Back to search

FRIPRO-Fri prosjektstøtte

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

Alternative title: Bruk av storskala persontilpassede omics-nettverk for å modellere forstyrrelser av genregulering innen kreft

Awarded: NOK 8.0 mill.

Project Number:


Project Period:

2021 - 2026

Funding received from:


Partner countries:

Cancer is a complex disease. Each individual tumor has a unique set of gene mutations and a distinctive pattern of how these genes function in the cancer cell. It has recently become clear that disruptions in interactions between genes and proteins are also unique and can drive tumor development and progression. In this project, we will develop advanced computational tools that can model the network of interactions for individual tumors. We will use these tools to identify those parts of the network where interactions are disrupted, leading to the development and progression of cancer. We will analyze each individual networks to understand why certain patients have a more aggressive disease than others and why some cancers do not respond to treatment. Finally, we will use data integration techniques and artificial intelligence to combine these networks with cancer mutations and clinical information, identifying new subgroups of cancers that that are caused by different biological alterations, and that may respond differently to cancer treatments. In summary, our proposed project will help to will help find mechanisms that drive each individual cancer, demonstrating the potential of precision network medicine.

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.

Publications from Cristin

No publications found

No publications found

No publications found

No publications found

Funding scheme:

FRIPRO-Fri prosjektstøtte

Funding Sources