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Integrated Analysis of Copy Number Alteration and RNA Expression Profiles of Cancer Using a High-Resolution Whole-Genome Oligonucleotide Array

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EXPERIMENTAL and MOLECULAR MEDICINE, Vol. 41, No. 7, 462-470, July 2009 Integrated analysis of copy number alteration and RNA expression profiles of cancer using a high-resolution whole-genome oligonucleotide array Seung-Hyun Jung1,2, Seung-Hun Shin1,2, targeting 7 CNA regions, detected by oligoarray only, Seon-Hee Yim2, Hye-Sun Choi1,2, and one non-CNA region to validate the oligoarray CNA Sug-Hyung Lee3 and Yeun-Jun Chung1,2,4 detection. All qPCR results were consistent with the oligoarray-CGH results. When we explored the possi- 1Department of Microbiology bility of combined interpretation of both DNA copy 2Integrated Research Center for Genome Polymorphism number and RNA expression profiles, mean DNA copy 3Department of Pathology number and RNA expression levels showed a sig- The Catholic University of Korea nificant correlation. In conclusion, this 30k oli- Seoul 137-701, Korea goarray-CGH system can be a reasonable choice for 4Corresponding author: Tel, 82-2-590-1214; analyzing whole genome CNAs and RNA expression Fax, 82-2-596-8969; E-mail, [email protected] profiles at a lower cost. DOI 10.3858/emm.2009.41.7.051 Keywords: cell line, tumor; gene dosage; gene ex- Accepted 2 February 2009 pression profiling; oligonucleotide array sequence analysis Abbreviations: array-CGH, microarray-based comparative genomic hybridization; CNA, copy number alteration; oligoarray, oligonu- cleotide microarrays; qPCR, quantitative PCR; RAR, repeatedly altered regions Introduction One of the hallmarks of cancer is copy number alteration (CNA) across the entire genome. CNA Abstract can affect the development or progression of human malignancies by altering the expression of Recently, microarray-based comparative genomic hy- cancer-related genes. Recently, microarray-based bridization (array-CGH) has emerged as a very efficient comparative genomic hybridization (array-CGH) has technology with higher resolution for the genome-wide emerged as a very efficient technology with higher identification of copy number alterations (CNA). resolution for the genome-wide identification and Although CNAs are thought to affect gene expression, characterization of CNAs (Pinkel et al., 1998; Yim there is no platform currently available for the integrated and Chung, 2004). Improved resolution enables CNA-expression analysis. To achieve high-resolution the identification of submicroscopic chromosomal copy number analysis integrated with expression pro- alterations which are less likely to be detected by files, we established human 30k oligoarray-based ge- conventional cytogenetics tools. These small-sized nome-wide copy number analysis system and explored chromosomal changes including repeatedly altered the applicability of this system for integrated genome regions (RAR) in various cancers are thought to and transcriptome analysis using MDA-MB-231 cell line. contain cancer-related genes (Albertson and Pinkel, 2003; Kim et al., 2006). In addition, whole-genome We compared the CNAs detected by the oligoarray with approach can help to understand the contribution those detected by the 3k BAC array for validation. The of CNAs to tumorigenesis and tumor behaviors oligoarray identified the single copy difference more such as metastasis in comprehensive terms. accurately and sensitively than the BAC array. Seven- In spite of technical advancement, the precise teen CNAs detected by both platforms in MDA-MB-231 identification of CNAs is still challenging and needs such as gains of 5p15.33-13.1, 8q11.22-8q21.13, further improvement. Most currently available BAC 17p11.2, and losses of 1p32.3, 8p23.3-8p11.21, and arrays provide the resolution of ∼1 mb, which 9p21 were consistently identified in previous studies makes it difficult to detect smaller than mb-sized on breast cancer. There were 122 other small CNAs CNAs (Brennan et al., 2004; Carvalho et al., 2004). (mean size 1.79 mb) that were detected by oligoarray Oligonucleotide microarrays (oligoarray) provide only, not by BAC-array. We performed genomic qPCR higher resolution and sensitivity in detecting sub- Integrated copy number-expression analysis using oligoarray 463 microscopic CNAs. Especially long oligoarray (＞60 for expression analysis. In this platform, 60mer-sized mers) guarantees increased signal intensity thanks oligo probes covering over 17,500 genes are spo- to improved hybridization kinetics, which enables tted across the whole chromosome, which is more reliable copy number analysis (Ylstra et al., basically the same to long oligoarrays for whole 2006). genome copy number analysis (Brennan et al., Since CNAs are thought to affect expression of 2004; Carvalho et al., 2004). In this study, we genes by changing genomic dosages or gene struc- explored the applicability of this system for integrated tures, it is important to interpret copy number status genome and transcriptome analysis using cancer together with gene expression profiles (Lynch, cell line. 2002). For example, genomic amplifications of 8q24, 11q13 and 17q12 are commonly observed to be associated with overexpression of MYC, CCND1 Results and ERBB1 in diverse cancers (Croce, 2008). Recently, integrated genome and transcriptome Comparison of CNA detection ability between analysis results for breast and lung cancers have oligoarray and BAC array been reported, for which different platforms were used separately for copy number and expression Firstly, to see how accurately single copy difference profiling (Dehan et al., 2007; Vincent-Salomon et would be detected by two different platforms, we al., 2008). However, there is no array platform hybridized normal male and female genomic DNA currently available for the integrated CNA-expre- onto the two platforms, oligoarray (30k, 100 kb ssion analysis. resolution on average) and BAC array (3k, 1mb To achieve high-resolution copy number analysis resolution on average). Although both platforms integrated with analysis of expression on the same successfully identified the single copy difference of platform, we established human 30k oligoarray- X chromosome between male and female, oligo- based genome-wide copy number-expression ana- array detected the difference more accurately than lysis system using the platform originally designed BAC array. In oligoarray-CGH results, mean log2 Figure 1. Whole genome array-CGH profiles of the MDA-MB-231 using 2 different arrays. (A) Profile by 30k oligoarray and (B) Profile by 3k BAC array. X axis represents individual chromosomes and Y axis represents the signal intensity ratio (tumor/normal) in log2 ratio. Red dots represent the probes above ratio zero and green dots represent below zero. 464 Exp. Mol. Med. Vol. 41(7), 462-470, 2009 Table 1. Genomic alterations identified by both BAC and oligoarrays in MDA-MB-231. CNA Size Clone Chr Map position Cytoband Cancer-related genes ID (mb) G1 PH_hs_0040486- 2 61,203,137- 5.4 2p15-2p14 PH_hs_0010160 66,652,876 G2 PH_hs_0017517- 2 68,736,091- 16.9 2p14-2p11.2 ANTXR1, TIA1, TGFA, MOBKL1B, PH_hs_0027696 85,662,429 DOK1, TMSB10, KCMF1, CAPG G3 PH_hs_0015309- 5 258,178- 39.1 5p15.33-5p13.1 AHRR, TERT, SRD5A1, AMACR, PH_hs_0027712 39,418,807 SKP2, GDNF G4 PH_hs_0030540- 6 71,660,593- 4.3 6q13-6q14.1 DDX43, EEF1A1 PH_hs_0031517 76,019,546 G5 PH_hs_0013046- 8 50,571,933- 30.5 8q11.22-8q21.13 RB1CC1, SDCBP, ASPH, CRH, COPS5, PH_hs_0035433 81,077,958 SULF1, NCOA2 G6 PH_hs_0031183- 10 57,787,294- 5.8 10q21.1-10q21.2 CDC2 PH_hs_0003766 63,623,495 G7 PH_hs_0039119- 14 19,987,030- 41.1 14q11.2-14q23.1 APEX1, NDRG2, MMP14, PSME1, PSME2, PH_hs_0028475 61,092,886 PRKD1, NFKBIA, RPS29, PTGER2, LGALS3 G8 PH_hs_0038941- 16 45,061,443- 1.4 16q11.2-16q12.1 PH_hs_0019422 46,478,325 G9 PH_hs_0020121- 17 1,368,228- 18.2 17p13.3-17p11.2 SERPINF1, HIC1, ALOX15, PELP1, PLD2, PH_hs_0027026 19,584,383 GP1BA, PFN1, XAF1, ALOX12, GABARAP, CLDN7, TNFSF12, TNFSF13, TP53, ALOX15B, PER1, AURKB, NTN1, MAP2K4, FLCN, PEMT, LLGL1, MAPK7, ALDH3A1 G10 PH_hs_0032483- 20 29,538,943- 32.6 20q11.21-20q13.33 ID1, BCL2L1, DNMT3B, E2F1, ASIP, PH_hs_0039471 62,139,615 RBL1, SRC, BLCAP, TGM2, TOP1, PLCG1, HNF4A, WISP2, STK4, SLPI, WFDC2, UBE2C, MMP9, CD40, EYA2, SNAI1, CEBPB, ZNF217, BCAS1, AURKA, TFAP2C, BMP7, LAMA5, GATA5, BIRC7, PTK6, TNFRSF6B L1 PH_hs_0035564- 1 48,232,776- 4.6 1p33-1p32.3 CDKN2C PH_hs_0025172 52,907,016 L2 PH_hs_0002020- 2 50,001,041- 9.3 2p16.3-2p16.1 RTN4 PH_hs_0032953 59,330,250 L3 PH_hs_0030124- 8 398,441- 39.5 8p23.3-8p11.21 MCPH1, ANGPT2, DEFB4, CLDN23, PH_hs_0000100 39,904,628 PINX1, GATA4, CTSB, DLC1, TUSC3, FGF20, CNOT7, PDGFRL, MTUS1, NAT1, NAT2, LPL, LZTS1, KIAA1967, TNFRSF10B, TNFRSF10C, TNFRSF10D, TNFRSF10A, NKX3-1, STC1, PTK2B, CLU, SCARA3, PBK, FZD3, WRN, EIF4EBP1, BAG4, FGFR1, INDO L4 PH_hs_0024727- 9 3,237,130- 23.5 9p24.2-9p21.2 JAK2, RLN2, CD274, IFNB1, IFNA1, MTAP, PH_hs_0032012 26,831,705 CDKN2A, CDKN2B, TUSC1 L5 PH_hs_0016939- 23 1,465,314- 15.2 Xp22.33-Xp22.2 CD99, TMSB4X, FIGF, BMX, GRPR PH_hs_0026494 16,685,297 L6 PH_hs_0004181- 23 22,175,974- 8.3 Xp22.11-Xp21.2 SAT1 PH_hs_0015032 30,487,677 L7 PH_hs_0009133- 23 123,334,353- 10.1 Xq25-Xq26.2 GPC3 PH_hs_0024909 133,461,868 signal intensity ratio value of the chromosome X and that of autosomes was -0.0020 (SD=0.06). probes in the female-versus-male hybridization Female versus male array-CGH plots from both was 0.78 (Standard Deviation (SD)=0.29) and that platforms are available in the Supplemental Data of autosomes was 0.0027 (SD=0.21). However, in Figure S1. BAC array-CGH results, mean log2 ratio value of To examine the sensitivity of two arrays for the chromosome X probes
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    www.nature.com/scientificreports OPEN OncoScore: a novel, Internet- based tool to assess the oncogenic potential of genes Received: 06 July 2016 Rocco Piazza1, Daniele Ramazzotti2, Roberta Spinelli1, Alessandra Pirola3, Luca De Sano4, Accepted: 15 March 2017 Pierangelo Ferrari3, Vera Magistroni1, Nicoletta Cordani1, Nitesh Sharma5 & Published: 07 April 2017 Carlo Gambacorti-Passerini1 The complicated, evolving landscape of cancer mutations poses a formidable challenge to identify cancer genes among the large lists of mutations typically generated in NGS experiments. The ability to prioritize these variants is therefore of paramount importance. To address this issue we developed OncoScore, a text-mining tool that ranks genes according to their association with cancer, based on available biomedical literature. Receiver operating characteristic curve and the area under the curve (AUC) metrics on manually curated datasets confirmed the excellent discriminating capability of OncoScore (OncoScore cut-off threshold = 21.09; AUC = 90.3%, 95% CI: 88.1–92.5%), indicating that OncoScore provides useful results in cases where an efficient prioritization of cancer-associated genes is needed. The huge amount of data emerging from NGS projects is bringing a revolution in molecular medicine, leading to the discovery of a large number of new somatic alterations that are associated with the onset and/or progression of cancer. However, researchers are facing a formidable challenge in prioritizing cancer genes among the variants generated by NGS experiments. Despite the development of a significant number of tools devoted to cancer driver prediction, limited effort has been dedicated to tools able to generate a gene-centered Oncogenic Score based on the evidence already available in the scientific literature.
  • Characterisation of Isomirs in Stem Cells

    Characterisation of Isomirs in Stem Cells

    Characterisation of isomiRs in stem cells Geok Chin Tan Institute of Reproductive and Developmental Biology Department of Surgery and Cancer Faculty of Medicine Imperial College London Thesis submitted to Imperial College London for the degree of Doctor of Philosophy 1 Statement of Originality All experiments included in this thesis were performed by me unless otherwise stated in the text. 2 Copyright Statement ‘The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work’ 3 Acknowledgements I would like to thank my supervisor Dr Nicholas Dibb for giving me the opportunity to work in his lab and for all of his guidance and support throughout my PhD, without which this project would not have been possible. I am also very grateful to Dr Wei Cui for teaching me the technique of stem cell culture, her comments on my project related to stem cells and as a wonderful co-supervisor. I would like to also thank Professor Malcolm Parker for his supports and advise on academic and non-academic related subjects. Many thanks to Elcie Chan for the generation of all the stem cell libraries which forms the platform for my project. My sincere thanks also to Gunter Meister for supplying the Argonaute antibodies, Leandro Castellano for the help in the design of RNA sponges, Laki Buluwela for the pTRIPz lentiviral vector and last but not least Alywn Dart from Charlotte Bevan group for the prostate cancer cell lines.