Prediction of desmoid tumor progression using miRNA expression profiling.

Desmoid tumor is a rare connective tissue tumor with locoregional aggressiveness but unpredictable behavior. The miRNA profile was ascertained for 26 patients included in the Desminib phase II trial and an independent validation cohort of 15 patients. Predictive and prognostic supervised analysis on the Desminib cohort failed to identify miRNAs differentially expressed between progressive and non-progressive patients under imatinib treatment or between progressive and non-progressive patients after discontinuation of imatinib. However, an unsupervised hierarchical clustering of the Desminib cohort identified two groups (A and B) of 13 patients each, where only the number of previous lines of treatment before inclusion in the study differed significantly between the two groups. Time to progression after discontinuation of imatinib was longer in group B than in group A. Fifteen miRNAs were highly statistically differentially expressed between groups A and B, targeting more than 3000 genes, including AGO1, BCL2, CDK6, SMAD4, PTEN, CCND1, VEGFA, and RB1. These results were confirmed in the independent validation cohort: hierarchical clustering of these 15 miRNAs identified two groups, in which time to recurrence was statistically different (28.8 months vs 68.8 months). These results provide the first indication of the prognostic value of miRNA expression profiling with a possible direct impact on patient management. A more precise miRNA signature must now be determined to select patients who would not benefit from surgical resection of their tumor and who ought to be monitored without treatment.

Modelling competition and dispersal in a statistical phylogeographic framework.

Competition between organisms influences the processes governing the colonization of new habitats. As a consequence, species or populations arriving first at a suitable location may prevent secondary colonization. Although adaptation to environmental variables (e.g., temperature, altitude, etc.) is essential, the presence or absence of certain species at a particular location often depends on whether or not competing species co-occur. For example, competition is thought to play an important role in structuring mammalian communities assembly. It can also explain spatial patterns of low genetic diversity following rapid colonization events or the "progression rule" displayed by phylogenies of species found on archipelagos. Despite the potential of competition to maintain populations in isolation, past quantitative analyses have largely ignored it because of the difficulty in designing adequate methods for assessing its impact. We present here a new model that integrates competition and dispersal into a Bayesian phylogeographic framework. Extensive simulations and analysis of real data show that our approach clearly outperforms the traditional Mantel test for detecting correlation between genetic and geographic distances. But most importantly, we demonstrate that competition can be detected with high sensitivity and specificity from the phylogenetic analysis of genetic variation in space.