A high-dimensional, deep-sequencing study of lung adenocarcinoma in female never-smokers

Genome-wide Detection of Chimeric Transcripts in Early-stage Non-small Cell Lung Cancer

BACKGROUND/AIM

Lung cancer remains the main culprit in cancer-related mortality worldwide. Transcript fusions play a critical role in the initiation and progression of multiple cancers. Treatment approaches based on specific targeting of discovered driver events, such as mutations in EGFR, and fusions in NTRK, ROS1, and ALK genes led to profound improvements in clinical outcomes. The formation of chimeric proteins due to genomic rearrangements or at the post-transcriptional level is widespread and plays a critical role in tumor initiation and progression. Yet, the fusion landscape of lung cancer remains underexplored.

MATERIALS AND METHODS

We used the JAFFA pipeline to discover transcript fusions in early-stage non-small cell lung cancer (NSCLC). The set of detected fusions was further analyzed to identify recurrent events, genes with multiple partners and fusions with high predicted oncogenic potential. Finally, we used a generalized linear model (GLM) to establish statistical associations between fusion occurrences and clinicopathological variables. RNA sequencing was used to discover and characterize transcript fusions in 270 NSCLC samples selected from the Glans-Look specimen repository. The samples were obtained during the early stages of disease prior to the initiation of chemo- or radiotherapy.

RESULTS

We identified a set of 792 fusions where 751 were novel, and 33 were recurrent. Four of the 33 recurrent fusions were significantly associated with clinicopathological variables. Several of the fusion partners were represented by well-established oncogenes ERBB4, BRAF, FGFR2, and MET.

CONCLUSION

The data presented in this study allow researchers to identify, select, and validate promising candidates for targeted clinical interventions.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

Systematic comparison of ranking aggregation methods for gene lists in experimental results

MOTIVATION

A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists.

RESULTS

In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20 000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets.

AVAILABILITY AND IMPLEMENTATION

The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses

Genome-wide transcriptome profiling identifies genes that are prone to differential expression (DE) across contexts, as well as genes with changes specific to the experimental manipulation. Distinguishing genes that are specifically changed in a context of interest from common differentially expressed genes (DEGs) allows more efficient prediction of which genes are specific to a given biological process under scrutiny. Currently, common DEGs or pathways can only be identified through the laborious manual curation of experiments, an inordinately time-consuming endeavor. Here we pioneer an approach, Specific cOntext Pattern Highlighting In Expression data (SOPHIE), for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be generated. We apply SOPHIE to diverse datasets including those from human, human cancer, and bacterial pathogen Pseudomonas aeruginosa. SOPHIE identifies common DEGs in concordance with previously described, manually and systematically determined common DEGs. Further molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional changes. SOPHIE's measure of specificity can complement log2 fold change values generated from traditional DE analyses. For example, by filtering the set of DEGs, one can identify genes that are specifically relevant to the experimental condition of interest. Consequently, these results can inform future research directions. All scripts used in these analyses are available at https://github.com/greenelab/generic-expression-patterns. Users can access https://github.com/greenelab/sophie to run SOPHIE on their own data.

Lung cancer risk in never-smokers: An overview of environmental and genetic factors

Lung cancer is the leading cause of cancer-related mortality globally, accounting for 1.8 million deaths in 2020. While the vast majority are caused by tobacco smoking, 15%-25% of all lung cancer cases occur in lifelong never-smokers. The International Agency for Research on Cancer (IARC) has classified multiple agents with sufficient evidence for lung carcinogenesis in humans, which include tobacco smoking, as well as several environmental exposures such as radon, second-hand tobacco smoke, outdoor air pollution, household combustion of coal and several occupational hazards. However, the IARC evaluation had not been stratified based on smoking status, and notably lung cancer in never-smokers (LCINS) has different epidemiological, clinicopathologic and molecular characteristics from lung cancer in ever-smokers. Among several risk factors proposed for the development of LCINS, environmental factors have the most available evidence for their association with LCINS and their roles cannot be overemphasized. Additionally, while initial genetic studies largely focused on lung cancer as a whole, recent studies have also identified genetic risk factors for LCINS. This article presents an overview of several environmental factors associated with LCINS, and some of the emerging evidence for genetic factors associated with LCINS. An increased understanding of the risk factors associated with LCINS not only helps to evaluate a never-smoker's personal risk for lung cancer, but also has important public health implications for the prevention and early detection of the disease. Conclusive evidence on causal associations could inform longer-term policy reform in a range of areas including occupational health and safety, urban design, energy use and particle emissions, and the importance of considering the impacts of second-hand smoke in tobacco control policy.

Finding differentially expressed sRNA-Seq regions with srnadiff

Small RNAs (sRNAs) encompass a great variety of molecules of different kinds, such as microRNAs, small interfering RNAs, Piwi-associated RNA, among others. These sRNAs have a wide range of activities, which include gene regulation, protection against virus, transposable element silencing, and have been identified as a key actor in determining the development of the cell. Small RNA sequencing is thus routinely used to assess the expression of the diversity of sRNAs, usually in the context of differentially expression, where two conditions are compared. Tools that detect differentially expressed microRNAs are numerous, because microRNAs are well documented, and the associated genes are well defined. However, tools are lacking to detect other types of sRNAs, which are less studied, and whose precursor RNA is not well characterized. We present here a new method, called srnadiff, which finds all kinds of differentially expressed sRNAs. To the extent of our knowledge, srnadiff is the first tool that detects differentially expressed sRNAs without the use of external information, such as genomic annotation or additional sequences of sRNAs.

The Fusion Gene Landscape in Taiwanese Patients with Non-Small Cell Lung Cancer

Simple Summary

Human cancer genomes show a variety of alterations, such as single base changes, deletions, insertions, copy number changes, and gene fusions. Analyzing fusion gene transcripts may yield a novel and effective approach for selecting cancer treatments. However, few comprehensive analyses of gene fusions in non-small cell lung cancer (NSCLC) patients have been performed. Here, we characterized the fusion gene landscape of NSCLC in a case study of Taiwanese lung cancer patients. We concluded that some fusion genes likely play driver roles in carcinogenesis, while others act as passengers. We demonstrated that by using RNA-sequencing to detect gene fusion events, putative therapeutic drug targets could be identified, potentially leading to more precise therapies for NSCLC.

Abstract

Background: Analyzing fusion gene transcripts may yield an effective approach for selecting cancer treatments. However, few comprehensive analyses of fusions in non-small cell lung cancer (NSCLC) patients have been performed. Methods: We enrolled 54 patients with NSCLC, and performed RNA-sequencing (RNA-Seq). STAR (Spliced Transcripts Alignment to a Reference)-Fusion was used to identify fusions. Results: Of the 218 fusions discovered, 24 had been reported and the rest were novel. Three fusions had the highest occurrence rates. After integrating our gene expression and fusion data, we found that samples harboring fusions containing ASXL1, CACNA1A, EEF1A1, and RET also exhibited increased expression of these genes. We then searched for mutations and fusions in cancer driver genes in each sample and found that nine patients carried both mutations and fusions in cancer driver genes. Furthermore, we found a trend for mutual exclusivity between gene fusions and mutations in the same gene, with the exception of DMD, and we found that EGFR mutations are associated with the number of fusion genes. Finally, we identified kinase gene fusions, and potentially druggable fusions, which may play roles in lung cancer therapy. Conclusion: The clinical use of RNA-Seq for detecting driver fusion genes may play an important role in the treatment of lung cancer.

Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty

Alternative splicing is a biological process during gene expression that allows a single gene to code for multiple proteins. However, splicing patterns can be altered in some conditions or diseases. Here, we present BANDITS, a R/Bioconductor package to perform differential splicing, at both gene and transcript level, based on RNA-seq data. BANDITS uses a Bayesian hierarchical structure to explicitly model the variability between samples and treats the transcript allocation of reads as latent variables. We perform an extensive benchmark across both simulated and experimental RNA-seq datasets, where BANDITS has extremely favourable performance with respect to the competitors considered.

Metabolic gene NR4A1 as a potential therapeutic target for non-smoking female non-small cell lung cancer patients

BACKGROUND

Although cigarette smoking is considered one of the key risk factors for lung cancer, 15% of male patients and 53% of female patients with lung cancer are non-smokers. Metabolic changes are critical features of cancer. Therapeutic target identification from a metabolic perspective in non-small cell lung cancer (NSCLC) tissue of female non-smokers has long been ignored.

RESULTS

Based on microarray data retrieved from Affymetrix expression arrays E-GEOD-19804, we found that the downregulated genes in non-smoking female NSCLC patients tended to participate in protein/amino acid and lipid metabolism, while upregulated genes were more involved in protein/amino acid and carbohydrate metabolism. Combining nutrient metabolic co-expression, protein-protein interaction network construction and overall survival assessment, we identified NR4A1 and TIE1 as potential therapeutic targets for NSCLC in female non-smokers. To accelerate the drug development for non-smoking female NSCLC patients, we identified nilotinib as a potential agonist targeting NR4A1 encoded protein by molecular docking and molecular dynamic stimulation. We also show that nilotinib inhibited proliferation and induced senescence of cells in non-smoking female NSCLC patients in vitro.

CONCLUSIONS

These results not only uncover nutrient metabolic characteristics in non-smoking female NSCLC patients, but also provide a new paradigm for identifying new targets and drugs for novel therapy for such patients.

Integrative Bioinformatics Analysis Reveals Potential Long Non-Coding RNA Biomarkers and Analysis of Function in Non-Smoking Females with Lung Cancer

BACKGROUND Lung cancer is the most lethal cancer worldwide. The aim of this study was to identify the tumor-related lncRNAs and explore their functions in female non-smokers with lung cancer. MATERIAL AND METHODS The gene expression microarray datasets GSE19804, GSE31210, and GSE31548 were downloaded from the Gene Expression Omnibus database. The differentially-expressed lncRNAs between non-smoking female lung cancer samples and non-tumor lung tissues were identified using GEO2R. RESULTS In total, 25, 40, and 15 differentially-expressed lncRNAs were obtained from GSE19804, GSE31210, and GSE31548 datasets (|logFC| >1, adj. P<0.05), respectively. Eight lncRNAs were screened out in all 3 datasets. Of these, 5 lncRNAs were up-regulated and 3 lncRNAs were down-regulated in lung cancer tissues compared to non-tumor lung tissues. Then, the target miRNAs of aberrantly expressed lncRNAs and target mRNAs corresponding to miRNAs were predicted. Subsequently, the ceRNA network with 8 key lncRNAs, 20 miRNAs, and 38 mRNAs were constructed. Functional and pathway enrichment analysis showed these target genes were mainly enriched in biological processes associated with protein binding, nucleus, metal ion binding, regulation of transcription from RNA polymerase II promoter, nucleic acid binding, cell differentiation, microRNAs in cancer, and the hippo signaling pathway. Survival analysis of these lncRNAs revealed that low LINC00968 (P=0.0067) and TBX5-AS1 (P=0.0028) expression were associated with unfavorable prognosis in never-smoking female lung cancer patients. CONCLUSIONS The present study promotes understanding of the molecular mechanism of the pathogenesis of non-smoking female lung cancer and provides potential biomarkers for diagnosis and treatment.

A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies

In genomic research, it is becoming increasingly popular to perform meta-analysis, the practice of combining results from multiple studies that target a common essential biological problem. Rank aggregation, a robust meta-analytic approach, consolidates such studies at the rank level. There exists extensive research on this topic, and various methods have been developed in the past. However, these methods have two major limitations when they are applied in the genomic context. First, they are mainly designed to work with full lists, whereas partial and/or top-ranked lists prevail in genomic studies. Second, the component studies are often clustered, and the existing methods fail to utilize such information. To address the above concerns, a Bayesian latent variable approach, called BiG, is proposed to formally deal with partial and top-ranked lists and incorporate the effect of clustering. Various reasonable prior specifications for variance parameters in hierarchical models are carefully studied and compared. Simulation results demonstrate the superior performance of BiG compared with other popular rank aggregation methods under various practical settings. A non-small-cell lung cancer data example is analyzed for illustration.

Bayesian estimation of differential transcript usage from RNA-seq data

Next generation sequencing allows the identification of genes consisting of differentially expressed transcripts, a term which usually refers to changes in the overall expression level. A specific type of differential expression is differential transcript usage (DTU) and targets changes in the relative within gene expression of a transcript. The contribution of this paper is to: (a) extend the use of cjBitSeq to the DTU context, a previously introduced Bayesian model which is originally designed for identifying changes in overall expression levels and (b) propose a Bayesian version of DRIMSeq, a frequentist model for inferring DTU. cjBitSeq is a read based model and performs fully Bayesian inference by MCMC sampling on the space of latent state of each transcript per gene. BayesDRIMSeq is a count based model and estimates the Bayes Factor of a DTU model against a null model using Laplace's approximation. The proposed models are benchmarked against the existing ones using a recent independent simulation study as well as a real RNA-seq dataset. Our results suggest that the Bayesian methods exhibit similar performance with DRIMSeq in terms of precision/recall but offer better calibration of False Discovery Rate.

Meta-analysis approaches to combine multiple gene set enrichment studies

In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta-analysis approaches recently developed for genome-wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size-adjusted enrichment score based on a one-sided Kolmogorov-Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.

[Omics technologies in diagnostics of lung adenocarcinoma]

To date lung adenocarcinoma (LAC) is the most common type of lung cancer. Numerous studies on LAC biology resulted in identification of crucial mutations in protooncogenes and activating neoplastic transformation pathways. Therapeutic approaches that significantly increase the survival rate of patients with LAC of different etiology have been developed and introduced into clinical practice. However, the main problem in the treatment of LAC is early diagnosis, taking into account both factors and mechanisms responsible in tumor initiation and progression. Identification of a wide biomarker repertoire with high specificity and reliability of detection appears to be a solution to this problem. In this context, proteins with differential expression in normal and pathological condition, suitable for detection in biological fluids are the most promising biomarkers. In this review we have analyzed literature data on studies aimed at search of LAC biomarkers. The major attention has been paid to protein biomarkers as the most promising and convenient subject of clinical diagnosis. The review also summarizes existing knowledge on posttranslational modifications, splice variants, isoforms, as well as model systems and transcriptome changes in LAC.

Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data

High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

snoRNA and piRNA expression levels modified by tobacco use in women with lung adenocarcinoma

Lung cancer is one of the most frequent types of cancer worldwide. Most patients are diagnosed at advanced stage and thus have poor prognosis. Smoking is a risk factor for lung cancer, however most smokers do not develop lung cancer while 20% of women with lung adenocarcinoma are non-smokers. Therefore, it is possible that these two groups present differences besides the smoking status, including differences in their gene expression signature. The altered expression patterns of non-coding RNAs in complex diseases make them potential biomarkers for diagnosis and treatment. We analyzed data from differentially and constitutively expressed PIWI-interacting RNAs and small nucleolar RNAs from publicly available small RNA high-throughput sequencing data in search of an expression pattern of non-coding RNA that could differentiate these two groups. Here, we report two sets of differentially expressed small non-coding RNAs identified in normal and tumoral tissues of women with lung adenocarcinoma, that discriminate between smokers and non-smokers. Our findings may offer new insights on metabolic alterations caused by tobacco and may be used for early diagnosis of lung cancer.

miR-376c promotes carcinogenesis and serves as a plasma marker for gastric carcinoma

Gastric carcinoma is highly prevalent throughout the world. Understanding the pathogenesis of this disease will benefit diagnosis and resolution. Studies show that miRNAs are involved in the tumorigenesis of gastric carcinoma. An initial screening followed by subsequent validation identified that miR-376c is up-regulated in gastric carcinoma tissue and the plasma of patients with the disease. In addition, the urinary level of miR-376c is also significantly increased in gastric carcinoma patients. The plasma miR-376c level was validated as a biomarker for gastric carcinoma, including early stage tumors. The induction of miR-376c was found to enrich the proliferation, migration and anchorage-independent growth of carcinoma cells and, furthermore, the repression of the expression of endogenous miR-376c was able to reduce such oncogenic phenotypes. ARID4A gene is a direct target of miR-376c. Knockdown of endogenous ARID4A increased the oncogenicity of carcinoma cells, while ARID4A was found to be drastically down-regulated in tumor tissue. Thus, expression levels of miR-376c and ARID4A mRNA tended to be opposing in tumor tissue. Our results demonstrate that miR-376c functions by suppressing ARID4A expression, which in turn enhances the oncogenicity of gastric carcinoma cells. It seems likely that the level of miR-376c in plasma and urine could act as invaluable markers for the detection of gastric carcinoma.

Exploring functions of long noncoding RNAs across multiple cancers through co-expression network

In contrast to protein-coding genes, long-noncoding RNAs (lncRNAs) are much less well understood, despite increasing evidence indicating a wide range of their biological functions, and possible roles in various cancers. Based on public RNA-seq datasets of four solid cancer types, we here utilize Weighted Correlation Network Analysis (WGCNA) to propose a strategy for exploring the functions of lncRNAs altered in more than two cancer types, which we call onco-lncRNAs. Results indicate that cancer-expressed lncRNAs show high tissue specificity and are weakly expressed, more so than protein-coding genes. Most of the 236 onco-lncRNAs we identified have not been reported to have associations with cancers before. Our analysis exploits co-expression network to reveal that onco-lncRNAs likely play key roles in the multistep development of human cancers, covering a wide range of functions in genome stability maintenance, signaling, cell adhesion and motility, morphogenesis, cell cycle, immune and inflammatory response. These observations contribute to a more comprehensive understanding of cancer-associated lncRNAs, while demonstrating a novel and efficient strategy for subsequent functional studies of lncRNAs.

Noncoding RNA Profiles in Tobacco- and Alcohol-Associated Diseases

Tobacco and alcohol are the leading environmental risk factors in the development of human diseases, such as cancer, cardiovascular disease, and liver injury. Despite the copious amount of research on this topic, by 2030, 8.3 million deaths are projected to occur worldwide due to tobacco use. The expression of noncoding RNAs, primarily microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), is modulated by tobacco and alcohol consumption. Drinking alcohol and smoking cigarettes can modulate the expression of miRNAs and lncRNAs through various signaling pathways, such as apoptosis, angiogenesis, and inflammatory pathways—primarily interleukin 6 (IL-6)/signal transducer and activator of transcription 3 (STAT3), which seems to play a major role in the development of diseases associated with these risk factors. Since they may be predictive and prognostic biomarkers, they can be used both as predictors of the response to therapy and as a targeted therapy. Further, circulating miRNAs might be valuable noninvasive tools that can be used to examine diseases that are related to the use of tobacco and alcohol. This review discusses the function of noncoding RNAs in cancer and other human tobacco- and alcohol-associated diseases.

Triple-layer dissection of the lung adenocarcinoma transcriptome: regulation at the gene, transcript, and exon levels

Lung adenocarcinoma is one of the most deadly human diseases. However, the molecular mechanisms underlying this disease, particularly RNA splicing, have remained underexplored. Here, we report a triple-level (gene-, transcript-, and exon-level) analysis of lung adenocarcinoma transcriptomes from 77 paired tumor and normal tissues, as well as an analysis pipeline to overcome genetic variability for accurate differentiation between tumor and normal tissues. We report three major results. First, more than 5,000 differentially expressed transcripts/exonic regions occur repeatedly in lung adenocarcinoma patients. These transcripts/exonic regions are enriched in nicotine metabolism and ribosomal functions in addition to the pathways enriched for differentially expressed genes (cell cycle, extracellular matrix receptor interaction, and axon guidance). Second, classification models based on rationally selected transcripts or exonic regions can reach accuracies of 0.93 to 1.00 in differentiating tumor from normal tissues. Of the 28 selected exonic regions, 26 regions correspond to alternative exons located in such regulators as tumor suppressor (GDF10), signal receptor (LYVE1), vascular-specific regulator (RASIP1), ubiquitination mediator (RNF5), and transcriptional repressor (TRIM27). Third, classification systems based on 13 to 14 differentially expressed genes yield accuracies near 100%. Genes selected by both detection methods include C16orf59, DAP3, ETV4, GABARAPL1, PPAR, RADIL, RSPO1, SERTM1, SRPK1, ST6GALNAC6, and TNXB. Our findings imply a multilayered lung adenocarcinoma regulome in which transcript-/exon-level regulation may be dissociated from gene-level regulation. Our described method may be used to identify potentially important genes/transcripts/exonic regions for the tumorigenesis of lung adenocarcinoma and to construct accurate tumor vs. normal classification systems for this disease.

Biomarker development in the precision medicine era: lung cancer as a case study

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.

Putative effectors for prognosis in lung adenocarcinoma are ethnic and gender specific

Lung adenocarcinoma possesses distinct patterns of EGFR/KRAS mutations between East Asian and Western, male and female patients. However, beyond the well-known EGFR/KRAS distinction, gender and ethnic specific molecular aberrations and their effects on prognosis remain largely unexplored. Association modules capture the dependency of an effector molecular aberration and target gene expressions. We established association modules from the copy number variation (CNV), DNA methylation and mRNA expression data of a Taiwanese female cohort. The inferred modules were validated in four external datasets of East Asian and Caucasian patients by examining the coherence of the target gene expressions and their associations with prognostic outcomes. Modules 1 (cis-acting effects with chromosome 7 CNV) and 3 (DNA methylations of UBIAD1 and VAV1) possessed significantly negative associations with survival times among two East Asian patient cohorts. Module 2 (cis-acting effects with chromosome 18 CNV) possessed significantly negative associations with survival times among the East Asian female subpopulation alone. By examining the genomic locations and functions of the target genes, we identified several putative effectors of the two cis-acting CNV modules: RAC1, EGFR, CDK5 and RALBP1. Furthermore, module 3 targets were enriched with genes involved in cell proliferation and division and hence were consistent with the negative associations with survival times. We demonstrated that association modules in lung adenocarcinoma with significant links of prognostic outcomes were ethnic and/or gender specific. This discovery has profound implications in diagnosis and treatment of lung adenocarcinoma and echoes the fundamental principles of the personalized medicine paradigm.

VAMP2-NRG1 Fusion Gene is a Novel Oncogenic Driver of Non-Small-Cell Lung Adenocarcinoma

INTRODUCTION

Neuregulin 1 (NRG1) has been discovered as the tail moiety of fusion genes with several distinct partner head genes in lung cancers. These fusion genes activate ERBB2/ERBB3 receptor-mediated cell signaling and thereby function as oncogenic drivers.

METHODS

We have carried out whole-transcriptome sequencing of 100 non-small-cell lung carcinoma tumors and isolated a novel fusion gene consisting of vesicle-associated membrane protein 2 (VAMP2) and NRG1. Reverse transcription-polymerase chain reaction and genomic DNA analysis were used to demonstrate interchromosomal translocation. Immunoblotting and soft agar assays were used to examine stimulating activity of the fusion gene through ERBB2/ERBB3 signaling pathway.

RESULTS

The most highly expressed splice variant of VAMP2-NRG1 fusion gene was shown to be membrane bound and display EGF-like domain of NRG1 extracellularly. VAMP2-NRG1 promotes anchorage-independent colony formation of H1568 lung adenocarcinoma cells. Ectopic expression of the fusion gene stimulates phosphorylation of ERBB2 and ERBB3 as well as downstream targets, AKT and ERK, confirming activation of the signaling pathway.

CONCLUSION

VAMP2-NRG1 is a novel oncogenic fusion gene representing a new addition to the list of NRG1 fusion genes, which together may form an important diagnostic and clinical category of lung adenocarcinoma cases.

Identification of Alternative Splicing and Fusion Transcripts in Non-Small Cell Lung Cancer by RNA Sequencing

Background

Lung cancer is the most common cause of cancer related death. Alterations in gene sequence, structure, and expression have an important role in the pathogenesis of lung cancer. Fusion genes and alternative splicing of cancer-related genes have the potential to be oncogenic. In the current study, we performed RNA-sequencing (RNA-seq) to investigate potential fusion genes and alternative splicing in non-small cell lung cancer.

Methods

RNA was isolated from lung tissues obtained from 86 subjects with lung cancer. The RNA samples from lung cancer and normal tissues were processed with RNA-seq using the HiSeq 2000 system. Fusion genes were evaluated using Defuse and ChimeraScan. Candidate fusion transcripts were validated by Sanger sequencing. Alternative splicing was analyzed using multivariate analysis of transcript sequencing and validated using quantitative real time polymerase chain reaction.

Results

RNA-seq data identified oncogenic fusion genes EML4-ALK and SLC34A2-ROS1 in three of 86 normal-cancer paired samples. Nine distinct fusion transcripts were selected using DeFuse and ChimeraScan; of which, four fusion transcripts were validated by Sanger sequencing. In 33 squamous cell carcinoma, 29 tumor specific skipped exon events and six mutually exclusive exon events were identified. ITGB4 and PYCR1 were top genes that showed significant tumor specific splice variants.

Conclusion

In conclusion, RNA-seq data identified novel potential fusion transcripts and splice variants. Further evaluation of their functional significance in the pathogenesis of lung cancer is required.

Molecular profile of lung cancer in never smokers

Tobacco smoking is the most common cause of lung cancer, but approximately 10–25% of patients with lung cancer are life-long never smokers. The cause of lung cancer in never smokers is unknown, although tobacco-smoke exposure may play a role in some of these patients. Lung cancer that develops in the absence of significant tobacco-smoke exposure appears to be a unique disease entity with novel genomic and epigenomic alterations and activation of molecular pathways that are not generally seen in tobacco-smoke-induced lung cancer. These molecular alterations are very likely responsible for the unique clinico-pathological features of lung cancer in never smokers (LCINS), and some of these molecular alterations – such as the activating EGFR TK mutations and EML4–ALK fusion – significantly influence therapeutic choices and treatment outcomes. In the last few years there has been a number of studies exploring the molecular characteristics of LCINS, and some of them have reported new and significant findings. Here we review the key findings from these studies and discuss their potential therapeutic implications.

Comparative transcriptome analysis reveals that the extracellular matrix receptor interaction contributes to the venous metastases of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of liver cancer in the world. Portal vein tumor thrombus (PVTT) is one of the most serious complications of HCC and is strongly correlated with a poor prognosis for HCC patients. However, the detailed mechanism of PVTT development remains to be explored. In this study, we present a large-scale transcriptome analysis, by RNA sequencing, of 11 patients diagnosed with HCC with PVTT. The dysregulated genes between HCC and PVTT suggested that the extracellular matrix receptor interaction was correlated with the venous metastases of HCC. Among all of the recurrent alternative splicing events, we identified exon 6 skipping of RPS24, which is likely to be a cancer driver. We also identified five common fusion genes between HCC and its corresponding PVTT samples, including ARID1A-GPATCH3, MDM1-NUP107, PTGES3-RARG, PRLR-TERT, and C9orf3-TMC1. All of these findings broaden our knowledge of PVTT development and may also contribute to the diagnosis and treatment of HCC patients with PVTT.

RNA-seq analysis of lung adenocarcinomas reveals different gene expression profiles between smoking and nonsmoking patients

Lung adenocarcinoma is caused by the combination of genetic and environmental effects, and smoking plays an important role in the disease development. Exploring the gene expression profile and identifying genes that are shared or vary between smokers and nonsmokers with lung adenocarcinoma will provide insights into the etiology of this complex cancer. We obtained RNA-seq data from paired normal and tumor tissues from 34 nonsmoking and 34 smoking patients with lung adenocarcinoma (GEO: GSE40419). R Bioconductor, edgeR, was adopted to conduct differential gene expression analysis between paired normal and tumor tissues. A generalized linear model was applied to identify genes that were differentially expressed in nonsmoker and smoker patients as well as genes that varied between these two groups. We identified 2273 genes that showed differential expression with FDR < 0.05 and |logFC| >1 in nonsmoker tumor versus normal tissues; 3030 genes in the smoking group; and 1967 genes were common to both groups. Sixty-eight and 70% of the identified genes were downregulated in nonsmoking and smoking groups, respectively. The 20 genes such as SPP1, SPINK1, and FAM83A with largest fold changes in smokers also showed similar large and highly significant fold changes in nonsmokers and vice versa, showing commonalities in expression changes for adenocarcinomas in both smokers and nonsmokers for these genes. We also identified 175 genes that were significantly differently expressed between tumor samples from nonsmoker and smoker patients. Gene expression profile varied substantially between smoker and nonsmoker patients with lung adenocarcinoma. Smoking patients overall showed far more complicated disease mechanism and have more dysregulation in their gene expression profiles. Our study reveals pathogenetic differences in smoking and nonsmoking patients with lung adenocarcinoma from transcriptome analysis. We provided a list of candidate genes for further study for disease detection and treatment in both smoking and nonsmoking patients with lung adenocarcinoma.

Next-generation sequencing for the diagnosis of hereditary pheochromocytoma and paraganglioma syndromes

PURPOSE OF REVIEW

About 40% of the neuroendocrine tumors pheochromocytomas and paragangliomas (PPGLs) are caused by an inherited mutation. Diagnostic genetic screening is recommended for patients and their families. However, the number of susceptibility genes involved is high and continues to grow, making conventional sequencing costly and burdensome. Next-generation sequencing (NGS) enables accurate, thorough, and cost-effective identification of inherited mutations. Here we review recent successes, limitations, and the future of NGS for diagnosis of pheochromocytoma and paraganglioma syndromes.

RECENT FINDINGS

NGS-based screen of genetic disorders in the clinical setting shows improved diagnostic rates over conventional tests. Both broad, whole-exome sequencing, and targeted NGS approaches have been tested for screening of PPGLs, with accurate mutation detection, higher speed, and reduced costs compared with current assays. Flexibility to expand the targeted gene set is immediate in whole-exome sequencing, and adjustable in targeted NGS, but both methods have limitations.

SUMMARY

The high degree of genetic heterogeneity and heritability of PPGLs make NGS an ideal medium for their diagnostic screening. However, improved detection of large genomic defects and underrepresented gene areas are needed before NGS can fully realize its potential as the premier option for routine genetic testing of these syndromes.

Integrated analysis of DNA methylation and microRNA regulation of the lung adenocarcinoma transcriptome

Lung adenocarcinoma, as a common type of non-small cell lung cancer (40%), poses a significant threat to public health worldwide. The present study aimed to determine the transcriptional regulatory mechanisms in lung adenocarcinoma. Illumina sequence data GSE 37764 including expression profiling, methylation profiling and non-coding RNA profiling of 6 never-smoker Korean female patients with non-small cell lung adenocarcinoma were obtained from the Gene Expression Omnibus (GEO) database. Differentially methylated genes, differentially expressed genes (DEGs) and differentially expressed microRNAs (miRNAs) between normal and tumor tissues of the same patients were screened with tools in R. Functional enrichment analysis of a variety of differential genes was performed. DEG-specific methylation and transcription factors (TFs) were analyzed with ENCODE ChIP-seq. The integrated regulatory network of DEGs, TFs and miRNAs was constructed. Several overlapping DEGs, such as v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) were screened. DEGs were centrally modified by histones of tri-methylation of lysine 27 on histone H3 (H3K27me3) and di-acetylation of lysine 12 or 20 on histone H2 (H2BK12/20AC). Upstream TFs of DEGs were enriched in different ChIP-seq clusters, such as glucocorticoid receptors (GRs). Two miRNAs (miR-126-3p and miR-30c-2-3p) and three TFs including homeobox A5 (HOXA5), Meis homeobox 1 (MEIS1) and T-box 5 (TBX5), played important roles in the integrated regulatory network conjointly. These DEGs, and DEG-related histone modifications, TFs and miRNAs may be important in the pathogenesis of lung adenocarcinoma. The present results may indicate directions for the next step in the study of the further elucidation and targeted prevention of lung adenocarcinoma.

Targeted Therapies in Non-Small Cell Lung Cancer-Beyond EGFR and ALK

Systemic therapy for non-small cell lung cancer (NSCLC) has undergone a dramatic paradigm shift over the past decade. Advances in our understanding of the underlying biology of NSCLC have revealed distinct molecular subtypes. A substantial proportion of NSCLC depends on oncogenic molecular aberrations (so-called "driver mutations") for their malignant phenotype. Personalized therapy encompasses the strategy of matching these subtypes with effective targeted therapies. EGFR mutations and ALK translocation are the most effectively targeted oncogenes in NSCLC. EGFR mutations and ALK gene rearrangements are successfully being targeted with specific tyrosine kinase inhibitors. The number of molecular subgroups of NSCLC continues to grow. The scope of this review is to discuss recent data on novel molecular targets as ROS1, BRAF, KRAS, HER2, c-MET, RET, PIK3CA, FGFR1 and DDR2. Thereby the review will focus on therapeutic strategies targeting these aberrations. Moreover, the emerging challenge of acquired resistance to initially effective therapies will be discussed.

Molecular histology of lung cancer: from targets to treatments

Lung cancer is the leading cause of cancer-related death worldwide with a 5-year survival rate of less than 15%, despite significant advances in both diagnostic and therapeutic approaches. Combined genomic and transcriptomic sequencing studies have identified numerous genetic driver mutations that are responsible for the development of lung cancer. In addition, molecular profiling studies identify gene products and their mutations which predict tumour responses to targeted therapies such as protein tyrosine kinase inhibitors and also can offer explanation for drug resistance mechanisms. The profiling of circulating micro-RNAs has also provided an ability to discriminate patients in terms of prognosis/diagnosis and high-throughput DNA sequencing strategies are beginning to elucidate cell signalling pathway mutations associated with oncogenesis, including potential stem cell associated pathways, offering the promise that future therapies may target this sub-population, preventing disease relapse post treatment and improving patient survival. This review provides an assessment of molecular profiling within lung cancer concerning molecular mechanisms, treatment options and disease-progression. Current areas of development within lung cancer profiling are discussed (i.e. profiling of circulating tumour cells) and future challenges for lung cancer treatment addressed such as detection of micro-metastases and cancer stem cells.

Protein domain-level landscape of cancer-type-specific somatic mutations

Identifying driver mutations and their functional consequences is critical to our understanding of cancer. Towards this goal, and because domains are the functional units of a protein, we explored the protein domain-level landscape of cancer-type-specific somatic mutations. Specifically, we systematically examined tumor genomes from 21 cancer types to identify domains with high mutational density in specific tissues, the positions of mutational hotspots within these domains, and the functional and structural context where possible. While hotspots corresponding to specific gain-of-function mutations are expected for oncoproteins, we found that tumor suppressor proteins also exhibit strong biases toward being mutated in particular domains. Within domains, however, we observed the expected patterns of mutation, with recurrently mutated positions for oncogenes and evenly distributed mutations for tumor suppressors. For example, we identified both known and new endometrial cancer hotspots in the tyrosine kinase domain of the FGFR2 protein, one of which is also a hotspot in breast cancer, and found new two hotspots in the Immunoglobulin I-set domain in colon cancer. Thus, to prioritize cancer mutations for further functional studies aimed at more precise cancer treatments, we have systematically correlated mutations and cancer types at the protein domain level.

Identification and validation of gene module associated with lung cancer through coexpression network analysis

Lung cancer, a tumor with heterogeneous biology, is influenced by a complex network of gene interactions. Therefore, elucidating the relationships between genes and lung cancer is critical to attain further knowledge on tumor biology. In this study, we performed weighted gene coexpression network analysis to investigate the roles of gene networks in lung cancer regulation. Gene coexpression relationships were explored in 58 samples with tumorous and matched non-tumorous lungs, and six gene modules were identified on the basis of gene coexpression patterns. The overall expression of one module was significantly higher in the normal group than in the lung cancer group. This finding was validated across six datasets (all p values <0.01). The particular module was highly enriched for genes belonging to the biological Gene Ontology category "response to wounding" (adjusted p value = 4.28 × 10(-10)). A lung cancer-specific hub network (LCHN) consisting of 15 genes was also derived from this module. A support vector machine based on classification model robustly separated lung cancer from adjacent normal tissues in the validation datasets (accuracy ranged from 91.7% to 98.5%) by using the LCHN gene signatures as predictors. Eight genes in the LCHN are associated with lung cancer. Overall, we identified a gene module associated with lung cancer, as well as an LCHN consisting of hub genes that may be candidate biomarkers and therapeutic targets for lung cancer. This integrated analysis of lung cancer transcriptome provides an alternative strategy for identification of potential oncogenic drivers.

Measuring the effect of inter-study variability on estimating prediction error

Background

The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in “batch-effects”) and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies.

Methods

Here we quantify the impact of these combined “study-effects” on a disease signature’s predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance.

Results

As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification.

Conclusions

We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when “sufficient” diversity has been achieved for learning a molecular signature likely to translate without significant loss of accuracy to new clinical settings.

Tumor heterogeneity and resistance to EGFR-targeted therapy in advanced nonsmall cell lung cancer: challenges and perspectives

Lung cancer, mostly nonsmall cell lung cancer, continues to be the leading cause of cancer-related death worldwide. With the development of tyrosine kinase inhibitors that selectively target lung cancer-related epidermal growth factor receptor mutations, management of advanced nonsmall cell lung cancer has been greatly transformed. Improvements in progression-free survival and life quality of the patients were observed in numerous clinical studies. However, overall survival is not prolonged because of later-acquired drug resistance. Recent studies reveal a heterogeneous subclonal architecture of lung cancer, so it is speculated that the tumor may rapidly adapt to environmental changes via a Darwinian selection mechanism. In this review, we aim to provide an overview of both spatial and temporal tumor heterogeneity as potential mechanisms underlying epidermal growth factor receptor tyrosine kinase inhibitor resistance in nonsmall cell lung cancer and summarize the possible origins of tumor heterogeneity covering theories of cancer stem cells and clonal evolution, as well as genomic instability and epigenetic aberrations in lung cancer. Moreover, investigational measures that overcome heterogeneity-associated drug resistance and new assays to improve tumor assessment are also discussed.

The OncoFinder algorithm for minimizing the errors introduced by the high-throughput methods of transcriptome analysis

The diversity of the installed sequencing and microarray equipment make it increasingly difficult to compare and analyze the gene expression datasets obtained using the different methods. Many applications requiring high-quality and low error rates cannot make use of available data using traditional analytical approaches. Recently, we proposed a new concept of signalome-wide analysis of functional changes in the intracellular pathways termed OncoFinder, a bioinformatic tool for quantitative estimation of the signaling pathway activation (SPA). We also developed methods to compare the gene expression data obtained using multiple platforms and minimizing the error rates by mapping the gene expression data onto the known and custom signaling pathways. This technique for the first time makes it possible to analyze the functional features of intracellular regulation on a mathematical basis. In this study we show that the OncoFinder method significantly reduces the errors introduced by transcriptome-wide experimental techniques. We compared the gene expression data for the same biological samples obtained by both the next generation sequencing (NGS) and microarray methods. For these different techniques we demonstrate that there is virtually no correlation between the gene expression values for all datasets analyzed (R² < 0.1). In contrast, when the OncoFinder algorithm is applied to the data we observed clear-cut correlations between the NGS and microarray gene expression datasets. The SPA profiles obtained using NGS and microarray techniques were almost identical for the same biological samples allowing for the platform-agnostic analytical applications. We conclude that this feature of the OncoFinder enables to characterize the functional states of the transcriptomes and interactomes more accurately as before, which makes OncoFinder a method of choice for many applications including genetics, physiology, biomedicine, and molecular diagnostics.

Lung cancer transcriptomes refined with laser capture microdissection

We evaluated the importance of tumor cell selection for generating gene signatures in non-small cell lung cancer. Tumor and nontumor tissue from macroscopically dissected (Macro) surgical specimens (31 pairs from 32 subjects) was homogenized, extracted, amplified, and hybridized to microarrays. Adjacent scout sections were histologically mapped; sets of approximately 1000 tumor cells and nontumor cells (alveolar or bronchial) were procured by laser capture microdissection (LCM). Within histological strata, LCM and Macro specimens exhibited approximately 67% to 80% nonoverlap in differentially expressed (DE) genes. In a representative subset, LCM uniquely identified 300 DE genes in tumor versus nontumor specimens, largely attributable to cell selection; 382 DE genes were common to Macro, Macro with preamplification, and LCM platforms. RT-qPCR validation in a 33-gene subset was confirmatory (ρ = 0.789 to 0.964, P = 0.0013 to 0.0028). Pathway analysis of LCM data suggested alterations in known cancer pathways (cell growth, death, movement, cycle, and signaling components), among others (eg, immune, inflammatory). A unique nine-gene LCM signature had higher tumor-nontumor discriminatory accuracy (100%) than the corresponding Macro signature (87%). Comparison with Cancer Genome Atlas data sets (based on homogenized Macro tissue) revealed both substantial overlap and important differences from LCM specimen results. Thus, cell selection via LCM enhances expression profiling precision, and confirms both known and under-appreciated lung cancer genes and pathways.

Unraveling tumor grading and genomic landscape in lung neuroendocrine tumors

Currently, grading in lung neuroendocrine tumors (NETs) is inherently defined by the histological classification based on cell features, mitosis count, and necrosis, for which typical carcinoids (TC) are low-grade malignant tumors with long life expectation, atypical carcinoids (AC) intermediate-grade malignant tumors with more aggressive clinical behavior, and large cell NE carcinomas (LCNEC) and small cell lung carcinomas (SCLC) high-grade malignant tumors with dismal prognosis. While Ki-67 antigen labeling index, highlighting the proportion of proliferating tumor cells, has largely been used in digestive NETs for assessing prognosis and assisting therapy decisions, the same marker does not play an established role in the diagnosis, grading, and prognosis of lung NETs. Next generation sequencing techniques (NGS), thanks to their astonishing ability to process in a shorter timeframe up to billions of DNA strands, are radically revolutionizing our approach to diagnosis and therapy of tumors, including lung cancer. When applied to single genes, panels of genes, exome, or the whole genome by using either frozen or paraffin tissues, NGS techniques increase our understanding of cancer, thus realizing the bases of precision medicine. Data are emerging that TC and AC are mainly altered in chromatin remodeling genes, whereas LCNEC and SCLC are also mutated in cell cycle checkpoint and cell differentiation regulators. A common denominator to all lung NETs is a deregulation of cell proliferation, which represents a biological rationale for morphologic (mitoses and necrosis) and molecular (Ki-67 antigen) parameters to successfully serve as predictors of tumor behavior (i.e., identification of pathological entities with clinical correlation). It is envisaged that a novel grading system in lung NETs based on the combined assessment of mitoses, necrosis, and Ki-67 LI may offer a better stratification of prognostic classes, realizing a bridge between molecular alterations, morphological features, and clinical behavior.

Identification of mutant genes with high-frequency, high-risk, and high-expression in lung adenocarcinoma

BACKGROUND

To identify mutant genes with high-frequency-risk-expression between lung adenocarcinoma and normal samples.

METHODS

The ribonucleic acid RNA-Seq data GSE34914 and GSE37765 were downloaded from the Gene Expression Omnibus database, including 12 lung adenocarcinoma samples and six controls. All RNA-Seq reads were processed and the gene-expression level was calculated. Single nucleotide variation (SNV) was analyzed and the locations of mutant sites were recorded. In addition, the frequency and risk-level of mutant genes were calculated. Gene Ontology (GO) functional analysis was performed. The reported cancer genes were searched in tumor suppressor genes, Cancer Genes, and the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

RESULTS

The SNV annotations of somatic mutation sites showed that 70% of mutation sites in the exon region occurred in the coding sequence (CDS). Thyroid hormone receptor interactor (TRIP)12 was identified with the highest frequency. A total of 118 mutant genes with high frequency and high-risk were selected and significantly enriched into several GO terms. No base mutation of cyclin C (CCNC) or RAB11A was recorded. At fragments per kilobase per million reads (FPKM) ≥ 56.5, reported tumor suppressor genes catenin (cadherin-associated protein), delta (CTNND)1, dual specificity phosphatase (DUSP)6, malate dehydrogenase (MDH)1 and RNA binding motif protein (RBM)5, were identified. Notably, signal transducer and activator of transcription 2 (STAT2) was the only transcription factor (TF) with high-risk mutation and its expression was detected.

CONCLUSION

For the mutant genes with high-frequency-risk-expression, CTNND1, DUSP6, MDH1 and RBM5 were identified. TRIP12 might be a potential cancer-related gene, and expression of TF STAT2 with high-risk was detected. These mutant gene candidates might promote the development of lung adenocarcinoma and provide new diagnostic potential targets for treatment.

RET and other genes: therapeutic targets in lung adenocarcinoma

SUMMARY: 

The RET fusion gene was recently identified as a new druggable driver gene present in 1–2% of lung adenocarcinomas (LADCs). Vandetanib (ZD6474) and cabozantininb (XL184), two RET tyrosine kinase inhibitors approved by US FDA for the therapy of medullary thyroid cancer, have demonstrated therapeutic effectiveness in a few RET fusion-positive LADC patients. Several clinical trials are under way to address the therapeutic effects of RET tyrosine kinase inhibitors, including these two drugs. Multiplex diagnosis of aberrations in druggable driver oncogenes, such as EGFR, ALK, RET, ROS1, HER2/ERBB2, BRAF and others, in clinical samples will facilitate the design of personalized therapies for LADC based on protein kinase inhibitors. The development of therapeutic methods targeting aberrations of other genes, such as chromatin remodeling genes, is necessary to further improve the treatment of LADC.

Abnormal gene expression and gene fusion in lung adenocarcinoma with high-throughput RNA sequencing

To explore the universal law of the abnormal gene expression and the structural variation of genes related to lung adenocarcinoma, the gene expression profile of GSE37765 were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) were analyzed with t-test and NOISeq tool, and the core DEGs were screened out by combining with another RNA-seq data containing totally 77 pairs of samples in 77 patients with lung adenocarcinoma. Moreover, the functional annotation of the core DEGs was performed by using the Database for Annotation Visualization and Integrated Discovery following selection of oncogene and tumor suppressor by combining with tumor suppressor genes and Cancer Genes database, and motif-finding of core DEGs was performed with motif-finding algorithm Seqpos. We also used Tophat-fusion tool to further explore the fusion genes. In total, 850 downregulated DEGs and 206 upregulated DEGs were screened out in lung adenocarcinoma tissues. Next, we selected 543 core DEGs, including 401 downregulated and 142 upregulated genes, and vasculature development (P=1.89E-06) was significantly enriched among downregulated core genes, as well as mitosis (P=6.26E-04) enriched among upregulated core genes. On the basis of the cellular localization analysis of core genes, wnt-1-induced secreted protein 1 (WISP1) and receptor (G protein-coupled) activity modifying protein 1 (RAMP1) identified mainly located in extracellular region and extracellular space. We also screened one oncogene, v-myb avian myeloblastosis viral oncogene homolog-like 2 (MYBL2). Moreover, transcription factor GATA2 was mined by motif-finding analysis. Finally, four fusion genes belonged to the human leukocyte antigen (HLA) family. WISP1, RAMP1, MYBL2 and GATA2 could be potential targets of treatment for lung adenocarcinoma and the fusion of HLA family genes might have important roles in lung adenocarcinoma.

Whole-genome sequencing identifies genomic heterogeneity at a nucleotide and chromosomal level in bladder cancer

Using complete genome analysis, we sequenced five bladder tumors accrued from patients with muscle-invasive transitional cell carcinoma of the urinary bladder (TCC-UB) and identified a spectrum of genomic aberrations. In three tumors, complex genotype changes were noted. All three had tumor protein p53 mutations and a relatively large number of single-nucleotide variants (SNVs; average of 11.2 per megabase), structural variants (SVs; average of 46), or both. This group was best characterized by chromothripsis and the presence of subclonal populations of neoplastic cells or intratumoral mutational heterogeneity. Here, we provide evidence that the process of chromothripsis in TCC-UB is mediated by nonhomologous end-joining using kilobase, rather than megabase, fragments of DNA, which we refer to as "stitchers," to repair this process. We postulate that a potential unifying theme among tumors with the more complex genotype group is a defective replication-licensing complex. A second group (two bladder tumors) had no chromothripsis, and a simpler genotype, WT tumor protein p53, had relatively few SNVs (average of 5.9 per megabase) and only a single SV. There was no evidence of a subclonal population of neoplastic cells. In this group, we used a preclinical model of bladder carcinoma cell lines to study a unique SV (translocation and amplification) of the gene glutamate receptor ionotropic N-methyl D-aspertate as a potential new therapeutic target in bladder cancer.