Transcript Folie 1 - uni

SP Transcription Factor network of cell migration
Summary
Hypothesis:
We hypothesize that, by generating a network model
for transcription factor regulation of EGF-induced
migration of breast epithelial cells, we will be able to
predict phenotypic efficacy of mono- and
combinatorial perturbations in anti-metastasis
therapy
SP Transcription Factor network of cell migration
Previous work
In collaboration with Yosef Yarden (Weizmann
Institute) we have identified @@@ TFs that are
regulated upon EGF-stimulation in MCF10A
cells and @@@ TFs with known relevance in
breast cancer that are all expressed in MCF10A.
In real-time measurements (RTCA) we found
that cell migration of unperturbed MCF10A cells
is induced about 4 hours after application of the
EGF stimulus. Consequently, we hypothesize
that de novo transcription is required for the
phenotypic switch. We individually knocked
down (RNAi) the TFs and screened for effects
on collective cell migration. This identified TFs
that either enhanced or abrogated migration in
MCF10A cells. We then intercrossed these TFs
with the findings made in expression profiling
experiments and prioritized a list of @@@ TFs
for further investigation.
time
4h
Fast signaling
Transcription
Phenotypic response
SP Transcription Factor network of cell migration
Workpackages
WP/Aim 1:
WP/Aim 2:
WP/Aim 3:
WP/Aim 4:
WP/Aim 5:
WP/Aim 6:
We will quantify transcription changes
after knock-down of individual TFs as
well as combinations to generate a
perturbation matrix of TFs and
regulated genes. Data from patient
samples will be integrated to determine
clinical significance of in vitro gene
expression patterns.
Steady-state causalities in the network
and links to target genes will be
determined by reverse engineering
approaches incorporating prior
knowledge.
The network model will be enriched by
time-resolved measurements as well as
information on RNA/protein turnover
thus capturing the dynamics of TF
network activities.
The network model will be challenged
and refined by testing synthetic
interactions of TFs.
The network model will be extended to
link global gene expression patterns,
epigenetic marks and phenotypic
responses to TF levels.
In vivo relevance of model predictions
will be established by targeting TFcombinations in mouse systems and
quantifying the impact on metastasis
formation.
Amit Nat Genet 2007
SP Transcription Factor network of cell migration
Internal Networking
Aim 1-3,5
Aim 1,5,6
Aim 1-3,5
Aim 1-3,5
Aim 1,3,6
Aim 2,3,5
Aim 3
Aim 5,6
Aim 6