GRAIL An E3 Ubiquitin Ligase that Inhibits Cytokine Gene Transcription Is Expressed in Anergic CD4+ T Cells

AbstractT cell anergy may serve to limit autoreactive T cell responses. We examined early changes in gene expression after antigen-TCR signaling in the presence (activation) or absence (anergy) of B7 costimulation. Induced expression of GRAIL (gene related to anergy in lymphocytes) was observed in anergic CD4+ T cells. GRAIL is a type I transmembrane protein that localizes to the endocytic pathway and bears homology to RING zinc-finger proteins. Ubiquitination studies in vitro support GRAIL function as an E3 ubiquitin ligase. Expression of GRAIL in retrovirally transduced T cell hybridomas dramatically limits activation-induced IL-2 and IL-4 production. Additional studies suggest that GRAIL E3 ubiquitin ligase activity and intact endocytic trafficking are critical for cytokine transcriptional regulation. Expression of GRAIL after an anergizing stimulus may result in ubiquitin-mediated regulation of proteins essential for mitogenic cytokine expression, thus positioning GRAIL as a key player in the induction of the anergic phenotype

Predicting ligand-dependent tumors from multi-dimensional signaling features

Targeted therapies have shown significant patient benefit in about 5–10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo

Abstract 1312: Predicting ligand-dependent tumors from multi-dimensional signaling features [Abstract]

Receptor tyrosine kinases (RTKs) are high-affinity cell surface receptors for growth factors that are frequently deregulated in cancer. Signaling through these receptors has been associated with increased cancer cell proliferation and resistance to cytotoxic therapies. To block this detrimental signaling, many companies are developing inhibitory antibodies against various RTKs. A key challenge in clinical studies is the optimal stratification of patients who may benefit from these therapies. For an RTK targeted antibody, the detection of the respective growth factor in the tumor microenvironment may be an important bio-marker. Beyond the physical presence of the growth factor, the decision whether a cancer cell will respond to growth factor-induced signals is governed by complex intra-cellular signaling networks. We compared different approaches to predict cellular responses and will highlight a hybrid approach that combines mechanistic modeling based on ordinary differential equations with a machine learning algorithm. The models are trained on in vitro drug response screens and then applied to predict response in patient samples. The mechanistic models are trained on quantitative data from signal transduction studies as well as RNAseq data for cellular characterization. Using the hybrid approach, a correlation between growth factor expression in the tumor microenvironment and its predicted response was identified. This supports the hypothesis of addiction of tumors to growth factors abundant in the tumor microenvironment, and might enable more robust patient stratification in the future