Antibody-mediated targeting of TNFR2 activates CD8 T cells in mice and promotes antitumor immunity.

Tumor necrosis factor receptor 2 (TNFR2) is the alternate receptor for TNF and can mediate both pro- and anti-inflammatory activities of T cells. Although TNFR2 has been linked to enhanced suppressive activity of regulatory T cells (T) in autoimmune diseases, the viability of TNFR2 as a target for cancer immunotherapy has been underappreciated. Here, we show that new murine monoclonal anti-TNFR2 antibodies yield robust antitumor activity and durable protective memory in multiple mouse cancer cell line models.

Recipes for Analysis of Molecular Networks Using the Data2Dynamics Modeling Environment.

Mechanistic models of biomolecular processes are established research tools that enable to quantitatively investigate dynamic features of biological processes such as signal transduction cascades. Often, these models aim at describing a large number of states, for instance concentrations of proteins and small molecules, as well as their interactions. Each modeled interaction increases the number of potentially unknown parameters like reaction rate constants or initial amount of proteins.

MM-131, a bispecific anti-Met/EpCAM mAb, inhibits HGF-dependent and HGF-independent Met signaling through concurrent binding to EpCAM.

Activation of the Met receptor tyrosine kinase, either by its ligand, hepatocyte growth factor (HGF), or via ligand-independent mechanisms, such as amplification or receptor overexpression, has been implicated in driving tumor proliferation, metastasis, and resistance to therapy. Clinical development of Met-targeted antibodies has been challenging, however, as bivalent antibodies exhibit agonistic properties, whereas monovalent antibodies lack potency and the capacity to down-regulate Met. Through computational modeling, we found that the potency of a monovalent antibody targeting Met could be dramatically improved by introducing a second binding site that recognizes an unrelated, highly expressed antigen on the tumor cell surface.

Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics.

Tumor ecosystems are composed of multiple cell types that communicate by ligand-receptor interactions. Targeting ligand-receptor interactions (for instance, with immune checkpoint inhibitors) can provide significant benefits for patients. However, our knowledge of which interactions occur in a tumor and how these interactions affect outcome is still limited.

Modeling chemotherapy-induced stress to identify rational combination therapies in the DNA damage response pathway.

Cells respond to DNA damage by activating complex signaling networks that decide cell fate, promoting not only DNA damage repair and survival but also cell death. We have developed a multiscale computational model that quantitatively links chemotherapy-induced DNA damage response signaling to cell fate. The computational model was trained and calibrated on extensive data from U2OS osteosarcoma cells, including the cell cycle distribution of the initial cell population, signaling data measured by Western blotting, and cell fate data in response to chemotherapy treatment measured by time-lapse microscopy.