‘Big data’ approaches for novel anti-cancer drug discovery

ABSTRACT

Introduction: The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological ‘big data’ are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions.

Areas covered: The authors discuss the current approaches that use ‘big data’ to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery.

Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.

Article highlights

• Large-scale studies of genetic mutations have identified at best a very small number of highly recurrent altered genes, with increasingly long tails of much more infrequently altered genes. Studies that cluster these rarer mutations are providing insight into biological understanding of the pathways involved in cancers.

• The patterns of mutations within driver genes enable identification of genes into oncogenes and tumour suppressors. Tumour suppressors cannot often be directly targeted. Instead analysis is required to find gene products that are synthetically lethal to the tumour suppressor and can be inhibited pharmacologically. The DNA damage response pathways are particularly fruitful sources of tumour suppressors that can be targeted in this way.

• The genetic dependencies of cancer cell lines can be profiled, identifying where the cancer has become addicted to support from altered pathways, allowing new therapeutic options. Analysis of omic data from cell lines tested with novel compounds has allowed genetic, lineage, and gene-expression-based predictors of drug sensitivity.

• Proteins can be assessed for druggability by modelling 3D structure and analysing the extent to which ‘pockets’ in the protein bind pharmacologically suitable molecules with high affinity and specificity. Where proteins are not suitable analysis is needed to find synthetic dosage lethal partners.

• Much of the data produced from these large-scale studies is publicly available, together with tools allowing integrated analysis.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties

Additional information

Funding

F Pearl is funded by the University of Sussex and G Benstead-Hume has an MRC Studentship MR/N50189X/1.