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

Abstract

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.

MAEL contributes to gastric cancer progression by promoting ILKAP degradation

“…Currently, there are numerous high-throughput datasets related to cancer that are freely available in public data repositories, such as TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus) and EGA (European Genome-phenome Archive). These “big data” have greatly facilitated the systematic analysis of the key genes and pathways involved in tumorigenesis [ 16 ]. Here, we used cancer genomics and transcriptomics data to systematically investigate the expression and clinical significance of MAEL .…”

Cancer cachexia: an orphan with a future

“…to be picked up by the research community and clinicians should be that we need better epidemiological data. As suggested previously, all available resources should be exploited for use, including big data approach, and experience from other disease should be considered as well . With existing International Classification of Disease code, only few studies exist .…”

“…As suggested previously, all available resources should be exploited for use, including big data approach, and experience from other disease should be considered as well. [6][7][8][9] With existing International Classification of Disease code, only few studies exist. 10 Nonetheless, because several countries have such nationwide resources, [11][12][13] these could be source of valuable information.…”

Biological network topology features predict gene dependencies in cancer cell lines

“…While identifying general essential genes or disease specific gene dependencies provides a better understanding of potential disease specific targets, loss of function screens are not readily available for the majority of individual cancer patients. Tools that could predict cell line specific gene dependencies from more readily available data such as mutations and gene expression may offer new opportunities for affordable tailored therapies (Charlton & Spicer, 2016;Benstead-Hume et al, 2017).…”