[Statlist] Nächster Talk: Freitag, 2. Oktober 2009 mit J. Rahnenführer

[Cecilia Rey]

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ETH and University of Zurich

Proff. A.D. Barbour - P. Buehlmann -  L. Held -
H.R. Kuensch - M. Maathuis - W. Stahel - S. van de Geer


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We are glad to announce the following talk

*Friday, October 2, 2009 15.15 - 17.00  HG  G 19.1 
*
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with Jörg Rahnenführer, Technische Universität Dortmund


Title:
Statistical methods for estimating cancer progression from genetic measurements

Abstract:
Human tumors are often associated with typical genetic events like 
tumor-specific chromosomal alterations. The identification of 
characteristic pathogenic routes in such tumors can improve the 
prediction of (disease-free) survival times und thus helps in choosing 
the optimal therapy. In recent years we have developed a biostatistical 
model for estimating the most likely pathways of chromosomal alterations from cross-sectional data. In this model progression is described by the irreversible, typically sequential, accumulation of somatic changes in cancer cells. The model was validated both statistically and clinically in various ways. We have also introduced a method to determine the optimal number of tree components based on a new BIC criterion.

The new model is characterized by a high level of interpretability. 
Further, it allows the introduction of a genetic progression score (GPS) 
that quantifies univariately the progression status of a disease. 
Progression of a single patient along such a model is typically 
correlated with increasingly poor prognosis. Using Cox regression models we could demonstrate that the GPS is a medically relevant prognostic factor that can be used to discriminate between patient subgroups with different expected clinical outcome. Both for prostate cancer patients and for patients with different types of brain tumors a higher GPS is correlated with shorter time to relapse or death.

The clinical relevance of such a disease progression model depends on 
the stability of the statistical model estimation process and on the 
predictive power of the derived progression score regarding survival 
times. Simulation studies show that the topology of our model can not 
always be estimated precisely. We present a study for determining the 
necessary sample size for recovering a true relationship between genetic progression and disease-free survival times. All studies are performed with the new R package Rtreemix for the estimation of such progression models. 

This abstract is also to be found under the following link:
http://stat.ethz.ch/talks/research_seminar

-- 
ETH Zürich
Seminar für Statistik
Cecilia Rey-Lutz, HG G10.3
Rämistrasse 101                    
CH-8092 Zurich		                      	
mail: rey at stat.math.ethz.ch    	  		
phone: +41 44 632 3438/fax: +41 44 632 1228