Mauro Delorenzi & Frédéric Schütz's group

What we do

In the Bioinformatics Core Facility (BCF), we promote trans-disciplinary collaborations between research teams in medicine, molecular biology, genetics, genomics, statistics, and bioinformatics. In particular, we carry out analyses of biomedical-genomics data with a focus on biomarker studies in cancer research, building on our specific expertise in statistical methods for genomics data analysis. Recently, we have concentrated on molecular heterogeneity and pathway activation patterns in cancer subtypes, but we are open to any kind of research direction.

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Highlights 2017

We investigate the molecular heterogeneity of tumours to find markers useful to assess prognosis and best treatments after surgical removal of the primary tumour or upon metastasis. One approach is based on a “direct” statistical analysis of the association between a molecular feature and the clinical variables. In a second approach, we first identify disease subtypes and then test their ability to predict e.g. the efficacy of chemo- and immunotherapies. We develop analysis pipelines that extract these and other important characteristics from raw tumour profile data, such as mutational features or tumour clonality. We reported molecular subtypes in colon cancer (Barras 2017) and T-cell lymphomas (Dobay 2017). We have been supporting the development of an empirical platform for personalized treatment optimization based on determining the sensitivity of a patient individual cancer cells to a panel of drugs, with applications to acute lymphoblastic leukaemia (Frismantas 2017).

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Main publications 2017

• Barras D et al. BRAF V600E Mutant Colorectal Cancer Subtypes Based on Gene Expression. Clin Cancer Res. 2017 Jan 1;23(1):104-115.
• Dobay MP et al. Integrative clinicopathological and molecular analyses of angioimmunoblastic T-cell lymphoma and other nodal lymphomas of follicular helper T-cell origin. Haematologica. 2017 Apr;102(4):e148-e151.
• Frismantas V et al. Ex vivo drug response profiling detects recurrent sensitivity patterns in drug-resistant acute lymphoblastic leukemia. Blood. 2017 Mar 16;129(11):e26-e37.

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