The recurrent architecture of tumour initiation, progression and drug sensitivity

Journal name:
Nature Reviews Cancer
Year published:
Published online


Recent studies across multiple tumour types are starting to reveal a recurrent regulatory architecture in which genomic alterations cluster upstream of functional master regulator (MR) proteins, the aberrant activity of which is both necessary and sufficient to maintain tumour cell state. These proteins form small, hyperconnected and autoregulated modules (termed tumour checkpoints) that are increasingly emerging as optimal biomarkers and therapeutic targets. Crucially, as their activity is mostly dysregulated in a post-translational manner, rather than by mutations in their corresponding genes or by differential expression, the identification of MR proteins by conventional methods is challenging. In this Opinion article, we discuss novel methods for the systematic analysis of MR proteins and of the modular regulatory architecture they implement, including their use as a valuable reductionist framework to study the genetic heterogeneity of human disease and to drive key translational applications.

At a glance


  1. The architecture of tumour checkpoints.
    Figure 1: The architecture of tumour checkpoints.

    a | The probability densities of normal and transformed cells are shown in a principal component (PC) projection that captures most of the sample variability of four tumour types: colorectal adenocarcinoma (COAD), kidney renal clear cell carcinoma (KIRC), uterine corpus endometrial cancer (UCEC) and prostate adenocarcinoma (PRAD).These distributions show a clear single-peak structure, suggesting that the regulatory logic of the tumour cell is effective in avoiding occupancy of states that are far away from the mean. Considering that cancer tissue may also be contaminated by extensive lymphocytic and stromal cell infiltration, the variance of the normal and tumour-associated distributions is of quite comparable magnitude. A comprehensive inventory of all tumour types in The Cancer Genome Atlas (TCGA) reveals that only a handful — such as head and neck squamous cell carcinoma (HNSC), kidney renal papillary cell carcinoma (KIRP) and liver hepatocellular carcinoma (LIHC) — present with substantially greater variance than the corresponding normal tissue. b | The proposed regulatory architecture implemented by master regulator (MR) proteins in tumour checkpoints is shown. MRs (blue spheres in shaded area) represent proteins the concerted, aberrant activity of which is both necessary and sufficient for cancer cell state maintenance. Their aberrant activity is induced by genes in their upstream pathways that are mutated in a specific patient (purple spheres) selected from a larger repertoire of candidate driver genes (green spheres), the mutation of which is recurrently detected in large cohorts. Passenger mutations (pale blue spheres) that are not upstream of MRs have no effect on tumour checkpoint activity and thus on the specific phenotype that the checkpoint regulates. Arrows in this diagram show regulatory and signalling interactions, that is, how one gene product regulates other gene products. Black arrows represent crucial top-down interactions leading from patient mutations first to activation of MR proteins in the tumour checkpoint and then to activation of downstream genetic programmes that are required for tumour phenotype presentation. Grey and blue arrows represent additional regulatory interactions that do not affect and are not affected by tumour checkpoint MRs, respectively. Dashed arrows represent feedback loops implemented either between the MR layer and the upstream modulators or between genes regulated by MR proteins and upstream MR modulators. The MR protein module in the shaded area represents the tumour checkpoint. Pink spheres represent genes that are differentially expressed as a result of the aberrant activity of MR proteins in the tumour checkpoint (that is, the tumour gene expression signature). Lightning bolts represent potential therapeutic interventions using pharmacological inhibitors. Inhibiting oncoproteins mutated in a large fraction (for example, 90%) of tumour subclones will cause relapse owing to the presence of rare, alternative subclones harbouring either alternative or bypass mutations. A bypass mutation is a mutation that activates the pathway downstream of the pharmacological intervention point. By contrast, inhibiting the tumour checkpoint may represent a more effective strategy, as it captures the effect of all upstream mutations.

  2. Dysregulation of homeostatic control following malignant transformation and activation of dystasis control mechanisms that are responsible for the stability of tumour cell state.
    Figure 2: Dysregulation of homeostatic control following malignant transformation and activation of dystasis control mechanisms that are responsible for the stability of tumour cell state.

    This figure shows how normal cell physiology is determined by the energetic landscape of its regulatory networks, enabling cells to follow specific developmental trajectories that are highly insensitive to genetic, epigenetic and environmental variability, thus achieving stable end point states. This process, also known as Waddington canalization23, is illustrated in a cartoon showing differentiation from haematopoietic stem cell (HSC), to multi-lymphoid progenitor (MLP) to a fully differentiated human B cell as a set of transitions to states of progressively lower energy and thus higher stability. Disruption of this regulatory landscape by genetic alterations and environmental signals leads to physiological state loss and emergence of novel, stable disease states, for example, diffuse large B cell lymphoma (DLBCL). When multiple, quasi-isoenergetic states emerge, they can lead to coexistence of cells representing distinct tumour phenotypes in the same tumour mass or to tumour cell reprogramming to different states following treatment, a process known as tumour plasticity. For example, it has been shown that cells representing both the mesenchymal and the proneural subtype of glioma can coexist in the same tumour107 and that a small fraction of cells treated with tumour necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) transiently reprogramme to a TRAIL-resistant state133. Whereas normal cell homeostasis presides over the stability of physiological cell states, by making them difficult to escape, we propose that a dysregulated form of these stability control processes (that is, tumour dystasis) is responsible for the stability of tumour-associated cell states and is mechanistically implemented by a small number of master regulator (MR) proteins in a tumour checkpoint.

  3. Diverse genetic alterations in upstream pathways contribute to aberrant NF-[kappa]B activity in DLBCL.
    Figure 3: Diverse genetic alterations in upstream pathways contribute to aberrant NF-κB activity in DLBCL.

    Systematic analysis of genes in pathways upstream of the nuclear factor-κB (NF-κB) complex revealed a large repertoire of diffuse large B cell lymphoma (DLBCL)-specific genetic alterations in B cell receptor (BCR) and myeloid differentiation primary response 88 (MYD88) pathways. The presence of these mutations leads to aberrant activation of the canonical p50–RELA heterodimer and associated tumour dependency. These mutations, which are more frequent in the activated B cell (ABC) subtype of DLBCL, have provided the rationale for the clinical development of several BCR pathway inhibitors, such as ibrutinib, a Bruton tyrosine kinase (BTK) inhibitor. CARD11, caspase recruitment domain family member 11; IFN, interferon; IL, interleukin; IRAK, IL-1 r eceptor-associated kinase; IRF4, interferon regulatory factor 4; ITAM, immune receptor tyrosine-based activation motif; JAK1, Janus kinase 1; MALT1, mucosa-associated lymphoid tissue lymphoma translocation protein 1; PKC, protein kinase C; STAT, signal transducer and activator of transcription; TIR, Toll–interleukin receptor; TRAF6, TNF receptor associated factor 6. Adapted with permission from Ref. 134, Nature Publishing Group.

  4. Protein activity inference from the expression of its regulatory targets.
    Figure 4: Protein activity inference from the expression of its regulatory targets.

    a | Protein activity is the ultimate result of a complex cascade of molecular processes, from transcription and translation, to post-translational modification, complex formation and localization to appropriate subcellular compartments. As a result, there are no individual assays that can accurately measure protein activity in proteome-wide fashion. Instead, we have proposed that an accurate estimator of protein activity is represented by the gene expression of its transcriptional targets, that is, its regulon. This rationale is implemented by the Virtual Inference of Protein Activity by Enriched Regulon Analysis (VIPER) algorithm, based on transcriptional targets inferred by reverse engineering algorithms such as Algorithm for the Accurate Reconstruction of Cellular Networks (ARACNe). b | When a protein is inactive its targets are randomly distributed in terms of differential expression. c | By contrast, when the same protein is aberrantly activated, its positively regulated targets become significantly overexpressed and its repressed targets become underexpressed. This can be effectively and quantitatively assessed by gene expression enrichment analysis methods. EGFR, epidermal growth factor receptor.

  5. Tumour checkpoint architecture of the mesenchymal subtype of glioblastoma.
    Figure 5: Tumour checkpoint architecture of the mesenchymal subtype of glioblastoma.

    Transcription factors involved in the activation of mesenchymal glioblastoma (MES-GBM) subtype are shown in purple. Overall, the six transcription factors shown in this figure — CCAAT/enhancer- binding protein-β (CEBPβ) and CEBPδ are represented by CEBP, for simplicity, as they form homodimers and heterodimers — control 74% of the genes in the mesenchymal signature of high-grade glioma. A region between 2 kb upstream and 2 kb downstream of the transcription start site of the target genes identified by Algorithm for the Accurate Reconstruction of Cellular Networks (ARACNe) was analysed for the presence of putative binding sites. When combined with analysis of gene expression profiles following short hairpin RNA (shRNA)-mediated silencing of these transcription factors, the latter were shown to bind and regulate the large majority of MES-GBM signature genes (shown in pink). In addition, CEBP (both β and δ subunits) and signal transducer and activator of transcription 3 (STAT3) were shown to regulate the other three transcription factors in the tumour checkpoint and to synergistically regulate the state of MES-GBM cells. ACTA2, actin α2; ACTN1, actinin α1; ANGPT2, angiopoietin 2; ANPEP, alanyl aminopeptidase; BACE2, β-site APP-cleaving enzyme 2; B4GALT1, β-1,4-galactosyltransferase 1; BHLHE40, class E basic helix–loop–helix protein 40; CA12, carbonic anhydrase 12; C1QTNF1, C1q and tumour necrosis factor related protein 1; C1R, complement C1r; C1RL, complement C1r subcomponent like; CHI3L1, chitinase 3 like 1; COL4A1, collagen type IV α1 chain; ECE1, endothelin converting enzyme 1; EFEMP2, EGF containing fibulin like extracellular matrix protein 2; EFNB2, ephrin B2; EHD2, EH domain containing 2; EMP1, epithelial membrane protein 1; ESM1, endothelial cell specific molecule 1; FCGR2A, Fc fragment of IgG receptor IIa; FLNA, filamin A; FOSL2, Fos-related antigen 2; FPRL1, formyl peptide receptor-like 1; HRH1, histamine receptor H1; ICAM1, intercellular adhesion molecule 1; IFITM, interferon induced transmembrane protein; IL32, interleukin-32; ITGA7, integrin subunit α7; LIF, leukaemia inhibitory factor; MMP, matrix metalloproteinase; MVP, major vault protein; MYH9, myosin heavy chain 9; MYL9, myosin light chain 9; NRP2, neuropilin 2; OSMR, oncostatin M receptor; PAPPA, pappalysin 1; PDLIM4; PDZ and LIM domain 4; PDPN, podoplanin; PELO, pelota homologue; PI3, peptidase inhibitor 3; PLA2G5, phospholipase A2 group V; PLAU, plasminogen activator, urokinase; PLAUR, PLAU receptor; PVRL2, poliovirus receptor-related 2; PTRF, polymerase I and transcript release factor; RRBP1, ribosome binding protein 1; RUNX1, runt-related transcription factor 1; SGSH, N-sulfoglucosamine sulfohydrolase; S100A11, S100 calcium binding protein A11; SLC39A8, solute carrier family 39 member 8; SOCS3, suppressor of cytokine signalling 3; TAGLN, transgelin; THBD, thrombomodulin; TIMP1, tissue inihibitor of metalloproteinase 1; TMEPAI, transmembrane prostate androgen-induced protein; TNC, tenascin C; TPP1, tripeptidyl peptidase 1; ZYX, zyxin. Adapted with permission from Ref. 10, Nature Publishing Group.


  1. Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929944 (2014).
  2. Shah, S. P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395399 (2012).
  3. Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98110 (2010).
  4. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346352 (2012).
  5. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 6170 (2012).
  6. Oakman, C., Santarpia, L. & Di Leo, A. Breast cancer assessment tools and optimizing adjuvant therapy. Nat. Rev. Clin. Oncol. 7, 725732 (2010).
  7. Chen, J. C. et al. Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks. Cell 159, 402414 (2014).
  8. Compagno, M. et al. Mutations of multiple genes cause deregulation of NF-κB in diffuse large B-cell lymphoma. Nature 459, 717721 (2009).
  9. Aytes, A. et al. Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell 25, 638651 (2014).
  10. Carro, M. S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318325 (2010).
  11. Bisikirska, B. et al. Elucidation and pharmacological targeting of novel molecular drivers of follicular lymphoma progression. Cancer Res. 76, 664674 (2016).
  12. Yang, J. et al. Twist, a master regulator of morphogenesis, plays an essential role in tumor metastasis. Cell 117, 927939 (2004).
  13. Nambu, J. R., Lewis, J. O., Wharton, K. A. Jr & Crews, S. T. The Drosophila single-minded gene encodes a helix–loop–helix protein that acts as a master regulator of CNS midline development. Cell 67, 11571167 (1991).
  14. Resnick, M. A., Tomso, D., Inga, A., Menendez, D. & Bell, D. Functional diversity in the gene network controlled by the master regulator p53 in humans. Cell Cycle 4, 10261029 (2005).
  15. Klapper, L. N., Kirschbaum, M. H., Sela, M. & Yarden, Y. Biochemical and clinical implications of the ErbB/HER signaling network of growth factor receptors. Adv. Cancer Res. 77, 2579 (2000).
  16. Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747752 (2000).
  17. Dalla-Favera, R. et al. Human c-myc oncogene is located on the region of chromosome 8 that is translocated in Burkitt lymphoma cells. Proc. Natl Acad. Sci. USA 79, 78247827 (1982).
  18. Klein, U. et al. Transcriptional analysis of the B cell germinal center reaction. Proc. Natl Acad. Sci. USA 100, 26392644 (2003).
  19. Akavia, U. D. et al. An integrated approach to uncover drivers of cancer. Cell 143, 10051017 (2010).
  20. Gu, J. et al. Gene module based regulator inference identifying miR-139 as a tumor suppressor in colorectal cancer. Mol. Biosyst. 10, 32493254 (2014).
  21. Ergun, A., Lawrence, C. A., Kohanski, M. A., Brennan, T. A. & Collins, J. J. A network biology approach to prostate cancer. Mol. Syst. Biol. 3, 82 (2007).
  22. Cannon, W. B. Organization for physiological homeostasis. Physiol. Rev. 9, 399431 (1929).
  23. Waddington, C. H. Canalization of development and genetic assimilation of acquired characters. Nature 183, 16541655 (1959).
  24. Waddington, C. H. Genetic assimilation. Adv. Genet. 10, 257293 (1961).
  25. Siegel, P. M. & Massague, J. Cytostatic and apoptotic actions of TGF-β in homeostasis and cancer. Nat. Rev. Cancer 3, 807821 (2003).
  26. Carter, S. B. Tissue homeostasis and the biological basis of cancer. Nature 220, 970974 (1968).
  27. Young, S. R. et al. Establishment and serial passage of cell cultures derived from LuCaP xenografts. Prostate 73, 12511262 (2013).
  28. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603607 (2012).
  29. Rodriguez-Barrueco, R. et al. Inhibition of the autocrine IL-6–JAK2–STAT3–calprotectin axis as targeted therapy for HR/HER2+ breast cancers. Genes Dev. 29, 16311648 (2015).
  30. Piovan, E. et al. Direct reversal of glucocorticoid resistance by AKT inhibition in acute lymphoblastic leukemia. Cancer Cell 24, 766776 (2013).
  31. Mitrofanova, A., Aytes, A., Shen, C., Abate-Shen, C. & Califano, A. A systems biology approach to predict drug response for human prostate cancer based on in vivo preclinical analyses of mouse models. Cell Rep. 12, 112 (2015).
  32. Davis, R. E., Brown, K. D., Siebenlist, U. & Staudt, L. M. Constitutive nuclear factor kappaB activity is required for survival of activated B cell-like diffuse large B cell lymphoma cells. J. Exp. Med. 194, 18611874 (2001).
  33. Ngo, V. N. et al. Oncogenically active MYD88 mutations in human lymphoma. Nature 470, 115119 (2011).
  34. Davis, R. E. et al. Chronic active B-cell-receptor signalling in diffuse large B-cell lymphoma. Nature 463, 8892 (2010).
  35. Luo, J., Solimini, N. L. & Elledge, S. J. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136, 823837 (2009).
  36. Wayne, D. W. Kolmogorov–Smirnov One-sample Test: Applied Nonparametric Statistics 2nd edn 319330 (PWS-Kent, 1990).
  37. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 1554515550 (2005).
  38. Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838847 (2016).
  39. 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).
  40. Castro, M. A. et al. Regulators of genetic risk of breast cancer identified by integrative network analysis. Nat. Genet. 48, 1221 (2016).
  41. Zhang, S. et al. Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer. Sci. Rep. 5, 16066 (2015).
  42. Yepes, S., Torres, M. M. & Lopez-Kleine, L. Regulatory network reconstruction reveals genes with prognostic value for chronic lymphocytic leukemia. BMC Genomics 16, 1002 (2015).
  43. Remo, A. et al. Systems biology analysis reveals NFAT5 as a novel biomarker and master regulator of inflammatory breast cancer. J. Transl Med. 13, 138 (2015).
  44. Fletcher, M. N. et al. Master regulators of FGFR2 signalling and breast cancer risk. Nat. Commun. 4, 2464 (2013).
  45. Tanaka, H. & Ogishima, S. Network biology approach to epithelial-mesenchymal transition in cancer metastasis: three stage theory. J. Mol. Cell Biol. 7, 253266 (2015).
  46. Piao, G. et al. A computational procedure for identifying master regulator candidates: a case study on diabetes progression in Goto–Kakizaki rats. BMC Syst. Biol. 6 (Suppl. 1), S2 (2012).
  47. Liang, Y. et al. Transcriptional network analysis identifies BACH1 as a master regulator of breast cancer bone metastasis. J. Biol. Chem. 287, 3353333544 (2012).
  48. Califano, A., Butte, A. J., Friend, S., Ideker, T. & Schadt, E. Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nat. Genet. 44, 841847 (2012).
  49. Pe'er, D. & Hacohen, N. Principles and strategies for developing network models in cancer. Cell 144, 864873 (2011).
  50. Tovar, H., Garcia-Herrera, R., Espinal-Enriquez, J. & Hernandez-Lemus, E. Transcriptional master regulator analysis in breast cancer genetic networks. Comput. Biol. Chem. 59B, 6777 (2015).
  51. Sartor, I. T., Zeidan-Chulia, F., Albanus, R. D., Dalmolin, R. J. & Moreira, J. C. Computational analyses reveal a prognostic impact of TULP3 as a transcriptional master regulator in pancreatic ductal adenocarcinoma. Mol. Biosyst. 10, 14611468 (2014).
  52. Lim, W. K., Lyashenko, E. & Califano, A. Master regulators used as breast cancer metastasis classifiers. Pac. Symp. Biocomput. 14, 504515 (2009).
  53. Ikiz, B. et al. Dissecting the regulatory machinery of neurodegeneration in an in vitro model of amyotrophic lateral sclerosis. Cell Rep. 12, 335345 (2015).
  54. Aubry, S. et al. Assembly and interrogation of Alzheimer's disease genetic networks reveal novel regulators of progression. PLoS ONE 10, e0120352 (2015).
  55. Brichta, L. et al. Identification of neurodegenerative factors using translatome-regulatory network analysis. Nat. Neurosci. 18, 13251333 (2015).
  56. Repunte-Canonigo, V. et al. Identifying candidate drivers of alcohol dependence-induced excessive drinking by assembly and interrogation of brain-specific regulatory networks. Genome Biol. 16, 68 (2015).
  57. Kushwaha, R. et al. Interrogation of a context-specific transcription factor network identifies novel regulators of pluripotency. Stem Cells 33, 367377 (2015).
  58. Ravasi, T. et al. An atlas of combinatorial transcriptional regulation in mouse and man. Cell 140, 744752 (2010).
  59. Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382390 (2005).
  60. ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799816 (2007).
  61. Sumazin, P. et al. An extensive microRNA-mediated network of RNA–RNA interactions regulates established oncogenic pathways in glioblastoma. Cell 147, 370381 (2011).
  62. Lewis, B. P., Shih, I. H., Jones-Rhoades, M. W., Bartel, D. P. & Burge, C. B. Prediction of mammalian microRNA targets. Cell 115, 787798 (2003).
  63. Friedlander, M. R. et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat. Biotechnol. 26, 407415 (2008).
  64. Rual, J. F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 11731178 (2005).
  65. Bandyopadhyay, S. et al. A human MAP kinase interactome. Nat. Methods 7, 801805 (2010).
  66. Wang, K. et al. Genome-wide identification of post-translational modulators of transcription factor activity in human B cells. Nat. Biotechnol. 27, 829839 (2009).
  67. Zhang, Q. C. et al. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 490, 556560 (2012).
  68. AlQuraishi, M., Koytiger, G., Jenney, A., MacBeath, G. & Sorger, P. K. A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks. Nat. Genet. 46, 13631367 (2014).
  69. Faith, J. J. et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5, e8 (2007).
  70. Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799805 (2004).
  71. Kundaje, A. et al. Learning regulatory programs that accurately predict differential expression with MEDUSA. Ann. NY Acad. Sci. 1115, 178202 (2007).
  72. Kramer, A., Green, J., Pollard, J. Jr & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523530 (2014).
  73. Maher, B. ENCODE: the human encyclopaedia. Nature 489, 4648 (2012).
  74. Palomero, T. et al. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc. Natl Acad. Sci. USA 103, 1826118266 (2006).
  75. Affara, M. et al. Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis. BMC Genomics 14, 23 (2013).
  76. Werner, H. M., Mills, G. B. & Ram, P. T. Cancer systems biology: a peek into the future of patient care? Nat. Rev. Clin. Oncol. 11, 167176 (2014).
  77. Saito, M. et al. BCL6 suppression of BCL2 via Miz1 and its disruption in diffuse large B cell lymphoma. Proc. Natl Acad. Sci. USA 106, 1129411299 (2009).
  78. Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308312 (2004).
  79. Phillips, H. S. et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157173 (2006).
  80. Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462477 (2013).
  81. Wapinski, O. L. et al. Hierarchical mechanisms for direct reprogramming of fibroblasts to neurons. Cell 155, 621635 (2013).
  82. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646674 (2011).
  83. Chudnovsky, Y. et al. ZFHX4 interacts with the NuRD core member CHD4 and regulates the glioblastoma tumor-initiating cell state. Cell Rep. 6, 313324 (2014).
  84. Taylor, B. S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 1122 (2010).
  85. US National Library of Medicine. (2014).
  86. Tzoneva, G. et al. Activating mutations in the NT5C2 nucleotidase gene drive chemotherapy resistance in relapsed ALL. Nat. Med. 19, 368371 (2013).
  87. Woyach, J. A. et al. Resistance mechanisms for the Bruton's tyrosine kinase inhibitor ibrutinib. N. Engl. J. Med. 370, 22862294 (2014).
  88. Flusberg, D. A. & Sorger, P. K. Modulating cell-to-cell variability and sensitivity to death ligands by co-drugging. Phys. Biol. 10, 035002 (2013).
  89. Fenner, A. Prostate cancer: next-generation RNA sequencing identifies gene signature of neuroendocrine differentiation in prostate tumors. Nat. Rev. Urol. 9, 8 (2012).
  90. Van Vlierberghe, P. & Ferrando, A. The molecular basis of T cell acute lymphoblastic leukemia. J. Clin. Invest. 122, 33983406 (2012).
  91. Wei, G. et al. Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell 10, 331342 (2006).
  92. Rubin, E. H. & Gilliland, D. G. Drug development and clinical trials—the path to an approved cancer drug. Nat. Rev. Clin. Oncol. 9, 215222 (2012).
  93. Shelanski, M. et al. A systems approach to drug discovery in Alzheimer's disease. Neurotherapeutics 12, 126131 (2015).
  94. Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710717 (2005).
  95. Hofree, M., Shen, J. P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 11081115 (2013).
  96. Alvarez, M. J., Chen, J. C. & Califano, A. DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes. Bioinformatics 31, 40324034 (2015).
  97. Weinstein, I. B. Cancer. Addiction to oncogenes—the Achilles heal of cancer. Science 297, 6364 (2002).
  98. Druker, B. J. et al. Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat. Med. 2, 561566 (1996).
  99. Wang, X., Haswell, J. R. & Roberts, C. W. Molecular pathways: SWI/SNF (BAF) complexes are frequently mutated in cancer—mechanisms and potential therapeutic insights. Clin. Cancer Res. 20, 2127 (2014).
  100. Vazquez, M., Valencia, A. & Pons, T. Structure-PPi: a module for the annotation of cancer-related single-nucleotide variants at protein-protein interfaces. Bioinformatics 31, 23972399 (2015).
  101. Morris, J. P. t., Wang, S. C. & Hebrok, M. KRAS, Hedgehog, Wnt and the twisted developmental biology of pancreatic ductal adenocarcinoma. Nat. Rev. Cancer 10, 683695 (2010).
  102. Dhomen, N. & Marais, R. BRAF signaling and targeted therapies in melanoma. Hematol. Oncol. Clin. North Am. 23, 529545 (2009).
  103. Davoli, A., Hocevar, B. A. & Brown, T. L. Progression and treatment of HER2-positive breast cancer. Cancer Chemother. Pharmacol. 65, 611623 (2010).
  104. Bansal, M. et al. A community computational challenge to predict the activity of pairs of compounds. Nat. Biotechnol. 32, 12131222 (2014).
  105. Woo, J. H. et al. Elucidating compound mechanism of action by network dysregulation analysis in pertubed cells. Cell 162, 441451 (2015).
  106. Tallarida, R. J. An overview of drug combination analysis with isobolograms. J. Pharmacol. Exp. Ther. 319, 17 (2006).
  107. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 13961401 (2014).
  108. Park, H. S. & Jun, C. H. A simple and fast algorithm for K-medoids clustering. Expert Systems With Appl. 36, 33363341 (2009).
  109. Lefebvre, C., Rieckhof, G. & Califano, A. Reverse-engineering human regulatory networks. Wiley Interdiscip. Rev. Syst. Biol. Med. 4, 311325 (2012).
  110. Linding, R. et al. Systematic discovery of in vivo phosphorylation networks. Cell 129, 14151426 (2007).
  111. Srivas, R. et al. Assembling global maps of cellular function through integrative analysis of physical and genetic networks. Nat. Protoc. 6, 13081323 (2011).
  112. Rhodes, D. R. et al. Mining for regulatory programs in the cancer transcriptome. Nat. Genet. 37, 579583 (2005).
  113. Lachmann, A. et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 24382444 (2010).
  114. Needham, C. J., Bradford, J. R., Bulpitt, A. J. & Westhead, D. R. Inference in Bayesian networks. Nat. Biotechnol. 24, 5153 (2006).
  115. Yeung, M. K., Tegner, J. & Collins, J. J. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl Acad. Sci. USA 99, 61636168 (2002).
  116. Martinez, M. R. et al. Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis. Proc. Natl Acad. Sci. USA 109, 26722677 (2012).
  117. Polynikis, A., Hogan, S. J. & di Bernardo, M. Comparing different ODE modelling approaches for gene regulatory networks. J. Theor. Biol. 261, 511530 (2009).
  118. Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249255 (2003).
  119. Horvath, S. & Dong, J. Geometric interpretation of gene coexpression network analysis. PLoS Comput. Biol. 4, e1000117 (2008).
  120. Jansen, R. et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449453 (2003).
  121. He, F., Balling, R. & Zeng, A. P. Reverse engineering and verification of gene networks: principles, assumptions, and limitations of present methods and future perspectives. J. Biotechnol. 144, 190203 (2009).
  122. Stolovitzky, G., Prill, R. J. & Califano, A. Lessons from the DREAM2 Challenges. Ann. NY Acad. Sci. 1158, 159195 (2009).
  123. Marbach, D. et al. Revealing strengths and weaknesses of methods for gene network inference. Proc. Natl Acad. Sci. USA 107, 62866291 (2010).
  124. Bussemaker, H. J., Li, H. & Siggia, E. D. Regulatory element detection using correlation with expression. Nat. Genet. 27, 167171 (2001).
  125. Fiedler, D. et al. Functional organization of the S. cerevisiae phosphorylation network. Cell 136, 952963 (2009).
  126. Mani, K. M. et al. A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol. Syst. Biol. 4, 169 (2008).
  127. Ideker, T. & Krogan, N. J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012).
  128. Barrios-Rodiles, M. et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science 307, 16211625 (2005).
  129. Saez-Rodriguez, J. et al. Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Res. 71, 54005411 (2011).
  130. Menashe, I. et al. Pathway analysis of breast cancer genome-wide association study highlights three pathways and one canonical signaling cascade. Cancer Res. 70, 44534459 (2010).
  131. Laoukili, J. et al. FoxM1 is required for execution of the mitotic programme and chromosome stability. Nature Cell Biol. 7, 126136 (2005).
  132. Basu, A. et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 11511161 (2013).
  133. Flusberg, D. A., Roux, J., Spencer, S. L. & Sorger, P. K. Cells surviving fractional killing by TRAIL exhibit transient but sustainable resistance and inflammatory phenotypes. Mol. Biol. Cell 24, 21862200 (2013).
  134. Roschewski, M., Staudt, L. M. & Wilson, W. H. Diffuse large B-cell lymphoma-treatment approaches in the molecular era. Nat. Rev. Clin. Oncol. 11, 1223 (2014).

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Author information


  1. Department of Systems Biology, Columbia University, and the Departments of Biomedical Informatics, Biochemistry and Molecular Biophysics, JP Sulzberger Columbia Genome Center, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York 10032, USA.

    • Andrea Califano
  2. DarwinHealth, Inc., 3960 Broadway, Suite 540, New York, New York 10032, USA.

    • Mariano J. Alvarez

Competing interests statement

A.C. is founder of DarwinHealth, Inc. M.J.A. has been employed by DarwinHealth, Inc. since March 2016.

Corresponding author

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Author details

  • Andrea Califano

    Andrea Califano is the Clyde and Helen Wu Professor of Chemical and Systems Biology, Funding Chair of the Department of Systems Biology, Director of the JP Sulzberger Columbia Genome Center and Associate Director for Bioinformatics of the Herbert Irving Comprehensive Cancer Center, Columbia University, New York, USA. His research interests are in the use of cellular network models to elucidate novel biomarkers, therapeutic targets and their small molecule modulators in cancer and in the translational application of these discoveries.

  • Mariano J. Alvarez

    Mariano J. Alvarez is the Chief Scientific Officer of DarwinHealth, Inc., New York, USA. He has participated in the development and application of key algorithms discussed in this Opinion article, including Master Regulator Inference Algorithm (MARINa), Virtual Inference of Protein Activity by Enriched Regulon Analysis (VIPER) and Driver-gene Inference by Genetical-Genomics and Information Theory (DIGGIT). His interests are in the use of systems biology approaches in precision medicine applications.

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