Summary BRAF is an attractive target for melanoma drug development. However, resistance to BRAF inhibitors is a significant clinical challenge. We describe a model of resistance to BRAF inhibitors developed by chronic treatment of BRAFV600E melanoma cells with the BRAF inhibitor SB-590885; these cells are cross resistant to other BRAF-selective inhibitors. Resistance involves flexible switching among the three RAF isoforms, underscoring the ability of melanoma cells to adapt to pharmacological challenges. IGF-1R/PI3K signaling was enhanced in resistant melanomas, and combined treatment with IGF-1R/PI3K and MEK inhibitors induced death of BRAF inhibitor-resistant cells. Increased IGFR-1R and pAKT levels in a post-relapse human tumor sample are consistent with a role for IGF-1R/PI3K-dependent survival in the development of resistance to BRAF inhibitors.

BRAF V600E is the most frequent oncogenic protein kinase mutation known. Furthermore, inhibitors targeting ''active'' protein kinases have demonstrated significant utility in the therapeutic repertoire against cancer. Therefore, we pursued the development of specific kinase inhibitors targeting B-Raf, and the V600E allele in particular. By using a structure-guided discovery approach, a potent and selective inhibitor of active B-Raf has been discovered. PLX4720, a 7-azaindole derivative that inhibits B-Raf V600E with an IC50 of 13 nM, defines a class of kinase inhibitor with marked selectivity in both biochemical and cellular assays. PLX4720 preferentially inhibits the active B-Raf V600E kinase compared with a broad spectrum of other kinases, and potent cytotoxic effects are also exclusive to cells bearing the V600E allele. Consistent with the high degree of selectivity, ERK phosphorylation is potently inhibited by PLX4720 in B-Raf V600E -bearing tumor cell lines but not in cells lacking oncogenic B-Raf. In melanoma models, PLX4720 induces cell cycle arrest and apoptosis exclusively in B-Raf V600E -positive cells. In B-Raf V600E -dependent tumor xenograft models, orally dosed PLX4720 causes significant tumor growth delays, including tumor regressions, without evidence of toxicity. The work described here represents the entire discovery process, from initial identification through structural and biological studies in animal models to a promising therapeutic for testing in cancer patients bearing B-Raf V600E -driven tumors.cancer ͉ cell signaling ͉ melanoma ͉ phosphorylation ͉ protein kinases O ncogenic mutations in the BRAF gene (1) correlate with increased severity and decreased response to chemotherapy in a wide variety of human tumors (2-4). Hence, direct therapeutic inhibition of oncogenic B-Raf kinase activity affords an avenue to treat these tumors. The therapeutic approach of targeting oncogenic kinase activity has proved very valuable in oncology (5, 6). Recently, we have described the technique termed scaffold-based drug discovery, a strategy for identifying small molecule inhibitors of cyclic nucleotide phosphodiesterases (7). Here, we describe an expansion of this strategy to discover a scaffold targeting protein kinases, and we report the elaboration of this scaffold into the potent and selective B-Raf V600E inhibitor PLX4720. Because a majority of all melanomas harbor an activating missense mutation (V600E) in the B-Raf oncogene (1), targeted inhibition of the V600E gene product is a particularly rational therapeutic goal in this otherwise therapy-resistant tumor type. Previous generations of B-Raf inhibitors possess Raf inhibitory activity at low nanomolar concentrations (8-13); however, the relative therapeutic efficacy of such inhibitors has been hampered by the lack of bioavailability or by the number of nonspecific targets that are also affected (14, 15). The development of highly specific and effectual inhibitors of the BRAF V600E gene product would provide insight into the true therapeutic rele...

Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by AE 60 days and in standard deviation by AE 20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground-or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS Correspondence: Michael A. White, tel. 1 1 435 797 3794, fax 1 1 435 797 187, trends could be detected for only 12% of North America and were divided between trends towards both earlier and later spring.

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Background: with ageing populations and increasing exposure to risk factors for chronic diseases, the prevalence of chronic disease multimorbidity is rising globally. There is little evidence on the determinants of multimorbidity and its impact on healthcare utilisation and health status in Europe. Methods: we used cross-sectional data from the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2011-12, which included nationally representative samples of persons aged 50 and older from 16 European nations. Negative binomial and logistic regression models were used to assess the association between number of chronic diseases and healthcare utilisation, self-perceived health, depression and reduction of functional capacity. Results: overall, 37.3% of participants reported multimorbidity; the lowest prevalence was in Switzerland (24.7%), the highest in Hungary (51.0%). The likelihood of having multimorbidity increased substantially with age. Number of chronic conditions was associated with greater healthcare utilisation in both primary (regression coefficient for medical doctor visits = 0.29, 95% CI = 0.27-0.30) and secondary setting (adjusted odds ratio (AOR) for having any hospitalisation in the last year = 1.49, 95% CI = 1.42-1.55) in all countries analysed. Number of chronic diseases was associated with fair/poor health status (AOR 2.13, 95% CI = 2.03-2.24), being depressed (AOR 1.48, 95% CI = 1.42-1.54) and reduced functional capacity (AOR 2.12, 95% CI = 2.02-2.22). Conclusion: multimorbidity is associated with greater healthcare utilisation, worse self-reported health status, depression and reduced functional capacity in European countries. European health systems should prioritise improving the management of patients with multimorbidity to improve their health status and increase healthcare efficiency.

BackgroundThe burden of non-communicable disease (NCDs) has grown rapidly in low- and middle-income countries (LMICs), where populations are ageing, with rising prevalence of multimorbidity (more than two co-existing chronic conditions) that will significantly increase pressure on already stretched health systems. We assess the impact of NCD multimorbidity on healthcare utilisation and out-of-pocket expenditures in six middle-income countries: China, Ghana, India, Mexico, Russia and South Africa.MethodsSecondary analyses of cross-sectional data from adult participants (>18 years) in the WHO Study on Global Ageing and Adult Health (SAGE) 2007–2010. We used multiple logistic regression to determine socio-demographic correlates of multimorbidity. Association between the number of NCDs and healthcare utilisation as well as out-of-pocket spending was assessed using logistic, negative binominal and log-linear models.ResultsThe prevalence of multimorbidity in the adult population varied from 3∙9% in Ghana to 33∙6% in Russia. Number of visits to doctors in primary and secondary care rose substantially for persons with increasing numbers of co-existing NCDs. Multimorbidity was associated with more outpatient visits in China (coefficient for number of NCD = 0∙56, 95% CI = 0∙46, 0∙66), a higher likelihood of being hospitalised in India (AOR = 1∙59, 95% CI = 1∙45, 1∙75), higher out-of-pocket expenditures for outpatient visits in India and China, and higher expenditures for hospital visits in Russia. Medicines constituted the largest proportion of out-of-pocket expenditures in persons with multimorbidity (88∙3% for outpatient, 55∙9% for inpatient visit in China) in most countries.ConclusionMultimorbidity is associated with higher levels of healthcare utilisation and greater financial burden for individuals in middle-income countries. Our study supports the WHO call for universal health insurance and health service coverage in LMICs, particularly for vulnerable groups such as the elderly with multimorbidity.

Spring phenology is thought to exert a major influence on the carbon (C) balance of temperate and boreal ecosystems. We investigated this hypothesis using four spring onset phenological indicators in conjunction with surface-atmosphere CO(2) exchange data from the conifer-dominated Howland Forest and deciduous-dominated Harvard Forest AmeriFlux sites. All phenological measures, including CO(2) source-sink transition dates, could be well predicted on the basis of a simple two-parameter spring warming model, indicating good potential for improving the representation of phenological transitions and their dynamic responsiveness to climate variability in land surface models. The date at which canopy-scale photosynthetic capacity reached a threshold value of 12 micromol m(-2) s(-1) was better correlated with spring and annual flux integrals than were either deciduous or coniferous bud burst dates. For all phenological indicators, earlier spring onset consistently, but not always significantly, resulted in higher gross primary productivity (GPP) and ecosystem respiration (RE) for both seasonal (spring months, April-June) and annual flux integrals. The increase in RE was less than that in GPP; depending on the phenological indicator used, a one-day advance in spring onset increased springtime net ecosystem productivity (NEP) by 2-4 g C m(-2) day(-1). In general, we could not detect significant differences between the two forest types in response to earlier spring, although the response to earlier spring was generally more pronounced for Harvard Forest than for Howland Forest, suggesting that future climate warming may favor deciduous species over coniferous species, at least in this region. The effect of earlier spring tended to be about twice as large when annual rather than springtime flux integrals were considered. This result is suggestive of both immediate and lagged effects of earlier spring onset on ecosystem C cycling, perhaps as a result of accelerated N cycling rates and cascading effects on N uptake, foliar N concentrations and photosynthetic capacity.

We conducted an inverse modeling analysis, using a variety of data streams (tower-based eddy covariance measurements of net ecosystem exchange, NEE, of CO2, chamber-based measurements of soil respiration, and ancillary ecological measurements of leaf area index, litterfall, and woody biomass increment) to estimate parameters and initial carbon (C) stocks of a simple forest C-cycle model, DALEC, using Monte Carlo procedures. Our study site is the spruce-dominated Howland Forest AmeriFlux site, in central Maine, USA. Our analysis focuses on: (1) full characterization of data uncertainties, and treatment of these uncertainties in the parameter estimation; (2) evaluation of how combinations of different data streams influence posterior parameter distributions and model uncertainties; and (3) comparison of model performance (in terms of both predicted fluxes and pool dynamics) during a 4-year calibration period (1997-2000) and a 4-year validation period ("forward run", 2001-2004). We find that woody biomass increment, and, to a lesser degree, soil respiration, measurements contribute to marked reductions in uncertainties in parameter estimates and model predictions as these provide orthogonal constraints to the tower NEE measurements. However, none of the data are effective at constraining fine root or soil C pool dynamics, suggesting that these should be targets for future measurement efforts. A key finding is that adding additional constraints not only reduces uncertainties (i.e., narrower confidence intervals) on model predictions, but at the same time also results in improved model predictions by greatly reducing bias associated with predictions during the forward run.