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International BCB-Workshop on Statistics and Cancer
Genomics
August 21, 2003
at
Magnus-Haus,
Am Kupfergraben 7, 10117 Berlin
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Preliminary
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Michael Boutros |
Genome-wide Functional Analysis of Cellular Phenotypes using RNAi
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One of the most critical demands following the completion of whole
genome
sequences is the systematic functional analysis of all predicted gene
products. Towards this goal, we established a methodology using
RNA-interference (RNAi) in cultured cells to functionally characterize
all
Drosophila genes required for specific cellular processes. We generated
a
library of 19,470 double-stranded RNAs (dsRNAs) designed to target by
RNAi
every predicted open reading frame in the Drosophila genome.
High-throughput
screens with this dsRNA library comprehensively identified genes
required
for cell growth and cell survival. Among the 438 targeted genes with the
most severe cell viability phenotypes were many without annotated
functions.
Novel candidate genes were further classified based on the similarity of
quantitative phenotypes with known genes required for cell growth and
cell
survival. In particular, this approach identified a candidate with a
further
demonstrated anti-apoptotic function encoded by a novel homolog of the
mammalian leukemic transcription factors AML1/2. We demonstrate that
RNAi
screens identify comprehensive sets of functionally related genes that
can
serve to predict novel gene functions on a genome-wide scale.
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Edgar Brunner |
Efficient Factorial Designs for cDNA-Microarray Experiments
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Based on the Kerr-Churchill ANOVA-model, a linear model for the
log-intensities of the expressions in the Cy3- and Cy5-channel is
considered. Estimability and variance of the effects of interest as well
as the efficiency of the statistics for treatment (variety) effects in
factorial designs are discussed. In this context, an average
E-optimality of a specific design for a certain set of effects is
discussed. As examples, the efficiencies of certain common reference-,
swap- , and loop- designs are compared in some one- and two-way layouts.
The analysis of the experimental data based on the designs is briefly
discussed.
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Peter Bühlmann |
Supervised Grouping of Genes
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A challenging task with expression measurements from thousands of genes
is
to reveal group of genes whose collective expression is strongly
associated
with an outcome (response) variable of interest such as an observed
tumor
type. Unlike unsupervised clustering algorithms, we construct groups of
genes by incorporating directly the observed response variable of
interest.
This is done within the framework of PEnalized LOgistic Regression
Analysis
and thus, we refer to our method as PELORA. We demonstrate empirically
that
PELORA identifies relatively few groups of genes whose expression
centroids
have excellent predictive potential (often superior to state-of-the-art
classification methods based on single genes) while still yielding a
drastic reduction in dimensionality. Moreover, PELORA can be used in
conjunction with additional clinical variables to come up with a model
and
competitive predictions for medical prognosis. Statistical inference in
such a model is proposed by leave-one-out bootstrap methods.
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Christine Sers, Oleg Tchernitsa, Ralf-Jürgen Kuban & Reinhold Schäfer |
RAS-oncogene dependent gene expression
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RAS proteins act as molecular switches transmitting signals from the
cell surface to the nucleus via a complex network of kinase cascades.
GTP-bound RAS proteins can activate of a number of effector proteins
among which Raf, Phosphatidylinositol kinase and RalGDS are the best
understood. These effectors stimulate cytoplasmic kinase cascades which
result in a profound transcriptional alteration of numerous genes. We
have analysed global RAS-dependent transcriptional alterations using
suppression subtractive hybridisation for the comparison of immortalized
and RAS transformed cells (Zuber et al. 2000). Hundreds of genes
encoding proteins with important roles in cell growth control,
cytoskeletal organisation and transcriptional regulation are either up
or down-regulated in RAS-transformed cells.
Further analysis using standard Northern Blots and high density
microarrays showed that subsets of genes are regulated by distinct
pathways. We were able to define a MAP/ERK module, a PI-3 kinase module
and a methylation-dependent module of signal-regulated transcriptional
targets. To define the temporal dependency of gene regulation in
response to RAS activtion, we used immortal rat fibroblasts harbouring
an IPTG-inducible HRAS oncogene. We analysed the expression of 286
selected RAS targets genes at distinct time points after the induction
of RAS expression using a cDNA array. We measured RAS protein levels and
activation of the MAP/ERK signaling pathway 10, 30 and 60 minutes and 2,
6, 12 , 24, 48 and 72 hours after addition of IPTG. RNA was prepared at
these time points and hybridized to the cDNA array. In total, 249 genes
were detectable. Among these, several groups were recognized which
revealed distinct patterns of up-or down-regulation following the
induction of RAS. Thirty-seven genes were found up-regulated and 44
genes were found down-regulated by a factor of 2 or more over time. The
most dramatic changes in gene expression were observed within the first
30 minutes after RAS activation and 24 to 48 hours later. For several
genes e.g. Fra-1, Srf, MMP3, RhoC and c-Jun such a bisphasic activation
was detected. In addition, the inactivation of potential classII tumor
suppressor genes (e.g. lox, clusterin) was found to decrease gradually
over time. These genes can be re-expression by 5-aza-deoxycytidine in
cells stably transformed by oncogenic RAS. This suggests that RAS
signalling down-regulates a subset of genes by activation of the
methylation machinery.
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Terry Speed |
Robustness and QC for sets of GeneChips
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Affymetrix offers a battery of measures for assessing the quality of a
GeneChip experiment, but almost all relate to the experimental process,
not directly to the end result, which is a measure of gene expression.
Further, they are all single chip measures, with generally recommended
ranges of values for the measures; they do not put each chip in the
context of other chips in the experiment. By contrast, RMA (robust
multi-chip analysis) attempts to do just this, robustly. We find various
by-products of the RMA calculation provide valuable quality information.
I will outline some of the ways we display and use this information.
(This talk describes joint work with Francois Collin, Ben Bolstad and
Julia Brettschneider of Berkeley, and Ken Simpson of Melbourne.)
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Mike West |
Statistical Modelling for Clinico-Genomics: Integrating Metagene
Signatures with Clinical Data in Models for Cancer Prognosis
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I'll talk about principles, models and practice of clinico-genomics --
the issues, ideas, methods and challenges in the contexts of modelling
and intgrating multiple forms of information, including traditional
clinical, pathological and demographic factors along with gene
expression patterns, to aid in clinical prognosis. Our work in
breast cancer provides the contexts, examples, experiences and
stimuli for open questions and challenges. One key goal of the talk
is to engage clinically-oriented biologists in conversations about
embracing rather more complex statistical models than is common in
clinical biostatistical practice.
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