Project description:Abstract: A subset of HER2 breast cancers with amplification of the TFAP2C gene locus becomes addicted to AP-2g. We sought to define AP-2g-regulated genes that control growth and invasiveness by comparing HER2 cell lines with differential response to TFAP2C knockdown. A set of 68 differentially expressed genes was identified, which included CDH5 and CDKN1A. Pathway analysis implicated the MAPK13/p38δ and retinoic acid regulatory nodes, which were confirmed to display divergent responses. The AP-2g gene signature was highly predictive of outcome in HER2-positive breast cancer patients. We conclude that AP-2g regulates a set of genes in HER2 breast cancer that drive cancer growth and invasiveness and that the AP-2g gene signature can predict outcome of patients with HER2 breast cancer. Results: Using an optimized data analysis workflow, we mapped about 50-75 million sequence reads per sample to the human genome Human Feb.2009 (GRCh37/hg19) (hg19). By RNA-seq analysis, knockdown of TFAP2C in HCC1954 with siRNA (compared to NT siRNA) identified 364 genes with significantly altered expression. To further create specificity for AP-2gamma-regulated genes, RNA-seq analysis was performed comparing expression in shHCC1954 with shTFAP2C cells vs. shNT; this analysis identified 8,986 genes with significantly altered expression. The two data sets were subsequently compared to identify a set of genes that were consistently altered with knockdown of TFAP2C by siRNA and shRNA. This comparison confirmed the identification of 152 AP-2gamma target genes in HCC1954 cells. Because HCC1954 demonstrated opposite growth regulation and invasiveness with knockdown of TFAP2C compared to SKBR3, we hypothesized that the AP-2gamma target genes responsible for growth and invasion would be differentially regulated with knockdown of TFAP2C in HCC1954 versus SKBR3. Hence, RNA-seq analysis was performed in SKBR3 after knockdown of TFAP2C with siRNA; in this analysis, a total of 3814 genes were significantly altered. The pattern of expression for the 152 AP-2gamma target genes identified in HCC1954 was subsequently compared to expression changes in SKBR3. Of note, only 79 of the 152 TFAP2C target genes in HCC1954 were found to change expression significantly in the RNA-seq data set from SKBR-3. Conclusions: Knockdown of TFAP2C with co-knockdown of CDH5 in SKBR-3 confirmed no significant effect on invasion, though there was a slight reduction in invasiveness with knockdown of CDH5 that failed to reach statistical significance (p=0.12). These findings support the conclusion that regulation of CDH5 and CDKN1A contribute to alterations of proliferation and invasiveness induced by knockdown of TFAP2C.

Project description:Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.

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Elucidating drug mechanistics and resistance in HER2-positive breast cancer cell lines by mathematical modeling

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