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Large-scale personalized omics networks to model the disruption of gene regulation in cancer


In the past decade, next generation sequencing technologies have been widely applied to study cancer. Large collaborative efforts have mapped various ‘omics landscapes, including gene expression, mutations, and methylation profiles, for a wide variety of cancer types. However, the impact of these approaches on patient outcomes has been limited. It has become clear that, in order to understand what drives cancer and to identify new biomarkers and therapeutic targets, we need to integrate multiple ‘omics data types to gain greater insight into the molecular interactions that occur in the development and progression of the disease. In this project, we propose to develop advanced computational tools to model transcriptional and post-transcriptional gene regulatory interactions in large-scale gene regulatory networks by integrating transcription factor and miRNA binding with target gene co-expression information. We will use an innovative mathematical approach to model these networks for individual cancer patients. We will develop new computational tools using methodologies from network science and machine learning to integrate these large-scale patient-specific networks with mutation data and with clinical information. We will apply our tools to large-scale pan-cancer datasets to map the pan-cancer atlas of gene regulation. For each cancer type, we will analyze individual patient networks in the context of heterogeneity, response to treatment, and survival. In parallel, we will perform a pan-cancer analysis to uncover similarities and differences in regulatory interactions across multiple cancer types. We will perform experimental validation to test the involvement of candidate regulatory interactions in cancer development. This work will advance the field of precision network medicine, improve our understanding of gene regulation in cancer, and identify the underlying biological mechanisms that drive cancer development, progression, and clinical phenotypes.

Project leader: Marieke Kuijjer

Started: 2021

Ends: 2025

Category: Universiteter

Sector: UoH-sektor

Budget: 7984000

Institution: Norsk senter for molekylærmedisin (NCMM)

Address: Oslo