Identifying Oncogenic Drivers in NSCLC Cells Harboring EGFR Kinase Domain Mutation with Resistance to EGFR TKI and Mesenchymal Phenotype

EGFR kinase domain mutant NSCLC cells are exquisitely dependent on mutant EGFR for cell survival and proliferation. Patients with mutant EGFR respond well to the EGFR inhibitors. However, acquired drug resistance greatly limits the efficacy of the treatment. About 15% of the resistant tumors present an evidence of epithelial to mesenchymal transition (EMT). We hypothesize that the induction of mesenchymal promotes aberrant upregulation of other oncogenic drivers to replace the oncogenic mutant EGFR. We observed overexpression of CXCR7 in NSCLC models of acquired resistance to EGFR TKI with mesenchymal phenotype. Additionally, our studies demonstrate that ectopically overexpressing CXCR7 in EGFR mutant NSCLC cells promotes EGFR TKI resistance and EMT. CXCR7 knockdown restores sensitivity to EGFR TKI and partially reverse EMT. In summary, our data suggested CXCR7 as a promising therapeutic target for preventing or overcoming EGFR TKI resistance in a subset of EGFR mutant NSCLC patients

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Comment: 45 page

Etiology and Clinical Characteristics of Influenza-Like Illness (ILI) in Outpatients in Beijing, June 2010 to May 2011

BACKGROUND: Since May 2009, exposure of the population of Beijing, China to pH1N1 has resulted in an increase in respiratory illnesses. Limited information is available on the etiology and clinical characteristics of the influenza-like illness (ILI) that ensued in adults following the pH1N1 pandemic. METHODS: Clinical and epidemiological data of ILI in adults was collected. A total of 279 throat swabs were tested for twelve respiratory viruses using multiplex RT-PCR. Clinical characteristics of influenza A in outpatients versus test-negative patients were compared using Pearson's χ2 and the Mann-Whitney U test. 190 swabs were tested for pH1N1 by virus isolation. Consultation rates for ILI were compared between 2009 and 2010. RESULTS: One or two virus were detected in 29% of the samples. Influenza A virus (FLU-A) accounted for 22.9% (64/279). Other viruses were present at a frequency less than 3.0%. Cough was significantly associated with Influenza A virus infection (χ2, p<0.001). The positive rate of FLU-A was consistent with changes in the ILI rate during the same period and there was a significant reduction in the incidence of ILI in 2010 when compared to 2009. During the 2010-2011 influenza season, the incidence peaked in January 2011 in Beijing and north China. CONCLUSIONS: Exposure to pH1N1 had no impact on typical influenza seasonal peaks, although FLU-A remained the predominant virus for 2010 in Beijing. Symptomatically, cough was associated with FLU-A infection. The positive rate of influenza virus was consistent with changes in the ILI rate during the same period and there was a significant reduction in the incidence of ILI in 2010 when compared to that of 2009

Highly pathogenic avian influenza H5N6 viruses exhibit enhanced affinity for human type sialic acid receptor and in-contact transmission in model ferrets

Since May 2014, highly pathogenic avian influenza H5N6 virus has been reported to cause six severe human infections three of which were fatal. The biological properties of this subtype, in particular its relative pathogenicity and transmissibility in mammals, are not known. We characterized the virus receptor-binding affinity, pathogenicity, and transmissibility in mice and ferrets of four H5N6 isolates derived from waterfowl in China from 2013-2014. All four H5N6 viruses have acquired a binding affinity for human-like SA alpha 2,6Gal-linked receptor to be able to attach to human tracheal epithelial and alveolar cells. The emergent H5N6 viruses, which share high sequence similarity with the human isolate A/Guangzhou/39715/2014 (H5N6), were fully infective and highly transmissible by direct contact in ferrets but showed less-severe pathogenicity than the parental H5N1 virus. The present results highlight the threat of emergent H5N6 viruses to poultry and human health and the need to closely track their continual adaptation in humans

Efficacy of BET bromodomain inhibition in Kras-mutant non-small cell lung cancer

PurposeAmplification of MYC is one of the most common genetic alterations in lung cancer, contributing to a myriad of phenotypes associated with growth, invasion and drug resistance. Murine genetics has established both the centrality of somatic alterations of Kras in lung cancer, as well as the dependency of mutant Kras tumors on MYC function. Unfortunately, drug-like small-molecule inhibitors of KRAS and MYC have yet to be realized. The recent discovery, in hematologic malignancies, that BET bromodomain inhibition impairs MYC expression and MYC transcriptional function established the rationale of targeting KRAS-driven NSCLC with BET inhibition.Experimental DesignWe performed functional assays to evaluate the effects of JQ1 in genetically defined NSCLC cells lines harboring KRAS and/or LKB1 mutations. Furthermore, we evaluated JQ1 in transgenic mouse lung cancer models expressing mutant kras or concurrent mutant kras and lkb1. Effects of bromodomain inhibition on transcriptional pathways were explored and validated by expression analysis.ResultsWhile JQ1 is broadly active in NSCLC cells, activity of JQ1 in mutant KRAS NSCLC is abrogated by concurrent alteration or genetic knock-down of LKB1. In sensitive NSCLC models, JQ1 treatment results in the coordinate downregulation of the MYC-dependent transcriptional program. We found that JQ1 treatment produces significant tumor regression in mutant kras mice. As predicted, tumors from mutant kras and lkb1 mice did not respond to JQ1.ConclusionBromodomain inhibition comprises a promising therapeutic strategy for KRAS mutant NSCLC with wild-type LKB1, via inhibition of MYC function. Clinical studies of BET bromodomain inhibitors in aggressive NSCLC will be actively pursued

Automated muscle histopathology analysis using CellProfiler

Abstract Background Histological assessment of skeletal muscle sections is important for the research of muscle physiology and diseases. Quantifiable measures of skeletal muscle often include mean fiber diameter, fiber size distribution, and centrally nucleated muscle fibers. These parameters offer insights into the dynamic adaptation of skeletal muscle cells during repeated cycles of degeneration and regeneration associated with many muscle diseases and injuries. Computational programs designed to obtain these parameters would greatly facilitate such efforts and offer significant advantage over manual image analysis, which is very labor-intensive and often subjective. Here, we describe a customized pipeline termed MuscleAnalyzer for muscle histology analysis based upon CellProfiler, a free, open-source software for measuring and analyzing cell images. Results The MuscleAnalyzer pipeline consists of loading, adjusting, and running a series of image-processing modules provided by CellProfiler. This pipeline was evaluated using wild-type and mdx muscle sections co-stained with laminin (to demarcate the muscle fiber boundaries) and 4′,6-diamidino-2-phenylindole (DAPI, to label the nuclei). The immunofluorescence images analyzed using the MuscleAnalyzer pipeline or manually yielded similar results in the number of muscle fibers per image (p = 0.42) and central nucleated fiber (CNF) percentage (p = 0.29) in mdx mice. However, for a total of 67 images, CellProfiler completed the analysis in ~ 10 min on a regular PC while it took an investigator ~ 3 h using the manual approach in order to quantify the number of muscle fibers and CNF. Moreover, the MuscleAnalyzer pipeline also provided the measurement of the cross-sectional area (CSA) and minimal Feret’s diameter (MFD) of muscle fibers, and thus fiber size distribution can be plotted. Conclusions Our data indicate that the MuscleAnalyzer pipeline can efficiently and accurately analyze laminin and DAPI co-stained muscle images in a batch format and provide quantitative measurements for muscle histological properties such as muscle fiber diameters, fiber size distribution, and CNF percentage