Classification of Cancer Patients Using Pathway Analysis and Network Clustering

Abstract

Molecular expression patterns have often been used for patient classification in oncology in an effort to improve prognostic prediction and treatment compatibility. This effort is, however, hampered by the highly heterogeneous data often seen in the molecular analysis of cancer. The lack of overall similarity between expression profiles makes it difficult to partition data using conventional data mining tools. In this chapter, the authors introduce a bioinformatics protocol that uses REACTOME pathways and patient–protein network structure (also called topology) as the basis for patient classification.