Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models.
Deep whole genome sequencing identifies recurrent genomic alterations in commonly used breast cancer cell lines and patient-derived xenograft models.
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DOI：
10.1186/s13058-022-01540-0
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发表时间：
2022-09-24
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乳腺癌细胞系（bcls）和患者来源的异种移植物（PDXs）是乳腺癌研究中最常用的模型。尽管这些模型被广泛使用，但它们的基因组测序是不完整的，以前的研究只集中在目标基因面板、全外显子组或浅全基因组测序上。深度全基因组测序是检测单核苷酸变异和索引、基因拷贝数和基因融合等结构事件最灵敏、最准确的方法。本文描述了使用Illumina X10平台对常用的BCCL和PDX模型进行深度全基因组测序（WGS），平均覆盖率约为60倍。与以前可用的测序数据相比，我们确定了新的基因组改变，包括碱基对分辨率的点突变和基因组重排。通过与公开可用的功能筛选数据的综合分析，我们注释了可能具有生物学意义的新基因组特征。CSMD1，以前被认为是多种癌症类型的肿瘤抑制基因，包括头颈癌、肺癌和乳腺癌，在50%的PDX模型中被发现缺失，这表明在侵袭性乳腺癌中起重要作用。我们的WGS数据为这些模型提供了全面的基因组测序资源。在线版本包含补充材料，可在10.1186/s13058-022-01540-0获得。
Breast cancer cell lines (BCCLs) and patient-derived xenografts (PDXs) are the most frequently used models in breast cancer research. Despite their widespread usage, genome sequencing of these models is incomplete, with previous studies only focusing on targeted gene panels, whole exome or shallow whole genome sequencing. Deep whole genome sequencing is the most sensitive and accurate method to detect single nucleotide variants and indels, gene copy number and structural events such as gene fusions. Here we describe deep whole genome sequencing (WGS) of commonly used BCCL and PDX models using the Illumina X10 platform with an average ~ 60 × coverage. We identify novel genomic alterations, including point mutations and genomic rearrangements at base-pair resolution, compared to previously available sequencing data. Through integrative analysis with publicly available functional screening data, we annotate new genomic features likely to be of biological significance. CSMD1, previously identified as a tumor suppressor gene in various cancer types, including head and neck, lung and breast cancers, has been identified with deletion in 50% of our PDX models, suggesting an important role in aggressive breast cancers. Our WGS data provides a comprehensive genome sequencing resource of these models. The online version contains supplementary material available at 10.1186/s13058-022-01540-0.
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影响因子：
64.8
作者：
Nik-Zainal S;Davies H;Staaf J;Ramakrishna M;Glodzik D;Zou X;Martincorena I;Alexandrov LB;Martin S;Wedge DC;Van Loo P;Ju YS;Smid M;Brinkman AB;Morganella S;Aure MR;Lingjærde OC;Langerød A;Ringnér M;Ahn SM;Boyault S;Brock JE;Broeks A;Butler A;Desmedt C;Dirix L;Dronov S;Fatima A;Foekens JA;Gerstung M;Hooijer GK;Jang SJ;Jones DR;Kim HY;King TA;Krishnamurthy S;Lee HJ;Lee JY;Li Y;McLaren S;Menzies A;Mustonen V;O'Meara S;Pauporté I;Pivot X;Purdie CA;Raine K;Ramakrishnan K;Rodríguez-González FG;Romieu G;Sieuwerts AM;Simpson PT;Shepherd R;Stebbings L;Stefansson OA;Teague J;Tommasi S;Treilleux I;Van den Eynden GG;Vermeulen P;Vincent-Salomon A;Yates L;Caldas C;van't Veer L;Tutt A;Knappskog S;Tan BK;Jonkers J;Borg Å;Ueno NT;Sotiriou C;Viari A;Futreal PA;Campbell PJ;Span PN;Van Laere S;Lakhani SR;Eyfjord JE;Thompson AM;Birney E;Stunnenberg HG;van de Vijver MJ;Martens JW;Børresen-Dale AL;Richardson AL;Kong G;Thomas G;Stratton MR
通讯作者：
Stratton MR
影响因子：
14.9
作者：
Gao Y;Shang S;Guo S;Li X;Zhou H;Liu H;Sun Y;Wang J;Wang P;Zhi H;Li X;Ning S;Zhang Y
通讯作者：
Zhang Y
影响因子：
64.8
作者：
Yu, Mamie;Selvaraj, Suresh K.;Neve, Richard M.
通讯作者：
Neve, Richard M.
影响因子：
50.3
作者：
Neve, Richard M.;Chin, Koei;Gray, Joe W.
通讯作者：
Gray, Joe W.
影响因子：
14.9
作者：
Quek XC;Thomson DW;Maag JL;Bartonicek N;Signal B;Clark MB;Gloss BS;Dinger ME
通讯作者：
Dinger ME