Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response

Looking for treatment clues in cancer’s web of mutation

3 July 2018

A computational analytics framework that appraises the effects of cancer’s genomic aberrations on biological networks, rather than just those of cancerous mutations on individual molecules, has been devised by a team of scientists from South Korea and Hong Kong led by KAIST’s Kwang-Hyun Cho.

Cho’s team tested their framework by analyzing the tumor suppressor gene p53, which is mutated in about 50 per cent of all cancers, making it a clear target for drug intervention. They were able to calculate how combinations of five therapeutics would affect p53’s signaling pathways, and hence impact drug response. The framework also classified cancer subtypes based on the congruency of their response to drug treatment.

With further development and the increasing availability of genomic data for a range of diseases, this computational framework could be adapted to benefit pharmacological response prediction for more illnesses and allow for medicine that is more personalized.

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References

1. Nature Communications 8, 1940 (2017). doi: 10.1038/s41467-017-02160-5