Lung Adenocarcinoma of Never Smokers and Smokers Harbor Differential Regions of Genetic Alteration and Exhibit Different Levels of Genomic Instability

Recent evidence suggests that the observed clinical distinctions between lung tumors in smokers and never smokers (NS) extend beyond specific gene mutations, such as EGFR, EML4-ALK, and KRAS, some of which have been translated into targeted therapies. However, the molecular alterations identified thus far cannot explain all of the clinical and biological disparities observed in lung tumors of NS and smokers. To this end, we performed an unbiased genome-wide, comparative study to identify novel genomic aberrations that differ between smokers and NS

An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer

<p>Abstract</p> <p>Background</p> <p>Genomics has substantially changed our approach to cancer research. Gene expression profiling, for example, has been utilized to delineate subtypes of cancer, and facilitated derivation of predictive and prognostic signatures. The emergence of technologies for the high resolution and genome-wide description of genetic and epigenetic features has enabled the identification of a multitude of causal DNA events in tumors. This has afforded the potential for large scale integration of genome and transcriptome data generated from a variety of technology platforms to acquire a better understanding of cancer.</p> <p>Results</p> <p>Here we show how multi-dimensional genomics data analysis would enable the deciphering of mechanisms that disrupt regulatory/signaling cascades and downstream effects. Since not all gene expression changes observed in a tumor are causal to cancer development, we demonstrate an approach based on multiple concerted disruption (MCD) analysis of genes that facilitates the rational deduction of aberrant genes and pathways, which otherwise would be overlooked in single genomic dimension investigations.</p> <p>Conclusions</p> <p>Notably, this is the first comprehensive study of breast cancer cells by parallel integrative genome wide analyses of DNA copy number, LOH, and DNA methylation status to interpret changes in gene expression pattern. Our findings demonstrate the power of a multi-dimensional approach to elucidate events which would escape conventional single dimensional analysis and as such, reduce the cohort sample size for cancer gene discovery.</p

Integrative Genomic Analyses Identify BRF2 as a Novel Lineage-Specific Oncogene in Lung Squamous Cell Carcinoma

William Lockwood and colleagues show that the focal amplification of a gene, BRF2, on Chromosome 8p12 plays a key role in squamous cell carcinoma of the lung

Divergent Genomic and Epigenomic Landscapes of Lung Cancer Subtypes Underscore the Selection of Different Oncogenic Pathways during Tumor Development

For therapeutic purposes, non-small cell lung cancer (NSCLC) has traditionally been regarded as a single disease. However, recent evidence suggest that the two major subtypes of NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SqCC) respond differently to both molecular targeted and new generation chemotherapies. Therefore, identifying the molecular differences between these tumor types may impact novel treatment strategy. We performed the first large-scale analysis of 261 primary NSCLC tumors (169 AC and 92 SqCC), integrating genome-wide DNA copy number, methylation and gene expression profiles to identify subtype-specific molecular alterations relevant to new agent design and choice of therapy. Comparison of AC and SqCC genomic and epigenomic landscapes revealed 778 altered genes with corresponding expression changes that are selected during tumor development in a subtype-specific manner. Analysis of >200 additional NSCLCs confirmed that these genes are responsible for driving the differential development and resulting phenotypes of AC and SqCC. Importantly, we identified key oncogenic pathways disrupted in each subtype that likely serve as the basis for their differential tumor biology and clinical outcomes. Downregulation of HNF4α target genes was the most common pathway specific to AC, while SqCC demonstrated disruption of numerous histone modifying enzymes as well as the transcription factor E2F1. In silico screening of candidate therapeutic compounds using subtype-specific pathway components identified HDAC and PI3K inhibitors as potential treatments tailored to lung SqCC. Together, our findings suggest that AC and SqCC develop through distinct pathogenetic pathways that have significant implication in our approach to the clinical management of NSCLC

Differential disruption of cell cycle pathways in small cell and non-small cell lung cancer

Lung cancer is the leading cause of cancer-related mortality in the world, with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) comprising the two major cell types. Although these cell types can be distinguished readily at the histological level, knowledge of their underlying molecular differences is very limited. In this study, we compared 14 SCLC cell lines against 27 NSCLC cell lines using an integrated array comparative genomic hybridisation and gene expression profiling approach to identify subtype-specific disruptions. Using stringent criteria, we have identified 159 of the genes that are responsible for the different biology of these cell types. Sorting of these genes by their biological functions revealed the differential disruption of key components involved in cell cycle pathways. Our novel comparative combined genome and transcriptome analysis not only identified differentially altered genes, but also revealed that certain shared pathways are preferentially disrupted at different steps in these cell types. Small cell lung cancer exhibited increased expression of MRP5, activation of Wnt pathway inhibitors, and upregulation of p38 MAPK activating genes, while NSCLC showed downregulation of CDKN2A, and upregulation of MAPK9 and EGFR. This information suggests that cell cycle upregulation in SCLC and NSCLC occurs through drastically different mechanisms, highlighting the need for differential molecular target selection in the treatment of these cancers

Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets1551sci