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Objective assessment of cancer genes for drug discovery

Nature Reviews Drug Discovery volume 12, pages 35–50 (2013)Cite this article

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Key Points

  • 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.

Abstract

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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Figure 1: Workflow with annotation scheme and assessment criteria.
Figure 2: Functional classes of proteins from the Cancer Gene Census.
Figure 3: Structural characterization of proteins from the Cancer Gene Census.
Figure 4: Multifaceted approach to identify suitable targets for drug discovery from the Cancer Gene Census.
Figure 5: Examples of predicted druggable cancer targets from the Cancer Gene Census.
Figure 6: A network view of evidence-based assessment of proteins from the Cancer Gene Census.

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References

  1. Jemal, A. et al. Global cancer statistics. CA Cancer J. Clin. 61, 69–90 (2011).

    Article  PubMed  Google Scholar 

  2. Meyerson, M., Gabriel, S. & Getz, G. Advances in understanding cancer genomes through second-generation sequencing. Nature Rev. Genet. 11, 685–696 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Freedman, M. L. et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nature Genet. 43, 513–518 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Hanash, S. & Taguchi, A. The grand challenge to decipher the cancer proteome. Nature Rev. Cancer 10, 652–660 (2010).

    Article  CAS  Google Scholar 

  5. Brough, R. et al. Functional viability profiles of breast cancer. Cancer Discov. 1, 260–273 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hoon, S. et al. An integrated platform of genomic assays reveals small-molecule bioactivities. Nature Chem. Biol. 4, 498–506 (2008).

    Article  CAS  Google Scholar 

  7. Workman, P. & Collins, I. Probing the probes: fitness factors for small molecule tools. Chem. Biol. 17, 561–577 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chin, L., Hahn, W. C., Getz, G. & Meyerson, M. Making sense of cancer genomic data. Genes Dev. 25, 534–555 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Garraway, L. A. & Jänne, P. A. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2, 214–226 (2012).

    Article  CAS  PubMed  Google Scholar 

  10. Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Stratton, M. R. Exploring the genomes of cancer cells: progress and promise. Science 331, 1553–1558 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. Al-Lazikani, B., Banerji, U. & Workman, P. Combinatorial drug therapy for cancer in the post-genomic era. Nature Biotech. 30, 679–692 (2012).

    Article  CAS  Google Scholar 

  13. de Bono, J. S. et al. Abiraterone and increased survival in metastatic prostate cancer. N. Engl. J. Med. 364, 1995–2005 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Chapman, P. B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Pammolli, F., Magazzini, L. & Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nature Rev. Drug Discov. 10, 428–438 (2011).

    Article  CAS  Google Scholar 

  17. 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).

    Article  CAS  PubMed  Google Scholar 

  18. 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).

    Article  CAS  PubMed  Google Scholar 

  19. Oltersdorf, T. et al. An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature 435, 677–681 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. 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).

    Article  CAS  PubMed  Google Scholar 

  21. Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993–998 (2010).

    Article  CAS  PubMed  Google Scholar 

  22. 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).

    Article  CAS  Google Scholar 

  23. Aguero, F. et al. Genomic-scale prioritization of drug targets: the TDR Targets database. Nature Rev. Drug Discov. 7, 900–907 (2008).

    Article  Google Scholar 

  24. Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. 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).

    Article  CAS  PubMed  Google Scholar 

  26. Begley, C. G. & Ellis, L. M. Drug development: raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. 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).

    Article  CAS  Google Scholar 

  28. Futreal, P. A. et al. A census of human cancer genes. Nature Rev. Cancer 4, 177–183 (2004).

    Article  CAS  Google Scholar 

  29. Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nature Rev. Drug Discov. 5, 993–996 (2006).

    Article  CAS  Google Scholar 

  30. Darnell, J. E. Transcription factors as targets for cancer therapy. Nature Rev. Cancer 2, 740–749 (2002).

    Article  CAS  Google Scholar 

  31. Moellering, R. E. et al. Direct inhibition of the NOTCH transcription factor complex. Nature 462, 182–188 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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).

    Article  CAS  PubMed  Google Scholar 

  33. Yu, H. & Jove, R. The STATs of cancer — new molecular targets come of age. Nature Rev. Cancer 4, 97–105 (2004).

    Article  CAS  Google Scholar 

  34. Seth, A. & Watson, D. K. ETS transcription factors and their emerging roles in human cancer. Eur. J. Cancer 41, 2462–2478 (2005).

    Article  CAS  PubMed  Google Scholar 

  35. Jones, K. A. Outsmarting a mastermind. Dev. Cell 17, 750–752 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Berriman, M. et al. The genome of the blood fluke Schistosoma mansoni. Nature 460, 352–358 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3, 96ra76 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sanseau, P. et al. Use of genome-wide association studies for drug repositioning. Nature Biotech. 30, 317–320 (2012).

    Article  CAS  Google Scholar 

  40. Kato, Y. et al. PPARγ insufficiency promotes follicular thyroid carcinogenesis via activation of the nuclear factor-kB signaling pathway. Oncogene 25, 2736–2747 (2005).

    Article  Google Scholar 

  41. 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).

    Article  Google Scholar 

  42. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Russo, D. et al. Thyrotropin receptor gene alterations in thyroid hyperfunctioning adenomas. J. Clin. Endocrinol. Metab. 81, 1548–1551 (1996).

    CAS  PubMed  Google Scholar 

  44. Parma, J. et al. Somatic mutations in the thyrotropin receptor gene cause hyperfunctioning thyroid adenomas. Nature 365, 649–651 (1993).

    Article  CAS  PubMed  Google Scholar 

  45. Polak, M. Hyperfunctioning thyroid adenoma and activating mutations in the TSH receptor gene. Arch. Med. Res. 30, 510–513 (1999).

    Article  CAS  PubMed  Google Scholar 

  46. Milas, M. et al. Effectiveness of peripheral thyrotropin receptor mRNA in follow-up of differentiated thyroid cancer. Ann. Surg. Oncol. 16, 473–480 (2009).

    Article  PubMed  Google Scholar 

  47. Neumann, S. et al. A low-molecular-weight antagonist for the human thyrotropin teceptor with therapeutic potential for hyperthyroidism. Endocrinology 149, 5945–5950 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Neumann, S. et al. A small molecule inverse agonist for the human thyroid-stimulating hormone receptor. Endocrinology 151, 3454–3459 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sekulic, A. et al. Efficacy and safety of vismodegib in advanced basal-cell carcinoma. N. Engl. J. Med. 366, 2171–2179 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  51. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zhang, J. et al. A novel retinoblastoma therapy from genomic and epigenetic analyses. Nature 481, 329–334 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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).

    Article  CAS  Google Scholar 

  54. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Surade, S. & Blundell, T. L. Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem. Biol. 19, 42–50 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Collins, I. & Workman, P. New approaches to molecular cancer therapeutics. Nature Chem. Biol. 2, 689–700 (2006).

    Article  CAS  Google Scholar 

  57. Balamurugan, K. et al. The tumour suppressor C/EBPδ inhibits FBXW7 expression and promotes mammary tumour metastasis. EMBO J. 29, 4106–4117 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Yang, L., Han, Y., Suarez Saiz, F. & Minden, M. D. A tumor suppressor and oncogene: the WT1 story. Leukemia 21, 868–876 (2007).

    Article  CAS  PubMed  Google Scholar 

  59. 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).

    Article  CAS  PubMed  Google Scholar 

  60. Wilson, J. J. & Kovall, R. A. Crystal structure of the CSL-Notch-mastermind ternary complex bound to DNA. Cell 124, 985–996 (2006).

    Article  CAS  PubMed  Google Scholar 

  61. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. DeAngelis, L. M. & Mellinghoff, I. K. Virchow 2011 or how to ID(H) human glioblastoma. J. Clin. Oncol. 29, 4473–4474 (2011).

    Article  CAS  PubMed  Google Scholar 

  64. 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).

    Article  CAS  Google Scholar 

  65. 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).

    Article  CAS  PubMed  Google Scholar 

  66. 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).

    Article  CAS  PubMed  Google Scholar 

  67. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Medina, P. P. et al. Frequent BRG1/SMARCA4-inactivating mutations in human lung cancer cell lines. Hum. Mutat. 29, 617–622 (2008).

    Article  CAS  PubMed  Google Scholar 

  69. 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).

    Article  CAS  PubMed  Google Scholar 

  70. 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).

    Article  CAS  PubMed  Google Scholar 

  71. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Verdine, G. L. in The Harvey Lectures: Series 102, 2006–2007 1–16 (Wiley-Blackwell; 2010).

    Book  Google Scholar 

  74. Filippakopoulos, P. et al. Selective inhibition of BET bromodomains. Nature 468, 1067–1073 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Halgren, T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des. 69, 146–148 (2007).

    Article  CAS  PubMed  Google Scholar 

  77. 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).

    Article  CAS  PubMed  Google Scholar 

  78. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Chen, X., Lin, Y., Liu, M. & Gilson, M. K. The Binding Database: data management and interface design. Bioinformatics 18, 130–139 (2002).

    Article  CAS  PubMed  Google Scholar 

  80. Shoemaker, R. H. The NCI60 human tumour cell line anticancer drug screen. Nature Rev. Cancer 6, 813–823 (2006).

    Article  CAS  Google Scholar 

  81. 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).

    Article  CAS  PubMed  Google Scholar 

  82. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, SM2 5NG, UK

    Mishal N. Patel, Mark D. Halling-Brown, Joseph E. Tym, Paul Workman & Bissan Al-Lazikani

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  1. Mishal N. Patel
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Correspondence to Paul Workman or Bissan Al-Lazikani.

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Competing interests

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.

Supplementary information

Supplementary information S1

Dataset, methodology and additional notes (PDF 1073 kb)

Supplementary information S2

Descriptions of Supplementry Table 2 (XLSX 135 kb)

Related links

Related links

FURTHER INFORMATION

canSAR Database

“Cancer Drug Targets: The March of the Lemmings” —Forbes website (6 July 2012)

Cancer fact sheet — World Health Organization (WHO)

Cancer Gene Census

Cancer Genome Project

ClinicalTrials.gov website

International Cancer Genome Consortium

Multidisciplinary protein annotation tool

The Cancer Genome Atlas

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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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