Genome scanning with array CGH delineates regional alterations in mouse islet carcinomas

Mixed-Lineage Leukaemia Gene Regulates Glucose-Sensitive Gene Expression and Insulin Secretion in Pancreatic Beta Cells

The escalating prevalence of diabetes mellitus underscores the need for a comprehensive understanding of pancreatic beta cell function. Interest in glucose effectiveness has prompted the exploration of novel regulatory factors. The myeloid/lymphoid or mixed-lineage leukaemia gene (MLL) is widely recognised for its role in leukemogenesis and nuclear regulatory mechanisms through its histone methyltransferase activity in active chromatin. However, its function within pancreatic endocrine tissues remains elusive. Herein, we unveil a novel role of MLL in glucose metabolism and insulin secretion. MLL knockdown in βHC-9 pancreatic beta cells diminished insulin secretion in response to glucose loading, paralleled by the downregulation of the glucose-sensitive genes SLC2a1 and SLC2a2. Similar observations were made in MLL heterozygous knockout mice (MLL+/-), which exhibited impaired glucose tolerance and reduced insulin secretion without morphological anomalies in pancreatic endocrine cells. The reduction in insulin secretion was independent of changes in beta cell mass or insulin granule morphology, suggesting the regulatory role of MLL in glucose-sensitive gene expression. The current results suggest that MLL interacts with circadian-related complexes to modulate the expression of glucose transporter genes, thereby regulating glucose sensing and insulin secretion. Our findings shed light on insulin secretion control, providing potential avenues for therapeutics against diabetes.

PFDN4 as a Prognostic Marker Was Associated with Chemotherapy Resistance through CREBP1/AURKA Pathway in Triple-Negative Breast Cancer

Breast cancer is the most common malignancy and its incidence is increasing. It is currently mainly treated by clinical chemotherapy, but chemoresistance remains poorly understood. Prefolded proteins 4 (PFDN4) are molecular chaperone complexes that bind to newly synthesized polypeptides and allow them to fold correctly to stabilize protein formation. This study aimed to investigate the role of PFDN4 in chemotherapy resistance in breast cancer. Our study found that PFDN4 was highly expressed in breast cancer compared to normal tissues and was statistically significantly associated with stage, nodal status, subclasses (luminal, HER2 positive and triple negative), triple-negative subtype and disease-specific survival by TCGA database analysis. CRISPR knockout of PFDN4 inhibited the growth of 89% of breast cancer cell lines, and the triple-negative cell line exhibited a stronger inhibitory effect than the non-triple-negative cell line. High PFDN4 expression was associated with poor overall survival in chemotherapy and resistance to doxorubicin and paclitaxel through the CREBP1/AURKA pathway in the triple-negative MDAMB231 cell line. This study provides insightful evidence for the value of PFDN4 in poor prognosis and chemotherapy resistance in breast cancer patients.

Translational challenges in pancreatic neuroendocrine tumor immunotherapy

Pancreatic neuroendocrine tumors are rare types of pancreatic cancer formed from islet cells of pancreas. Clinical presentation of pancreatic neuroendocrine tumors depends on both tumor progression and hormone secretion status, which generate several complications in both diagnosis and treatment. Despite numerous strategies, treatment of patients with pancreatic neuroendocrine tumors still needs improvement. It is suggested that immune response modulation may be essential in the regulation of pancreatic neuroendocrine tumor progression and patient's symptomology. Accumulating evidence indicates that immunotherapy seems to be a promising treatment option for patients with pancreatic neuroendocrine tumors. Nevertheless, several challenges in pre-clinical and clinical studies are present. This review provides knowledge about microenvironment of pancreatic neuroendocrine tumors including significance of cytokine and chemokine as well as specific immune cell types. Additionally, in vitro and in vivo models of pancreatic neuroendocrine tumors and translational challenges are highlighted.

Fstl1/DIP2A/MGMT signaling pathway plays important roles in temozolomide resistance in glioblastoma

Temozolomide was recognized as the first-line therapy for glioblastoma to prolong the survival of patients noticeably, while recent clinical studies found that some patients were not sensitive to temozolomide treatment. The possible mechanisms seemed to be methylguanine-DNA-methyltransferase (MGMT), mismatch repair, PARP, etc. And the abnormal expression of MGMT might be the most direct factor. In this study, we provide evidence that Fstl1 plays a vital role in temozolomide resistance by sequentially regulating DIP2A protein distribution, H3K9 acetylation (H3K9Ac), and MGMT transcription. As a multifunctional protein widely distributed in cells, DIP2A cooperates with the HDAC2-DMAP1 complex to enhance H3K9Ac deacetylation, prevent MGMT transcription, and increase temozolomide sensitivity. Fstl1, a glycoprotein highly expressed in glioblastoma, competitively binds DIP2A to block DIP2A nuclear translocation, so as to hinder DIP2A from binding the HDAC2-DMAP1 complex. The overexpression of Fstl1 promoted the expression of MGMT in association with increased promoter H3K9Ac. Upregulation of Fstl1 enhanced temozolomide resistance, whereas Fstl1 silencing obviously sensitized GBM cells to temozolomide both in vivo and in vitro. Moreover, DIP2A depletion abolished the effects of Fstl1 on MGMT expression and temozolomide resistance. These findings highlight an important role of Fstl1 in the regulation of temozolomide resistance by modulation of DIP2A/MGMT signaling.

Deficiency of Follistatin-Like Protein 1 Accelerates the Growth of Breast Cancer Cells at Lung Metastatic Sites

Purpose

Follistatin-like protein 1 (FSTL1) is a secreted glycoprotein that has been shown to play a role in various types of cancer. However, the clinical significance and function of FSTL1 in breast cancer have not been reported. We investigated the role of FSTL1 in breast cancer in this study.

Methods

Enzyme-linked immunosorbent assays, western blot analysis, and reverse transcription polymerase chain reaction were used to monitor the expression of FSTL1 in breast cancer tissue and in serum samples from breast cancer patients. We employed a 4T1 breast cancer model and Fstl1 ^+/- mice for in vivo studies. Hematoxylin and eosin staining, western blot analysis, and RNA sequencing were used to analyze the effect of FSTL1 on primary tumor growth and lung metastasis.

Results

We demonstrated that the expression of FSTL1 is reduced in both the breast cancer tissue and the serum of breast cancer patients. We showed that reduced levels of FSTL1 in serum correlate with elevated expression of Ki-67 and epidermal growth factor receptor (EGFR) in cancer tissues. Moreover, lowered expression of FSTL1 was associated with decreased survival in breast cancer patients. Experiments on the Fstl1 ^+/- mouse model established that FSTL1 deficiency had no effect on primary tumor growth, but increased the lung metastases of breast cancer cells, resulting in reduced survival of tumor-bearing mice. RNA sequencing found significantly reduced expression of Egln3 and increased expression of EGFR in Fstl1 ^+/- mice. Thus, our results suggest that FSTL1 may affect the expression of EGFR through Egln3, inhibiting the proliferation of breast cancer cells at lung metastatic sites.

Conclusion

In conclusion, we suggest a suppressor role of FSTL1 in breast cancer lung metastasis. Furthermore, FSTL1 may represent a potential prognostic biomarker and a candidate therapeutic target in breast cancer patients.

Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

Quantitative, in situ analysis of mRNAs and proteins with subcellular resolution

We describe here a method, termed immunoFISH, for simultaneous in situ analysis of the composition and distribution of proteins and individual RNA transcripts in single cells. Individual RNA molecules are labeled by hybridization and target proteins are concurrently stained using immunofluorescence. Multicolor fluorescence images are acquired and analyzed to determine the abundance, composition, and distribution of hybridized probes and immunofluorescence. We assessed the ability of immunoFISH to simultaneous quantify protein and transcript levels and distribution in cultured HER2 positive breast cancer cells and human breast tumor samples. We demonstrated the utility of this assay in several applications including demonstration of the existence of a layer of normal myoepithelial KRT14 expressing cells that separate HER2+ cancer cells from the stromal and immune microenvironment in HER2+ invasive breast cancer. Our studies show that immunoFISH provides quantitative information about the spatial heterogeneity in transcriptional and proteomic features that exist between and within cells.

Magellan: A Web Based System for the Integrated Analysis of Heterogeneous Biological Data and Annotations; Application to DNA Copy Number and Expression Data in Ovarian Cancer

Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves be able to perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries. We have developed a web-based system, Magellan, which allows the upload, storage, and analysis of multivariate data and textual or numerical annotations. Data and annotations are treated as abstract entities, to maximize the different types of information the system can store and analyze. Annotations can be used in analyses/visualizations, as a means of subsetting data to reduce dimensionality, or as a means of projecting variables from one data type or data set to another. Analytical methods are deployed within Magellan such that new functionalities can be added in a straightforward fashion. Using Magellan, we performed an integrated analysis of genome-wide comparative genomic hybridization (CGH), mRNA expression, and clinical data from ovarian tumors. Analyses included the use of permutation-based methods to identify genes whose mRNA expression levels correlated with patient survival, a nearest neighbor classifier to predict patient survival from CGH data, and curated annotations such as genomic position and derived annotations such as statistical computations to explore the quantitative relationship between CGH and mRNA expression data.

Role of the long non-coding RNA PVT1 in the dysregulation of the ceRNA-ceRNA network in human breast cancer

Recent findings have identified competing endogenous RNAs (ceRNAs) as the drivers in many disease conditions, including cancers. The ceRNAs indirectly regulate each other by reducing the amount of microRNAs (miRNAs) available to target messenger RNAs (mRNAs). The ceRNA interactions mediated by miRNAs are modulated by a titration mechanism, i.e. large changes in the ceRNA expression levels either overcome, or relieve, the miRNA repression on competing RNAs; similarly, a very large miRNA overexpression may abolish competition. The ceRNAs are also called miRNA "decoys" or miRNA "sponges" and encompass different RNAs competing with each other to attract miRNAs for interactions: mRNA, long non-coding RNAs (lncRNAs), pseudogenes, or circular RNAs. Recently, we developed a computational method for identifying ceRNA-ceRNA interactions in breast invasive carcinoma. We were interested in unveiling which lncRNAs could exert the ceRNA activity. We found a drastic rewiring in the cross-talks between ceRNAs from the physiological to the pathological condition. The main actor of this dysregulated lncRNA-associated ceRNA network was the lncRNA PVT1, which revealed a net biding preference towards the miR-200 family members in normal breast tissues. Despite its up-regulation in breast cancer tissues, mimicked by the miR-200 family members, PVT1 stops working as ceRNA in the cancerous state. The specific conditions required for a ceRNA landscape to occur are still far from being determined. Here, we emphasized the importance of the relative concentration of the ceRNAs, and their related miRNAs. In particular, we focused on the withdrawal in breast cancer tissues of the PVT1 ceRNA activity and performed a gene expression and sequence analysis of its multiple isoforms. We found that the PVT1 isoform harbouring the binding site for a representative miRNA of the miR-200 family shows a drastic decrease in its relative concentration with respect to the miRNA abundance in breast cancer tissues, providing a plausibility argument to the breakdown of the sponge program orchestrated by the oncogene PVT1.

The broken "Off" switch in cancer signaling: PP2A as a regulator of tumorigenesis, drug resistance, and immune surveillance

Aberrant activation of signal transduction pathways can transform a normal cell to a malignant one and can impart survival properties that render cancer cells resistant to therapy. A diverse set of cascades have been implicated in various cancers including those mediated by serine/threonine kinases such RAS, PI3K/AKT, and PKC. Signal transduction is a dynamic process involving both "On" and "Off" switches. Activating mutations of RAS or PI3K can be viewed as the switch being stuck in the "On" position resulting in continued signaling by a survival and/or proliferation pathway. On the other hand, inactivation of protein phosphatases such as the PP2A family can be seen as the defective "Off" switch that similarly can activate these pathways. A problem for therapeutic targeting of PP2A is that the enzyme is a hetero-trimer and thus drug targeting involves complex structures. More importantly, since PP2A isoforms generally act as tumor suppressors one would want to activate these enzymes rather than suppress them. The elucidation of the role of cellular inhibitors like SET and CIP2A in cancer suggests that targeting these proteins can have therapeutic efficacy by mechanisms involving PP2A activation. Furthermore, drugs such as FTY-720 can activate PP2A isoforms directly. This review will cover the current state of knowledge of PP2A role as a tumor suppressor in cancer cells and as a mediator of processes that can impact drug resistance and immune surveillance.

A semiparametric Bayesian model for comparing DNA copy numbers

We propose a two-step method for the analysis of copy number data. We first define the partitions of genome aberrations and conditional on the partitions we introduce a semiparametric Bayesian model for the analysis of multiple samples from patients with different subtypes of a disease. While the biological interest is to identify regions of differential copy numbers across disease subtypes, our model also includes sample-specific random effects that account for copy number alterations between different samples in the same disease subtype. We model the subtype and sample-specific effects using a random effects mixture model. The subtype's main effects are characterized by a mixture distribution whose components are assigned Dirichlet process priors. The performance of the proposed model is examined using simulated data as well as a breast cancer genomic data set.

Molecular Karyotyping: Array CGH Quality Criteria for Constitutional Genetic Diagnosis

Array CGH (comparative genomic hybridization) enables the identification of chromosomal copy number changes. The availability of clone sets covering the human genome opens the possibility for the widespread use of array CGH for both research and diagnostic purposes. In this manuscript we report on the parameters that were critical for successful implementation of the technology, assess quality criteria, and discuss the potential benefits and pitfalls of the technology for improved pre- and postnatal constitutional genetic diagnosis. We propose to name the genome-wide array CGH “molecular karyotyping,” in analogy with conventional karyotyping that uses staining methods to visualize chromosomes.

Jagged1 DNA Copy Number Variation Is Associated with Poor Outcome in Liver Cancer

Notch signaling abnormalities are reported to be involved in the acceleration of malignancy in solid tumors and stem cell formation or regeneration in various organs. We analyzed specific genes for DNA copy number variations in liver cancer cells and investigated whether these factors relate to clinical outcome. Chromosome 20p, which includes the ligand for Notch pathways, Jagged1, was found to be amplified in several types of hepatoma cells, and its mRNA was up-regulated according to α-fetoprotein gene expression levels. Notch inhibition using Jagged1 shRNA and γ-secretase inhibitors produced significant suppression of cell growth in α-fetoprotein-producing cells with suppression of downstream genes. Using in vivo hepatoma models, the administration of γ-secretase inhibitors resulted in reduced tumor sizes and effective Notch inhibition with widespread apoptosis and necrosis of viable tumor cells. The γ-secretase inhibitors suppressed cell growth of the epithelial cell adhesion molecule-positive fraction in hepatoma cells, indicating that Notch inhibitors could suppress the stem cell features of liver cancer cells. Even in clinical liver cancer samples, the expression of α-fetoprotein and Jagged1 showed significant correlation, and amplification of the copy number of Jagged1 was associated with Jagged1 mRNA expression and poor survival after liver cancer surgical resection. In conclusion, amplification of Jagged1 contributed to mRNA expression that activates the Jagged1-Notch signaling pathway in liver cancer and led to poor outcome.

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings.

Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling

Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses.

A Cross-Species Analysis in Pancreatic Neuroendocrine Tumors Reveals Molecular Subtypes with Distinctive Clinical, Metastatic, Developmental, and Metabolic Characteristics

Seeking to assess the representative and instructive value of an engineered mouse model of pancreatic neuroendocrine tumors (PanNET) for its cognate human cancer, we profiled and compared mRNA and miRNA transcriptomes of tumors from both. Mouse PanNET tumors could be classified into two distinctive subtypes, well-differentiated islet/insulinoma tumors (IT) and poorly differentiated tumors associated with liver metastases, dubbed metastasis-like primary (MLP). Human PanNETs were independently classified into these same two subtypes, along with a third, specific gene mutation-enriched subtype. The MLP subtypes in human and mouse were similar to liver metastases in terms of miRNA and mRNA transcriptome profiles and signature genes. The human/mouse MLP subtypes also similarly expressed genes known to regulate early pancreas development, whereas the IT subtypes expressed genes characteristic of mature islet cells, suggesting different tumorigenesis pathways. In addition, these subtypes exhibit distinct metabolic profiles marked by differential pyruvate metabolism, substantiating the significance of their separate identities.

SIGNIFICANCE

This study involves a comprehensive cross-species integrated analysis of multi-omics profiles and histology to stratify PanNETs into subtypes with distinctive characteristics. We provide support for the RIP1-TAG2 mouse model as representative of its cognate human cancer with prospects to better understand PanNET heterogeneity and consider future applications of personalized cancer therapy.

Multiple genes identified as targets for 20q13.12-13.33 gain contributing to unfavorable clinical outcomes in patients with hepatocellular carcinoma

BACKGROUND

Recurrent chromosome 20q gain is implicated in progressive cancer behaviors and has been associated with clinical outcomes in multiple types of cancer; however, its prognostic significance in hepatocellular carcinoma (HCC) and the involved genes remain unclear.

METHODS

Array comparative genomic hybridization and expression arrays were used to detect copy number alterations (CNAs) and expression levels, respectively. The associations between CNAs in 20q and outcomes were analyzed on 66 patients, for which the follow-up period was 2.6-73.3 months. One hundred seventeen tumors were further investigated to identify target genes in the potentially outcome-related CNAs.

RESULTS

Regional or whole 20q gain was detected in 24 (36.4%) of the 66 HCC cases. The most recurrent gains were 20q11.21-12, 20q12-13.12, 20q13.12-13.33 and 20q13.33. Of the CNAs, 20q13.12-13.33 gain was significantly associated with reduced extrohepatic metastasis-free and overall survival, as well as with elevated postoperative AFP level, tumor vascular invasion and advanced tumor stage. Multivariate Cox analysis identified 20q13.12-13.33 gain as an independent prognostic marker for metastasis (HR 3.73, 95% CI 1.08-12.87) and death (HR 3.00, 95% CI 1.26-7.13). A panel of 19 genes in 20q13.12-13.33 was significantly overexpressed in HCCs with gain compared to HCCs without. High expression (greater than median) for 5 of the 19 genes, DDX27, B4GALT5, RNF114, ZFP64 and PFDN4, correlated significantly with vascular invasion, and high RNF114 expression also with advanced tumor stage.

CONCLUSIONS

Gain at 20q13.12-13.33 is a prognostic marker of metastasis and death, and DDX27, B4GALT5, RNF114, ZFP64, and PFDN4 are probable target genes which may be involved together in the unfavorable outcomes of HCC patients.

Hybrid algorithms for multiple change-point detection in biological sequences

Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences in high resolution. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many complex diseases. In this study we propose two distinct hybrid algorithms that combine efficient sequential change-point detection procedures (the Shiryaev-Roberts procedure and the cumulative sum control chart (CUSUM) procedure) with the Cross-Entropy method, which is an evolutionary stochastic optimization technique to estimate both the number of change-points and their corresponding locations in aCGH data. The proposed hybrid algorithms are applied to both artificially generated data and real aCGH experimental data to illustrate their usefulness. Our results show that the proposed methodologies are effective in detecting multiple change-points in biological sequences of continuous measurements.

MACC1 expression levels as a novel prognostic marker for colorectal cancer

Metastasis-associated in colon cancer-1 (MACC1) is key in promoting tumor proliferation and invasion, and is mediated by the hepatocyte growth factor (HGF) and mesenchymal-epithelial transition factor. Previous reports have revealed that MACC1 is a novel oncogene that is expressed in various types of gastrointestinal cancer. The present study comprised of 174 patients who underwent curative surgery for colorectal cancer (CRC). The correlation between gene expression and clinical parameters of the patients was assessed. It was identified that patients exhibiting high MACC1 expression levels were statistically more susceptible to distant metastases and a poor prognosis, and those exhibiting low MACC1 expression showed improved disease-free and overall survival than those with high expression. Therefore, the present data indicates that MACC1 expression levels may present as a prognostic factor in CRC patients.

ANXA9 gene expression in colorectal cancer: A novel marker for prognosis

Annexin A9 (ANXA9) is involved with the interaction with membrane phospholipids in a Ca²⁺-dependent manner. A previous study has shown that ANXA9 expression is associated with bone metastasis in breast cancer, whereas its significance in colorectal cancer (CRC) is unknown. The present study was comprised of 100 patients who underwent surgery for CRC. The correlation between gene expression and the clinical parameters of the patients was assessed. Patients with high ANXA9 expression were statistically susceptible to a relatively worse prognosis, and those with low ANXA9 expression showed improved overall survival compared with those with high expression. In conclusion, the present data suggests that ANXA9 expression is a prognostic factor in CRC patients.

Mapping of the chromosomal amplification 1p21-22 in bladder cancer

Background

The aim of the study was to characterize a recurrent amplification at chromosomal region 1p21-22 in bladder cancer.

Methods

ArrayCGH (aCGH) was performed to identify DNA copy number variations in 7 clinical samples and 6 bladder cancer cell lines. FISH was used to map the amplicon at 1p21-22 in the cell lines. Gene expression microarrays and qRT-PCR were used to study the expression of putative target genes in the region.

Results

aCGH identified an amplification at 1p21-22 in 10/13 (77%) samples. The minimal region of the amplification was mapped to a region of about 1 Mb in size, containing a total of 11 known genes. The highest amplification was found in SCaBER squamous cell carcinoma cell line. Four genes, TMED5, DR1, RPL5 and EVI5, showed significant overexpression in the SCaBER cell line compared to all the other samples tested. Oncomine database analysis revealed upregulation of DR1 in superficial and infiltrating bladder cancer samples, compared to normal bladder.

Conclusions

In conclusions, we have identified and mapped chromosomal amplification at 1p21-22 in bladder cancer as well as studied the expression of the genes in the region. DR1 was found to be significantly overexpressed in the SCaBER, which is a model of squamous cell carcinoma. However, the overexpression was found also in a published clinical sample cohort of superficial and infiltrating bladder cancers. Further studies with more clinical material are needed to investigate the role of the amplification at 1p21-22.

Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer

Background

Non-coding RNAs (ncRNAs) are emerging as key regulators of many cellular processes in both physiological and pathological states. Moreover, the constant discovery of new non-coding RNA species suggests that the study of their complex functions is still in its very early stages. This variegated class of RNA species encompasses the well-known microRNAs (miRNAs) and the most recently acknowledged long non-coding RNAs (lncRNAs). Interestingly, in the last couple of years, a few studies have shown that some lncRNAs can act as miRNA sponges, i.e. as competing endogenous RNAs (ceRNAs), able to reduce the amount of miRNAs available to target messenger RNAs (mRNAs).

Results

We propose a computational approach to explore the ability of lncRNAs to act as ceRNAs by protecting mRNAs from miRNA repression. A seed match analysis was performed to validate the underlying regression model. We built normal and cancer networks of miRNA-mediated sponge interactions (MMI-networks) using breast cancer expression data provided by The Cancer Genome Atlas.

Conclusions

Our study highlights a marked rewiring in the ceRNA program between normal and pathological breast tissue, documented by its “on/off” switch from normal to cancer, and vice-versa. This mutually exclusive activation confers an interesting character to ceRNAs as potential oncosuppressive, or oncogenic, protagonists in cancer. At the heart of this phenomenon is the lncRNA PVT1, as illustrated by both the width of its antagonist mRNAs in normal-MMI-network, and the relevance of the latter in breast cancer. Interestingly, PVT1 revealed a net binding preference towards the mir-200 family as the bone of contention with its rival mRNAs.

Absence/presence calling in microarray-based CGH experiments with non-model organisms

Structural variations in genomes are commonly studied by (micro)array-based comparative genomic hybridization. The data analysis methods to infer copy number variation in model organisms (human, mouse) are established. In principle, the procedures are based on signal ratios between test and reference samples and the order of the probe targets in the genome. These procedures are less applicable to experiments with non-model organisms, which frequently comprise non-sequenced genomes with an unknown order of probe targets. We therefore present an additional analysis approach, which does not depend on the structural information of a reference genome, and quantifies the presence or absence of a probe target in an unknown genome. The principle is that intensity values of target probes are compared with the intensities of negative-control probes and positive-control probes from a control hybridization, to determine if a probe target is absent or present. In a test, analyzing the genome content of a known bacterial strain: Staphylococcus aureus MRSA252, this approach proved to be successful, demonstrated by receiver operating characteristic area under the curve values larger than 0.9995. We show its usability in various applications, such as comparing genome content and validating next-generation sequencing reads from eukaryotic non-model organisms.

SV40 TAg mouse models of cancer

The discovery of a number of viruses with the ability to induce tumours in animals and transform human cells has vastly impacted cancer research. Much of what is known about tumorigenesis today regarding tumour drivers and tumour suppressors has been discovered through experiments using viruses. The SV40 virus has proven extremely successful in generating transgenic models of many human cancer types and this review provides an overview of these models and seeks to give evidence as to their relevance in this modern era of personalised medicine and technological advancements.

Identification of chromosomal copy number variations and novel candidate loci in hereditary nonpolyposis colorectal cancer with mismatch repair proficiency

The pathogenesis of microsatellite stable hereditary non-polyposis colorectal cancers (MSS HNPCC) is unclear. To identify genomic regions that might be involved in MSS HNPCC pathogenesis, we selected 20 pairs of MSS HNPCC for a genome-wide study using copy number variation targeted (CNV-targeted) CytoScan HD Array. A remarkably increased frequency of 20q gain (70%) and high levels of copy-neutral loss of heterozygosity (40%) were observed. The most frequent tumor-specific CNVs included amplifications (7p21.3-15.1, 8q13.3-24.3, 13q14.1-33.3 and 20q12-13.33) and deletions (8p11.23-23.1, 15q11.2-26.1, 17p13.1-13.3 and 18q11.2-21.33). In addition, 10 novel CNVs were discovered and led to identification of WDR16 and RAPGEF5 as candidate genes involved in tumorigenesis, displaying a robust correlation between expression and genomic alterations. Moreover, WDR16 and RAPGEF5 exhibited altered protein expression levels as assessed by immunohistochemistry (IHC) in 41 other independent samples. Finally, high consistencies (68-84%) were observed between CNVs by Array and quantitative PCR. These findings are important for further elucidating MSS HNPCC pathogenesis.

Genomic copy number alterations in clear cell renal carcinoma: associations with case characteristics and mechanisms of VHL gene inactivation

Array comparative genomic hybridization was used to identify copy number alterations in clear cell renal cell carcinoma (ccRCC) patient tumors to identify associations with patient/clinical characteristics. Of 763 ccRCC patients, 412 (54%) provided frozen biopsies. Clones were analyzed for significant copy number differences, adjusting for multiple comparisons and covariates in multivariate analyses. Frequent alterations included losses on: 3p (92.2%), 14q (46.8%), 8p (38.1%), 4q (35.4%), 9p (32.3%), 9q (31.8%), 6q (30.8%), 3q (29.4%), 10q (25.7%), 13q (24.5%), 1p (23.5%) and gains on 5q (60.2%), 7q (39.6%), 7p (30.6%), 5p (26.5%), 20q (25.5%), 12q (24.8%), 12p (22.8%). Stage and grade were associated with 1p, 9p, 9q, 13q and 14q loss and 12q gain. Males had more alterations compared with females, independent of stage and grade. Significant differences in the number/types of alterations were observed by family cancer history, age at diagnosis and smoking status. Von Hippel–Lindau (VHL) gene inactivation was associated with 3p loss (P<E-05), and these cases had fewer alterations than wild-type cases. The fragile site flanking the FHIT locus (3p14.2) represented a unique breakpoint among VHL hypermethylated cases, compared with wild-type cases and those with sequence changes. This is the first study of its size to investigate copy number alterations among cases with extensive patient, clinical/risk factor information. Patients characterized by VHL wild-type gene status (vs sequence alterations) and male (vs female) cases had more copy number alterations regardless of diagnostic stage and grade, which could relate to poor prognosis.

Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM).

Array-based comparative genomics is a standard approach for the identification of DNA copy number polymorphisms between closely related genomes. The huge amounts of data produced by these experiments require efficient and accurate bioinformatics tools for the identification of copy number polymorphisms. Hidden Markov Models (HMMs) are frequently used for analyzing such data sets, but current models are based on first-order HMMs only having limited capabilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. We develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling these dependencies to overcome this limitation. In an in-depth case study with Arabidopsis thaliana, we find that parsimonious higher-order HMMs clearly improve the identification of copy number polymorphisms in comparison to standard first-order HMMs and other frequently used methods. Functional analysis of identified polymorphisms revealed details of genomic differences between the accessions C24 and Col-0 of Arabidopsis thaliana. An additional study on human cell lines further indicates that parsimonious HMMs are well-suited for the analysis of Array-CGH data.

Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that utilizes information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, referred to as BDSAcgh (Bayesian Detection of Shared Aberrations in aCGH), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alteration that correspond to local false discovery rates. We evaluate the operating characteristics of our method via simulations and an application using a lung cancer aCGH data set.

Bayesian Hidden Markov Modeling of Array CGH Data

Genomic alterations have been linked to the development and progression of cancer. The technique of comparative genomic hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data.We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Because the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulation-based inference. Publicly available data on pancreatic adenocarcinoma, glioblastoma multiforme, and breast cancer are analyzed, and comparisons are made with some widely used algorithms to illustrate the reliability and success of the technique.

A hidden Markov model-based algorithm for identifying tumour subtype using array CGH data

Background

The recent advancement in array CGH (aCGH) research has significantly improved tumor identification using DNA copy number data. A number of unsupervised learning methods have been proposed for clustering aCGH samples. Two of the major challenges for developing aCGH sample clustering are the high spatial correlation between aCGH markers and the low computing efficiency. A mixture hidden Markov model based algorithm was developed to address these two challenges.

Results

The hidden Markov model (HMM) was used to model the spatial correlation between aCGH markers. A fast clustering algorithm was implemented and real data analysis on glioma aCGH data has shown that it converges to the optimal cluster rapidly and the computation time is proportional to the sample size. Simulation results showed that this HMM based clustering (HMMC) method has a substantially lower error rate than NMF clustering. The HMMC results for glioma data were significantly associated with clinical outcomes.

Conclusions

We have developed a fast clustering algorithm to identify tumor subtypes based on DNA copy number aberrations. The performance of the proposed HMMC method has been evaluated using both simulated and real aCGH data. The software for HMMC in both R and C++ is available in ND INBRE website http://ndinbre.org/programs/bioinformatics.php.

Differential contribution to neuroendocrine tumorigenesis of parallel egfr signaling in cancer cells and pericytes

Factors associated with tumor sensitivity to epidermal growth factor receptor (EGFR) inhibitors in the context of wild-type EGFR remain elusive. This study investigates the mechanistic basis of responsiveness to EGFR inhibitors in the RIP1-Tag2 (RT2) mouse model of pancreatic neuroendocrine tumorigenesis (PNET). Upon treatment of RT2 mice with EGFR inhibitors, PNET tumors harboring wild-type, nonamplified alleles of Egfr grow at a markedly reduced rate and display a significant increase in tumor cell apoptosis, as well as reduced neovascularization. The authors identify Tgf-α and Hb-egf as key limiting mediators of separable pathological functions of Egfr in neuroendocrine tumor progression: Tgf-α mutant tumors present with an elevated apoptotic index, whereas Hb-egf mutant lesions exhibit decreased angiogenic switching and neovascularization. This study not only associates Tgf-α and Hb-egf expression with wild-type Egfr oncogenicity but also ascribes the proangiogenic activity of Egfr in this tumor model to a novel mesenchymal Hb-egf/Egfr signaling axis, whereby endothelial and pericyte-derived Hb-egf activates Egfr specifically in tumor-associated perivascular cells, leading to increased pericyte coverage of the tumor endothelium and enhanced angiogenesis.

Haploinsufficient and predominant expression of multiple endocrine neoplasia type 1 (MEN1)-related genes, MLL, p27Kip1 and p18Ink4C in endocrine organs

Multiple endocrine neoplasia type 1 (MEN1) is a rare autosomal dominantly inherited syndrome characterized by parathyroid, gastro-entero-pancreatic and anterior pituitary tumors. Although the tissue selectivity of tumors in specific endocrine organs is the very essence of MEN1, the mechanisms underlying the tissue-selectivity of tumors remain unknown. The product of the Men1 gene, menin, and mixed lineage leukemia (MLL) have been found to cooperatively regulate p27(Kip1)/CDKN1B (p27) and p18(Ink4C)/CDKN2C (p18) genes. However, there are no reports on the tissue distribution of these MEN1-related genes. We investigated the expression of these genes in the endocrine and non-endocrine organs of wild-type, Men1 knockout and MLL knockout mice. Men1 mRNA was expressed at a similar level in endocrine and non-endocrine organs. However, MLL, p27 and p18 mRNAs were predominantly expressed in the endocrine organs. Notably, p27 and MLL mRNAs were expressed in the pituitary gland at levels approximately 12- and 17-fold higher than those in the liver. The heterozygotes of Men1 knockout mice the levels of MLL, p27 and p18 mRNAs did not differ from those in the wild-type mice. In contrast, heterozygotes of MLL knockout mice showed significant reductions in p27 mRNA as well as protein levels in the pituitary and p27 and p18 in the pancreatic islets, but not in the liver. This study demonstrated for the first time the predominant expression MEN1-related genes, particularly MLL and p27, in the endocrine organs, and a tissue-specific haploinsuffiency of MLL, but not menin, may lead to a decrease in levels of p27 and p18 mRNAs in endocrine organs. These findings may provide basic information for understanding the mechanisms of tissue selectivity of the tumorigenesis in patients with MEN1.

Von Hippel-Lindau (VHL) inactivation in sporadic clear cell renal cancer: associations with germline VHL polymorphisms and etiologic risk factors

Renal tumor heterogeneity studies have utilized the von Hippel-Lindau VHL gene to classify disease into molecularly defined subtypes to examine associations with etiologic risk factors and prognosis. The aim of this study was to provide a comprehensive analysis of VHL inactivation in clear cell renal tumors (ccRCC) and to evaluate relationships between VHL inactivation subgroups with renal cancer risk factors and VHL germline single nucleotide polymorphisms (SNPs). VHL genetic and epigenetic inactivation was examined among 507 sporadic RCC/470 ccRCC cases using endonuclease scanning and using bisulfite treatment and Sanger sequencing across 11 CpG sites within the VHL promoter. Case-only multivariate analyses were conducted to identify associations between alteration subtypes and risk factors. VHL inactivation, either through sequence alterations or promoter methylation in tumor DNA, was observed among 86.6% of ccRCC cases. Germline VHL SNPs and a haplotype were associated with promoter hypermethylation in tumor tissue (OR = 6.10; 95% CI: 2.28-16.35, p = 3.76E-4, p-global = 8E-5). Risk of having genetic VHL inactivation was inversely associated with smoking due to a higher proportion of wild-type ccRCC tumors [former: OR = 0.70 (0.20-1.31) and current: OR = 0.56 (0.32-0.99); P-trend = 0.04]. Alteration prevalence did not differ by histopathologic characteristics or occupational exposure to trichloroethylene. ccRCC cases with particular VHL germline polymorphisms were more likely to have VHL inactivation through promoter hypermethylation than through sequence alterations in tumor DNA, suggesting that the presence of these SNPs may represent an example of facilitated epigenetic variation (an inherited propensity towards epigenetic variation) in renal tissue. A proportion of tumors from current smokers lacked VHL alterations and may represent a biologically distinct clinical entity from inactivated cases.

Discovering tumor suppressor genes through genome-wide copy number analysis

Classical tumor suppressor gene discovery has largely involved linkage analysis and loss-of-heterozygosity (LOH) screens, followed by detailed mapping of relatively large chromosomal regions. Subsequent efforts made use of genome-wide PCR-based methods to detect rare homozygous deletions. More recently, high-resolution genomic arrays have been applied to cancer gene discovery. However, accurate characterization of regions of genomic loss is particularly challenging due to sample heterogeneity, the small size of deleted regions and the high frequency of germline copy number polymorphisms. Here, we review the application of genome-wide copy number analysis to the specific problem of identifying tumor suppressor genes.

Shared acquired genomic changes in zebrafish and human T-ALL

T-cell acute lymphoblastic leukemia (T-ALL) is a challenging clinical entity with high rates of induction failure and relapse. To discover the genetic changes occurring in T-ALL, and those contributing to relapse, we studied zebrafish (Danio rerio) T-ALL samples using array comparative genomic hybridization (aCGH). We performed aCGH on 17 T-ALLs from four zebrafish T-ALL models, and evaluated similarities between fish and humans by comparing all D. rerio genes with copy number aberrations (CNAs) with a cohort of 75 published human T-ALLs analyzed by aCGH. Within all D. rerio CNAs, we identified 893 genes with human homologues and found significant overlap (67%) with the human CNA dataset. In addition, when we restricted our analysis to primary T-ALLs (14 zebrafish and 61 human samples), 10 genes were recurrently altered in > 3 zebrafish cancers and ≥ 4 human cases, suggesting a conserved role for these loci in T-ALL transformation across species. We also conducted iterative allo-transplantation with three zebrafish malignancies. This technique selects for aggressive disease, resulting in shorter survival times in successive transplant rounds and modeling refractory and relapsed human T-ALL. Fifty-five percent of original CNAs were preserved after serial transplantation, demonstrating clonality between each primary and passaged leukemia. Cancers acquired an average of 34 new CNAs during passaging. Genes in these loci may underlie the enhanced malignant behavior of these neoplasias. We also compared genes from CNAs of passaged zebrafish malignancies with aCGH results from 50 human T-ALL patients who failed induction, relapsed or would eventually relapse. Again, many genes (88/164) were shared by both datasets. Further, nine recurrently altered genes in passaged D. rerio T-ALL were also found in multiple human T-ALL cases. These results suggest that zebrafish and human T-ALLs are similar at the genomic level, and are governed by factors that have persisted throughout evolution.

Making sense of cancer genomic data

High-throughput tools for nucleic acid characterization now provide the means to conduct comprehensive analyses of all somatic alterations in the cancer genomes. Both large-scale and focused efforts have identified new targets of translational potential. The deluge of information that emerges from these genome-scale investigations has stimulated a parallel development of new analytical frameworks and tools. The complexity of somatic genomic alterations in cancer genomes also requires the development of robust methods for the interrogation of the function of genes identified by these genomics efforts. Here we provide an overview of the current state of cancer genomics, appraise the current portals and tools for accessing and analyzing cancer genomic data, and discuss emerging approaches to exploring the functions of somatically altered genes in cancer.

Strategies for genotyping

The identification of genomic loci linked to or associated with human disease has been greatly facilitated by the evolution of genotyping strategies and techniques. The success of these strategies continues to be based upon clear clinical assessment, accurate sample handling, and careful data management, but also increasingly upon experimental design. Technological advances in the field of genotyping have permitted increasingly complex and large population studies to be performed. An understanding of publicly available genetic variation databases, including an awareness of the limitations of these data, and an appreciation of the strategic approaches that should be used to exploit this information will provide tremendous insight for researchers are aiming to utilize this accessible technology. As genome-wide association studies (GWAS) and Next Generation (NextGen) sequencing become the mainstays of genetic analyses, it is important that their technical strengths and limitations, as well as their impact on study design, be understood before use in a linkage or genetic association study.

A new normalizing algorithm for BAC CGH arrays with quality control metrics

The main focus in pin-tip (or print-tip) microarray analysis is determining which probes, genes, or oligonucleotides are differentially expressed. Specifically in array comparative genomic hybridization (aCGH) experiments, researchers search for chromosomal imbalances in the genome. To model this data, scientists apply statistical methods to the structure of the experiment and assume that the data consist of the signal plus random noise. In this paper we propose "SmoothArray", a new method to preprocess comparative genomic hybridization (CGH) bacterial artificial chromosome (BAC) arrays and we show the effects on a cancer dataset. As part of our R software package "aCGHplus," this freely available algorithm removes the variation due to the intensity effects, pin/print-tip, the spatial location on the microarray chip, and the relative location from the well plate. removal of this variation improves the downstream analysis and subsequent inferences made on the data. Further, we present measures to evaluate the quality of the dataset according to the arrayer pins, 384-well plates, plate rows, and plate columns. We compare our method against competing methods using several metrics to measure the biological signal. With this novel normalization algorithm and quality control measures, the user can improve their inferences on datasets and pinpoint problems that may arise in their BAC aCGH technology.

Detecting genomic aberrations using products in a multiscale analysis

Genomic instability, such as copy-number losses and gains, occurs in many genetic diseases. Recent technology developments enable researchers to measure copy numbers at tens of thousands of markers simultaneously. In this article, we propose a nonparametric approach for detecting the locations of copy-number changes and provide a measure of significance for each change point. The proposed test is based on seeking scale-based changes in the sequence of copy numbers, which is ordered by the marker locations along the chromosome. The method leads to a natural way to estimate the null distribution for the test of a change point and adjusted p-values for the significance of a change point using a step-down maxT permutation algorithm to control the family-wise error rate. A simulation study investigates the finite sample performance of the proposed method and compares it with a more standard sequential testing method. The method is illustrated using two real data sets.

Detecting cancer gene networks characterized by recurrent genomic alterations in a population

High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.

Bayesian Frequentist hybrid Model wth Application to the Analysis of Gene Copy Number Changes

Gene copy number (GCN) changes are common characteristics of many genetic diseases. Comparative genomic hybridization (CGH) is a new technology widely used today to screen the GCN changes in mutant cells with high resolution genome-wide. Statistical methods for analyzing such CGH data have been evolving. Existing methods are either frequentist's, or full Bayesian. The former often has computational advantage, while the latter can incorporate prior information into the model, but could be misleading when one does not have sound prior information. In an attempt to take full advantages of both approaches, we develop a Bayesian-frequentist hybrid approach, in which a subset of the model parameters is inferred by the Bayesian method, while the rest parameters by the frequentist's. This new hybrid approach provides advantages over those of the Bayesian or frequentist's method used alone. This is especially the case when sound prior information is available on part of the parameters, and the sample size is relatively small. Spatial dependence and false discovery rate are also discussed, and the parameter estimation is efficient. As an illustration, we used the proposed hybrid approach to analyze a real CGH data.

Assessing Population Level Genetic Instability via Moving Average

Tumoral tissues tend to generally exhibit aberrations in DNA copy number that are associated with the development and progression of cancer. Genotyping methods such as array-based comparative genomic hybridization (aCGH) provide means to identify copy number variation across the entire genome. To address some of the shortfalls of existing methods of DNA copy number data analysis, including strong model assumptions, lack of accounting for sampling variability of estimators, and the assumption that clones are independent, we propose a simple graphical approach to assess population-level genetic alterations over the entire genome based on moving average. Furthermore, existing methods primarily focus on segmentation and do not examine the association of covariates with genetic instability. In our methods, covariates are incorporated through a possibly mis-specified working model and sampling variabilities of estimators are approximated using a resampling method that is based on perturbing observed processes. Our proposal, which is applicable to partial, entire or multiple chromosomes, is illustrated through application to aCGH studies of two brain tumor types, meningioma and glioma.

A bayesian analysis for identifying DNA copy number variations using a compound poisson process

To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in the data. Motivated by the specific features of aCGH data, we developed a novel method that takes into account the estimation of a change point or locus of the CNV in aCGH data with its associated biomarker position on the chromosome using a compound Poisson process. We used a Bayesian approach to derive the posterior probability for the estimation of the CNV locus. To detect loci of multiple CNVs in the data, a sliding window process combined with our derived Bayesian posterior probability was proposed. To evaluate the performance of the method in the estimation of the CNV locus, we first performed simulation studies. Finally, we applied our approach to real data from aCGH experiments, demonstrating its applicability.

DNA copy number aberrations and disruption of the p16Ink4a/Rb pathway in radiation-induced and spontaneous rat mammary carcinomas

Chromosomal amplifications and deletions are thought to be important events in spontaneous and radiation-induced carcinogenesis. To clarify how ionizing radiation induces mammary carcinogenesis, we characterized genomic copy number aberrations for gamma-ray-induced rat mammary carcinomas using microarray-based comparative genomic hybridization. We examined 14 carcinomas induced by gamma radiation (2 Gy) and found 26 aberrations, including trisomies of chromosomes 4 and 10 for three and one carcinomas, respectively, an amplification of the chromosomal region 1q12 in two carcinomas, and deletions of the chromosomal regions 3q35q36, 5q32 and 7q11 in two, two and four carcinomas, respectively. These aberrations were not observed in seven spontaneous mammary carcinomas. The expression of p16Ink4a and p19Arf, which are located in the chromosomal region 5q32, was always up-regulated except for a carcinoma with a homozygous deletion of region 5q32. The up-regulation was not accounted for by gene mutations or promoter hypomethylation. However, the amounts of Rb and its mRNA were down-regulated in these carcinomas, indicating a disruption of the p16Ink4a/Rb pathway. This is the first report of array CGH analysis for radiation-induced mammary tumors, which reveals that they show distinct DNA copy number aberration patterns that are different from those of spontaneous tumors and those reported previously for chemically induced tumors.

Polymorphic genetic control of tumor invasion in a mouse model of pancreatic neuroendocrine carcinogenesis

Cancer is a disease subject to both genetic and environmental influences. In this study, we used the RIP1-Tag2 (RT2) mouse model of islet cell carcinogenesis to identify a genetic locus that influences tumor progression to an invasive growth state. RT2 mice inbred into the C57BL/6 (B6) background develop both noninvasive pancreatic neuroendocrine tumors (PNET) and invasive carcinomas with varying degrees of aggressiveness. In contrast, RT2 mice inbred into the C3HeB/Fe (C3H) background are comparatively resistant to the development of invasive tumors, as are RT2 C3HB6(F1) hybrid mice. Using linkage analysis, we identified a 13-Mb locus on mouse chromosome 17 with significant linkage to the development of highly invasive PNETs. A gene residing in this locus, the anaplastic lymphoma kinase (Alk), was expressed at significantly lower levels in PNETs from invasion-resistant C3H mice compared with invasion-susceptible B6 mice, and pharmacological inhibition of Alk led to reduced tumor invasiveness in RT2 B6 mice. Collectively, our results demonstrate that tumor invasion is subject to polymorphic genetic control and identify Alk as a genetic modifier of invasive tumor growth.

A survey of analysis software for array-comparative genomic hybridisation studies to detect copy number variation

Copy number variants (CNVs) create a major source of variation among individuals and populations. Array-based comparative genomic hybridisation (aCGH) is a powerful method used to detect and compare the copy numbers of DNA sequences at high resolution along the genome. In recent years, several informatics tools for accurate and efficient CNV detection and assessment have been developed. In this paper, most of the well known algorithms, analysis software and the limitations of that software will be briefly reviewed.

Gene expression profiling of mouse p53-deficient epidermal carcinoma defines molecular determinants of human cancer malignancy

Background

The epidermal specific ablation of Trp53 gene leads to the spontaneous development of aggressive tumors in mice through a process that is accelerated by the simultaneous ablation of Rb gene. Since alterations of p53-dependent pathway are common hallmarks of aggressive, poor prognostic human cancers, these mouse models can recapitulate the molecular features of some of these human malignancies.

Results

To evaluate this possibility, gene expression microarray analysis was performed in mouse samples. The mouse tumors display increased expression of cell cycle and chromosomal instability associated genes. Remarkably, they are also enriched in human embryonic stem cell gene signatures, a characteristic feature of human aggressive tumors. Using cross-species comparison and meta-analytical approaches, we also observed that spontaneous mouse tumors display robust similarities with gene expression profiles of human tumors bearing mutated TP53, or displaying poor prognostic outcome, from multiple body tissues. We have obtained a 20-gene signature whose genes are overexpressed in mouse tumors and can identify human tumors with poor outcome from breast cancer, astrocytoma and multiple myeloma. This signature was consistently overexpressed in additional mouse tumors using microarray analysis. Two of the genes of this signature, AURKA and UBE2C, were validated in human breast and cervical cancer as potential biomarkers of malignancy.

Conclusions

Our analyses demonstrate that these mouse models are promising preclinical tools aimed to search for malignancy biomarkers and to test targeted therapies of prospective use in human aggressive tumors and/or with p53 mutation or inactivation.