Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Abstract

Modern approaches to biomedical research and diagnostics targeted towards precisionmedicine are generating 'big data' across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework formultimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework fromamore detailed perspective. PICan, built around a relational database storing curatedmultimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices wemade within the context of our local infrastructure and specific needs, but also highlighting alternative approaches thatmay better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigmfor howmodern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative teameffort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.