Systems biology in biomarker development in cancer signalling therapy

Drug-diagnostic co-development (CDx) in personalized cancer therapy requires reliable models describing molecular mechanisms of drug action and therapeutic response for the development of the predictive biomarkers of efficacy and toxicity of the drugs in specific cohorts of patients. Systems biology combining both computational and experimental techniques is now recognised as a powerful tool in next-generation drug and biomarker development. Integrative approach of systems biology to the analysis of large-scale omics data proposes the systems-based techniques to develop predictive molecular biomarkers of effectiveness of drug and combination therapy targeting complex oncogenic signalling pathways in cancer patients with different mutation make-ups. In this review we discuss advantages of systems approach applicable to the development of effective biomarker-driven therapy and tackling one of the main challenges in the personalised therapy, de novo and acquired resistance. In this connection, we discuss the development of systems biomarkers which integrate the concurrent responses of multiple cross-communicating signalling pathways activated by drug intervention and define a phenotypic signature of the therapeutic responses in different patients’ cohorts. Identification of systems biomarkers using the network-based approach of systems biology is critically important at a current stage of drug development rapidly transformed to clinical phenotype-driven drug development.

We also show that application of an extensive arsenal of computational systems biology methods can significantly contribute to the development of in silico CDx assay model to facilitate the development of systems biomarkers and support drug-diagnostics co-development. In addition, we advocate that in silico CDx assay model should be developed in parallel with in vitro CDx assay based on multi-omics data produced in all stages of drug development. Finally, we discuss the bioinformatics methods of the large-scale functional analysis of gene variants for systems biomarker identification based on high-throughput screening technology.