Wrzeszczynski Kazimierz O, Varadan Vinay, Kamalakaran Sitharthan, Levine Douglas A, Dimitrova Nevenka, Lucito Robert
Bioinformatics and Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Methods Mol Biol. 2013;1049:35-51. doi: 10.1007/978-1-62703-547-7_4.
The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to discover genes critical to the development, progression, and therapeutic resistance of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatics analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We identify changes in DNA methylation and expression specifically for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic gene function from integrative analysis of three modalities: copy number variation, DNA methylation, and gene expression. Our method (1) calculates the extent of genomic and epigenetic alterations of defined tumor suppressor and oncogenic features for the functional prediction of significant ovarian cancer gene candidates and (2) identifies the functional activity or inactivity of known tumor suppressors and oncogenes in ovarian cancer. We applied our protocol on 42 primary serous ovarian cancer samples using MOMA-ROMA representational array assays. Additionally, we provide the basis for incorporating epigenetic profiles of ovarian tumors for the purposes of platinum-free survival prediction in the context of TCGA data.
从原发性肿瘤细胞中鉴定基因和表观遗传改变已成为发现对癌症发生、发展和治疗耐药性至关重要的基因的常用方法。我们试图鉴定那些对肿瘤内基因功能影响最大的基因和表观遗传异常。首先，我们对覆盖卵巢癌肿瘤细胞遗传图谱的拷贝数变异（CNV）和DNA甲基化进行生物信息学分析。我们特别关注拷贝数变异，将其作为预测卵巢肿瘤中肿瘤抑制基因和癌基因的基础基因组特性。我们专门鉴定所有扩增和缺失基因的DNA甲基化和表达变化。我们通过对拷贝数变异、DNA甲基化和基因表达这三种模式的综合分析，从统计学上定义肿瘤抑制基因和致癌基因的功能。我们的方法（1）计算定义的肿瘤抑制和致癌特征的基因组和表观遗传改变程度，用于功能性预测重要的卵巢癌基因候选物，（2）鉴定卵巢癌中已知肿瘤抑制基因和癌基因的功能活性或无活性。我们使用MOMA - ROMA代表性阵列分析方法，对42例原发性浆液性卵巢癌样本应用了我们的方案。此外，我们还提供了在TCGA数据背景下，纳入卵巢肿瘤表观遗传图谱以预测无铂生存期的依据。
