Chromosomal aberrations associated with lung cancer are frequently observed in the long arm of chromosome 6. A candidate susceptibility locus at 6q23-25 for lung cancer was recently identified; however, no tumor suppressor genes inactivated by mutation have been identified in this locus. Genetic, epigenetic, gene expression, and in silico screening approaches were used to select 43 genes located in 6q12-27 for characterization of methylation status. Twelve (28%) genes were methylated in at least one lung cancer cell line, and methylation of 8 genes was specific to lung cancer cell lines. Five of the 8 genes with the highest prevalence for methylation in cell lines (TCF21, SYNE1, AKAP12, IL20RA, and ACAT2) were examined in primary lung adenocarcinoma samples from smokers (n = 100) and never smokers (n = 75). The prevalence for methylation of these genes was 81%, 50%, 39%, 26%, and 14%, respectively, and did not differ by smoking status or age at diagnosis. Transcription of SYNE1, AKAP12, and IL20RA was completely silenced by hypermethylation and could be restored after treatment with 5-aza-2-deoxycytidine. Significant associations were found between methylation of SYNE1 and TCF21, SYNE1 and AKAP12, and AKAP12 and IL20RA, indicating a coordinated inactivation of these genes in tumors. A higher prevalence for methylation of these genes was not associated with early-onset lung cancer cases, most likely precluding their involvement in familial susceptibility to this disease. Together, our results indicate that frequent inactivation of multiple candidate tumor suppressor genes within chromosome 6q likely contributes to development of sporadic lung cancer.

Aberrant promoter hypermethylation is one of the major mechanisms in carcinogenesis and some critical growth regulatory genes have shown commonality in methylation across solid tumors. Twenty-six genes, 14 identified through methylation in colon and breast cancers, were evaluated using primary lung adenocarcinomas (n 5 175) from current, former and never smokers. Tumor specificity of methylation was validated through comparison of 14 lung cancer cell lines to normal human bronchial epithelial cells derived from bronchoscopy of 20 cancer-free smokers. Twenty-five genes were methylated in 11-81% of primary tumors. Prevalence for methylation of TNFRSF10C, BHLHB5 and BOLL was significantly higher in adenocarcinomas from never smokers than smokers. The relation between methylation of individual genes was examined using pairwise comparisons. A significant association was seen between 138 (42%) of the possible 325 pairwise comparisons. Most notably, methylation of MMP2, BHLHB4 or p16 was significantly associated with methylation of 16-19 other genes, thus predicting for a widespread methylation phenotype. Kaplan-Meier log-rank test and proportional hazard models identified a significant association between methylation of SULF2 (a pro-growth, -angiogenesis and -migration gene) and better patient survival (hazard ratio 5 0.23). These results demonstrate a high degree of commonality for targeted silencing of genes between lung and other solid tumors and suggest that promoter hypermethylation in cancer is a highly co-ordinated event.

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

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Therapeutic angiogenesis is one promising strategy for the treatment of ischemic heart disease, which is the leading cause of death globally. In recent years, extracellular vesicles (EVs) have quickly gained much attention as a cell-free approach to stimulate angiogenesis. However, clinical applications of EVs are limited by their insufficient targeting capability. Herein, we introduce a method to enhance therapeutic angiogenesis based on platelet membrane-engineered EVs. Methods: Platelet-mimetic EVs (P-EVs) were fabricated by fusing the membranes of EVs with platelet membranes by extrusion. A mouse model of myocardial ischemia reperfusion (MI/R) was established and injected with PBS, EVs, and P-EVs to evaluate their targeting ability and therapeutic angiogenesis efficacy. Results: P-EVs inherited the adhesive proteins and natural targeting ability to injured vasculature of platelets and retained the pro-angiogenic potential of EVs. In the MI/R model, P-EVs preferentially accumulated in the injured endothelium of the ischemic hearts and enhanced the angiogenesis potency of EVs. Conclusions: This engineering strategy to modify pre-isolated EVs with platelet membranes by membrane fusion bestows EVs with the targeting ability of platelets and offers an exciting opportunity to design other targeted EVs fused with cell membranes from different sources for therapeutic angiogenesis.

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Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

BackgroundGlioma is the most aggressive and lethal brain tumor in humans, it comprises about 30 per cent of all brain tumors and central nervous system tumors.PurposeThe objective of this study was to create novel brain-targeting nanoliposomes to encapsulate curcumin as a promising option for glioma therapy.Patients and methodsHuman glioma cells (U251MG) were used to determine cell uptake efficiency and possible internalization mechanism of the curcumin-loaded nanoliposomes modified by a brain-targeting peptide RDP. In addition, intracranial glioma mice model was prepared by transplantation of U251MG cells into the mice striatum, and then the liposomes were intravenously administered into the glioma-bearing mice to evaluate the anti-glioma activity.ResultsRDP-modified liposomes (RCL) could enter the brain and glioma region, and were internalized by the glioma cells perhaps through acetylcholine receptor-mediated endocytosis pathway. Furthermore, the RCL prolonged the survival time of the glioma-bearing mice from 23 to 33 days, and the inhibition mechanism of the RCL on glioma cell was partly due to cell cycle arrest at the S phase and induction of cell apoptosis.ConclusionThis study would provide a potential approach for targeted delivery of drug-loaded liposomes for glioma treatment.