BCR-ABL-mediated upregulation of PRAME is responsible for knocking down TRAIL in CML patients

Tumor necrosis factor-related apoptosis-inducing ligand-TNFSF10 (TRAIL), a member of the TNF-alpha family and a death receptor ligand, was shown to selectively kill tumor cells. Not surprisingly, TRAIL is downregulated in a variety of tumor cells, including BCR-ABL-positive leukemia. Although we know much about the molecular basis of TRAIL-mediated cell killing, the mechanism responsible for TRAIL inhibition in tumors remains elusive because (a) TRAIL can be regulated by retinoic acid (RA); (b) the tumor antigen preferentially expressed antigen of melanoma (PRAME) was shown to inhibit transcription of RA receptor target genes through the polycomb protein, enhancer of zeste homolog 2 (EZH2); and (c) we have found that TRAIL is inversely correlated with BCR-ABL in chronic myeloid leukemia (CML) patients. Thus, we decided to investigate the association of PRAME, EZH2 and TRAIL in BCR-ABL-positive leukemia. Here, we demonstrate that PRAME, but not EZH2, is upregulated in BCR-ABL cells and is associated with the progression of disease in CML patients. There is a positive correlation between PRAME and BCR-ABL and an inverse correlation between PRAME and TRAIL in these patients. Importantly, knocking down PRAME or EZH2 by RNA interference in a BCR-ABL-positive cell line restores TRAIL expression. Moreover, there is an enrichment of EZH2 binding on the promoter region of TRAIL in a CML cell line. This binding is lost after PRAME knockdown. Finally, knocking down PRAME or EZH2, and consequently induction of TRAIL expression, enhances Imatinib sensibility. Taken together, our data reveal a novel regulatory mechanism responsible for lowering TRAIL expression and provide the basis of alternative targets for combined therapeutic strategies for CML. Oncogene (2011) 30, 223-233; doi:10.1038/onc.2010.409; published online 13 September 2010

Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies.