Combined BRAF, EGFR, and MEK Inhibition in Patients with BRAFV600E-Mutant Colorectal Cancer

Although BRAF inhibitor monotherapy yields response rates >50% in BRAFV600-mutant melanoma, only ~5% with BRAFV600E colorectal cancer (CRC) respond. Preclinical studies suggest that lack of efficacy in BRAFV600E CRC is due to adaptive feedback reactivation of MAPK signaling, often mediated by EGFR. This clinical trial evaluated BRAF and EGFR inhibition with dabrafenib (D) + panitumumab (P) \ub1 MEK inhibition with trametinib (T) to achieve greater MAPK suppression and improved efficacy in 142 patients with BRAFV600E CRC. Confirmed response rates for D+P, D+T+P, and T+P were 10%, 21%, and 0%, respectively. Pharmacodynamic analysis of paired pre- and on-treatment biopsies found that efficacy of D+T+P correlated with increased MAPK suppression. Serial cell-free DNA analysis revealed additional correlates of response and emergence of KRAS and NRAS mutations on disease progression. Thus, targeting adaptive feedback pathways in BRAFV600E CRC can improve efficacy, but MAPK reactivation remains an important primary and acquired resistance mechanism

Workgroup 2 Position Paper, APPLY COST Action CA17132

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Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD)

•Extensive drifter observations allow new understanding to data assimilation.•Background error covariance is the point at which assumptions have historically been placed.•Components of background error covariance are tested to determine impact.•Amplitude of background error covariance is a critical factor.•Time correlation in background errors must be considered in 3DVar and 4DVar.Ocean prediction systems rely on an array of assumptions to optimize their data assimilation schemes. Many of these remain untested, especially at smaller scales, because sufficiently dense observations are very rare. A set of 295 drifters deployed in July 2012 in the north-eastern Gulf of Mexico provides a unique opportunity to test these systems down to scales previously unobtainable. In this study, background error covariance assumptions in the 3DVar assimilation process are perturbed to understand the effect on the solution relative to the withheld dense drifter data. Results show that the amplitude of the background error covariance is an important factor as expected, and a proposed new formulation provides added skill. In addition, the background error covariance time correlation is important to allow satellite observations to affect the results over a period longer than one daily assimilation cycle. The results show the new background error covariance formulations provide more accurate placement of frontal positions, directions of currents and velocity magnitudes. These conclusions have implications for the implementation of 3DVar systems as well as the analysis interval of 4DVar systems