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Using cancer interactome cell map for analysis of and interpretation of cancer omics data

Using cancer interactome cell map for analysis of and interpretation of cancer omics data

By In Aiv Internship On June 1, 2018


Internship title: Using cancer interactome cell map for analysis of and interpretation of cancer omics data

LABORATORY
Name: Bioinformatics and Computational Systems Biology of Cancer
Affiliation: U900 Institut Curie – INSERM – Mines ParisTech
Address: 26 rue d’Ulm 75248 PARIS CEDEX 05 – FRANCE
E-mail: inna.kuperstein@curie.fr

LAB Director
Name: Emmanuel BARILLOT
Phone number: 3315246980
E-mail: emmanuel.barillot@curie.fr

SUPERVISOR
Name: Andrei ZINOVYEV and Inna KUPERSTEIN
Phone number: 33156246989
E-mail: andrei.zinovyev@curie.fr

Subject Keywords: systems biology
protein-protein interaction maps
signalling networks
NaviCell, ACSN
gene signatures
cancer patient stratification

Tools and methodologies: Experience of programming in a high-level language used in data science (R, Java, MATLAB, etc.). Familiarity with multivariate statistics for the analysis of genomics data (hypothesis testing, exploratory analysis, etc.). Cancer biology and molecular mechanisms knowledge is an advantage. Good English is essential.
Summary of lab’s interests: The “Bioinformatics and Computational Systems Biology of Cancer” Unit (U900 INSERM, Mines ParisTech, Institut Curie) involves about 90 researchers and students. It is a very active and growing interdisciplinary team of biologists, physicians, mathematicians, statisticians, physicists and computer scientists (http://u900.curie.fr). The Computational Systems Biology of Cancer team is embedded into the unit. This research group focuses on deciphering determinants of tumorigenesis and tumor progression. The group aims to propose new strategies to combat the disease. The domains of expertise are big data analysis; signaling network construction and mathematical modeling; study of synthetic interactions in cancer mechanisms, drug response prediction, patient stratification and others (http://sysbio.curie.fr).
Project summary: Background
Systems biology is an inter-disciplinary field that studies interactions between components of biological systems and how they determine phenotypes like cell fate or drug response. Among other activities, systems biology integrates computational approaches with the molecular knowledge for representation of biological processes as comprehensive signalling networks as Atlas of Cancer Signalling Network (ACSN, http://acsn.curie.fr). The network construction can be data-driven, based on omics data for instance, proteome data. The resulting interactome maps (http://interactome-cmp.ucsf.edu) can serve a resource for studying network perturbations in human diseases as cancer.

Aim of the project
Develop a method for integrating Cancer Cell Map interactome data with cancer signalling pathway databases (ACSN) and available public transcriptomic data (TCGA). The methods aims at creating network-based signatures of cancer drivers and quantifying these signatures in large omics datasets. The method will serve to better understand the molecular mechanisms of cancer progression and develop biomarkers for improving prognosis and treatment.

Tasks
1). Using protein interactome data obtained from proteomics analysis for head and neck cancer cell lines, determine network-based signatures of cancer drivers.
2). Apply methods for quantification of gene signatures using large-scale omics data (such as ROMA), to the network-based signatures.
3). Use the resulting sample scores to associate the network-based signatures of head and neck cancer drivers to available clinical and biological data
4). Explore the connection between the network-based signatures of head and neck cancer drivers and signaling pathways described in ACSN database
5). Develop a method for visualizing the results of the analysis on top of molecular maps using NaviCell tool.

Interdisciplinary aspect of the project: The project will be performed in collaboration with the team of Dr. Nevan KROGAN from Gladstone institute (https://gladstone.org/our-science/people/nevan-krogan).

The project is international, inter-institutional and interdisciplinary at the intersection of biology (proteome, cell lines, mutants), computational systems biology (networks, data analysis, methods development) and clinical research (patient stratification, signatures).

The results of the work may lead to one or more publications.