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Genomic Alterations Identified by Array Comparative Genomic Hybridization

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BMC Cancer BioMed Central Research article Open Access Genomic alterations identified by array comparative genomic hybridization as prognostic markers in tamoxifen-treated estrogen receptor-positive breast cancer Wonshik Han1, Mi-Ryung Han2, Jason Jongho Kang3, Ji-Yeon Bae2, Ji Hyun Lee3, Young Ju Bae3, Jeong Eon Lee1, Hyuk-Jae Shin1, Ki-Tae Hwang1, Sung-Eun Hwang1, Sung-Won Kim1 and Dong-Young Noh*1,2 Address: 1Department of Surgery, Seoul National University College of Medicine, Seoul, Korea, 2Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea and 3Macrogen Inc., Seoul, Korea Email: Wonshik Han - [email protected]; Mi-Ryung Han - [email protected]; Jason Jongho Kang - [email protected]; Ji- Yeon Bae - [email protected]; Ji Hyun Lee - [email protected]; Young Ju Bae - [email protected]; Jeong Eon Lee - [email protected]; Hyuk-Jae Shin - [email protected]; Ki-Tae Hwang - [email protected]; Sung- Eun Hwang - [email protected]; Sung-Won Kim - [email protected]; Dong-Young Noh* - [email protected] * Corresponding author Published: 12 April 2006 Received: 10 November 2005 Accepted: 12 April 2006 BMC Cancer2006, 6:92 doi:10.1186/1471-2407-6-92 This article is available from: http://www.biomedcentral.com/1471-2407/6/92 © 2006Han et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: A considerable proportion of estrogen receptor (ER)-positive breast cancer recurs despite tamoxifen treatment, which is a serious problem commonly encountered in clinical practice. We tried to find novel prognostic markers in this subtype of breast cancer. Methods: We performed array comparative genomic hybridization (CGH) with 1,440 human bacterial artificial chromosome (BAC) clones to assess copy number changes in 28 fresh-frozen ER- positive breast cancer tissues. All of the patients included had received at least 1 year of tamoxifen treatment. Nine patients had distant recurrence within 5 years (Recurrence group) of diagnosis and 19 patients were alive without disease at least 5 years after diagnosis (Non-recurrence group). Results: Potential prognostic variables were comparable between the two groups. In an unsupervised clustering analysis, samples from each group were well separated. The most common regions of gain in all samples were 1q32.1, 17q23.3, 8q24.11, 17q12-q21.1, and 8p11.21, and the most common regions of loss were 6q14.1-q16.3, 11q21-q24.3, and 13q13.2-q14.3, as called by CGH-Explorer software. The average frequency of copy number changes was similar between the two groups. The most significant chromosomal alterations found more often in the Recurrence group using two different statistical methods were loss of 11p15.5-p15.4, 1p36.33, 11q13.1, and 11p11.2 (adjusted p values <0.001). In subgroup analysis according to lymph node status, loss of 11p15 and 1p36 were found more often in Recurrence group with borderline significance within the lymph node positive patients (adjusted p = 0.052). Conclusion: Our array CGH analysis with BAC clones could detect various genomic alterations in ER-positive breast cancers, and Recurrence group samples showed a significantly different pattern of DNA copy number changes than did Non-recurrence group samples. Page 1 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:92 http://www.biomedcentral.com/1471-2407/6/92 Background [10-14]. Similar to chromosomal CGH, array CGH com- The incidence of breast cancer has been rapidly increasing pares the abundance of specific genomic sequences in in Korea and it has been the most frequent malignancy in whole-tumor DNA relative to normal reference genomes. Korean women since 2002 [1]. Breast cancer is a highly Array CGH can provide higher resolution than conven- heterogeneous disease both histologically and molecu- tional CGH with more accurate mapping of regions that larly, and hormone receptor-positive and -negative contain oncogenes or tumor suppressor genes [15]. With tumors are quite distinct biologically. Recent gene expres- more and more array CGH data emerging, there is a need sion profiling has identified hormone receptors as a fun- for efficient algorithms that automatically select regions of damental parameter for distinguishing breast cancers, gains and losses. Recently, various software items have suggesting a molecular difference according to hormone been released to make this complex analysis possible [16- receptor status [2]. More than 50% of breast cancer cases 21]. are estrogen-dependent, and treatment with estrogen antagonists that inhibit estrogen receptor (ER) action, par- In this study, we used array CGH to assess DNA copy ticularly tamoxifen, has contributed to a dramatic reduc- number changes in 28 fresh-frozen ER-positive breast can- tion in breast cancer mortality. However, a substantial cer tissue samples. A program for array CGH data analysis, proportion of patients expressing ER either fail to respond the CGH-Explorer and Analysis of Copy Error (ACE) algo- initially or become progressively resistant to endocrine rithm by Lingjærde et al. [20], was used for calling gains therapies [3]. Thus, it would be ideal to predict therapeu- and losses. The purpose of this study was to elucidate tic efficacy for each patient before treatment is initiated. whether DNA copy number changes in the primary tumor can predict a patient's prognosis and tamoxifen respon- Genetic and epigenetic alterations are important steps in siveness in ER-positive breast cancer and to identify the the development of malignancies and may contribute to corresponding chromosomal regions and genes. disease progression during treatment. Similarly, genetic alterations may play a role in the development of Methods tamoxifen resistance [4]. Searches for genes exhibiting Patients and tumor specimens altered expression in resistant breast cancer cells have A total of 28 primary invasive breast cancer tissues been performed using differential display, Serial Analysis selected from the frozen tissue archives in the Cancer of Gene Expression (SAGE), Comparative Genomic Research Institute, Seoul National University, were used Hybridization (CGH), and expression microarray, and in this study. All tumors were excised between November several marker genes have been identified using these 1996 and February 2001 and were histopathologically techniques [5-9]. However, the exact molecular mecha- confirmed as invasive ductal carcinoma. No in situ cancers nism, other than ER expression, underlying tamoxifen were included. This study was conducted under the response and resistance is not yet understood. approval of the Institutional Review Board of Seoul National University Hospital. Informed consent was Array CGH has been used to localize copy number obtained from all participants prior to surgery. All changes associated with human breast and other cancers patients received tamoxifen as an adjuvant endocrine Table 1: Clinicopathological characteristics of 28 patients with estrogen receptor-positive invasive ductal carcinoma Characteristic Recurrence group N (%) Non-recurrence group N (%) p value Age (range) 41.0 (24–62) 43.1 (29–65) 0.576 T stage T1 2 (22.2) 2 (10.5) T2 7 (77.8) 17 (89.5) 0.409 Lymph node metastasis Negative 2 (22.2) 6 (31.6) Positive 7 (77.8) 13 (68.4) 0.609 Histological grade 1–2 4 (44.4) 11 (57.9) 3 5 (55.6) 8 (42.1) 0.505 Nuclear grade 1–2 4 (44.4) 7 (36.8) 3 5 (55.6) 12 (63.2) 0.700 PR Positive 6 (66.7) 14 (73.7) Negative 3 (33.3) 5 (26.3) 0.701 Page 2 of 13 (page number not for citation purposes) BMC Cancer 2006, 6:92 http://www.biomedcentral.com/1471-2407/6/92 Table 2: Most frequent genomic aberrations (>20%) in 28 estrogen receptor-positive invasive ductal carcinomas Gain (core*) Frequency (%)† Loss (core) Frequency (%) 1q21.1-q44 (q32.1) 50.0–71.4 6q11.1-q27 (q14.1-16.3) 28.6–42.9 17q23.2-q25.3 (q23.3) 57.1–67.9 11q13.5-q24.3 (q21-24.3) 21.4–42.9 8q11.21-q24.3 (q24.11) 42.9–64.3 13q12.11-q33.3 (q13.2-14.3) 25.0-39.3 17q12-q22 (q12-21.1) 35.7–53.6 1p32.3-p13.3 (p22.3-21.1) 21.4–32.1 8p12-p11.1 (p11.21) 35.7–42.9 4q12-q35.2 (q13.1-34.3) 21.4–32.1 19p13.3-p13.11 (p13.12-13.11) 35.7–39.3 22q11.21-q12.3 (q11.21) 25.0–32.1 19q12-q13.42 (q12-13.2) 35.7–39.3 3p21.1-p13 (p14.3-14.1) 21.4–28.6 20q11.21-q13.33 (q13.13-13.32) 25.0–39.3 4p15.32-4p12 28.6 5p15.33 35.7 6p12.3-p11.2 (p12.3) 25.0–28.6 16p13.3-p11.2 35.7 9p24.3-p21.2 (p22.3-21.2) 21.4–28.6 3q24-q29 (q29) 21.4–32.1 5q21.2-q21.3 25.0 22q11.21-q13.33 (q11.21) 21.4–32.1 8p23.2-p21.1 (8p22-21.1) 21.4-25.0 1p36.33-p35.3 (1p36.33) 21.4–28.6 11p15.5-p15.2 (p15.4-15.2) 21.4-25.0 9q33.3-q34.3 (q34.11-34.3) 25.0–28.6 11p11.2-p11.12 25.0 20p11.22-p11.21 (p11.21) 25.0–28.6 14q24.3-q31.3 (q31.1-31.3) 21.4–25.0 11q13.3 25.0–28.6 10q23.31-q25.3 21.4 1p35.3 25.0 17p13.3-p11.2 21.4 5p15.32-p12 (p13.2-13.1) 21.4–25.0 22q13.2-13.33 21.4 13q34 25.0 16q24.3 25.0 3q12.1-q13.33 21.4 7q22.1 21.4 7q36.3 21.4 17p11.2 21.4 *Region of the highest frequency of alteration within a given range of gain or loss regions †Percentage of patients who have gain/loss.
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    bioRxiv preprint doi: https://doi.org/10.1101/2020.07.18.210328; this version posted April 12, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Dynamic regulatory module networks for inference of cell type-specific transcriptional networks Alireza Fotuhi Siahpirani1,2,+, Sara Knaack1+, Deborah Chasman1,8,+, Morten Seirup3,4, Rupa Sridharan1,5, Ron Stewart3, James Thomson3,5,6, and Sushmita Roy1,2,7* 1Wisconsin Institute for Discovery, University of Wisconsin-Madison 2Department of Computer Sciences, University of Wisconsin-Madison 3Morgridge Institute for Research 4Molecular and Environmental Toxicology Program, University of Wisconsin-Madison 5Department of Cell and Regenerative Biology, University of Wisconsin-Madison 6Department of Molecular, Cellular, & Developmental Biology, University of California Santa Barbara 7Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison 8Present address: Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Wisconsin-Madison +These authors contributed equally. *To whom correspondence should be addressed. 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.18.210328; this version posted April 12, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract Multi-omic datasets with parallel transcriptomic and epigenomic measurements across time or cell types are becoming increasingly common.
  • Statistical and Bioinformatic Analysis of Hemimethylation Patterns in Non-Small Cell Lung Cancer Shuying Sun1* , Austin Zane2, Carolyn Fulton3 and Jasmine Philipoom4

    Statistical and Bioinformatic Analysis of Hemimethylation Patterns in Non-Small Cell Lung Cancer Shuying Sun1* , Austin Zane2, Carolyn Fulton3 and Jasmine Philipoom4

    Sun et al. BMC Cancer (2021) 21:268 https://doi.org/10.1186/s12885-021-07990-7 RESEARCH ARTICLE Open Access Statistical and bioinformatic analysis of hemimethylation patterns in non-small cell lung cancer Shuying Sun1* , Austin Zane2, Carolyn Fulton3 and Jasmine Philipoom4 Abstract Background: DNA methylation is an epigenetic event involving the addition of a methyl-group to a cytosine- guanine base pair (i.e., CpG site). It is associated with different cancers. Our research focuses on studying non-small cell lung cancer hemimethylation, which refers to methylation occurring on only one of the two DNA strands. Many studies often assume that methylation occurs on both DNA strands at a CpG site. However, recent publications show the existence of hemimethylation and its significant impact. Therefore, it is important to identify cancer hemimethylation patterns. Methods: In this paper, we use the Wilcoxon signed rank test to identify hemimethylated CpG sites based on publicly available non-small cell lung cancer methylation sequencing data. We then identify two types of hemimethylated CpG clusters, regular and polarity clusters, and genes with large numbers of hemimethylated sites. Highly hemimethylated genes are then studied for their biological interactions using available bioinformatics tools. Results: In this paper, we have conducted the first-ever investigation of hemimethylation in lung cancer. Our results show that hemimethylation does exist in lung cells either as singletons or clusters. Most clusters contain only two or three CpG sites. Polarity clusters are much shorter than regular clusters and appear less frequently. The majority of clusters found in tumor samples have no overlap with clusters found in normal samples, and vice versa.