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

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. SP Transcription Factor network of cell migration Previous work time Fast signaling Transcription Phenotypic response 4h

WP/Aim 1: 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. WP/Aim 2: Steady-state causalities in the network and links to target genes will be determined by reverse engineering approaches incorporating prior knowledge. WP/Aim 3: 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. WP/Aim 4: The network model will be challenged and refined by testing synthetic interactions of TFs. WP/Aim 5: The network model will be extended to link global gene expression patterns, epigenetic marks and phenotypic responses to TF levels. WP/Aim 6: In vivo relevance of model predictions will be established by targeting TF- combinations in mouse systems and quantifying the impact on metastasis formation. SP Transcription Factor network of cell migration Workpackages Amit Nat Genet 2007

Aim 1-3,5 Aim 1,3,6 Aim 6 Aim 1,5,6Aim 5,6 Aim 2,3,5 Aim 3 Aim 1-3,5 SP Transcription Factor network of cell migration Internal Networking