Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

A census of pathway maps in cancer systems biology

An Author Correction to this article was published on 13 January 2021

This article has been updated

Abstract

A key goal of cancer systems biology is to use big data to elucidate the molecular networks by which cancer develops. However, to date there has been no systematic evaluation of how far these efforts have progressed. In this Analysis, we survey six major systems biology approaches for mapping and modelling cancer pathways with attention to how well their resulting network maps cover and enhance current knowledge. Our sample of 2,070 systems biology maps captures all literature-curated cancer pathways with significant enrichment, although the strong tendency is for these maps to recover isolated mechanisms rather than entire integrated processes. Systems biology maps also identify previously underappreciated functions, such as a potential role for human papillomavirus-induced chromosomal alterations in ovarian tumorigenesis, and they add new genes to known cancer pathways, such as those related to metabolism, Hippo signalling and immunity. Notably, we find that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed. Finally, few of these findings have yet been clinically translated, leaving ample opportunity for future translational studies. Periodic surveys of cancer pathway maps, such as the one reported here, are critical to assess progress in the field and identify underserved areas of methodology and cancer biology.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Structure of the analysis.
Fig. 2: Cancer systems biology approaches covered in this analysis.
Fig. 3: Coverage of LCpathways by SBmaps.
Fig. 4: Assessment of relative research coverage of cancer pathways by systems biology.
Fig. 5: Representative SBmaps not previously reported in the literature.
Fig. 6: Potential new mechanisms emerging from cancer systems biology studies.

Similar content being viewed by others

Change history

References

  1. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Cox, J. & Mann, M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem. 80, 273–299 (2011).

    Article  CAS  PubMed  Google Scholar 

  5. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Wang, H. et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1459–1544 (2016).

    Article  Google Scholar 

  7. Hoadley, K. A. et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173, 291–304 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang, J. et al. International Cancer Genome Consortium Data Portal — a one-stop shop for cancer genomics data. Database 2011, bar026 (2011).

    PubMed  PubMed Central  Google Scholar 

  9. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kuenzi, B. M. et al. Nature Reviews Cancer - SBmaps. NDEx .org http://www.ndexbio.org/#/networkset/7cd9b57c-8322-11e9-848d-0ac135e8bacf (2019).

  11. Pratt, D. et al. NDEx, the Network Data Exchange. Cell Syst. 1, 302–305 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kuenzi, B. M. et al. Nature Reviews Cancer - LCpathways. NDEx.org http://www.ndexbio.org/#/networkset/d01d40d4-fcdd-11e8-8438-0ac135e8bacf (2019).

  13. Kandasamy, K. et al. NetPath: a public resource of curated signal transduction pathways. Genome Biol. 11, R3 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Schaefer, C. F. et al. PID: the Pathway Interaction Database. Nucleic Acids Res. 37, D674–D679 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Perfetto, L. et al. SIGNOR: a database of causal relationships between biological entities. Nucleic Acids Res. 44, D548–D554 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Chinchor, N. MUC-4 evaluation metrics. in Proc. of the Fourth Message Understanding Conference 22–29 (Morgan Kaufmann, 1992).

  17. Shibuya, M. Vascular endothelial growth factor (VEGF) and its receptor (VEGFR) signaling in angiogenesis. Genes Cancer 2, 1097–1105 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Zhang, F. et al. A network medicine approach to build a comprehensive atlas for the prognosis of human cancer. Brief. Bioinform. 17, 1044–1059 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Park, S. et al. An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types. Bioinformatics 32, 1643–1651 (2016).

    Article  CAS  PubMed  Google Scholar 

  20. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

  21. Xiong, S. et al. structural basis for auto-inhibition of the NDR1 kinase domain by an atypically long activation segment. Structure 26, 1101–1115.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Grasso, C. S. et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sit, S.-T. & Manser, E. Rho GTPases and their role in organizing the actin cytoskeleton. J. Cell Sci. 124, 679–683 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Stoeger, T., Gerlach, M., Morimoto, R. I. & Amaral, L. A. N. Large-scale investigation of the reasons why potentially important genes are ignored. PLoS Biol. 16, e2006643 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Bai, Y. et al. Adaptive responses to dasatinib-treated lung squamous cell cancer cells harboring DDR2 mutations. Cancer Res. 74, 7217–7228 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Seiler, M. et al. Somatic mutational landscape of splicing factor genes and their functional consequences across 33 cancer types. Cell Rep. 23, 282–296 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Warburg, O. & Minami, S. Versuche an Überlebendem Carcinom-gewebe. Klin. Wochenschr. 2, 776–777 (1923).

    Article  Google Scholar 

  28. Horlbeck, M. A. et al. Mapping the genetic landscape of human cells. Cell 174, 953–967.e22 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Shen, J. P. et al. Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 14, 573–576 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhao, D. et al. Combinatorial CRISPR-Cas9 metabolic screens reveal critical redox control points dependent on the KEAP1-NRF2 regulatory axis. Mol. Cell 69, 699–708 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ashton, T. M., McKenna, W. G., Kunz-Schughart, L. A. & Higgins, G. S. Oxidative phosphorylation as an emerging target in cancer therapy. Clin. Cancer Res. 24, 2482–2490 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  CAS  Google Scholar 

  35. Bordbar, A. et al. Model‐driven multi‐omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol. Syst. Biol. 8, 558 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Domblides, C., Lartigue, L. & Faustin, B. Control of the antitumor immune response by cancer metabolism. Cells 8, (2019).

  37. Duarte, N. C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl Acad. Sci. USA 104, 1777–1782 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ma, H. et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 3, 135 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419–425 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Mardinoglu, A. et al. Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol. Syst. Biol. 9, 649 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Mardinoglu, A. et al. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5, 3083 (2014).

    Article  PubMed  CAS  Google Scholar 

  42. Yizhak, K., Chaneton, B., Gottlieb, E. & Ruppin, E. Modeling cancer metabolism on a genome scale. Mol. Syst. Biol. 11, 817 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Shlomi, T., Benyamini, T., Gottlieb, E., Sharan, R. & Ruppin, E. Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. PLoS Comput. Biol. 7, e1002018 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jerby, L. et al. Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Res. 72, 5712–5720 (2012).

    Article  CAS  PubMed  Google Scholar 

  45. Nam, H. et al. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks. PLoS Comput. Biol. 10, e1003837 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Agren, R. et al. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol. Syst. Biol. 10, 721 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Resendis-Antonio, O., Checa, A. & Encarnación, S. Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect. PLoS One 5, e12383 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

    Article  CAS  Google Scholar 

  49. Baldwin, A., Pirisi, L. & Creek, K. E. NFI-Ski interactions mediate transforming growth factor beta modulation of human papillomavirus type 16 early gene expression. J. Virol. 78, 3953–3964 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Wilting, S. M. et al. Genomic profiling identifies common HPV-associated chromosomal alterations in squamous cell carcinomas of cervix and head and neck. BMC Med. Genomics 2, 32 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Bodelon, C. et al. Chromosomal copy number alterations and HPV integration in cervical precancer and invasive cancer. Carcinogenesis 37, 188–196 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Wu, Q.-J. et al. Detection of human papillomavirus-16 in ovarian malignancy. Br. J. Cancer 89, 672 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Jeannot, E., Harlé, A., Holmes, A. & Sastre-Garau, X. Nuclear factor I X is a recurrent target for HPV16 insertions in anal carcinomas. Genes Chromosomes Cancer 57, 638–644 (2018).

    Article  CAS  PubMed  Google Scholar 

  54. zur Hausen, H. Papillomaviruses causing cancer: evasion from host-cell control in early events in carcinogenesis. J. Natl Cancer Inst. 92, 690–698 (2000).

    Article  CAS  PubMed  Google Scholar 

  55. Marullo, R. et al. HPV16 E6 and E7 proteins induce a chronic oxidative stress response via NOX2 that causes genomic instability and increased susceptibility to DNA damage in head and neck cancer cells. Carcinogenesis 36, 1397–1406 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Roos, P., Orlando, P. A., Fagerstrom, R. M. & Pepper, J. W. In North America, some ovarian cancers express the oncogenes of preventable human papillomavirus HPV-18. Sci. Rep. 5, 8645 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ingerslev, K. et al. High-risk HPV is not associated with epithelial ovarian cancer in a Caucasian population. Infect. Agent. Cancer 11, 39 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Rosa, M. I. et al. The prevalence of human papillomavirus in ovarian cancer: a systematic review. Int. J. Gynecol. Cancer 23, 437–441 (2013).

    Article  PubMed  Google Scholar 

  59. Meng, Z., Moroishi, T. & Guan, K.-L. Mechanisms of Hippo pathway regulation. Genes. Dev. 30, 1–17 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Shin, S.-Y. et al. Functional roles of multiple feedback loops in extracellular signal-regulated kinase and Wnt signaling pathways that regulate epithelial-mesenchymal transition. Cancer Res. 70, 6715–6724 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Aldridge, B. B., Saez-Rodriguez, J., Muhlich, J. L., Sorger, P. K. & Lauffenburger, D. A. Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling. PLoS Comput. Biol. 5, e1000340 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Kolch, W., Calder, M. & Gilbert, D. When kinases meet mathematics: the systems biology of MAPK signalling. FEBS Lett. 579, 1891–1895 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Orton, R. J. et al. Computational modelling of the receptor-tyrosine-kinase-activated MAPK pathway. Biochem. J. 392, 249–261 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Heinrich, R., Neel, B. G. & Rapoport, T. A. Mathematical models of protein kinase signal transduction. Mol. Cell 9, 957–970 (2002).

    Article  CAS  PubMed  Google Scholar 

  65. Pan, S. Modeling the mitogen activated protein (MAP)-kinase pathway using ordinary differential equations. Comput. Biol. Bioinf. 1, 6–9 (2013).

    Google Scholar 

  66. Tran, P. T. et al. Survival and death signals can predict tumor response to therapy after oncogene inactivation. Sci. Transl Med. 3, 103ra99 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Claas, A. M., Atta, L., Gordonov, S., Meyer, A. S. & Lauffenburger, D. A. Systems modeling identifies divergent receptor tyrosine kinase reprogramming to MAPK pathway inhibition. Cell. Mol. Bioeng. 11, 451–469 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Morris, M. K., Clarke, D. C., Osimiri, L. C. & Lauffenburger, D. A. Systematic analysis of quantitative logic model ensembles predicts drug combination effects on cell signaling networks. CPT Pharmacomet. Syst. Pharmacol. 5, 544–553 (2016).

    Article  CAS  Google Scholar 

  69. Gierut, J. J. et al. Network-level effects of kinase inhibitors modulate TNF-α–induced apoptosis in the intestinal epithelium. Sci. Signal. 8, ra129 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Lorz, A., Botesteanu, D.-A. & Levy, D. Modeling cancer cell growth dynamics in vitro in response to antimitotic drug treatment. Front. Oncol. https://doi.org/10.3389/fonc.2017.00189 (2017).

  71. Palacios-Moreno, J. et al. Neuroblastoma tyrosine kinase signaling networks involve FYN and LYN in endosomes and lipid rafts. PLoS Comput. Biol. 11, e1004130 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Choudhary, K. S. et al. EGFR signal-network reconstruction demonstrates metabolic crosstalk in EMT. PLoS Comput. Biol. 12, e1004924 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Gill, M. K. et al. A feed forward loop enforces YAP/TAZ signaling during tumorigenesis. Nat. Commun. 9, 3510 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. O’Connor, C. M. et al. Inactivation of PP2A by a recurrent mutation drives resistance to MEK inhibitors. Oncogene 39, 703–717 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838–847 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Coleman, D. J. et al. BET bromodomain inhibition blocks the function of a critical AR-independent master regulator network in lethal prostate cancer. Oncogene 38, 5658–5669 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Risom, T. et al. Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat. Commun. 9, 3815 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Echeverria, G. V. et al. Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state. Sci. Transl Med. 11, eaav0936 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Parker, L. A. et al. Diagnostic biomarkers: are we moving from discovery to clinical application? Clin. Chem. 64, 1657–1667 (2018).

    Article  CAS  PubMed  Google Scholar 

  80. Poste, G. Bring on the biomarkers. Nature 469, 156–157 (2011).

    Article  CAS  PubMed  Google Scholar 

  81. Carbone, D. P. et al. Prognostic and predictive role of the VeriStrat plasma test in patients with advanced non-small-cell lung cancer treated with erlotinib or placebo in the NCIC Clinical Trials Group BR.21 trial. J. Thorac. Oncol. 7, 1653–1660 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Amann, J. M. et al. Genetic and proteomic features associated with survival after treatment with erlotinib in first-line therapy of non-small cell lung cancer in Eastern Cooperative Oncology Group 3503. J. Thorac. Oncol. 5, 169–178 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Filho, O. M., Ignatiadis, M. & Sotiriou, C. Genomic Grade Index: an important tool for assessing breast cancer tumor grade and prognosis. Crit. Rev. Oncol. Hematol. 77, 20–29 (2011).

    Article  Google Scholar 

  84. Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Jerevall, P.-L. et al. Prognostic utility of HOXB13:IL17BR and molecular grade index in early-stage breast cancer patients from the Stockholm trial. Br. J. Cancer 104, 1762–1769 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Ma, X.-J. et al. A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer. Clin. Cancer Res. 14, 2601–2608 (2008).

    Article  CAS  PubMed  Google Scholar 

  87. Filipits, M. et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin. Cancer Res. 17, 6012–6020 (2011).

    Article  CAS  PubMed  Google Scholar 

  88. Sparano, J. A. et al. Prospective validation of a 21-gene expression assay in breast cancer. N. Engl. J. Med. 373, 2005–2014 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Cronin, M. et al. Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin. Chem. 53, 1084–1091 (2007).

    Article  CAS  PubMed  Google Scholar 

  90. van ’t Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    Article  Google Scholar 

  91. Silvestri, G. A. et al. A bronchial genomic classifier for the diagnostic evaluation of lung cancer. N. Eng. J. Med. 373, 243–251 (2015).

    Article  CAS  Google Scholar 

  92. Yeoh, E.-J. et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1, 133–143 (2002).

    Article  CAS  PubMed  Google Scholar 

  93. Stein, R. C. et al. OPTIMA prelim: a randomised feasibility study of personalised care in the treatment of women with early breast cancer. Health Technol. Assess. Winch. Engl. 20, 1–201 (2016).

    Article  Google Scholar 

  94. Michiels, S., Ternès, N. & Rotolo, F. Statistical controversies in clinical research: prognostic gene signatures are not (yet) useful in clinical practice. Ann. Oncol. 27, 01–09 (2016).

    Google Scholar 

  95. Cheng, D. T. et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). J. Mol. Diagn. 17, 251–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Harris, J. FDA approves FoundationOne CDx, CMS agrees to cover. OncLive, November (2017).

  97. Dacic, S. & Nikiforova, M. N. Present and future molecular testing of lung carcinoma. Adv. Anat. Pathol. 21, 94–99 (2014).

    Article  CAS  PubMed  Google Scholar 

  98. Rashdan, S. & Gerber, D. E. Going into BATTLE: umbrella and basket clinical trials to accelerate the study of biomarker-based therapies. Ann. Transl Med. 4, 529 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Biankin, A. V., Piantadosi, S. & Hollingsworth, S. J. Patient-centric trials for therapeutic development in precision oncology. Nature 526, 361–370 (2015).

    Article  CAS  PubMed  Google Scholar 

  100. Senft, D., Leiserson, M. D. M., Ruppin, E. & Ronai, Z. A. Precision oncology: the road ahead. Trends Mol. Med. 23, 874–898 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Mailman, M. D. et al. The NCBI dbGaP database of genotypes and phenotypes. Nat. Genet. 39, 1181–1186 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Clough, E. & Barrett, T. The Gene Expression Omnibus database. Methods Mol. Biol. Clifton NJ 1418, 93–110 (2016).

    Article  Google Scholar 

  103. Jones, P. et al. PRIDE: a public repository of protein and peptide identifications for the proteomics community. Nucleic Acids Res. 34, D659–D663 (2006).

    Article  CAS  PubMed  Google Scholar 

  104. Mani, R., St.Onge, R. P., Hartman, J. L., Giaever, G. & Roth, F. P. Defining genetic interaction. Proc. Natl. Acad. Sci. 105, 3461–3466 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Collins, S. R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007).

    Article  CAS  PubMed  Google Scholar 

  106. Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Havugimana, P. C. et al. A census of human soluble protein complexes. Cell 150, 1068–1081 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Brückner, A., Polge, C., Lentze, N., Auerbach, D. & Schlattner, U. Yeast two-hybrid, a powerful tool for systems biology. Int. J. Mol. Sci. 10, 2763–2788 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Remy, I. & Michnick, S. W. Application of protein-fragment complementation assays in cell biology. BioTechniques 42, 137–145 (2007).

    Article  CAS  PubMed  Google Scholar 

  111. Bürckstümmer, T. et al. An efficient tandem affinity purification procedure for interaction proteomics in mammalian cells. Nat. Methods 3, 1013–1019 (2006).

    Article  PubMed  CAS  Google Scholar 

  112. Dunham, W. H., Mullin, M. & Gingras, A.-C. Affinity-purification coupled to mass spectrometry: basic principles and strategies. Proteomics 12, 1576–1590 (2012).

    Article  CAS  PubMed  Google Scholar 

  113. Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7, S7 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Lefebvre, C. et al. A human B‐cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol. Syst. Biol. 6, 377 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Olow, A. et al. An atlas of the human kinome reveals the mutational landscape underlying dysregulated phosphorylation cascades in cancer. Cancer Res. 76, 1733–1745 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Lachmann, A., Giorgi, F. M., Lopez, G. & Califano, A. ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics 32, 2233–2235 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Leiserson, M. D. M. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015).

    Article  CAS  PubMed  Google Scholar 

  118. Vandin, F., Upfal, E. & Raphael, B. J. Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18, 507–522 (2011).

    Article  CAS  PubMed  Google Scholar 

  119. Jia, P. & Zhao, Z. VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data. PLoS Comput. Biol. 10, e1003460 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Park, J. et al. AF1q is a novel TCF7 co-factor which activates CD44 and promotes breast cancer metastasis. Oncotarget 6, 20697–20710 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Hofree, M., Shen, J. P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 1108–1115 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Bidkhori, G. et al. Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes. Proc. Natl Acad. Sci. 115, E11874–E11883 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Bordbar, A., Monk, J. M., King, Z. A. & Palsson, B. O. Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 15, 107–120 (2014).

    Article  CAS  PubMed  Google Scholar 

  124. Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    Article  CAS  PubMed  Google Scholar 

  125. Olcina, M. M. et al. Mutations in an innate immunity pathway are associated with poor overall survival outcomes and hypoxic signaling in cancer. Cell Rep. 25, 3721–3732.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Babaei, S., Hulsman, M., Reinders, M. & de Ridder, J. Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion. BMC Bioinformatics 14, 29 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the support for this work provided by grants from the US National Institutes of Health to T.I. (CA209891, CA184427, ES014811) and B.M.K. (CA212456).

Author information

Authors and Affiliations

Authors

Contributions

B.M.K. researched data for the article. B.M.K. and T.I. discussed the content and wrote, reviewed and edited the manuscript.

Corresponding author

Correspondence to Trey Ideker.

Ethics declarations

Competing interests

T.I. is a co-founder of Data4Cure and has an equity interest. T.I. is on the Scientific Advisory Board of Ideaya BioSciences, Inc., has an equity interest, and receives income. The terms of these arrangements have been reviewed and approved by the University of California San Diego in accordance with its conflict of interest policies. B.M.K. declares no competing interests.

Additional information

Peer review information

Nature Reviews Cancer thanks A. Mardinoglu, M. Vidal, D. Hill and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

American Association for Cancer Research project Genomics Evidence Neoplasia Information Exchange: http://www.aacr.org/Research/Research/Pages/aacr-project-genie.aspx

BioModels: https://www.ebi.ac.uk/biomodels/

Cancer Systems Biology Consortium: http://csbconsortium.org/

CellML: https://www.cellml.org/

ClinicalTrials.gov: https://clinicaltrials.gov/

Gene Ontology: http://geneontology.org/

GitHub: https://github.com/

Human Protein Atlas: http://proteinatlas.org/

ISRCTN: https://www.isrctn.com/

Network Data Exchange: http://www.ndexbio.org

Oncology Research Information Exchange Network: http://oriencancer.org

Quantum Immuno-oncology Lifelong Trial programme: https://clinicaltrials.gov/ct2/results?term=QUILT

Supplementary information

Glossary

Fuzzy logic

A predictive model that attempts to use vague or imprecise information to obtain accurate predictions and solve complex problems.

Adjacency matrix

A square matrix used to represent the structure of a finite network in which rows and columns represent nodes in the network and the binary elements of the matrix represent the edges.

Interaction list

A simple, tabular network representation containing two columns (source and target) detailing the edges of a network.

Cytoscape

An open-source software platform for visualizing complex networks and integrating these with any type of attribute data for further analyses.

Functional enrichment analysis

A method to identify collections of genes or proteins (often disease-associated pathways) that are over represented or under represented in a large set of genes or proteins.

Hypergeometric test

A statistical test used to calculate the statistical significance of having drawn specific successes from a given population, often used to identify subpopulations that are over represented or under represented in that population.

F score

A measure of a test’s accuracy that takes into account both the precision and the recall of the test to compute the score. Similarly to precision and recall, the F score has a highest value of 1 and a lowest value of 0.

STRING

A database of known and predicted protein–protein interactions that includes both direct (physical) and indirect (functional) interactions.

Epistasis

The phenomenon whereby genetic alterations at two or more genetic loci (for example, mutations or deletions in different genes) produce a phenotype that is unexpected on the basis of the phenotypes of each of the single genetic alterations.

CRISPR interference

A genetic perturbation technique that allows sequence specific repression of gene expression in prokaryotic and eukaryotic cells.

Network diffusion

A method to analyse how the topology of a network impacts how information spreads across a given network.

Striatin-interacting phosphatase and kinase (STRIPAK) and integrator complex

An evolutionarily conserved supramolecular protein complex which regulates the phosphorylation status and therefore activation status of various pathways.

k-nearest neighbours model

A non-parametric machine learning method used for classification and regression tasks that learns to classify new cases on the basis of a similarity measure (for example, distance functions).

Basket trials

Trials designed to test the effects of a single drug, or a combination of drugs, in a variety of cancer types on the basis of the presence of a specific biomarker.

Umbrella trials

Trials designed to test the effect of different drugs on the basis of the presence of different biomarkers within a single cancer type.

k-fold cross validation

A resampling procedure used to evaluate machine learning models on a limited data sample by repeatedly splitting the data into training and test sets.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuenzi, B.M., Ideker, T. A census of pathway maps in cancer systems biology. Nat Rev Cancer 20, 233–246 (2020). https://doi.org/10.1038/s41568-020-0240-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41568-020-0240-7

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer