Methylation of <i>TOX2</i> promoter CpG island.

<p><b>A</b>) Combined bisulfite modification and restriction analysis (COBRA) depicts methylation of <i>TOX2</i> promoter CpG island in normal and cancer samples. Complete, partial, or no methylation could be seen from digestion of all, some, or none of the PCR products in the presence of the <i>BstU1</i> (+) enzyme compared to no enzyme (−) control. MDA-MB-231 and MDA-MB-435 in all the figures are abbreviated as M-231 and M-435, respectively. <b>B</b>) Bisulfite sequencing was used to validate methylation results obtained through COBRA and MSP assays and to determine the degree and distribution of methylation at 51 CpG sites across <i>TOX2</i> promoter CpG island. Five clones were sequenced per sample and methylation status of each clone (1/5^th of a circle) at the specified CpG site is shown as methylated (filled) or unmethylated (open).</p

<i>TOX2</i> expression in normal and cancer cells.

<p>(<b>A</b>) Genomic structure of <i>TOX2</i>. Top box: Predicted transcript variants of <i>TOX2</i> (var.1-4) currently used as reference sequence for <i>Homo sapiens</i> chromosome 20, GRCh37.p2, (GenBank accession number NC_000020.10). Bottom box: Transcripts sequenced from human cells (var.5 and 6). Small arrows indicate the location and direction of primer binding sites; T#F or T#R (forward or reverse primers for TaqMan assays) and G#F or G#R (forward or reverse primers for gel-based assays). (<b>B</b>) Expression of <i>TOX2</i> transcript variants 5 and 6 and the house keeping gene beta-actin in distant normal lung tissue (DNLT), HBEC, and various lung and breast cancer cell lines. In Vehicle-treated (S, for sham) lung cancer (H1838, H2009) and breast cancer (T47D) cell lines with methylated promoter CpG island, both transcripts were silenced and expression of both was primarily restored with 5-Aza-2′-deoxycytidne (D) but not trichostatin A (T) treatment. (<b>C and D</b>) TaqMan assays that use distinct primer sets from those used for gel-based assays confirmed results shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034850#pone-0034850-g002" target="_blank">Figure 2B</a>. (<b>C</b>) Expression of TOX2 var.5 or both (var.5 & 6) in lung tumors (n = 20) relative to DNLT (n = 10) obtained from NSCLC patients. (<b>D</b>) Expression of <i>TOX2</i> var.5 or both (var.5+6) in TSA or DAC treated lung and breast cancer cell lines relative to Vehicle-treated (Sham) cell lines.</p

Genome-wide impact of epigenetic inactivation of <i>TOX2</i> and <i>TOX3</i>.

<p>Transfection of (<b>A</b>) <i>Calu-3</i> and <i>MDA-MB-231 (M-231)</i> with siRNAs targeting <i>TOX2</i> (<i>siTOX2</i>) or (<b>B</b>) <i>Calu-3</i> and <i>MCF-7</i> targeting <i>TOX3</i> (<i>siTOX3</i>) reduced expression of these genes by 70–86% compared to cells transfected with control siRNA (siControl). (<b>C and D</b>) However, knockdown of these genes did not change the migration potential of these cells. Genome-wide gene expression assays comparing <i>Calu-3</i> cells transfected with (<b>E</b>) siControl vs. siTOX2 or (<b>F</b>) siControl vs. siTOX3 revealed genes and pathways modulated by epigenetic inactivation of these genes.</p

Relative expression of TOX subfamily genes in normal lung tissue.

<p>(<b>A</b>) Expression of each gene was quantified using TaqMan assays and the level of <i>TOX4</i>, which is unmethylated in all samples and expressed the highest in normal lung tissue, was used as a reference to calculate the relative level of the remaining genes. * p = 0.03, ** p<0.001, *** p<0.0001 compared to <i>TOX4</i>. (<b>B</b>) COBRA conducted as described for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034850#pone-0034850-g001" target="_blank">Figure 1A</a>. (<b>C</b>) <i>TOX</i> expression was measured relative to its expression in <i>MCF-7</i> (Top left) or vehicle treated <i>MDA-MB-231 (M-231)</i>, <i>T47D</i>, or <i>MCF-7</i>. (<b>D</b>) Transfection of <i>M-231</i> with siTOX reduced its expression by 75% compared to siControl (left) but this did not alter the migration potential of the cells.</p

Prevalence for promoter CpG island hypermethylation of <i>TOX</i> subfamily of genes.

A<p>Methylation of <i>TOX</i> was significantly more prevalent in breast than lung tumor (p<0.001). In contrast, <i>TOX3</i> methylation was more common in lung than breast tumor (p<0.001).</p>B<p>Among NSCLC patients, the prevalence for <i>TOX2</i> methylation in current smokers was significantly higher than never smokers (p<0.05) as well as current non-smokers (former and never smokers combined) (p<0.05).</p>C<p><i>TOX3</i> methylation in primary lung tumors was marginally more prevalent in never smokers compared to current or former smokers (p = 0.05).</p>D<p><i>TOX3</i> methylation in primary lung tumors was more prevalent in squamous cell carcinoma compared to adenocarcinoma (p = 0.05).</p

Neoantigen Prediction Pipeline

abstract: Cells become cancerous due to changes in their genetic makeup. In cancers, an altered amino acid due to a tumor mutation can result in proteins that are identified as "foreign" by the immune system. An MHC molecule will bind to these "foreign" peptide fragments, also called neoantigens. There are 2 classes of MHC molecules. While the MHC I complex is found in all cells with a nucleus, MHC II complexes are mostly found in antigen presenting cells (APCs), such as macrophages, B cells, and dendritic cells. The MHC molecule then presents the neoantigen on the cell's surface. If an immune cell, such as a T-cell, is able to bind to the neoantigen, it can then destroy the tumor cell. However, there are molecules that act as checkpoints on certain immune cells that have to be activated or inactivated to start an immune response. This ensures that healthy cells are not being killed. However, sometimes cancer cells can find ways to use these checkpoints to avoid being attacked. An example of immunotherapy which has had clinical successes is checkpoint blockade inhibition, which means blocking the activity of immune checkpoint proteins in order to release the "brakes" on the immune system to increase its ability to destroy cancer cells. Studies have found that there is a correlation between mutational load and response to immunotherapy. The goal of this project is to create a pipeline that identifies tumor neoantigens. This involved researching various softwares and implementing them to work together. This project involved developing a neoantigen prediction pipeline, which works with TGen's genomics pipeline, to help understand a patient's immune response. The neoantigen prediction pipeline first creates two protein fastas from the high quality non-synonymous mutations, frameshifts, codon insertions, and codon deletions from vcfmerger. One of the protein fastas includes the mutations, while the other one does not representing the wildtype protein. The pipeline then predicts both classes of HLA genotypes of the MHC molecules using DNA or RNA expression in the form of fastqs. The protein fastas and each HLA are fed into IEDB to obtain peptide-MHC binding predictions. Wildtype peptides and neoantigens with low binding affinities are then removed. RNA expression information is then added into the final text file from dseq and sailfish files from TGen's genomics pipeline

Additional file 7: Table S1. of Whole exome sequencing identifies novel candidate genes that modify chronic obstructive pulmonary disease susceptibility

The information of Sanger sequencing validation for seven exonic variants of TACC2

Genetic Associations and Architecture of Asthma-COPD Overlap

Background Some people have characteristics of both asthma and COPD (asthma-COPD overlap), and evidence suggests they experience worse outcomes than those with either condition alone. Research Question What is the genetic architecture of asthma-COPD overlap, and do the determinants of risk for asthma-COPD overlap differ from those for COPD or asthma? Study Design and Methods We conducted a genome-wide association study in 8,068 asthma-COPD overlap case subjects and 40,360 control subjects without asthma or COPD of European ancestry in UK Biobank (stage 1). We followed up promising signals (P Results We selected 31 independent variants for further investigation in stage 2, and discovered eight novel signals (P Interpretation We identified eight signals for asthma-COPD overlap, which may represent loci that predispose to type 2 inflammation, and serious long-term consequences of asthma.</p