NCCR and other Projects
The BCF is working on or collaborating in a high number of projects.
A general overview is given here, followed by a list of collaborationg groups.
Many of them are documented on the web pages of the
BCF staff members.
Database of gene expression and clinical data
One serious shortcoming of most microarray studies is the lack of sufficient sample size.
This may be partially addressed by pooling all existing data and by adopting methods
from statistical meta-analysis. For many of these datasets, the expression or clinical data
are not conveniently organized. This requires manual collection and interpretation,
followed by standardization of vocabulary and data representation. We are setting up a
Genomic Medical Database, that is a curated collection of RNA / protein quantification
and associated clinical data. We also map corresponding probes on different platforms to
each other to facilitate the integrated study of multiple data sets. The focus is on breast
cancer and the usability of the clinical and survival data, but the concept could be
Discovery of cancer subtypes, prognostic and predictive profiles
In collaborations with different biomedical teams, we apply bioinformatics and
biostatistical methods to discover new informative markers and profiles, which can
improve treatment decisions by oncologists. We described a new subtype of breast
cancer, which might be responsive to anti-androgen drugs, in collaboration with Dr.
Herve Bonnefoi at Geneva Hospital and Dr. Richard Iggo at the University of St Andrews
(Scotland). In a continuation of the project, we search for profiles predictive of the
sensitivity of tumors to the two major drugs for systemic chemotherapy against breast
tumors, anthracyclines and taxanes. In collaboration with Dr. Christos Sotiriou and the
Institut Jules Bordet in Bruxelles we have discovered a method to determinate a "gene
expression" grade index (GGI), that is a powerful prognostic factor and that could
supplant histologic grade in clinical practice. We are currently aiming at further
improving the prognostic system with additional informative marker genes and studying
gene-gene dependencies to better understand the molecular and mathematical basis of the
risk of metastasis.
Improved programs for analyzing gene expression and association with phenotypes
There are a number of useful techniques from classical statistical analysis, that are
underused in bioinformatics because they are not implemented to be effectively combined
between them and with modern techniques of significance analysis in the multiple testing
setting. We are developing a software package (GlimEx) in which we are integrating such
methods. The core of the project is a C code library that can be used in a flexible way to
solve heavy computational tasks. The aim is to give to analysts a tool for efficient
integrated analysis of large data sets from multiple sources, freeing them from a number
of tedious tasks, so that they can concentrate on specific issues such as result
interpretation and identifying bias and confounding. Interfaces are developed to allow for
use of the library from within R, in combination with other R and Bioconductor
packages. GlimEx is planned to include different types of models for response (survival,
logistic, etc.), built-in meta-analytical capabilities, adjustment methods to account for
other (clinical) factors, multiple-testing correction methods and cross-validation
algorithms for testing predictive models.
Other software developments
MAMOT is a program we developed to allow for the use of simple hidden Markov models
by non specialists and that we is being used in a collaboration with Dr. P. Bucher for
generating models of Transcription factor binding sites.and for teaching purposes.
Below are some links to the groups we collaborate with.
1. NCCR molecular oncology program
Monika Hegi (CHUV Lausanne )
(EORTC Brain Cancer Group)
(EORTC trial "Temozolomide")
(St Andrews, Scotland)
(EORTC trial "p53")
(EORTC Breast Cancer Group)
(Experimental Pathology Lausanne)
Nathalie Rufer (ISREC)
Cathrin Brisken (ISREC)
(Institut Jules Bordet, Bruxelles)
3. University BERN
6. SCIENTIFIC COMPUTING
7. DNA ARRAY FACILITIES
Manfredo Quadroni PAFL
(Protein Analysis Facility PAFL)
(Peptide Chemistry Facility PPCF)
(University Geneva, Biochemistry )