Identifying and validating disease-causing genes that are viable as drug targets is a key challenge in drug discovery.
Large-scale multi-omics initiatives are deepening our understanding of cancer and providing an unbiased view of possible molecular mechanisms of the disease. Such studies usually result in sizeable lists — often hundreds — of potential cancer drug targets, most of which are not members of well-understood cancer pathways.
The selection a small number of genes for in-depth biological validation is thus often done in an ad hoc manner, thereby running the risk of bias or neglecting potentially druggable and therapeutically important novel targets.
We describe an objective, systematic, multifaceted computational approach of assessing biological and chemical space that draws on unprecedented volumes of multidisciplinary data, simultaneously, to assess large gene lists.
We utilize our new approach to evaluate 479 cancer genes from the Cancer Gene Census as an exemplar list and demonstrate the power of such an unbiased approach in rapidly unveiling potential therapeutic opportunities.
This analysis reveals the tension between biological relevance versus chemical tractability and highlights major gaps in available knowledge that can be addressed to aid objective decision-making.
We hypothesize drug repurposing opportunities and identify potentially druggable cancer proteins that are as yet poorly explored in the chemical space — despite their biological relevance — and we propose these proteins for in-depth chemical and biological studies.
We also illustrate how the mapping of biological and chemical data distillations onto cellular networks can provide deeper insights and potentially guide rational drug combination experiments.
We provide a live web-based portal to allow simultaneous annotation of up to 500 genes that can be applied to any human gene list. We propose that by using our approach alongside a researcher's own biological knowledge, stronger, more rational and unbiased decisions about target selection can be made that could lead to the discovery of a new generation of novel and chemically tractable therapeutic targets.
Selecting the best targets is a key challenge for drug discovery, and achieving this effectively, efficiently and systematically is particularly important for prioritizing candidates from the sizeable lists of potential therapeutic targets that are now emerging from large-scale multi-omics initiatives, such as those in oncology. Here, we describe an objective, systematic, multifaceted computational assessment of biological and chemical space that can be applied to any human gene set to prioritize targets for therapeutic exploration. We use this approach to evaluate an exemplar set of 479 cancer-associated genes, reveal the tension between biological relevance and chemical tractability, and describe major gaps in available knowledge that could be addressed to aid objective decision-making. We also propose drug repurposing opportunities and identify potentially druggable cancer-associated proteins that have been poorly explored with regard to the discovery of small-molecule modulators, despite their biological relevance.
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Jemal, A. et al. Global cancer statistics. CA Cancer J. Clin. 61, 69–90 (2011).
Meyerson, M., Gabriel, S. & Getz, G. Advances in understanding cancer genomes through second-generation sequencing. Nature Rev. Genet. 11, 685–696 (2010).
Freedman, M. L. et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nature Genet. 43, 513–518 (2011).
Hanash, S. & Taguchi, A. The grand challenge to decipher the cancer proteome. Nature Rev. Cancer 10, 652–660 (2010).
Brough, R. et al. Functional viability profiles of breast cancer. Cancer Discov. 1, 260–273 (2011).
Hoon, S. et al. An integrated platform of genomic assays reveals small-molecule bioactivities. Nature Chem. Biol. 4, 498–506 (2008).
Workman, P. & Collins, I. Probing the probes: fitness factors for small molecule tools. Chem. Biol. 17, 561–577 (2010).
Chin, L., Hahn, W. C., Getz, G. & Meyerson, M. Making sense of cancer genomic data. Genes Dev. 25, 534–555 (2011).
Garraway, L. A. & Jänne, P. A. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2, 214–226 (2012).
Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).
Stratton, M. R. Exploring the genomes of cancer cells: progress and promise. Science 331, 1553–1558 (2011).
Al-Lazikani, B., Banerji, U. & Workman, P. Combinatorial drug therapy for cancer in the post-genomic era. Nature Biotech. 30, 679–692 (2012).
de Bono, J. S. et al. Abiraterone and increased survival in metastatic prostate cancer. N. Engl. J. Med. 364, 1995–2005 (2011).
Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).
Chapman, P. B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011).
Pammolli, F., Magazzini, L. & Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nature Rev. Drug Discov. 10, 428–438 (2011).
Yap, T. A. & Workman, P. Exploiting the cancer genome: strategies for the discovery and clinical development of targeted molecular therapeutics. Annu. Rev. Pharmacol. Toxicol. 52, 549–573 (2012).
Edfeldt, F. N., Folmer, R. H. & Breeze, A. L. Fragment screening to predict druggability (ligandability) and lead discovery success. Drug Discov. Today 16, 284–287 (2011).
Oltersdorf, T. et al. An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature 435, 677–681 (2005).
Verdine, G. & Walensky, L. The challenge of drugging undruggable targets in cancer: lessons learned from targeting BCL-2 family members. Clin. Cancer Res. 13, 7264–7270 (2007).
Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993–998 (2010).
Iorns, E., Lord, C. J., Turner, N. & Ashworth, A. Utilizing RNA interference to enhance cancer drug discovery. Nature Rev. Drug Discov. 6, 556–568 (2007).
Aguero, F. et al. Genomic-scale prioritization of drug targets: the TDR Targets database. Nature Rev. Drug Discov. 7, 900–907 (2008).
Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).
Halling-Brown, M. D. Bulusu, K.C., Patel, M., Tym, J.E. & Al-Lazikani, B. canSAR: an integrated cancer public translational research and drug discovery resource. Nucleic Acids Res. 40, D947–D956 (2012).
Begley, C. G. & Ellis, L. M. Drug development: raise standards for preclinical cancer research. Nature 483, 531–533 (2012).
Prinz, F., Schlange, T. & Asadullah, K. Believe it or not: how much can we rely on published data on potential drug targets? Nature Rev. Drug Discov. 10, 712 (2011).
Futreal, P. A. et al. A census of human cancer genes. Nature Rev. Cancer 4, 177–183 (2004).
Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nature Rev. Drug Discov. 5, 993–996 (2006).
Darnell, J. E. Transcription factors as targets for cancer therapy. Nature Rev. Cancer 2, 740–749 (2002).
Moellering, R. E. et al. Direct inhibition of the NOTCH transcription factor complex. Nature 462, 182–188 (2009).
Wang, C.-Y., Mayo, M. W. & Baldwin, A. S. TNF- and cancer therapy-induced apoptosis: potentiation by inhibition of NF-kB. Science 274, 784–787 (1996).
Yu, H. & Jove, R. The STATs of cancer — new molecular targets come of age. Nature Rev. Cancer 4, 97–105 (2004).
Seth, A. & Watson, D. K. ETS transcription factors and their emerging roles in human cancer. Eur. J. Cancer 41, 2462–2478 (2005).
Jones, K. A. Outsmarting a mastermind. Dev. Cell 17, 750–752 (2009).
Faisal, A. et al. The aurora kinase inhibitor CCT137690 downregulates MYCN and sensitizes MYCN-amplified neuroblastoma in vivo. Mol. Cancer Ther. 10, 2115–2123 (2011).
Berriman, M. et al. The genome of the blood fluke Schistosoma mansoni. Nature 460, 352–358 (2009).
Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3, 96ra76 (2011).
Sanseau, P. et al. Use of genome-wide association studies for drug repositioning. Nature Biotech. 30, 317–320 (2012).
Kato, Y. et al. PPARγ insufficiency promotes follicular thyroid carcinogenesis via activation of the nuclear factor-kB signaling pathway. Oncogene 25, 2736–2747 (2005).
Copland, J. A. et al. Novel high-affinity PPARγ agonist alone and in combination with paclitaxel inhibits human anaplastic thyroid carcinoma tumor growth via p21WAF1//CIP1. Oncogene 25, 2304–2317 (2005).
Demetri, G. D. et al. Induction of solid tumor differentiation by the peroxisome proliferator-activated receptor-γ ligand troglitazone in patients with liposarcoma. Proc. Natl Acad. Sci. USA 96, 3951–3956 (1999).
Russo, D. et al. Thyrotropin receptor gene alterations in thyroid hyperfunctioning adenomas. J. Clin. Endocrinol. Metab. 81, 1548–1551 (1996).
Parma, J. et al. Somatic mutations in the thyrotropin receptor gene cause hyperfunctioning thyroid adenomas. Nature 365, 649–651 (1993).
Polak, M. Hyperfunctioning thyroid adenoma and activating mutations in the TSH receptor gene. Arch. Med. Res. 30, 510–513 (1999).
Milas, M. et al. Effectiveness of peripheral thyrotropin receptor mRNA in follow-up of differentiated thyroid cancer. Ann. Surg. Oncol. 16, 473–480 (2009).
Neumann, S. et al. A low-molecular-weight antagonist for the human thyrotropin teceptor with therapeutic potential for hyperthyroidism. Endocrinology 149, 5945–5950 (2008).
Neumann, S. et al. A small molecule inverse agonist for the human thyroid-stimulating hormone receptor. Endocrinology 151, 3454–3459 (2010).
Sekulic, A. et al. Efficacy and safety of vismodegib in advanced basal-cell carcinoma. N. Engl. J. Med. 366, 2171–2179 (2012).
The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).
Friedberg, J. W. et al. Inhibition of Syk with fostamatinib disodium has significant clinical activity in non-Hodgkin lymphoma and chronic lymphocytic leukemia. Blood 115, 2578–2585 (2010).
Zhang, J. et al. A novel retinoblastoma therapy from genomic and epigenetic analyses. Nature 481, 329–334 (2012).
Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).
Yuan, Y., Liao, Y. M., Hsueh, C. T. & Mirshahidi, H. R. Novel targeted therapeutics: inhibitors of MDM2, ALK and PARP. J. Hematol. Oncol. 4, 16 (2011).
Surade, S. & Blundell, T. L. Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem. Biol. 19, 42–50 (2012).
Collins, I. & Workman, P. New approaches to molecular cancer therapeutics. Nature Chem. Biol. 2, 689–700 (2006).
Balamurugan, K. et al. The tumour suppressor C/EBPδ inhibits FBXW7 expression and promotes mammary tumour metastasis. EMBO J. 29, 4106–4117 (2010).
Yang, L., Han, Y., Suarez Saiz, F. & Minden, M. D. A tumor suppressor and oncogene: the WT1 story. Leukemia 21, 868–876 (2007).
Ueno, N. T., Yu, D. & Hung, M. C. E1A: tumor suppressor or oncogene? Preclinical and clinical investigations of E1A gene therapy. Breast Cancer 8, 285–293 (2001).
Wilson, J. J. & Kovall, R. A. Crystal structure of the CSL-Notch-mastermind ternary complex bound to DNA. Cell 124, 985–996 (2006).
Ward, P. S. et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting α-ketoglutarate to 2-hydroxyglutarate. Cancer Cell 17, 225–234 (2010).
Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).
DeAngelis, L. M. & Mellinghoff, I. K. Virchow 2011 or how to ID(H) human glioblastoma. J. Clin. Oncol. 29, 4473–4474 (2011).
Bolton, E. E., Wang, Y., Thiessen, P. A. & Bryant, S. H. PubChem: integrated platform of small molecules and biological activities. Annu. Rep. Comput. Chem. 4, 217–241 (2008).
Smith, T. M., Hicks-Berger, C. A., Kim, S. & Kirley, T. L. Cloning, expression, and characterization of a soluble calcium-activated nucleotidase, a human enzyme belonging to a new family of extracellular nucleotidases. Arch. Biochem. Biophys. 406, 105–115 (2002).
Hermans, K. G. et al. Two unique novel prostate-specific and androgen-regulated fusion partners of ETV4 in prostate cancer. Cancer Res. 68, 3094–3098 (2008).
Gerhardt, J. et al. The androgen-regulated calcium-activated nucleotidase 1 (CANT1) is commonly overexpressed in prostate cancer and is tumor-biologically relevant in vitro. Am. J. Pathol. 178, 1847–1860 (2011).
Medina, P. P. et al. Frequent BRG1/SMARCA4-inactivating mutations in human lung cancer cell lines. Hum. Mutat. 29, 617–622 (2008).
Alessio, N. et al. The BRG1 ATPase of chromatin remodeling complexes is involved in modulation of mesenchymal stem cell senescence through RB-P53 pathways. Oncogene 29, 5452–5463 (2010).
Sentani, K. et al. Increased expression but not genetic alteration of BRG1, a component of the SWI/SNF complex, is associated with the advanced stage of human gastric carcinomas. Pathobiology 69, 315–320 (2001).
Emmanuel, C. et al. Comparison of expression profiles in ovarian epithelium in vivo and ovarian cancer identifies novel candidate genes involved in disease pathogenesis. PLoS ONE 6, e17617 (2011).
Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).
Verdine, G. L. in The Harvey Lectures: Series 102, 2006–2007 1–16 (Wiley-Blackwell; 2010).
Filippakopoulos, P. et al. Selective inhibition of BET bromodomains. Nature 468, 1067–1073 (2010).
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
Halgren, T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des. 69, 146–148 (2007).
Luangdilok, S. et al. Syk tyrosine kinase is linked to cell motility and progression in squamous cell carcinomas of the head and neck. Cancer Res. 67, 7907–7916 (2007).
Vidler, L. R., Brown, N., Knapp, S. & Hoelder, S. Druggability analysis and structural classification of bromodomain acetyl-lysine binding sites. J. Med. Chem. 55, 7346–7359 (2012).
Chen, X., Lin, Y., Liu, M. & Gilson, M. K. The Binding Database: data management and interface design. Bioinformatics 18, 130–139 (2002).
Shoemaker, R. H. The NCI60 human tumour cell line anticancer drug screen. Nature Rev. Cancer 6, 813–823 (2006).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Gileadi, O. et al. The scientific impact of the Structural Genomics Consortium: a protein family and ligand-centered approach to medically-relevant human proteins. J. Struct. Funct. Genomics 8, 107–119 (2007).
This work was supported by Cancer Research UK (grant numbers C309/A8274 and C309/A11566). P.W. is a Cancer Research UK Life Fellow. The authors acknowledge additional funding from Cancer Research UK to the Cancer Research UK Cancer Centre and from the UK National Health Service (NHS) to the National Institute for Health Research (NIHR) Biomedical Research Centre at The Institute of Cancer Research and Royal Marsden Hospital, UK. The authors thank K. Bulusu for technical help, and thank J. Blagg, M. Garnett and U. McDermott for valuable discussions and comments. Author contributions: B.A.L. conceived the project and designed the analysis; M.P., M.H.B. and B.A.L. performed the data analysis and informatics and wrote the paper; P.W. provided biological analysis and insights and wrote the paper; J.T. developed the target annotation tool.
M.P., J.T., P.W. and B.A.L. are employees of The Institute of Cancer Research (ICR), which has a commercial interest in inhibitors of cytochrome P450-C17 (CYP17), heat shock protein 90 (HSP90), phosphoinositide 3-kinase (PI3K), protein kinase B (PKB), histone deacetylase and other targets, and operates a 'Rewards to Inventors' scheme. P.W. and colleagues at ICR have received research funding from Cougar Biotechnology, Johnson & Johnson, Vernalis, Yamanouchi, Piramed Pharma (acquired by Roche), Astex Pharmaceuticals, AstraZeneca, Sareum, Merck Serono and Chroma Therapeutics. P.W. is a consultant and/or a member of the scientific advisory board for Novartis, Piramed Pharma, Astex Pharmaceuticals, Chroma Therapeutics, Kudos Pharmaceuticals, Wilex and Nextech Invest.
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Patel, M., Halling-Brown, M., Tym, J. et al. Objective assessment of cancer genes for drug discovery. Nat Rev Drug Discov 12, 35–50 (2013). https://doi.org/10.1038/nrd3913
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