Network-Guided Analysis of Genes with Altered Somatic Copy Number and Gene Expression Reveals Pathways Commonly Perturbed in Metastatic Melanoma

Cancer genomes frequently contain somatic copy number alterations (SCNA) that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression (‘SCNA-genes’) in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer

Statistical Methods in Integrative Genomics

Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions

Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays

<p>Abstract</p> <p>Background</p> <p>The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.</p> <p>Results</p> <p>The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.</p> <p>Conclusions</p> <p>We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.</p

Integrated Genomics Of Susceptiblity To Therapy-Related Leukemia

Therapy-related acute myeloid leukemia t-AML is a secondary, generally incurable, malignancy attributable to the chemotherapeutic treatment of an initial disease. Although there is a genetic component to susceptibility to therapy-related leukemias in mice, little is understood either about the contributing loci, or the mechanisms by which susceptibility factors mediate their effect. An improved understanding of susceptibility factors and the biological processes in which they act may lead to the development of t-AML prevention strategies. In this thesis work, we identified expression networks that are associated with t-AML susceptibility in mice. These networks are robust in that they emerge from distinct methods of analysis and from different gene expression data sets of hematopoietic stem and progenitor lineages. These networks are enriched in genes involved in cell cycle and DNA repair, suggesting that these processes play a role in susceptibility. By integrating gene expression and genetic information we prioritized network nodes for experimental validation as contributors to expression networks and t-AML susceptibility. Network analysis and node prioritization required a comprehensive map of genetic variation in mouse, which was not available at the outset of this thesis work. Specifically, DNA copy number variations: CNVs), defined as genomic sequences that are polymorphic in copy number and range in length from 1,000 to several million base pairs, were largely uncharacterized in inbred mice. We developed a computational approach, Washington University Hidden Markov Model: wuHMM), to identify CNVs from high-density array comparative genomic hybridization data, accounting for the high degree of polymorphism that occur between mouse strains. Using wuHMM we analyzed the copy number content of the mouse genome: 20 strains) to a sub-10-kb resolution, finding over 1,300 CNV-regions: CNVRs), most of which are \u3c 10 kb in length, are found in more than one strain, and span 3.2%: 85 Mb) of the reference genome. These CNVRs, along with haplotype blocks we derived from publicly available SNP data, were integrated into susceptibility expression network analysis. In addition to addressing questions regarding t-MDS/AML susceptibility, we also used this data to assess the potential functional impact of copy number variation by mapping expression profiles to CNVRs. In hematopoietic stem and progenitor cells, up to 28% of strain-dependent expression variation is associated with copy number variation, supporting the role of germline CNVs as key contributors to natural phenotypic variation

The application of genomic technologies to cancer and companion diagnostics.

This thesis describes work undertaken by the author between 1996 and 2014. Genomics is the study of the genome, although it is also often used as a catchall phrase and applied to the transcriptome (study of RNAs) and methylome (study of DNA methylation). As cancer is a disease of the genome the rapid advances in genomic technology, specifically microarrays and next generation sequencing, are creating a wave of change in our understanding of its molecular pathology. Molecular pathology and personalised medicine are being driven by discoveries in genomics, and genomics is being driven by the development of faster, better and cheaper genome sequencing. The next decade is likely to see significant changes in the way cancer is managed for individual cancer patients as next generation sequencing enters the clinic. In chapter 3 I discuss how ERBB2 amplification testing for breast cancer is currently dominated by immunohistochemistry (a single-gene test); and present the development, by the author, of a semi-quantitative PCR test for ERBB2 amplification. I also show that estimating ERBB2 amplification from microarray copy-number analysis of the genome is possible. In chapter 4 I present a review of microarray comparison studies, and outline the case for careful and considered comparison of technologies when selecting a platform for use in a research study. Similar, indeed more stringent, care needs to be applied when selecting a platform for use in a clinical test. In chapter 5 I present co-authored work on the development of amplicon and exome methods for the detection and quantitation of somatic mutations in circulating tumour DNA, and demonstrate the impact this can have in understanding tumour heterogeneity and evolution during treatment. I also demonstrate how next-generation sequencing technologies may allow multiple genetic abnormalities to be analysed in a single test, and in low cellularity tumours and/or heterogenous cancers. Keywords: Genome, exome, transcriptome, amplicon, next-generation sequencing, differential gene expression, RNA-seq, ChIP-seq, microarray, ERBB2, companion diagnostic

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

Targeted next generation sequencing identifies clinically actionable mutations in patients with melanoma

Somatic sequencing of cancers has produced new insight into tumorigenesis, tumor heterogeneity, and disease progression, but the vast majority of genetic events identified are of indeterminate clinical significance. Here we describe a NextGen sequencing approach to fully analyze 248 genes, including all those of known clinical significance in melanoma. This strategy features solution capture of DNA followed by multiplexed, high-throughput sequencing, and was evaluated in 31 melanoma cell lines and 18 tumor tissues from patients with metastatic melanoma. Mutations in melanoma cell lines correlated with their sensitivity to corresponding small molecule inhibitors, confirming, for example, lapatinib sensitivity in ERBB4 mutant lines and identifying a novel activating mutation of BRAF. The latter event would not have been identified by clinical sequencing and was associated with responsiveness to a BRAF kinase inhibitor. This approach identified focal copy number changes of PTEN not found by standard methods, such as comparative genomic hybridization (CGH). Actionable mutations were found in 89% of the tumor tissues analyzed, 56% of which would not be identified by standard-of-care approaches. This work shows that targeted sequencing is an attractive approach for clinical use in melanoma

Prediction of "BRCAness" in breast cancer by array comparative genomic hybridization

Predicting the likelihood that an individual is a BRCA mutation carrier is the first step to genetic counseling, followed by germ-line mutation testing in many family cancer clinics. Individuals who have been diagnosed as BRCA mutation-positive are offered special medical care; however, clinical management in the case of a negative test result or an unclassified variant in BRCA1 or BRCA2 can be difficult. Since it is estimated that 15% of BRCA mutation carriers are missed by current diagnostics and assessment of the clinical significance of many unclassified variants is complex and time consuming, new strategies are emerging that are able to predict BRCA dysfunction based on molecular tumor information (BRCAness) rather than on family history. This thesis starts with reviewing the importance of BRCA status assessment, followed by the studies performed by SA Joosse in which array comparative genomic hybridization has been utilized for the prediction of the involvement of BRCA in tumorigenesis.Dutch Cancer Society (KWF)UBL - phd migration 201

Looking for factors predicting outcome of breast carcinoma using tissue microarrays

Mammacarcinoom is de meest voorkomende maligniteit onder westerse vrouwen. ‘Klassieke’ prognostische indicatoren zoals tumorgrootte, tumorgraad en de expressie van hormoonreceptoren (ER en PR) en de HER2/neu receptor zijn goede voorspellers van uitkomst in grote groepen patiënten met mammacarcinoom. De uitkomst van een individuele patiënt valt echter slecht te voorspellen met behulp van deze indicatoren. In de afgelopen jaren zijn vele microarray-technieken geïntroduceerd. Deze technieken maken het mogelijk de expressie van vele genen of eiwitten in een patiënt te onderzoeken. Een van deze microarray-technieken is de tissue microarray (TMA)-techniek. Met behulp van deze techniek kan tumormateriaal van maximaal 300 patiënten in een paraffineblokje verzameld worden. Dit maakt het mogelijk om snel en efficiënt de expressie van een eiwit in grote series patiënten te testen. Het doel van dit proefschrift, zoals in hoofdstuk 1 geformuleerd, was om de toepasbaarheid van de tissue microarray-techniek in de evaluatie van nieuwe prognostische en predictieve eiwitmarkers voor het mammacarcinoom te onderzoeken. In hoofdstuk 2 wordt een overzicht gegeven van de meest gebruikte microarray-technieken (oligo-/cDNA array, CGH arrays, PCR array en tissue microarrays). Deze technieken worden gebruikt om markers te identificeren die geassocieerd zijn met tumorprogressie (ontwikkeling van metastasen, een lokaal recidief of kanker-gerelateerd overlijden). Oligo-/cDNA arrays zijn de meest gebruikte microarrays en maken het mogelijk tot 50000 genen in een experiment te onderzoeken. Elk experiment levert een genprofiel op van een testsample. Deze technieken zijn zeer kostbaar (een Mammaprint experiment kost €2.675,-) en dusdanig complex dat het alleen maar mogelijk is deze technieken te gebruiken in relatief kleine studiegroepen. PCR- en TMAtechnieken zijn daarentegen goed toepasbaar in grote studiegroepen, maar hebben als nadeel dat er slechts één gen of eiwit per experiment onderzocht kan worden. Daarom kunnen de verschillende microarray-technieken heel goed complementair aan elkaar zijn: DNA- microarray-technieken kunnen goed gebruikt worden om hypotheses te genereren. PCR en TMA daarentegen zijn goede methodes om de belangrijkste uitkomsten van deze DNA- microarraystudies te valideren in grotere studiegroepen en deze te vertalen naar klinisch toepasbare testen