Integrated analyses of copy number variations and gene expression in lung adenocarcinoma

Gene dosage compensation: Origins, criteria to identify compensated genes, and mechanisms including sensor loops as an emerging systems-level property in cancer

The gene dosage compensation hypothesis presents a mechanism through which the expression of certain genes is modulated to compensate for differences in the dose of genes when additional chromosomes are present. It is one of the means through which cancer cells actively cope with the potential damaging effects of aneuploidy, a hallmark of most cancers. Dosage compensation arises through several processes, including downregulation or overexpression of specific genes and the relocation of dosage-sensitive genes. In cancer, a majority of compensated genes are generally thought to be regulated at the translational or post-translational level, and include the basic components of a compensation loop, including sensors of gene dosage and modulators of gene expression. Post-translational regulation is mostly undertaken by a general degradation or aggregation of remaining protein subunits of macromolecular complexes. An increasingly important role has also been observed for transcriptional level regulation. This article reviews the process of targeted gene dosage compensation in cancer and other biological conditions, along with the mechanisms by which cells regulate specific genes to restore cellular homeostasis. These mechanisms represent potential targets for the inhibition of dosage compensation of specific genes in aneuploid cancers. This article critically examines the process of targeted gene dosage compensation in cancer and other biological contexts, alongside the criteria for identifying genes subject to dosage compensation and the intricate mechanisms by which cells orchestrate the regulation of specific genes to reinstate cellular homeostasis. Ultimately, our aim is to gain a comprehensive understanding of the intricate nature of a systems-level property. This property hinges upon the kinetic parameters of regulatory motifs, which we have termed "gene dosage sensor loops." These loops have the potential to operate at both the transcriptional and translational levels, thus emerging as promising candidates for the inhibition of dosage compensation in specific genes. Additionally, they represent novel and highly specific therapeutic targets in the context of aneuploid cancer.

Multi-label transcriptional classification of colorectal cancer reflects tumor cell population heterogeneity

BACKGROUND

Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information.

METHODS

A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS) was developed for single-sample classification.

RESULTS

Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification.

CONCLUSIONS

These results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.

Expression characteristics and their functional role of IGFBP gene family in pan-cancer

BACKGROUND

Insulin-like growth factor binding proteins (IGFBPs) are critical regulators of the biological activities of insulin-like growth factors. The IGFBP family plays diverse roles in different types of cancer, which we still lack comprehensive and pleiotropic understandings so far.

METHODS

Multi-source and multi-dimensional data, extracted from The Cancer Genome Atlas (TCGA), Oncomine, Cancer Cell Line Encyclopedia (CCLE), and the Human Protein Atlas (HPA) was used for bioinformatics analysis by R language. Immunohistochemistry and qRT-PCR were performed to validate the results of the database analysis results. Bibliometrics and literature review were used for summarizing the research progress of IGFBPs in the field of tumor.

RESULTS

The members of IGFBP gene family are differentially expressed in various cancer types. IGFBPs expression can affect prognosis of different cancers. The expression of IGFBPs expression is associated with multiple signal transduction pathways. The expression of IGFBPs is significantly correlated with tumor mutational burden, microsatellite instability, tumor stemness and tumor immune microenvironment. The qRT-PCR experiments verified the lower expression of IGFBP2 and IGFBP6 in gastric cancer and the lower expression of IGFBP6 in colorectal cancer. Immunohistochemistry validated a marked downregulation of IGFBP2 protein in gastric cancer tissues. The keywords co-occurrence analysis of IGFBP related publications in cancer showed relative research have been more concentrating on the potential of IGFBPs as tumor diagnostic and prognostic markers and developing cancer therapies.

CONCLUSIONS

These findings provide frontier trend of IGFBPs related research and new clues for identifying novel therapeutic targets for various cancers.

Non-canonical EphA2 activation underpins PTEN-mediated metastatic migration and poor clinical outcome in prostate cancer

Background

The key process of mesenchymal to amoeboid transition (MAT), which enables prostate cancer (PCa) transendothelial migration and subsequent development of metastases in red bone marrow stroma, is driven by phosphorylation of EphA2^S897 by pAkt, which is induced by the omega-6 polyunsaturated fatty acid arachidonic acid. Here we investigate the influence of EphA2 signalling in PCa progression and long-term survival.

Methods

The mechanisms underpinning metastatic biopotential of altered EphA2 signalling in relation to PTEN status were assessed in vitro using canonical (EphA2^D739N) and non-canonical (EphA2^S897G) PC3-M mutants, interrogation of publicly available PTEN-stratified databases and clinical validation using a PCa TMA (n = 177) with long-term follow-up data. Spatial heterogeneity of EphA2 was assessed using a radical prostatectomy cohort (n = 67).

Results

Non-canonical EphA2 signalling via pEphA2^S897 is required for PCa transendothelial invasion of bone marrow endothelium. High expression of EphA2 or pEphA2^S897 in a PTEN^low background is associated with poor overall survival. Expression of EphA2, pEphA2^S897 and the associated MAT marker pMLC2 are spatially regulated with the highest levels found within lesion areas within 500 µm of the prostate margin.

Conclusion

EphA2 MAT-related signalling confers transendothelial invasion. This is associated with a substantially worse prognosis in PTEN-deficient PCa.

A novel HIF1α-STIL-FOXM1 axis regulates tumor metastasis

BACKGROUND

Metastasis is the major cause of morbidity and mortality in cancer that involves in multiple steps including epithelial-mesenchymal transition (EMT) process. Centrosome is an organelle that functions as the major microtubule organizing center (MTOC), and centrosome abnormalities are commonly correlated with tumor aggressiveness. However, the conclusive mechanisms indicating specific centrosomal proteins participated in tumor progression and metastasis remain largely unknown.

METHODS

The expression levels of centriolar/centrosomal genes in various types of cancers were first examined by in silico analysis of the data derived from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and European Bioinformatics Institute (EBI) datasets. The expression of STIL (SCL/TAL1-interrupting locus) protein in clinical specimens was further assessed by Immunohistochemistry (IHC) analysis and the oncogenic roles of STIL in tumorigenesis were analyzed using in vitro and in vivo assays, including cell migration, invasion, xenograft tumor formation, and metastasis assays. The transcriptome differences between low- and high-STIL expression cells were analyzed by RNA-seq to uncover candidate genes involved in oncogenic pathways. The quantitative polymerase chain reaction (qPCR) and reporter assays were performed to confirm the results. The chromatin immunoprecipitation (ChIP)-qPCR assay was applied to demonstrate the binding of transcriptional factors to the promoter.

RESULTS

The expression of STIL shows the most significant increase in lung and various other types of cancers, and is highly associated with patients' survival rate. Depletion of STIL inhibits tumor growth and metastasis. Interestingly, excess STIL activates the EMT pathway, and subsequently enhances cancer cell migration and invasion. Importantly, we reveal an unexpected role of STIL in tumor metastasis. A subset of STIL translocate into nucleus and associate with FOXM1 (Forkhead box protein M1) to promote tumor metastasis and stemness via FOXM1-mediated downstream target genes. Furthermore, we demonstrate that hypoxia-inducible factor 1α (HIF1α) directly binds to the STIL promoter and upregulates STIL expression under hypoxic condition.

CONCLUSIONS

Our findings indicate that STIL promotes tumor metastasis through the HIF1α-STIL-FOXM1 axis, and highlight the importance of STIL as a promising therapeutic target for lung cancer treatment.

MYC dosage compensation is mediated by miRNA-transcription factor interactions in aneuploid cancer

We hypothesize that dosage compensation of critical genes arises from systems-level properties for cancer cells to withstand the negative effects of aneuploidy. We identified several candidate genes in cancer multiomics data and developed a biocomputational platform to construct a mathematical model of their interaction network with micro-RNAs and transcription factors, where the property of dosage compensation emerged for MYC and was dependent on the kinetic parameters of its feedback interactions with three micro-RNAs. These circuits were experimentally validated using a genetic tug-of-war technique to overexpress an exogenous MYC, leading to overexpression of the three microRNAs involved and downregulation of endogenous MYC. In addition, MYC overexpression or inhibition of its compensating miRNAs led to dosage-dependent cytotoxicity in MYC-amplified colon cancer cells. Finally, we identified negative correlation of MYC dosage compensation with patient survival in TCGA breast cancer patients, highlighting the potential of this mechanism to prevent aneuploid cancer progression.

Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction

BACKGROUND

Over the past decades, approaches for diagnosing and treating cancer have seen significant improvement. However, the variability of patient and tumor characteristics has limited progress on methods for prognosis prediction. The development of high-throughput omics technologies now provides multiple approaches for characterizing tumors. Although a large number of published studies have focused on integration of multi-omics data and use of pathway-level models for cancer prognosis prediction, there still exists a gap of knowledge regarding the prognostic landscape across multi-omics data for multiple cancer types using both gene-level and pathway-level predictors.

METHODS

In this study, we systematically evaluated three often available types of omics data (gene expression, copy number variation and somatic point mutation) covering both DNA-level and RNA-level features. We evaluated the landscape of predictive performance of these three omics modalities for 33 cancer types in the TCGA using a Lasso or Group Lasso-penalized Cox model and either gene or pathway level predictors.

RESULTS

We constructed the prognostic landscape using three types of omics data for 33 cancer types on both the gene and pathway levels. Based on this landscape, we found that predictive performance is cancer type dependent and we also highlighted the cancer types and omics modalities that support the most accurate prognostic models. In general, models estimated on gene expression data provide the best predictive performance on either gene or pathway level and adding copy number variation or somatic point mutation data to gene expression data does not improve predictive performance, with some exceptional cohorts including low grade glioma and thyroid cancer. In general, pathway-level models have better interpretative performance, higher stability and smaller model size across multiple cancer types and omics data types relative to gene-level models.

CONCLUSIONS

Based on this landscape and comprehensively comparison, models estimated on gene expression data provide the best predictive performance on either gene or pathway level. Pathway-level models have better interpretative performance, higher stability and smaller model size relative to gene-level models.

CNVIntegrate: the first multi-ethnic database for identifying copy number variations associated with cancer

Human copy number variations (CNVs) and copy number alterations (CNAs) are DNA segments (>1000 base pairs) of duplications or deletions with respect to the reference genome, potentially causing genomic imbalance leading to diseases such as cancer. CNVs further cause genetic diversity in healthy populations and are predominant drivers of gene/genome evolution. Initiatives have been taken by the research community to establish large-scale databases to comprehensively characterize CNVs in humans. Exome Aggregation Consortium (ExAC) is one such endeavor that catalogs CNVs, of nearly 60 000 healthy individuals across five demographic clusters. Furthermore, large projects such as the Catalogue of Somatic Mutations in Cancer (COSMIC) and the Cancer Cell Line Encyclopedia (CCLE) combine CNA data from cancer-affected individuals and large panels of human cancer cell lines, respectively. However, we lack a structured and comprehensive CNV/CNA resource including both healthy individuals and cancer patients across large populations. CNVIntegrate is the first web-based system that hosts CNV and CNA data from both healthy populations and cancer patients, respectively, and concomitantly provides statistical comparisons between copy number frequencies of multiple ethnic populations. It further includes, for the first time, well-cataloged CNV and CNA data from Taiwanese healthy individuals and Taiwan Breast Cancer data, respectively, along with imported resources from ExAC, COSMIC and CCLE. CNVIntegrate offers a CNV/CNA-data hub for structured information retrieval for clinicians and scientists towards important drug discoveries and precision treatments. Database URL: http://cnvintegrate.cgm.ntu.edu.tw/.

High prevalence of APOA1/C3/A4/A5 alterations in luminal breast cancers among young women in East Asia

In East Asia, the breast cancer incidence rate among women aged <50 years has rapidly increased. Emerging tumors are distinctly characterized by a high prevalence of estrogen receptor (ER)-positive/human epidermal growth factor receptor (HER2)-negative cancer. In the present study, we identified unique genetic alterations in these emerging tumors. We analyzed gene copy number variations (CNVs) in breast tumors from 120 Taiwanese patients, and obtained public datasets of CNV and gene expression (GE). The data regarding CNV and GE were separately compared between East Asian and Western patients, and the overlapping genes identified in the comparisons were explored to identify the gene-gene interaction networks. In the age <50 years/ER + /HER2- subgroup, tumors of East Asian patients exhibited a higher frequency of copy number loss in APOA1/C3/A4/A5, a lipid-metabolizing gene cluster (33 vs. 10%, P < .001) and lower APOA1/C3/A4/A5 expressions than tumors of Western patients. These copy number loss related- and GE-related results were validated in another Taiwanese cohort and in two GE datasets, respectively. The copy number loss was significantly associated with poor survival among Western patients, but not among East Asian patients. Lower APOA1, APOC3, and APOA5 expressions were associated with higher ESTIMATE immune scores, indicating an abundance of tumor-infiltrating immune cells. In conclusion, APOA1/C3/A4/A5 copy number loss was more prevalent in luminal breast tumors among East Asian women aged <50 years, and its immunomodulatory effect on the tumor microenvironment possibly plays various roles in the tumor biology of East Asian patients.

A Novel Computational Framework to Predict Disease-Related Copy Number Variations by Integrating Multiple Data Sources

Copy number variation (CNV) may contribute to the development of complex diseases. However, due to the complex mechanism of path association and the lack of sufficient samples, understanding the relationship between CNV and cancer remains a major challenge. The unprecedented abundance of CNV, gene, and disease label data provides us with an opportunity to design a new machine learning framework to predict potential disease-related CNVs. In this paper, we developed a novel machine learning approach, namely, IHI-BMLLR (Integrating Heterogeneous Information sources with Biweight Mid-correlation and L1-regularized Logistic Regression under stability selection), to predict the CNV-disease path associations by using a data set containing CNV, disease state labels, and gene data. CNVs, genes, and diseases are connected through edges and then constitute a biological association network. To construct a biological network, we first used a self-adaptive biweight mid-correlation (BM) formula to calculate correlation coefficients between CNVs and genes. Then, we used logistic regression with L1 penalty (LLR) function to detect genes related to disease. We added stability selection strategy, which can effectively reduce false positives, when using self-adaptive BM and LLR. Finally, a weighted path search algorithm was applied to find top D path associations and important CNVs. The experimental results on both simulation and prostate cancer data show that IHI-BMLLR is significantly better than two state-of-the-art CNV detection methods (i.e., CCRET and DPtest) under false-positive control. Furthermore, we applied IHI-BMLLR to prostate cancer data and found significant path associations. Three new cancer-related genes were discovered in the paths, and these genes need to be verified by biological research in the future.

High dimensional model representation of log likelihood ratio: binary classification with SNP data

BACKGROUND

Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sample, high-dimensional nature of SNP data, weak effect of each SNP on the outcome, and highly non-linear SNP interactions are several key factors complicating the analysis. Additionally, SNPs take a finite number of values which may be best understood as ordinal or categorical variables, but are treated as continuous ones by many algorithms.

METHODS

We use the theory of high dimensional model representation (HDMR) to build appropriate low dimensional glass-box models, allowing us to account for the effects of feature interactions. We compute the second order HDMR expansion of the log-likelihood ratio to account for the effects of single SNPs and their pairwise interactions. We propose a regression based approach, called linear approximation for block second order HDMR expansion of categorical observations (LABS-HDMR-CO), to approximate the HDMR coefficients. We show how HDMR can be used to detect pairwise SNP interactions, and propose the fixed pattern test (FPT) to identify statistically significant pairwise interactions.

RESULTS

We apply LABS-HDMR-CO and FPT to synthetically generated HAPGEN2 data as well as to two GWAS cancer datasets. In these examples LABS-HDMR-CO enjoys superior accuracy compared with several algorithms used for SNP classification, while also taking pairwise interactions into account. FPT declares very few significant interactions in the small sample GWAS datasets when bounding false discovery rate (FDR) by 5%, due to the large number of tests performed. On the other hand, LABS-HDMR-CO utilizes a large number of SNP pairs to improve its prediction accuracy. In the larger HAPGEN2 dataset FTP declares a larger portion of SNP pairs used by LABS-HDMR-CO as significant.

CONCLUSION

LABS-HDMR-CO and FPT are interesting methods to design prediction rules and detect pairwise feature interactions for SNP data. Reliably detecting pairwise SNP interactions and taking advantage of potential interactions to improve prediction accuracy are two different objectives addressed by these methods. While the large number of potential SNP interactions may result in low power of detection, potentially interacting SNP pairs, of which many might be false alarms, can still be used to improve prediction accuracy.

Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profile curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype associations by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of "copy number profile curves" to describe the CNV profile of an individual, and the "common area under the curve (cAUC) kernel" to model the multi-feature CNV effects. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between- and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank.

Multi-omics Analysis of Primary Cell Culture Models Reveals Genetic and Epigenetic Basis of Intratumoral Phenotypic Diversity

Uncovering the functionally essential variations related to tumorigenesis and tumor progression from cancer genomics data is still challenging due to the genetic diversity among patients, and extensive inter- and intra-tumoral heterogeneity at different levels of gene expression regulation, including but not limited to the genomic, epigenomic, and transcriptional levels. To minimize the impact of germline genetic heterogeneities, in this study, we establish multiple primary cultures from the primary and recurrent tumors of a single patient with hepatocellular carcinoma (HCC). Multi-omics sequencing was performed for these cultures that encompass the diversity of tumor cells from the same patient. Variations in the genome sequence, epigenetic modification, and gene expression are used to infer the phylogenetic relationships of these cell cultures. We find the discrepancy among the relationships revealed by single nucleotide variations (SNVs) and transcriptional/epigenomic profiles from the cell cultures. We fail to find overlap between sample-specific mutated genes and differentially expressed genes (DEGs), suggesting that most of the heterogeneous SNVs among tumor stages or lineages of the patient are functionally insignificant. Moreover, copy number alterations (CNAs) and DNA methylation variation within gene bodies, rather than promoters, are significantly correlated with gene expression variability among these cell cultures. Pathway analysis of CNA/DNA methylation-related genes indicates that a single cell clone from the recurrent tumor exhibits distinct cellular characteristics and tumorigenicity, and such an observation is further confirmed by cellular experiments both in vitro and in vivo. Our systematic analysis reveals that CNAs and epigenomic changes, rather than SNVs, are more likely to contribute to the phenotypic diversity among subpopulations in the tumor. These findings suggest that new therapeutic strategies targeting gene dosage and epigenetic modification should be considered in personalized cancer medicine. This culture model may be applied to the further identification of plausible determinants of cancer metastasis and relapse.

Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations

BACKGROUND

Gene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms.

RESULTS

In this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse.

CONCLUSIONS

Computer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1.

Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

MOTIVATION

Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy.

RESULTS

In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions.

AVAILABILITY AND IMPLEMENTATION

The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Aneuploidy and gene expression: is there dosage compensation?

Aneuploidy (i.e., abnormal chromosome number) is the leading cause of miscarriage and congenital defects in humans. Moreover, aneuploidy is ubiquitous in cancer. The deleterious phenotypes associated with aneuploidy are likely a result of the imbalance in the levels of gene products derived from the additional chromosome(s). Here, we summarize the current knowledge on how the presence of extra chromosomes impacts gene expression. We describe studies that have found a strict correlation between gene dosage and transcript levels as wells as studies that have found a less stringent correlation, hinting at the possible existence of dosage compensation mechanisms. We conclude by peering into the epigenetic changes found in aneuploid cells and outlining current knowledge gaps and potential areas of future investigation.

Ras Downstream Effector GGCT Alleviates Oncogenic Stress

How cells adapt to oncogenic transformation-associated cellular stress and become fully transformed is still unknown. Here we identified a novel GGCT-regulated glutathione (GSH)-reactive oxygen species (ROS) metabolic pathway in oncogenic stress alleviation. We identified GGCT as a target of oncogenic Ras and that it is required for oncogenic Ras-induced primary mouse cell proliferation and transformation and in vivo lung cancer formation in the LSL-Kras G12D mouse model. However, GGCT deficiency is compatible with normal mouse development, suggesting that GGCT can be a cancer-specific therapeutic target. Genetically amplified GGCT locus further supports the oncogenic driving function of GGCT. In summary, our study not only identifies an oncogenic function of GGCT but also identifies a novel regulator of GSH metabolism, with implications for further understanding of oncogenic stress and cancer treatment.

•

GGCT is a target of Ras and is required for Ras-induced cancer formation

•

GGCT deletion is compatible with normal mouse development and tissue function

•

GGCT genomic locus is amplified in multiple human cancer types

•

GGCT could alleviate oncogenic stress by regulating GSH-ROS metabolism

Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. High mortality in LUAD motivates us to stratify the patients into high- and low-risk groups, which is beneficial for the clinicians to design a personalized therapeutic regimen. To robustly predict the risk, we identified a set of robust prognostic gene signatures and critical pathways based on ten gene expression datasets by the meta-analysis-based Cox regression model, 25 of which were selected as predictors of multivariable Cox regression model by MMPC algorithm. Gene set enrichment analysis (GSEA) identified the Aurora-A pathway, the Aurora-B pathway, and the FOXM1 transcription factor network as prognostic pathways in LUAD. Moreover, the three prognostic pathways were also the biological processes of G2-M transition, suggesting that hyperactive G2-M transition in cell cycle was an indicator of poor prognosis in LUAD. The validation in the independent datasets suggested that overall survival differences were observed not only in all LUAD patients, but also in those with a specific TNM stage, gender, and age group. The comprehensive analysis demonstrated that prognostic signatures and the prognostic model by the large-scale gene expression analysis were more robust than models built by single data based gene signatures in LUAD overall survival prediction.

Gene set enrichment analysis and meta-analysis identified 12 key genes regulating and controlling the prognosis of lung adenocarcinoma

The aim of the present study was to analyze lung adenocarcinoma-associated microarray data and identify potentially crucial genes. The gene expression profiles were downloaded from the Gene Expression Omnibus database and 6 datasets, of which 2 were discarded and 4 were retained, were preprocessed using packages in the R computing language. Subsequently, Gene Set Enrichment Analysis (GSEA) and meta-analysis was used to screen the common pathways and differentially expressed genes at the transcriptional level. The genes detected from GSEA through The Cancer Genome Atlas databases were subsequently examined, and the crucial genes by survival data were identified. Pathways of the crucial genes were obtained using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the online website Database for Annotation, Visualization and Integrated Discovery (DAVID) tool, and the pathways of crucial genes that were upregulated or downregulated were matched using the Venn method to identify the common crucial pathways. Furthermore, on the basis of the common crucial pathways, key genes that are closely associated with the development and progression of lung adenocarcinoma were identified with the KEGG pathway of DAVID. Additional information was obtained through Gene Ontology annotation. A total of two key pathways, including cell cycle and DNA replication, as well as 12 key genes [DNA polymerase δ subunit 2, DNA replication licensing factor MCM4, MCM6, mitotic checkpoint serine/threonine-protein kinase BUB1, BUB1β, mitotic spindle assembly checkpoint protein MAD2A, dual specificity protein kinase TTK, M-phase inducer phosphatase 1, cell division control protein 45 homolog, cyclin-dependent kinase inhibitor 1C, pituitary tumor-transforming gene 1 protein and polo-like kinase 1] were identified. These key pathways and genes may be studied in future studies involving gene transfection/knockdown, which may provide insights into the prognosis of lung adenocarcinoma. Additional studies are required to confirm their biological function.

Genome-Wide Copy Number Variation Association Study of Atrial Fibrillation Related Thromboembolic Stroke

Atrial fibrillation (AF) is a common cardiac arrhythmia and is one of the major causes of ischemic stroke. In addition to the clinical factors such as CHADS2 or CHADS2-VASC score, the impact of genetic factors on the risk of thromboembolic stroke in patients with AF has been largely unknown. Single-nucleotide polymorphisms in several genomic regions have been found to be associated with AF. However, these loci do not contribute to all the genetic risks of AF or AF related thromboembolic risks, suggesting that there are other genetic factors or variants not yet discovered. In the human genome, copy number variations (CNVs) could also contribute to disease susceptibility. In the present study, we sought to identify CNVs determining the AF-related thromboembolic risk. Using a genome-wide approach in 109 patients with AF and thromboembolic stroke and 14,666 controls from the Taiwanese general population (Taiwan Biobank), we first identified deletions in chromosomal regions 1p36.32-1p36.33, 5p15.33, 8q24.3 and 19p13.3 and amplifications in 14q11.2 that were significantly associated with AF-related stroke in the Taiwanese population. In these regions, 148 genes were involved, including several microRNAs and long non-recoding RNAs. Using a pathway analysis, we found deletions in GNB1, PRKCZ, and GNG7 genes related to the alpha-adrenergic receptor signaling pathway that play a major role in determining the risk of an AF-related stroke. In conclusion, CNVs may be genetic predictors of a risk of a thromboembolic stroke for patients with AF, possibly pointing to an impaired alpha-adrenergic signaling pathway in the mechanism of AF-related thromboembolism.

Transcriptional Aneuploidy Responses of Brassica rapa-oleracea Monosomic Alien Addition Lines (MAALs) Derived From Natural Allopolyploid B. napus

Establishing the whole set of aneuploids, for one naturally evolved allopolyploid species, provides a unique opportunity to elucidate the transcriptomic response of the constituent subgenomes to serial aneuploidy. Previously, the whole set of monosomic alien addition lines (MAALs, C1-C9) with each of the nine C subgenome chromosomes, added to the extracted A subgenome, was developed in the context of the allotetraploid Brassica napus donor “Oro,” after the restitution of the ancestral B. rapa (RBR Oro) was realized. Herein, transcriptomic analysis using high-throughput technology was conducted to detect gene expression alterations in these MAALs and RBR. Compared to diploid RBR, the genes of all of the MAALs showed various degrees of dysregulated expressions that resulted from cis effects and more prevailing trans effects. In addition, the trans-effect on gene expression in MAALs increased with higher levels of homology between the recipient A subgenome and additional C subgenome chromosomes, instead of gene numbers of extra chromosomes. A total of 10 trans-effect dysregulated genes, among all pairwise comparisons, were mainly involved in the function of transporter activity. Furthermore, highly expressed genes were more prone to downregulation and vice-versa, suggesting a common trend for transcriptional pattern responses to aneuploidy. These results provided a comprehensive insight of the impact of gene expression of individual chromosomes, in one subgenome, on another intact subgenome for one allopolyploid with a long evolutionary history.

A pan-cancer study of copy number gain and up-regulation in human oncogenes

AIM

There has been limited research on CNVs in oncogenes and we conducted a systematic pan-cancer analysis of CNVs and their gene expression changes. The aim of the present study was to provide an insight into the relationships between gene expression and oncogenesis.

MAIN METHODS

We collected all the oncogenes from ONGene database and overlapped with CNVs TCGA tumour samples from Catalogue of Somatic Mutations in Cancer database. We further conducted an integrative analysis of CNV with gene expression using the data from the matched TCGA tumour samples.

KEY FINDINGS

From our analysis, we found 637 oncogenes associated with CNVs in 5900 tumour samples. There were 204 oncogenes with frequent copy number of gain (CNG). These 204 oncogenes were enriched in cancer-related pathways including the MAPK cascade and Ras GTPases signalling pathways. By using corresponding tumour samples data to perform integrative analyses of CNVs and gene expression changes, we identified 95 oncogenes with consistent CNG occurrence and up-regulation in the tumour samples, which may represent the recurrent driving force for oncogenesis. Surprisingly, eight oncogenes shown concordant CNG and gene up-regulation in at least 250 tumour samples: INTS8 (355), ECT2 (326), LSM1 (310), DDHD2 (298), COPS5 (286), EIF3E (281), TPD52 (258) and ERBB2 (254).

SIGNIFICANCE

As the first report about abundant CNGs on oncogene and concordant change of gene expression, our results may be valuable for the design of CNV-based cancer diagnostic strategy.

Functional interaction-based nonlinear models with application to multiplatform genomics data

Functional regression allows for a scalar response to be dependent on a functional predictor; however, not much work has been done when a scalar exposure that interacts with the functional covariate is introduced. In this paper, we present 2 functional regression models that account for this interaction and propose 2 novel estimation procedures for the parameters in these models. These estimation methods allow for a noisy and/or sparsely observed functional covariate and are easily extended to generalized exponential family responses. We compute standard errors of our estimators, which allows for further statistical inference and hypothesis testing. We compare the performance of the proposed estimators to each other and to one found in the literature via simulation and demonstrate our methods using a real data example.

Identification of novel prognosis-related genes associated with cancer using integrative network analysis

Prognosis identifies the seriousness and the chances of survival of a cancer patient. However, it remains a challenge to identify the key cancer genes in prognostic studies. In this study, we collected 2064 genes that were related to prognostic studies by using gene expression measurements curated from published literatures. Among them, 1820 genes were associated with copy number variations (CNVs). The further functional enrichment on 889 genes with frequent copy number gains (CNGs) revealed that these genes were significantly associated with cancer pathways including regulation of cell cycle, cell differentiation and mitogen-activated protein kinase (MAPK) cascade. We further conducted integrative analyses of CNV and their target genes expression using the data from matched tumour samples of The Cancer Genome Atlas (TCGA). Ultimately, 95 key prognosis-related genes were extracted, with concordant CNG events and increased up-regulation in at least 300 tumour samples. These genes, and the number of samples in which they were found, included: ACTL6A (399), ATP6V1C1 (425), EBAG9 (412), FADD (308), MTDH (377), and SENP5 (304). This study provides the first observation of CNV in prognosis-related genes across pan-cancer. The systematic concordance between CNG and up-regulation of gene expression in these novel prognosis-related genes may indicate their prognostic significance.

Expression of epithelial-mesenchymal transition-related genes increases with copy number in multiple cancer types

Epithelial-mesenchymal transition (EMT) is a cellular process through which epithelial cells transform into mesenchymal cells. EMT-implicated genes initiate and promote cancer metastasis because mesenchymal cells have greater invasive and migration capacities than epithelial cells. In this pan-cancer analysis, we explored the relationship between gene expression changes and copy number variations (CNVs) for EMT-implicated genes. Based on curated 377 EMT-implicated genes from the literature, we identified 212 EMT-implicated genes associated with more frequent copy number gains (CNGs) than copy number losses (CNLs) using data from The Cancer Genome Atlas (TCGA). Then by correlating these CNV data with TCGA gene expression data, we identified 71 EMT-implicated genes with concordant CNGs and gene up-regulation in 20 or more tumor samples. Of those, 14 exhibited such concordance in over 110 tumor samples. These 14 genes were predominantly apoptosis regulators, which may implies that apoptosis is critical during EMT. Moreover, the 71 genes with concordant CNG and up-regulation were largely involved in cellular functions such as phosphorylation cascade signaling. This is the first observation of concordance between CNG and up-regulation of specific genes in hundreds of samples, which may indicate that somatic CNGs activate gene expression by increasing the gene dosage.

Integrated analysis of SNP, CNV and gene expression data in genetic association studies

Integrative approaches that combine multiple forms of data can more accurately capture pathway associations and so provide a comprehensive understanding of the molecular mechanisms that cause complex diseases. Association analyses based on single nucleotide polymorphism (SNP) genotypes, copy number variant (CNV) genotypes, and gene expression profiles are the 3 most common paradigms used for gene set/pathway enrichment analyses. Many work has been done to leverage information from 2 types of data from these 3 paradigms. However, to the best of our knowledge, there is no work done before to integrate the 3 paradigms all together. In this article, we present an integrated analysis that combine SNP, CNV, and gene expression data to generate a single gene list. We present different methods to compare this gene list with the other 3 possible lists that result from the combinations of the following pairs of data: SNP genotype with gene expression, CNV genotype with gene expression, and SNP genotype with CNV genotype. The comparison is done using 3 different cancer datasets and 2 different methods of comparison. Our results show that integrating SNP, CNV, and gene expression data give better association results than integrating any pair of 3 data.

Mitochondrial UQCRB as a new molecular prognostic biomarker of human colorectal cancer

Ubiquinol cytochrome c reductase binding protein (UQCRB) is important for mitochondrial complex III stability, electron transport, cellular oxygen sensing and angiogenesis. However, its potential as a prognostic marker in colorectal cancer (CRC) remains unclear. The aim of this study was to determine whether UQCRB can be used as a diagnostic molecular marker for CRC. The correlation between the expression of three genes (UQCRB, UQCRFS1 and MT-CYB) in the mitochondrial respiratory chain complex III and clinico-pathological features was determined. Compared to non-tumor tissues, UQCRB gene expression was upregulated in CRC tissues. Gene and protein expression of the genes were positively correlated. Copy number variation (CNV) differences in UQCRB were observed in CRC tissues (1.32-fold) compared to non-tumor tissues. The CNV of UQCRB in CRC tissues increased proportionally with gene expression and clinical stage. Single-nucleotide polymorphisms in the 3′-untranslated region of UQCRB (rs7836698 and rs10504961) were investigated, and the rs7836698 polymorphism was associated with CRC clinical stage. DNA methylation of the UQCRB promoter revealed that most CRC patients had high methylation levels (12/15 patients) in CRC tissues compared to non-tumor tissues. UQCRB overexpression and CNV gain were correlated with specific CRC clinico-pathological features, indicating clinical significance as a prognostic predictor in CRC. Gene structural factors may be more important than gene transcription repression factors with respect to DNA methylation in UQCRB overexpression. Our results provide novel insights into the critical role of UQCRB in regulating CRC, supporting UQCRB as a new candidate for the development of diagnostics for CRC patients.

Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) represents a genomically unstable cancer type with extensive copy number aberrations. The relationship of gene copy number alterations and subsequent mRNA levels has only fragmentarily been described. The aim of this study was to conduct a genome-wide analysis of gene copy number gains and corresponding gene expression levels in a clinically well annotated NSCLC patient cohort (n = 190) and their association with survival. While more than half of all analyzed gene copy number-gene expression pairs showed statistically significant correlations (10,296 of 18,756 genes), high correlations, with a correlation coefficient >0.7, were obtained only in a subset of 301 genes (1.6%), including KRAS, EGFR and MDM2. Higher correlation coefficients were associated with higher copy number and expression levels. Strong correlations were frequently based on few tumors with high copy number gains and correspondingly increased mRNA expression. Among the highly correlating genes, GO groups associated with posttranslational protein modifications were particularly frequent, including ubiquitination and neddylation. In a meta-analysis including 1,779 patients we found that survival associated genes were overrepresented among highly correlating genes (61 of the 301 highly correlating genes, FDR adjusted p<0.05). Among them are the chaperone CCT2, the core complex protein NUP107 and the ubiquitination and neddylation associated protein CAND1. In conclusion, in a comprehensive analysis we described a distinct set of highly correlating genes. These genes were found to be overrepresented among survival-associated genes based on gene expression in a large collection of publicly available datasets.

Primary patient-derived lung adenocarcinoma cell culture challenges the association of cancer stem cells with epithelial-to-mesenchymal transition

The cancer stem cell (CSC) and epithelial-to-mesenchymal transition (EMT) models have been closely associated and used to describe both the formation of metastasis and therapy resistance. We established a primary lung cell culture from a patient in a clinically rare and unique situation of primary resistant disease. This culture consisted of two biologically profoundly distinct adenocarcinoma cell subpopulations, which differed phenotypically and genotypically. One subpopulation initiated and sustained in spheroid cell culture (LT22s) whereas the other subpopulation was only capable of growth and proliferation under adherent conditions (LT22a). In contrast to our expectations, LT22s were strongly associated with the epithelial phenotype, and expressed additionally CSC markers ALDH1 and CD133, whereas the LT22a was characterized as mesenchymal with lack of CSC markers. The LT22s cells also demonstrated an invasive behavior and mimicked gland formation. Finally, LT22s were more resistant to Cisplatin than LT22a cells. We demonstrate a primary lung adenocarcinoma cell culture derived from a patient with resistant disease, with epithelial aggressive subpopulation of cells associated with stem cell features and therapy resistance. Our findings challenge the current model associating CSC and disease resistance mainly to mesenchymal cells and may have important clinical implications.

Deregulated ALG-2/HEBP2 axis alters microtubule dynamics and mitotic spindle behavior to stimulate cancer development

Cancer cells are characterized by genomic instability, resulting in the accumulation of mutations that promote cancer progression. One way that genomic instability can arise is through improper regulation of the microtubule cytoskeleton that impacts the function of the mitotic spindle. In this study, we have identified a critical role for the interaction between apoptosis-linked gene 2 (ALG-2) and heme-binding protein 2 (HEBP2) in the above processes. Our data show that the gene copy numbers and mRNA levels for both ALG-2 and HEBP2 are significantly upregulated in breast and lung cancer. Coexpression of ALG-2 and HEBP2 markedly increases the cytoplasmic pool of ALG-2 and alters the subcellular distribution of HEBP2. Our data further reveal that abnormality in the ALG-2/HEBP2 interaction impairs spindle orientation and positioning during mitosis. In addition, this complex appears to modulate the dynamic properties of microtubules in cancer cells. These finding thus uncover an important function for deregulated ALG-2/HEBP2 axis in cancer development by influencing microtubule dynamics and spindle behavior, providing novel insight into the etiology and pathogenesis of cancer.

iGC-an integrated analysis package of gene expression and copy number alteration

BACKGROUND

With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual.

RESULTS

We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance.

CONCLUSION

The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package's source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html .

Identifying anti-cancer drug response related genes using an integrative analysis of transcriptomic and genomic variations with cell line-based drug perturbations

Background

Clinical responses to anti-cancer therapies often only benefit a defined subset of patients. Predicting the best treatment strategy hinges on our ability to effectively translate genomic data into actionable information on drug responses.

Results

To achieve this goal, we compiled a comprehensive collection of baseline cancer genome data and drug response information derived from a large panel of cancer cell lines. This data set was applied to identify the signature genes relevant to drug sensitivity and their resistance by integrating CNVs and the gene expression of cell lines with in vitro drug responses. We presented an efficient in-silico pipeline for integrating heterogeneous cell line data sources with the simultaneous modeling of drug response values across all the drugs and cell lines. Potential signature genes correlated with drug response (sensitive or resistant) in different cancer types were identified. Using signature genes, our collaborative filtering-based drug response prediction model outperformed the 44 algorithms submitted to the DREAM competition on breast cancer cells. The functions of the identified drug response related signature genes were carefully analyzed at the pathway level and the synthetic lethality level. Furthermore, we validated these signature genes by applying them to the classification of the different subtypes of the TCGA tumor samples, and further uncovered their in vivo implications using clinical patient data.

Conclusions

Our work may have promise in translating genomic data into customized marker genes relevant to the response of specific drugs for a specific cancer type of individual patients.

Concordance of copy number loss and down-regulation of tumor suppressor genes: a pan-cancer study

Background

Tumor suppressor genes (TSGs) encode the guardian molecules to control cell growth. The genomic alteration of TSGs may cause tumorigenesis and promote cancer progression. So far, investigators have mainly studied the functional effects of somatic single nucleotide variants in TSGs. Copy number variation (CNV) is another important form of genetic variation, and is often involved in cancer biology and drug treatment, but studies of CNV in TSGs are less represented in literature. In addition, there is a lack of a combinatory analysis of gene expression and CNV in this important gene set. Such a study may provide more insights into the relationship between gene dosage and tumorigenesis. To meet this demand, we performed a systematic analysis of CNVs and gene expression in TSGs to provide a systematic view of CNV and gene expression change in TSGs in pan-cancer.

Results

We identified 1170 TSGs with copy number gain or loss in 5846 tumor samples. Among them, 207 TSGs tended to have copy number loss (CNL), from which fifteen CNL hotspot regions were identified. The functional enrichment analysis revealed that the 207 TSGs were enriched in cancer-related pathways such as P53 signaling pathway and the P53 interactome. We further performed integrative analyses of CNV with gene expression using the data from the matched tumor samples. We found 81 TSGs with concordant CNL events and decreased gene expression in the tumor samples we examined. Remarkably, seven TSGs displayed concordant CNL and gene down-regulation in at least 50 tumor samples: MTAP (212 samples), PTEN (139), MCPH1 (85), FBXO25 (67), SMAD4 (64), TRIM35 (57), and RB1 (54). Specifically to MTAP, this concordance was found in 14 cancer types, an observation that is not much reported in literature yet. Further network-based analysis revealed that these TSGs with concordant CNL and gene down-regulation were highly connected.

Conclusions

This study provides a draft landscape of CNV in pan-cancer. Our findings of systematic concordance between CNL and down-regulation of gene expression may help better understand the TSG biology in tumorigenesis and cancer progression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2904-y) contains supplementary material, which is available to authorized users.

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.

Sirolimus and Everolimus Pathway: Reviewing Candidate Genes Influencing Their Intracellular Effects

Sirolimus (SRL) and everolimus (EVR) are mammalian targets of rapamycin inhibitors (mTOR-I) largely employed in renal transplantation and oncology as immunosuppressive/antiproliferative agents. SRL was the first mTOR-I produced by the bacterium Streptomyces hygroscopicus and approved for several medical purposes. EVR, derived from SRL, contains a 2-hydroxy-ethyl chain in the 40th position that makes the drug more hydrophilic than SRL and increases oral bioavailability. Their main mechanism of action is the inhibition of the mTOR complex 1 and the regulation of factors involved in a several crucial cellular functions including: protein synthesis, regulation of angiogenesis, lipid biosynthesis, mitochondrial biogenesis and function, cell cycle, and autophagy. Most of the proteins/enzymes belonging to the aforementioned biological processes are encoded by numerous and tightly regulated genes. However, at the moment, the polygenic influence on SRL/EVR cellular effects is still not completely defined, and its comprehension represents a key challenge for researchers. Therefore, to obtain a complete picture of the cellular network connected to SRL/EVR, we decided to review major evidences available in the literature regarding the genetic influence on mTOR-I biology/pharmacology and to build, for the first time, a useful and specific “SRL/EVR genes-focused pathway”, possibly employable as a starting point for future in-depth research projects.

Sequential model selection-based segmentation to detect DNA copy number variation

Array-based CGH experiments are designed to detect genomic aberrations or regions of DNA copy-number variation that are associated with an outcome, typically a state of disease. Most of the existing statistical methods target on detecting DNA copy number variations in a single sample or array. We focus on the detection of group effect variation, through simultaneous study of multiple samples from multiple groups. Rather than using direct segmentation or smoothing techniques, as commonly seen in existing detection methods, we develop a sequential model selection procedure that is guided by a modified Bayesian information criterion. This approach improves detection accuracy by accumulatively utilizing information across contiguous clones, and has computational advantage over the existing popular detection methods. Our empirical investigation suggests that the performance of the proposed method is superior to that of the existing detection methods, in particular, in detecting small segments or separating neighboring segments with differential degrees of copy-number variation.

Genome-wide screening identifies a KCNIP1 copy number variant as a genetic predictor for atrial fibrillation

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Previous genome-wide association studies had identified single-nucleotide polymorphisms in several genomic regions to be associated with AF. In human genome, copy number variations (CNVs) are known to contribute to disease susceptibility. Using a genome-wide multistage approach to identify AF susceptibility CNVs, we here show a common 4,470-bp diallelic CNV in the first intron of potassium interacting channel 1 gene (KCNIP1) is strongly associated with AF in Taiwanese populations (odds ratio=2.27 for insertion allele; P=6.23 × 10(-24)). KCNIP1 insertion is associated with higher KCNIP1 mRNA expression. KCNIP1-encoded protein potassium interacting channel 1 (KCHIP1) is physically associated with potassium Kv channels and modulates atrial transient outward current in cardiac myocytes. Overexpression of KCNIP1 results in inducible AF in zebrafish. In conclusions, a common CNV in KCNIP1 gene is a genetic predictor of AF risk possibly pointing to a functional pathway.

Integrated analyses of copy number variations and gene differential expression in lung squamous-cell carcinoma

Background

Although numerous efforts have been made, the pathogenesis underlying lung squamous-cell carcinoma (SCC) remains unclear. This study aimed to identify the CNV-driven genes by an integrated analysis of both the gene differential expression and copy number variation (CNV).

Results

A higher burden of the CNVs was found in 10–50 kb length. The 16 CNV-driven genes mainly located in chr 1 and chr 3 were enriched in immune response [e.g. complement factor H (CFH) and Fc fragment of IgG, low affinity IIIa, receptor (FCGR3A)], starch and sucrose metabolism [e.g. amylase alpha 2A (AMY2A)]. Furthermore, 38 TFs were screened for the 9 CNV-driven genes and then the regulatory network was constructed, in which the GATA-binding factor 1, 2, and 3 (GATA1, GATA2, GATA3) jointly regulated the expression of TP63.

Conclusions

The above CNV-driven genes might be potential contributors to the development of lung SCC.

Deciphering the Correlation between Breast Tumor Samples and Cell Lines by Integrating Copy Number Changes and Gene Expression Profiles

Breast cancer is one of the most common cancers with high incident rate and high mortality rate worldwide. Although different breast cancer cell lines were widely used in laboratory investigations, accumulated evidences have indicated that genomic differences exist between cancer cell lines and tissue samples in the past decades. The abundant molecular profiles of cancer cell lines and tumor samples deposited in the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas now allow a systematical comparison of the breast cancer cell lines with breast tumors. We depicted the genomic characteristics of breast primary tumors based on the copy number variation and gene expression profiles and the breast cancer cell lines were compared to different subgroups of breast tumors. We identified that some of the breast cancer cell lines show high correlation with the tumor group that agrees with previous knowledge, while a big part of them do not, including the most used MCF7, MDA-MB-231, and T-47D. We presented a computational framework to identify cell lines that mostly resemble a certain tumor group for the breast tumor study. Our investigation presents a useful guide to bridge the gap between cell lines and tumors and helps to select the most suitable cell line models for personalized cancer studies.

Identification of regulatory SNPs associated with genetic modifications in lung adenocarcinoma

Background

Although much research effort has been devoted to elucidating lung cancer, the molecular mechanism of tumorigenesis still remains unclear. A major challenge to improve the understanding of lung cancer is the difficulty of identifying reproducible differentially expressed genes across independent studies, due to their low consistency. To enhance the reproducibility of the findings, an integrated analysis was performed to identify regulatory SNPs. Thirty-two pairs of tumor and adjacent normal lung tissue specimens were analyzed using Affymetrix U133plus2.0, Affymetrix SNP 6.0, and Illumina Infinium Methylation microarrays. Copy number variations (CNVs) and methylation alterations were analyzed and paired t-tests were used to identify differentially expressed genes.

Results

A total of 505 differentially expressed genes were identified, and their dysregulated patterns moderately correlated with CNVs and methylation alterations based on the hierarchical clustering analysis. Subsequently, three statistical approaches were performed to explore regulatory SNPs, which revealed that the genotypes of 551 and 66 SNPs were associated with CNV and changes in methylation, respectively. Among them, downstream transcriptional dysregulation was observed in 9 SNPs for CNVs and 4 SNPs for methylation alterations.

Conclusions

In summary, these identified SNPs concurrently showed the same direction of gene expression changes with genetic modifications, suggesting their pivotal roles in the genome for non-smoking women with lung adenocarcinoma.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-015-1053-8) contains supplementary material, which is available to authorized users.

A meta-analysis strategy for gene prioritization using gene expression, SNP genotype, and eQTL data

In order to understand disease pathogenesis, improve medical diagnosis, or discover effective drug targets, it is important to identify significant genes deeply involved in human disease. For this purpose, many earlier approaches attempted to prioritize candidate genes using gene expression profiles or SNP genotype data, but they often suffer from producing many false-positive results. To address this issue, in this paper, we propose a meta-analysis strategy for gene prioritization that employs three different genetic resources—gene expression data, single nucleotide polymorphism (SNP) genotype data, and expression quantitative trait loci (eQTL) data—in an integrative manner. For integration, we utilized an improved technique for the order of preference by similarity to ideal solution (TOPSIS) to combine scores from distinct resources. This method was evaluated on two publicly available datasets regarding prostate cancer and lung cancer to identify disease-related genes. Consequently, our proposed strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of genetic resources, while making good use of potential complementarities among available resources.

Evaluation of copy number variation and gene expression in neurofibromatosis type-1-associated malignant peripheral nerve sheath tumours

BACKGROUND

Neurofibromatosis type-1 (NF1) is a complex neurogenetic disorder characterised by the development of benign and malignant tumours of the peripheral nerve sheath (MPNSTs). Whilst biallelic NF1 gene inactivation contributes to benign tumour formation, additional cellular changes in gene structure and/or expression are required to induce malignant transformation. Although few molecular profiling studies have been performed on the process of progression of pre-existing plexiform neurofibromas to MPNSTs, the integrated analysis of copy number alterations (CNAs) and gene expression is likely to be key to understanding the molecular mechanisms underlying NF1-MPNST tumorigenesis. In a pilot study, we employed this approach to identify genes differentially expressed between benign and malignant NF1 tumours.

RESULTS

SPP1 (osteopontin) was the most differentially expressed gene (85-fold increase in expression), compared to benign plexiform neurofibromas. Short hairpin RNA (shRNA) knockdown of SPP1 in NF1-MPNST cells reduced tumour spheroid size, wound healing and invasion in four different MPNST cell lines. Seventy-six genes were found to exhibit concordance between CNA and gene expression level.

CONCLUSIONS

Pathway analysis of these genes suggested that glutathione metabolism and Wnt signalling may be specifically involved in NF1-MPNST development. SPP1 is associated with malignant transformation in NF1-associated MPNSTs and could prove to be an important target for therapeutic intervention.

Personalized identification of altered pathways in cancer using accumulated normal tissue data

MOTIVATION

Identifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individual's pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual are often unavailable in clinical situation. Therefore, we suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patient's matched normal data are unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it with accumulated normal samples. We propose and examine personalized extensions of pathway statistics, overrepresentation analysis and functional class scoring, to generate individualized pathway aberrance score.

RESULTS

Collected microarray data of normal tissue of lung and colon mucosa are served as reference to investigate a number of cancer individuals of lung adenocarcinoma (LUAD) and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival-related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified 'amino acid synthesis and interconversion' pathway is a good indicator of LUAD (Area Under the Curve (AUC) 0.982 at independent validation). Clinical importance of the method is providing pathway interpretation of single cancer, even though its matched normal data are unavailable.

AVAILABILITY AND IMPLEMENTATION

The method was implemented using the R software, available at our Web site: http://bibs.snu.ac.kr/ipas.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Establishment of a New Ovarian Cancer Cell Line CA5171

A new cell line, CA5171, derived from a chemotherapy-naive, high-grade undifferentiated ovarian carcinoma was established and characterized. The CA5171 cells presented with cobblestone morphology and a doubling time of 24 hours. Gene mutation analysis showed that the cells belonged to the type II ovarian cancer pathway with mutations of PIK3CA, PTEN, and TP53. Single-nucleotide polymorphism array analysis showed no homozygous gene deletion; however, several loci of gene copy number gains were noted in chromosome 1, 2, 5, 9, 10, 12, 15, 16, 20, and X. The in vitro and in vivo experiments showed that the cells were sensitive to paclitaxel and doxorubicin, but resistant to cisplatin. The cells also presented epithelial-mesenchymal transition properties that may have been related to their invasion and migration potential. The CA5171 cells show the potential as a new cell line for studies on epithelial ovarian carcinoma.

Integrated exon level expression analysis of driver genes explain their role in colorectal cancer

Integrated analysis of genomic and transcriptomic level changes holds promise for a better understanding of colorectal cancer (CRC) biology. There is a pertinent need to explain the functional effect of genome level changes by integrating the information at the transcript level. Using high resolution cytogenetics array, we had earlier identified driver genes by ‘Genomic Identification of Significant Targets In Cancer (GISTIC)’ analysis of paired tumour-normal samples from colorectal cancer patients. In this study, we analyze these driver genes at three levels using exon array data – gene, exon and network. Gene level analysis revealed a small subset to experience differential expression. These results were reinforced by carrying out separate differential expression analyses (SAM and LIMMA). ATP8B1 was found to be the novel gene associated with CRC that shows changes at cytogenetic, gene and exon levels. Splice index of 29 exons corresponding to 13 genes was found to be significantly altered in tumour samples. Driver genes were used to construct regulatory networks for tumour and normal groups. There were rearrangements in transcription factor genes suggesting the presence of regulatory switching. The regulatory pattern of AHR gene was found to have the most significant alteration. Our results integrate data with focus on driver genes resulting in highly enriched novel molecules that need further studies to establish their role in CRC.

ADAM9 promotes lung cancer metastases to brain by a plasminogen activator-based pathway

The transmembrane cell adhesion protein ADAM9 has been implicated in cancer cell migration and lung cancer metastasis to the brain, but the underpinning mechanisms are unclear and clinical support has been lacking. Here, we demonstrate that ADAM9 enhances the ability of tissue plasminogen activator (tPA) to cleave and stimulate the function of the promigratory protein CDCP1 to promote lung metastasis. Blocking this mechanism of cancer cell migration prolonged survival in tumor-bearing mice and cooperated with dexamethasone and dasatinib (a dual Src/Abl kinase inhibitor) treatment to enhance cytotoxic treatment. In clinical specimens, high levels of ADAM9 and CDCP1 correlated with poor prognosis and high risk of mortality in patients with lung cancer. Moreover, ADAM9 levels in brain metastases derived from lung tumors were relatively higher than the levels observed in primary lung tumors. Our results show how ADAM9 regulates lung cancer metastasis to the brain by facilitating the tPA-mediated cleavage of CDCP1, with potential implications to target this network as a strategy to prevent or treat brain metastatic disease.

Identification of gene expression biomarkers for predicting radiation exposure

A need for more accurate and reliable radiation dosimetry has become increasingly important due to the possibility of a large-scale radiation emergency resulting from terrorism or nuclear accidents. Although traditional approaches provide accurate measurements, such methods usually require tedious effort and at least two days to complete. Therefore, we provide a new method for rapid prediction of radiation exposure. Eleven microarray datasets were classified into two groups based on their radiation doses and utilized as the training samples. For the two groups, Student's t-tests and resampling tests were used to identify biomarkers, and their gene expression ratios were used to develop a prediction model. The performance of the model was evaluated in four independent datasets, and Ingenuity pathway analysis was performed to characterize the associated biological functions. Our meta-analysis identified 29 biomarkers, showing approximately 90% and 80% accuracy in the training and validation samples. Furthermore, the 29 genes significantly participated in the regulation of cell cycle, and 19 of them are regulated by three well-known radiation-modulated transcription factors: TP53, FOXM1 and ERBB2. In conclusion, this study demonstrates a reliable method for identifying biomarkers across independent studies and high and reproducible prediction accuracy was demonstrated in both internal and external datasets.

Integrated analysis of copy number variation and genome-wide expression profiling in colorectal cancer tissues

Integrative analyses of multiple genomic datasets for selected samples can provide better insight into the overall data and can enhance our knowledge of cancer. The objective of this study was to elucidate the association between copy number variation (CNV) and gene expression in colorectal cancer (CRC) samples and their corresponding non-cancerous tissues. Sixty-four paired CRC samples from the same patients were subjected to CNV profiling using the Illumina HumanOmni1-Quad assay, and validation was performed using multiplex ligation probe amplification method. Genome-wide expression profiling was performed on 15 paired samples from the same group of patients using the Affymetrix Human Gene 1.0 ST array. Significant genes obtained from both array results were then overlapped. To identify molecular pathways, the data were mapped to the KEGG database. Whole genome CNV analysis that compared primary tumor and non-cancerous epithelium revealed gains in 1638 genes and losses in 36 genes. Significant gains were mostly found in chromosome 20 at position 20q12 with a frequency of 45.31% in tumor samples. Examples of genes that were associated at this cytoband were PTPRT, EMILIN3 and CHD6. The highest number of losses was detected at chromosome 8, position 8p23.2 with 17.19% occurrence in all tumor samples. Among the genes found at this cytoband were CSMD1 and DLC1. Genome-wide expression profiling showed 709 genes to be up-regulated and 699 genes to be down-regulated in CRC compared to non-cancerous samples. Integration of these two datasets identified 56 overlapping genes, which were located in chromosomes 8, 20 and 22. MLPA confirmed that the CRC samples had the highest gains in chromosome 20 compared to the reference samples. Interpretation of the CNV data in the context of the transcriptome via integrative analyses may provide more in-depth knowledge of the genomic landscape of CRC.

Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA

With accumulating research on the interconnections among different types of genomic regulations, researchers have found that multidimensional genomic studies outperform one-dimensional studies in multiple aspects. Among many sources of multidimensional genomic data, The Cancer Genome Atlas (TCGA) provides the public with comprehensive profiling data on >30 cancer types, making it an ideal test bed for conducting and comparing different analyses. In this article, the analysis goal is to apply several existing methods and associate multidimensional genomic measurements with cancer outcomes in particular prognosis, with special focus on the predictive power of genomic signatures. We exploit clinical data and four types of genomic measurement including mRNA gene expression, DNA methylation, microRNA and copy number alterations for breast invasive carcinoma, glioblastoma multiforme, acute myeloid leukemia and lung squamous cell carcinoma collected by TCGA. To accommodate the high dimensionality, we extract important features using Principal Component Analysis, Partial Least Squares and Least Absolute Shrinkage and Selection Operator (Lasso), which are representative of dimension reduction and variable selection techniques and have been extensively adopted, and fit Cox survival models with combined important features. We calibrate the predictive power of each type of genomic measurement for the prognosis of four cancer types and find that the results vary across cancers. Our analysis also suggests that for most of the cancers in our study and the adopted methods, there is no substantial improvement in prediction when adding other genomic measurement after gene expression and clinical covariates have been included in the model. This is consistent with the findings that molecular features measured at the transcription level affect clinical outcomes more directly than those measured at the DNA/epigenetic level.

Identification of reproducible gene expression signatures in lung adenocarcinoma

BACKGROUND

Lung cancer is the leading cause of cancer-related death worldwide. Tremendous research efforts have been devoted to improving treatment procedures, but the average five-year overall survival rates are still less than 20%. Many biomarkers have been identified for predicting survival; challenges arise, however, in translating the findings into clinical practice due to their inconsistency and irreproducibility. In this study, we proposed an approach by identifying predictive genes through pathways.

RESULTS

The microarrays from Shedden et al. were used as the training set, and the log-rank test was performed to select potential signature genes. We focused on 24 cancer-related pathways from 4 biological databases. A scoring scheme was developed by the Cox hazard regression model, and patients were divided into two groups based on the medians. Subsequently, their predictability and generalizability were evaluated by the 2-fold cross-validation and a resampling test in 4 independent datasets, respectively. A set of 16 genes related to apoptosis execution was demonstrated to have good predictability as well as generalizability in more than 700 lung adenocarcinoma patients and was reproducible in 4 independent datasets. This signature set was shown to have superior performances compared to 6 other published signatures. Furthermore, the corresponding risk scores derived from the set were found to associate with the efficacy of the anti-cancer drug ZD-6474 targeting EGFR.

CONCLUSIONS

In summary, we presented a new approach to identify reproducible survival predictors for lung adenocarcinoma, and the identified genes may serve as both prognostic and predictive biomarkers in the future.