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Objective assessment of cancer genes for drug discovery

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.


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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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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Corresponding authors

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


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 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).

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