Translational bioinformatics and statistics (TBS)

mauro_delorenzi-8314.jpg (Mauro Delorenzi)

Mauro DELORENZI
Doctor
Laboratory M. Delorenzi

Department of oncology UNIL CHUV

Phone +41 21 545 44 00
Email Mauro.delorenzi@unil.ch

 

 

Research interest

• Bioinformatics support for research groups

• Data-analysis & statistics services

• Discovery-oriented collaborations in fundamental and clinical cancer research

• Development and validation of prognostic and predictive signatures

• Development and comparative testing of methods

• Training in statistics & statistical computing  with applications (elementary to advanced)

Research group projects

Methods for data exploration and visualization

One approach focuses on improving computational methods that are important to more efficiently investigate and visualize relationships among data, across variables describing samples (like mutations or gene expression) and outcomes of interest (like patient survival or response to treatment). The aim is to allow the analyst to quickly extract and understand the most salient information in the data, at the level of interesting explanatory power and appropriate mathematical models, as well as at the level of potential quality issues and of strategies of data adjustments or filtering and stratification.

Delorenzi 1.jpg

Investigation of consistent relations between gene-expression data and survival signatures over multiple independent datasets.

Signatures: Methods for Translating from Research to Application

We think that a main reason of difficulty in translating multi-variable prediction models from research to clinical settings is projects typically rely on intricate and unstable processing steps to provide supposedly bias-free datasets to discover and train new omics signatures.

But in unprocessed data, some variables likely capture systematic biases. These relations can be exploited in a predictor to render it more robust to changing measurement conditions. We develop statistical approaches suitable for practical applications of this principle of  “self-normalizing predictors”, which we believe might turn out to be more easily translated to applications, especially if clinical applications are aimed at, where it is crucial to be able to reliably analyze single samples one by one in real time as they enter the clinic.

Molecular characterization of cancer subtypes and exploration of treatment efficacy signatures 

We have explored heterogeneity of gene expression in various cancer types, in particular primary colon cancer, summarized it with a system of five subtypes (of which one formed by tumors with a mixed profile) and later contributed to an international consortium that established a consensus of four well characterized molecular subtypes CMS1-CMS4 and a group of unclassified not well characterized tumors.

Ongoing efforts aim at expanding the study to metastatic colon cancer and to other cancer types.

We help clinical and pharma groups in the analysis of gene expression and genetic profiles of tumors, especially in the retrospective and exploratory investigation of clinical trials data, searching for significant relations between tumor features and treatment efficacies. The aim is to test if any biomarkers of response can be identified and then validated in successive studies. 

Delorenzi 2.jpg Delorenzi 3.jpgDelorenzi 4.jpg

Selected publications

  • Cardoso F et al.  70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. 2016 N Engl J Med. 2016;25;375(8):717-29. PMID: 27557300
  • Barras D et al.  BRAF V600E mutant colorectal cancer subtypes based on gene expression. 2016 Clin Cancer Res. 2016 Jun 27. pii: clincanres.0140.2016. PMID: 27354468
  • Guinney J etal. The consensus molecular subtypes of colorectal cancer. 2015 Nat Med. 2015 Nov;21(11):1350-6. doi: 10.1038/nm.3967. PMID: 26457759
  • Missiaglia E et al. Distal and proximal colon cancers differ in terms of molecular, pathological and clinical features. 2014 Ann Oncol. 2014 Oct;25(10):1995-2001. PMID: 25057166
  • Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. 2013. BMC Bioinformatics. 2013 Mar 9;14:91. doi: 10.1186/1471-2105-14-91

Group members - core

  • Dr Paolo ANGELINO
    Senior bioinformatician
  • Dr Pierre BADY
    Senior biostatistician
  • Dr David BARRAS
    Senior bioinformatician
  • Dr Linda DIB
    senior bioinformatician
  • Dr Andrew JANOWCZYK
    Senior bioinformatician
  • Dr Sina NASSIRI
    Bioinformatician
  • Patrick RÖLLI
    PhD student
  • Dr Frédéric SCHÜTZ
    Senior statistician
  • Dr Petros TSANTOULIS
    Visiting scientist (long-term)
  • Dr Asa WIRAPATI
    Senior bioinformatician
  • Dr Nadine ZANGGER
    Senior bioinformatician

Additional current members:

  • Dr Sara FONSECA
    Visiting scientist (short-term)
  • Dr Giovanni PRIVITERA
    Intern
  • Dr Tania WYSS
    Intern
Ch. des Boveresses 155 - CH-1066 Epalinges
Switzerland
Tel. +41 21 692 59 92
Fax +41 21 692 59 95
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