Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack

Abstract

The post-genomic era promises to pave the way to a personalized understanding of disease processes, with technological and analytical advances helping to solve some of the world's health challenges. Despite extraordinary progress in our understanding of cancer pathogenesis, the disease remains one of the world's major medical problems. New therapies and diagnostic procedures to guide their clinical application are urgently required. OncoTrack, a consortium between industry and academia, supported by the Innovative Medicines Initiative, signifies a new era in personalized medicine, which synthesizes current technological advances in omics techniques, systems biology approaches, and mathematical modeling. A truly personalized molecular imprint of the tumor micro-environment and subsequent diagnostic and therapeutic insight is gained, with the ultimate goal of matching the "right" patient to the "right" drug and identifying predictive biomarkers for clinical application. This comprehensive mapping of the colon cancer molecular landscape in tandem with crucial, clinical functional annotation for systems biology analysis provides unprecedented insight and predictive power for colon cancer management. Overall, we show that major biotechnological developments in tandem with changes in clinical thinking have laid the foundations for the OncoTrack approach and the future clinical application of a truly personalized approach to colon cancer theranostics.

Keywords: Biomarkers; Colon cancer; Next-generation sequencing; Personalized medicine; Tumor heterogeneity.

Figure 1

OncoTrack – shifting the theranostic paradigm. In the course of the OncoTrack program, a comprehensive and systematic molecular interrogation of the primary tumor, metastases and paired healthy tissue, comprising whole genome, exome, transcriptome, methylome, and global proteomes of colon cancer patients is carried out. In tandem, serum and plasma is collected to aid biomarker detection to support prediction of clinical outcomes. To gain insight into the response of individual tumors to drugs, 3D cell cultures of tumors and mouse xenograft models are used, supported by corresponding omics analysis (transcriptomes, proteomes, and ideally, exomes and methylomes are analyzed before and during treatments), to facilitate prediction of patient response and development of drug resistance. Resultant data from individual tumors and patients is used to seed the ModCell™ integrative systems biology predictive platform [84, 85] to focus therapeutic strategies and identify biomarkers for stratification and management of patients. In turn, computer model predictions and experimental models are used to predict tumor responders and non-responders and ultimately identify biomarkers to direct therapeutic strategy.

Figure 2

PyBioS and ModCell™. PyBioS (pybios.molgen.mpg.de) and ModCell™ represent an integrative systems biology predictive platform that uses a global network model to predict individual outcomes following virtual treatment. Analysis of patient-derived omics and experimental data from individual tumors is integrated with existing information regarding the consequences of cancer related mutations on a molecular pathway level and their functional effects on the cellular and organism level. Based on a Monte Carlo type strategy, the model samples parameter vectors from a random distribution with statistical significance testing [84, 85]. In this way, information on cellular processes, such as cellular signaling pathways and drug interactions can be integrated, and the model can be applied to investigate the qualitative and quantitative behavior of the underlying biological system given specific perturbations, such as targeted drugs or mutations. The model can predict changes in key components (e.g. expression of specific genes, alteration in abundance of specific growth factors in autocrine loops, and inactivation of tumor suppressors) under different conditions (stimulation with growth factors, mutations, different drugs, and drug combinations at different concentrations), to provide patient-specific predictions and biomarker identification.