The novel ATP-competitive inhibitor of the MET hepatocyte growth factor receptor EMD1214063 displays inhibitory activity against selected MET-mutated variants

The receptor tyrosine kinase MET is a prime target in clinical oncology due to its aberrant activation and involvement in the pathogenesis of a broad spectrum of malignancies. Similar to other targeted kinases, primary and secondary mutations seem to represent an important resistance mechanism to MET inhibitors. Here, we report the biologic activity of a novel MET inhibitor, EMD1214063, on cells that ectopically express the mutated MET variants M1268T, Y1248H, H1112Y, L1213V, H1112L, V1110I, V1206L, and V1238I. Our results demonstrate a dose-dependent decrease in MET autophosphorylation in response to EMD1214063 in five out of the eight cell lines (IC50 2-43nM). Blockade of MET by EMD1214063 was accompanied by a reduced activation of downstream effectors in cells expressing EMD1214063-sensitive mutants. In all sensitive mutant-expressing lines, EMD1214063 altered cell cycle distribution, primarily with an increase in G1 phase. EMD1214063 strongly influenced MET-driven biological functions, such as cellular morphology, MET-dependent cell motility and anchorage-independent growth. To assess the in vivo efficacy of EMD1214063, we used a xenograft tumor model in immunocompromised mice bearing NIH3T3 cells expressing sensitive and resistant MET mutated variants. Animals were randomized for the treatment with EMD1214063 (50mg/kg/day) or vehicle only. Remarkably, five days of EMD1214063 treatment resulted in a complete regression of the sensitive H1112L-derived tumors, while tumor growth remained unaffected in mice with L1213V tumors and in vehicle-treated animals. Collectively, the current data identifies EMD1214063 as a potent MET small molecule inhibitor with selective activity towards mutated MET variants.

Matrix models for quantifying competitive intransitivity from species abundance data

In a network of competing species, a competitive intransitivity occurs when the ranking of competitive abilities does not follow a linear hierarchy (A > B > C but C > A). A variety of mathematical models suggests that intransitive networks can prevent or slow down competitive exclusion and maintain biodiversity by enhancing species coexistence. However, it has been difficult to assess empirically the relative importance of intransitive competition because a large number of pairwise species competition experiments are needed to construct a competition matrix that is used to parameterize existing models. Here we introduce a statistical framework for evaluating the contribution of intransitivity to community structure using species abundance matrices that are commonly generated from replicated sampling of species assemblages. We provide metrics and analytical methods for using abundance matrices to estimate species competition and patch transition matrices by using reverse-engineering and a colonization-competition model. These matrices provide complementary metrics to estimate the degree of intransitivity in the competition network of the sampled communities. Benchmark tests reveal that the proposed methods could successfully detect intransitive competition networks, even in the absence of direct measures of pairwise competitive strength. To illustrate the approach, we analyzed patterns of abundance and biomass of five species of necrophagous Diptera and eight species of their hymenopteran parasitoids that co-occur in beech forests in Germany. We found evidence for a strong competitive hierarchy within communities of flies and parasitoids. However, for parasitoids, there was a tendency towards increasing intransitivity in higher weight classes, which represented larger resource patches. These tests provide novel methods for empirically estimating the degree of intransitivity in competitive networks from observational datasets. They can be applied to experimental measures of pairwise species interactions, as well as to spatio-temporal samples of assemblages in homogenous environments or environmental gradients.