Discordance of Mutation Statuses of Epidermal Growth Factor Receptor and K-ras between Primary Adenocarcinoma of Lung and Brain Metastasis.

Mutations on epidermal growth factor receptor (EGFR) of adenocarcinomas of lung have been found to be associated with increased sensitivity to EGFR tyrosine kinase inhibitors and K-ras mutations may correlate with primary resistance. We aimed to explore the discordant mutation statuses of EGFR and K-ras between primary tumors and matched brain metastases in adenocarcinomas of lung. We used a sensitive Scorpion ARMS method to analyze EGFR mutation, and Sanger sequencing followed by allele-specific real-time polymerase chain reaction to analyze K-ras mutation. Forty-nine paired tissues with both primary adenocarcinoma of lung and matched brain metastasis were collected. Thirteen patients (26.5%) were discordant for the status of EGFR between primary and metastatic sites. K-ras gene could be checked in paired specimens from 33 patients, thirteen patients (39.6%) were discordant for the status of K-ras. In primary lung adenocarcinoma, there were 14 patients of mutant EGFR had mutant K-ras synchronously. This study revealed that the status of EGFR mutation in lung adenocarcinomas is relatively consistent between primary and metastatic sites compared to K-ras mutation. However, there are still a few cases of adenocarcinoma of lung showing discordance for the status of EGFR mutation. Repeated analysis of EGFR mutation is highly recommended if tissue from metastatic or recurrent site is available for the evaluation of target therapy.

A geometric network model of intrinsic grey-matter connectivity of the human brain.

Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuroscience is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct 'shortcuts' through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections.