Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

Scientific Reports
Marcela FrancoAndrey Alexeyenko

Abstract

Despite the widening range of high-throughput platforms and exponential growth of generated data volume, the validation of biomarkers discovered from large-scale data remains a challenging field. In order to tackle cancer heterogeneity and comply with the data dimensionality, a number of network and pathway approaches were invented but rarely systematically applied to this task. We propose a new method, called NEAmarker, for finding sensitive and robust biomarkers at the pathway level. scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways. These dimensions are then correlated with phenotype variables. The method was first tested using in vitro data from three anti-cancer drug screens and then on clinical data of The Cancer Genome Atlas. It proved superior to the single-gene and alternative enrichment analyses in terms of (1) universal applicability to different data types with a possibility of cross-platform integration, (2) consistency of the discovered correlates between independent drug screens, and (3) ability to explain differential survival of treated patients. Our new screen of anti-cancer compounds validated the performance of multivariate models...Continue Reading

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Citations

Sep 15, 2019·BMC Cancer·Konstantinos Karakostis, Robin Fåhraeus

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Methods Mentioned

BETA
RNA-seq
exome sequencing
phylogenetic profiles

Software Mentioned

ZGSEA
PWNEA
PLAGE
SPIA
PARADIGM
STRING
EnrichNet
affymetrix
ssGSEA SPIA
DegreeCox

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