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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


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.

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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.

Change history


  1. 1.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 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).

    CAS  PubMed  Google Scholar 

  4. 4.

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

    CAS  PubMed  Google Scholar 

  5. 5.

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

    CAS  PubMed  Google Scholar 

  6. 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).

    Google Scholar 

  7. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 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. 9.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kuenzi, B. M. et al. Nature Reviews Cancer - SBmaps. NDEx .org (2019).

  11. 11.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Kuenzi, B. M. et al. Nature Reviews Cancer - LCpathways. (2019).

  13. 13.

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

    PubMed  PubMed Central  Google Scholar 

  14. 14.

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

    CAS  PubMed  Google Scholar 

  15. 15.

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

    CAS  PubMed  Google Scholar 

  16. 16.

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

  17. 17.

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

    PubMed  PubMed Central  Google Scholar 

  18. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    CAS  PubMed  Google Scholar 

  20. 20.

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

  21. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

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

    CAS  PubMed  Google Scholar 

  24. 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).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

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

    Google Scholar 

  28. 28.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

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

    PubMed  PubMed Central  Google Scholar 

  35. 35.

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

    PubMed  PubMed Central  Google Scholar 

  36. 36.

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

  37. 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).

    CAS  PubMed  Google Scholar 

  38. 38.

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

    PubMed  PubMed Central  Google Scholar 

  39. 39.

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

    CAS  PubMed  Google Scholar 

  40. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 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).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

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

    PubMed  PubMed Central  Google Scholar 

  43. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

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

    CAS  PubMed  Google Scholar 

  45. 45.

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

    PubMed  PubMed Central  Google Scholar 

  46. 46.

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

    PubMed  PubMed Central  Google Scholar 

  47. 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).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

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

    Google Scholar 

  49. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 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).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

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

    CAS  PubMed  Google Scholar 

  52. 52.

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

    PubMed  PubMed Central  Google Scholar 

  53. 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).

    CAS  PubMed  Google Scholar 

  54. 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).

    CAS  PubMed  Google Scholar 

  55. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 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).

    PubMed  PubMed Central  Google Scholar 

  58. 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).

    PubMed  Google Scholar 

  59. 59.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 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).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

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

    CAS  PubMed  Google Scholar 

  63. 63.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

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

    CAS  PubMed  Google Scholar 

  65. 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. 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).

    PubMed  PubMed Central  Google Scholar 

  67. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 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).

    CAS  Google Scholar 

  69. 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).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Lorz, A., Botesteanu, D.-A. & Levy, D. Modeling cancer cell growth dynamics in vitro in response to antimitotic drug treatment. Front. Oncol. (2017).

  71. 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).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

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

    PubMed  PubMed Central  Google Scholar 

  73. 73.

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

    PubMed  PubMed Central  Google Scholar 

  74. 74.

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

    PubMed  PubMed Central  Google Scholar 

  75. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

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

    PubMed  PubMed Central  Google Scholar 

  78. 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).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

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

    CAS  PubMed  Google Scholar 

  80. 80.

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

    CAS  PubMed  Google Scholar 

  81. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 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).

    PubMed  PubMed Central  Google Scholar 

  83. 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).

    Google Scholar 

  84. 84.

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

    PubMed  PubMed Central  Google Scholar 

  85. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 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).

    CAS  PubMed  Google Scholar 

  87. 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).

    CAS  PubMed  Google Scholar 

  88. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 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).

    CAS  PubMed  Google Scholar 

  90. 90.

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

    Google Scholar 

  91. 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).

    CAS  Google Scholar 

  92. 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).

    CAS  PubMed  Google Scholar 

  93. 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).

    Google Scholar 

  94. 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. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

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

  97. 97.

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

    CAS  PubMed  Google Scholar 

  98. 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).

    PubMed  PubMed Central  Google Scholar 

  99. 99.

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

    CAS  PubMed  Google Scholar 

  100. 100.

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

    PubMed  PubMed Central  Google Scholar 

  101. 101.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

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

    Google Scholar 

  103. 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).

    CAS  PubMed  Google Scholar 

  104. 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).

    CAS  PubMed  Google Scholar 

  105. 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).

    CAS  PubMed  Google Scholar 

  106. 106.

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

    PubMed  PubMed Central  Google Scholar 

  107. 107.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 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).

    PubMed  PubMed Central  Google Scholar 

  110. 110.

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

    CAS  PubMed  Google Scholar 

  111. 111.

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

    PubMed  Google Scholar 

  112. 112.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 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).

    PubMed  PubMed Central  Google Scholar 

  114. 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).

    PubMed  PubMed Central  Google Scholar 

  115. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 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).

    CAS  Google Scholar 

  118. 118.

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

    CAS  PubMed  Google Scholar 

  119. 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).

    PubMed  PubMed Central  Google Scholar 

  120. 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).

    PubMed  PubMed Central  Google Scholar 

  121. 121.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 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).

    CAS  PubMed  Google Scholar 

  123. 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).

    CAS  PubMed  Google Scholar 

  124. 124.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 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).

    PubMed  PubMed Central  Google Scholar 

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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




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.

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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.

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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.

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American Association for Cancer Research project Genomics Evidence Neoplasia Information Exchange:


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Supplementary information


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.


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.


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


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.

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Kuenzi, B.M., Ideker, T. A census of pathway maps in cancer systems biology. Nat Rev Cancer 20, 233–246 (2020).

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