ETV4 and ETV5 drive synovial sarcoma through cell cycle and DUX4 embryonic pathway control

Synovial sarcoma is an aggressive malignancy with no effective treatments for patients with metastasis. The synovial sarcoma fusion SS18-SSX, which recruits the SWI/SNF-BAF chromatin remodeling and polycomb repressive complexes, results in epigenetic activation of FGF receptor (FGFR) signaling. In genetic FGFR-knockout models, culture, and xenograft synovial sarcoma models treated with the FGFR inhibitor BGJ398, we show that FGFR1, FGFR2, and FGFR3 were crucial for tumor growth. Transcriptome analyses of BGJ398-treated cells and histological and expression analyses of mouse and human synovial sarcoma tumors revealed prevalent expression of two ETS factors and FGFR targets, ETV4 and ETV5. We further demonstrate that ETV4 and ETV5 acted as drivers of synovial sarcoma growth, most likely through control of the cell cycle. Upon ETV4 and ETV5 knockdown, we observed a striking upregulation of DUX4 and its transcriptional targets that activate the zygotic genome and drive the atrophy program in facioscapulohumeral dystrophy patients. In addition to demonstrating the importance of inhibiting all three FGFRs, the current findings reveal potential nodes of attack for the cancer with the discovery of ETV4 and ETV5 as appropriate biomarkers and molecular targets, and activation of the embryonic DUX4 pathway as a promising approach to block synovial sarcoma tumors

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks

Estimated Parameters in the Nonlinear State-Space Model for the JAK-STAT Pathway.

<p>Estimated Parameters in the Nonlinear State-Space Model for the JAK-STAT Pathway.</p

Scheme of the JAK-STAT pathway.

<p>Scheme of the JAK-STAT pathway.</p

Scheme of the Ras/Raf/MEK/ERK Pathway.

<p>Scheme of the Ras/Raf/MEK/ERK Pathway.</p

The predicted dynamic behavior of unphosphorylated STAT5 (<i>x</i>₁), tyrosine phosphorylated STAT5 monomers (<i>x</i>₂) and dimers (<i>x</i>₃) in the cytoplasm, and STAT5 molecules in the nucleus (<i>x</i>₄) in the JAK-STAT pathway.

<p>The predicted dynamic behavior of unphosphorylated STAT5 (<i>x</i>₁), tyrosine phosphorylated STAT5 monomers (<i>x</i>₂) and dimers (<i>x</i>₃) in the cytoplasm, and STAT5 molecules in the nucleus (<i>x</i>₄) in the JAK-STAT pathway.</p

Figure 3

<p>3A. The estimated parameter <i>θ</i>₁ for the simulated data. 3B. The estimated parameter <i>θ</i>₂ for the simulated data.</p