Welcome!

CSO
The Computational Systems Oncology lab integrates algorithmic design, data science, and molecular biology approaches to address relevant questions in cancer biology and therapeutics.

Our group is embedded within the Department of Computational Biology of the University of Lausanne and a member of the Swiss Cancer Center Leman, the Swiss Institute of Bioinformatics, the Center of Precision Oncology of the CHUV and Swiss Institute for Experimental Cancer Research (ISREC)

TMFOCE

Research in brief

Cancer Evolutionary Dependencies

Cancer emerges through the occurrence and selection of molecular alterations. We aim at understanding factors that favor or veto selection of specific alterations, a.k.a. evolutionary dependencies (EDs). In particular, we focus on concurrent or mutually exclusive selection of genetic alteations and whether these EDs can inform response to therapy.

Cancer Cell Plasticity

Cancer cells can change their phenotype or even their identity without modifying their genetic code. We are interested in understanding epigenetic and transcriptional reprogramming programs that underlie cancer cell plasticity. We study features of plastic reprogramming among different patients and within individual tumors, using cutting edge single cell and spatial -omics technologies.

Chromatin 3D Architecture

A key paradigm in biology is that structure determines function. Whether and to what extent this holds true for chromatin 3D architectures remains an open question. Here, we study chromatin structural changes in response to cancer genetic variants and epigenetic reprogramming. Our goal is to decipher chromatin plasticity and determine how it is hijacked in and/or it influences tumor phenotypes.

We want you!

Currently open positions:

  • Post-doc position available in our lab!

    (UPDATED March, 2023) We are looking for outstanding candidates for a a post-doctoral position in our group! We're looking for scientist to join our team studying the interplay between cancer alterations on chromatin 3D conformation. The successful applicant will work in interdisciplinary team combining development and application of new algorithmic approaches, high-throughput data generation, and functional genomics approaches. Requirements
    A Ph.D. in computer science or related field is preferred, but applicants with a Ph.D. in Life Sciences are also welcomed to apply. Previous experience in cancer epigenetics and Hi-C data analysis is highly preferred. At least one first/main author publication in a peer-reviewed journal is required.

    To Apply
    Just send me an email with your CV and name of 3 referees.
    if you can't figure out my email..maybe you shouldn't apply ;)
    Cheers!