An Integrative MuSiCO Algorithm: From the Patient-Specific Transcriptional Profiles to Novel Checkpoints in Disease Pathobiology

Abstract

Strong efforts are invested in the field of cancer and other multifactorial diseases to evaluate the applicability of gene expression patterns for identification of novel disease-relevant checkpoints and nomination of promising biomarkers for disease and/or targets. Deciphering the disease complexity demands the implementation of a holistic approach, which covers the levels of the biological hierarchy from molecules to functional gene network(s) and biological pathways and further to disease (patho)mechanisms and clinical relevance. In this chapter we describe the systems biology-based integrative algorithm, named by us as MuSiCO/fromMultigeneSignature to Patient-OrientatedClinicalOutcome, and discuss its applicability for translational research. This innovative approach is based on the implementation of consecutive analytical modules integrating advanced gene expression profiling of clinical patient specimens, prognostic/predictive modeling, digital pathology, and systems biology. It consolidates in-depth expertise from diverse scientific and medical disciplines and hereby bridges systems biology and systems medicine to maximize the benefit of the patient.