arrayMap 2014: an updated cancer genome resource

Candidate targets of copy number deletion events across 17 cancer types

Genome variation is the direct cause of cancer and driver of its clonal evolution. While the impact of many point mutations can be evaluated through their modification of individual genomic elements, even a single copy number aberration (CNA) may encompass hundreds of genes and therefore pose challenges to untangle potentially complex functional effects. However, consistent, recurring and disease-specific patterns in the genome-wide CNA landscape imply that particular CNA may promote cancer-type-specific characteristics. Discerning essential cancer-promoting alterations from the inherent co-dependency in CNA would improve the understanding of mechanisms of CNA and provide new insights into cancer biology and potential therapeutic targets. Here we implement a model using segmental breakpoints to discover non-random gene coverage by copy number deletion (CND). With a diverse set of cancer types from multiple resources, this model identified common and cancer-type-specific oncogenes and tumor suppressor genes as well as cancer-promoting functional pathways. Confirmed by differential expression analysis of data from corresponding cancer types, the results show that for most cancer types, despite dissimilarity of their CND landscapes, similar canonical pathways are affected. In 25 analyses of 17 cancer types, we have identified 19 to 169 significant genes by copy deletion, including RB1, PTEN and CDKN2A as the most significantly deleted genes among all cancer types. We have also shown a shared dependence on core pathways for cancer progression in different cancers as well as cancer type separation by genome-wide significance scores. While this work provides a reference for gene specific significance in many cancers, it chiefly contributes a general framework to derive genome-wide significance and molecular insights in CND profiles with a potential for the analysis of rare cancer types as well as non-coding regions.

The Progenetix oncogenomic resource in 2021

In cancer, copy number aberrations (CNAs) represent a type of nearly ubiquitous and frequently extensive structural genome variations. To disentangle the molecular mechanisms underlying tumorigenesis as well as identify and characterize molecular subtypes, the comparative and meta-analysis of large genomic variant collections can be of immense importance. Over the last decades, cancer genomic profiling projects have resulted in a large amount of somatic genome variation profiles, however segregated in a multitude of individual studies and datasets. The Progenetix project, initiated in 2001, curates individual cancer CNA profiles and associated metadata from published oncogenomic studies and data repositories with the aim to empower integrative analyses spanning all different cancer biologies. During the last few years, the fields of genomics and cancer research have seen significant advancement in terms of molecular genetics technology, disease concepts, data standard harmonization as well as data availability, in an increasingly structured and systematic manner. For the Progenetix resource, continuous data integration, curation and maintenance have resulted in the most comprehensive representation of cancer genome CNA profiling data with 138 663 (including 115 357 tumor) copy number variation (CNV) profiles. In this article, we report a 4.5-fold increase in sample number since 2013, improvements in data quality, ontology representation with a CNV landscape summary over 51 distinctive National Cancer Institute Thesaurus cancer terms as well as updates in database schemas, and data access including new web front-end and programmatic data access. Database URL: progenetix.org.

Signatures of Discriminative Copy Number Aberrations in 31 Cancer Subtypes

Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies have been focused on the association of CNA to pre-selected "driver" genes with limited application to rare drivers and other genomic elements. In this study, we developed a bioinformatics pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification.

The Landscape of Somatic Copy Number Alterations in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide. Somatic copy number alterations (CNAs) play a significant role in the development of this lethal cancer. In this study, we present a meta-analysis of CNAs for a total of 1,395 HNSCC samples. Publicly available R packages and in-house scripts were used for genomic array data processing, including normalization, segmentation and CNA calling. We detected 125 regions of significant gains or losses using GISTIC algorithm and found several potential driver genes in these regions. The incidence of chromothripsis in HNSCC was estimated to be 6%, and the chromosome pulverization hotspot regions were detected. We determined 323 genomic locations significantly enriched for breakpoints, which indicate HNSCC-specific genomic instability regions. Unsupervised clustering of genome-wide CNA data revealed a sub-cluster predominantly composed of nasopharynx tumors and presented a large proportion of HPV-positive samples. These results will facilitate the discovery of therapeutic candidates and extend our molecular understanding of HNSCC.

Comprehensive identification and characterization of somatic copy number alterations in triple‑negative breast cancer

Triple‑negative breast cancer (TNBC) accounts for ~15% of all breast cancer diagnoses each year. Patients with TNBC tend to have a higher risk for early relapse and a worse prognosis. TNBC is characterized by extensive somatic copy number alterations (CNAs). However, the DNA CNA profile of TNBC remains to be extensively investigated. The present study assessed the genomic profile of CNAs in 201 TNBC samples, aiming to identify recurrent CNAs that may drive the pathogenesis of TNBC. In total, 123 regions of significant amplification and deletion were detected using the Genomic Identification of Significant Targets in Cancer algorithm, and potential driver genes for TNBC were identified. A total of 31 samples exhibited signs of chromothripsis and revealed chromosome pulverization hotspot regions. The present study further determined 199 genomic locations that were significantly enriched for breakpoints, which indicated TNBC‑specific genomic instability regions. Unsupervised hierarchical clustering of tumors resulted in three main subgroups that exhibited distinct CNA profiles, which may reveal the heterogeneity of molecular mechanisms in TNBC subgroups. These results will extend the molecular understanding of TNBC and will facilitate the discovery of therapeutic and diagnostic target candidates.

Chromothripsis Detection and Characterization Using the CTLPScanner Web Server

Accurate detection of chromothripsis event is important to study the mechanisms underlying this phenomenon. CTLPScanner ( http://cgma.scu.edu.cn/CTLPScanner/ ) is a web-based tool for identification and annotation of chromothripsis-like pattern (CTLP) in genomic array data. In this chapter, we illustrate the utility of CTLPScanner for screening chromosome pulverization regions and give interpretation of the results. The web interface offers a set of parameters and thresholds for customized screening. We also provide practical recommendations for effective chromothripsis detection. In addition to the user data processing module, CTLPScanner contains more than 50,000 preprocessed oncogenomic arrays, which allow users to explore the presence of chromothripsis signatures from public data resources.

Germline and Somatic NF1 Alterations Are Linked to Increased HER2 Expression in Breast Cancer

NF1 germline mutation predisposes to breast cancer. NF1 mutations have also been proposed as oncogenic drivers in sporadic breast cancers. To understand the genomic and histologic characteristics of these breast cancers, we analyzed the tumors with NF1 germline mutations and also examined the genomic and proteomic profiles of unselected tumors. Among 14 breast cancer specimens from 13 women affected with neurofibromatosis type 1 (NF1), 9 samples (NF + BrCa) underwent genomic copy number (CN) and targeted sequencing analysis. Mutations of NF1 were identified in two samples and TP53 were in three. No mutation was detected in ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, NBN, PALB2, PTEN, RAD50, and STK11 HER2 (ErbB2) overexpression was detected by IHC in 69.2% (9/13) of the tumors. CN gain/amplification of ERBB2 was detected in 4 of 9 with DNA analysis. By evaluating HER2 expression and NF1 alterations in unselected invasive breast cancers in TCGA datasets, we discovered that among samples with ERBB2 CN gain/amplification, the HER2 mRNA and protein expression were much more pronounced in NF1-mutated/deleted samples in comparison with NF1-unaltered samples. This finding suggests a synergistic interplay between these two genes, potentially driving the development of breast cancer harboring NF1 mutation and ERBB2 CN gain/amplification. NF1 gene loss of heterozygosity was observed in 4 of 9 NF + BrCa samples. CDK4 appeared to have more CN gain in NF + BrCa and exhibited increased mRNA expression in TCGA NF1--altered samples. Cancer Prev Res; 11(10); 655-64. ©2018 AACR.

segment_liftover : a Python tool to convert segments between genome assemblies

The process of assembling a species’ reference genome may be performed in a number of iterations, with subsequent genome assemblies differing in the coordinates of mapped elements. The conversion of genome coordinates between different assemblies is required for many integrative and comparative studies. While currently a number of bioinformatics tools are available to accomplish this task, most of them are tailored towards the conversion of single genome coordinates. When converting the boundary positions of segments spanning larger genome regions, segments may be mapped into smaller sub-segments if the original segment’s continuity is disrupted in the target assembly. Such a conversion may lead to a relevant degree of data loss in some circumstances such as copy number variation (CNV) analysis, where the quantitative representation of a genomic region takes precedence over base-specific accuracy. segment_liftover aims at continuity-preserving remapping of genome segments between assemblies and provides features such as approximate locus conversion, automated batch processing and comprehensive logging to facilitate processing of datasets containing large numbers of structural genome variation data.

Customized Array Comparative Genomic Hybridization Analysis of 25 Phosphatase-encoding Genes in Colorectal Cancer Tissues

BACKGROUND/AIM

Molecular mechanisms of alterations in protein tyrosine phosphatases (PTPs) genes in cancer have been previously described and include chromosomal aberrations, gene mutations, and epigenetic silencing. However, little is known about small intragenic gains and losses that may lead to either changes in expression or enzyme activity and even loss of protein function.

MATERIALS AND METHODS

The aim of this study was to investigate 25 phosphatase genes using customized array comparative genomic hybridization in 16 sporadic colorectal cancer tissues.

RESULTS

The analysis revealed two unique small alterations: of 2 kb in PTPN14 intron 1 and of 1 kb in PTPRJ intron 1. We also found gains and losses of whole PTPs gene sequences covered by large chromosome aberrations.

CONCLUSION

In our preliminary studies using high-resolution custom microarray we confirmed that PTPs are frequently subjected to whole-gene rearrangements in colorectal cancer, and we revealed that non-polymorphic intragenic changes are rare.

Cytogenetic Resources and Information

The main databases devoted stricto sensu to cancer cytogenetics are the "Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer" ( http://cgap.nci.nih.gov/Chromosomes/Mitelman ), the "Atlas of Genetics and Cytogenetics in Oncology and Haematology" ( http://atlasgeneticsoncology.org ), and COSMIC ( http://cancer.sanger.ac.uk/cosmic ).However, being a complex multistep process, cancer cytogenetics are broadened to "cytogenomics," with complementary resources on: general databases (nucleic acid and protein sequences databases; cartography browsers: GenBank, RefSeq, UCSC, Ensembl, UniProtKB, and Entrez Gene), cancer genomic portals associated with recent international integrated programs, such as TCGA or ICGC, other fusion genes databases, array CGH databases, copy number variation databases, and mutation databases. Other resources such as the International System for Human Cytogenomic Nomenclature (ISCN), the International Classification of Diseases for Oncology (ICD-O), and the Human Gene Nomenclature Database (HGNC) allow a common language.Data within the scientific/medical community should be freely available. However, most of the institutional stakeholders are now gradually disengaging, and well-known databases are forced to beg or to disappear (which may happen!).

CNARA: reliability assessment for genomic copy number profiles

Background

DNA copy number profiles from microarray and sequencing experiments sometimes contain wave artefacts which may be introduced during sample preparation and cannot be removed completely by existing preprocessing methods. Besides, large derivative log ratio spread (DLRS) of the probes correlating with poor DNA quality is sometimes observed in genome screening experiments and may lead to unreliable copy number profiles. Depending on the extent of these artefacts and the resulting misidentification of copy number alterations/variations (CNA/CNV), it may be desirable to exclude such samples from analyses or to adapt the downstream data analysis strategy accordingly.

Results

Here, we propose a method to distinguish reliable genomic copy number profiles from those containing heavy wave artefacts and/or large DLRS. We define four features that adequately summarize the copy number profiles for reliability assessment, and train a classifier on a dataset of 1522 copy number profiles from various microarray platforms. The method can be applied to predict the reliability of copy number profiles irrespective of the underlying microarray platform and may be adapted for those sequencing platforms from which copy number estimates could be computed as a piecewise constant signal. Further details can be found at https://github.com/baudisgroup/CNARA.

Conclusions

We have developed a method for the assessment of genomic copy number profiling data, and suggest to apply the method in addition to and after other state-of-the-art noise correction and quality control procedures. CNARA could be instrumental in improving the assessment of data used for genomic data mining experiments and support the reliable functional attribution of copy number aberrations especially in cancer research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3074-7) contains supplementary material, which is available to authorized users.

CTLPScanner: a web server for chromothripsis-like pattern detection

Chromothripsis is a recently observed phenomenon in cancer cells in which one or several chromosomes shatter into pieces with subsequent inaccurate reassembly and clonal propagation. This type of event generates a potentially vast number of mutations within a relatively short-time period, and has been considered as a new paradigm in cancer development. Despite recent advances, much work is still required to better understand the molecular mechanisms of this phenomenon, and thus an easy-to-use tool is in urgent need for automatically detecting and annotating chromothripsis. Here we present CTLPScanner, a web server for detection of chromothripsis-like pattern (CTLP) in genomic array data. The output interface presents intuitive graphical representations of detected chromosome pulverization region, as well as detailed results in table format. CTLPScanner also provides additional information for associated genes in chromothripsis region to help identify the potential candidates involved in tumorigenesis. To assist in performing meta-data analysis, we integrated over 50 000 pre-processed genomic arrays from The Cancer Genome Atlas and Gene Expression Omnibus into CTLPScanner. The server allows users to explore the presence of chromothripsis signatures from public data resources, without carrying out any local data processing. CTLPScanner is freely available at http://cgma.scu.edu.cn/CTLPScanner/.

The 2015 Nucleic Acids Research Database Issue and molecular biology database collection

The 2015 Nucleic Acids Research Database Issue contains 172 papers that include descriptions of 56 new molecular biology databases, and updates on 115 databases whose descriptions have been previously published in NAR or other journals. Following the classification that has been introduced last year in order to simplify navigation of the entire issue, these articles are divided into eight subject categories. This year's highlights include RNAcentral, an international community portal to various databases on noncoding RNA; ValidatorDB, a validation database for protein structures and their ligands; SASBDB, a primary repository for small-angle scattering data of various macromolecular complexes; MoonProt, a database of 'moonlighting' proteins, and two new databases of protein-protein and other macromolecular complexes, ComPPI and the Complex Portal. This issue also includes an unusually high number of cancer-related databases and other databases dedicated to genomic basics of disease and potential drugs and drug targets. The size of NAR online Molecular Biology Database Collection, http://www.oxfordjournals.org/nar/database/a/, remained approximately the same, following the addition of 74 new resources and removal of 77 obsolete web sites. The entire Database Issue is freely available online on the Nucleic Acids Research web site (http://nar.oxfordjournals.org/).

Interactive analysis of large cancer copy number studies with Copy Number Explorer

Copy number abnormalities (CNAs) such as somatically-acquired chromosomal deletions and duplications drive the development of cancer. As individual tumor genomes can contain tens or even hundreds of large and/or focal CNAs, a major difficulty is differentiating between important, recurrent pathogenic changes and benign changes unrelated to the subject's phenotype. Here we present Copy Number Explorer, an interactive tool for mining large copy number datasets. Copy Number Explorer facilitates rapid visual and statistical identification of recurrent regions of gain or loss, identifies the genes most likely to drive CNA formation using the cghMCR method and identifies recurrently broken genes that may be disrupted or fused. The software also allows users to identify recurrent CNA regions that may be associated with differential survival.

AVAILABILITY AND IMPLEMENTATION

Copy Number Explorer is available under the GNU public license (GPL-3). Source code is available at: https://sourceforge.net/projects/copynumberexplorer/

CONTACT

scott.newman@emory.edu.