognostic cancer subtypes. PLoS Comput. Biol. 7, e1002227. https://doi.org/10.1371/journal.pcbi.1002227
  84. Zhang, H., Liu, T., Zhang, Z., Payne, S.H., Zhang, B., McDermott, J.E., Zhou, J.Y., Petyuk, V.A., Chen, L., Ray, D., et al. (2016). Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755-765. https://doi.org/10.1016/j.cell.2016.05.069