Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia.
Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia.
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DOI：
10.1371/journal.pgen.1003997
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发表时间：
2013
期刊：
影响因子：
4.5
通讯作者：
Aerts S
中科院分区：
文献类型：
--
作者：
Atak ZK;Gianfelici V;Hulselmans G;De Keersmaecker K;Devasia AG;Geerdens E;Mentens N;Chiaretti S;Durinck K;Uyttebroeck A;Vandenberghe P;Wlodarska I;Cloos J;Foà R;Speleman F;Cools J;Aerts S
RNA-seq是一种很有前途的技术，可以对蛋白质编码基因进行重新测序，以识别单核苷酸变体（SNV），同时获得有关结构变异和基因表达扰动的信息。我们询问RNA-seq是否适用于检测T细胞急性淋巴细胞白血病（T-ALL）中的驱动突变。这些白血病是由基因融合、转录因子的过度表达以及癌基因和肿瘤抑制基因中的协同点突变的组合引起的。我们通过高覆盖率配对末端RNA-seq分析了31例T-ALL患者样本和18个T-ALL细胞系。首先，我们通过将结果与外显子组重测序数据进行比较来优化RNA-seq数据中SNV的检测。我们确定了已知的驱动基因与经常性的蛋白质改变的变化，以及几个新的候选人，包括H3 F3 A，PTK 2B和STAT 5 B。接下来，我们通过标准化和批量效应去除从RNA-seq数据中确定准确的基因表达水平，并使用这些将患者分类为T-ALL亚型。最后，我们检测到基因融合，其中一些可以解释关键驱动基因如TLX 1，PLAG 1，LMO 1或NKX 2 -1的过度表达;其他导致编码活化激酶（SSBP 2-FER和TPM 3-JAK 2）或涉及MLLT 10的新融合转录物。总之，我们提出了新的分析管道，用于变体调用，变体过滤和RNA-seq数据的表达标准化，并成功地将其应用于检测T-ALL中的易位，点突变，INDEL，外显子跳跃事件和表达扰动。对致癌过程中潜在的体细胞突变的探索是当今癌症研究的中心主题。包括扩增子再测序、外显子组再测序、全基因组再测序和SNP阵列的高通量基因组学方法已经有助于对跨癌症类型的驱动基因进行编目。到目前为止，通过RNA-seq的转录组测序主要用于检测融合基因，而很少有研究评估其用于SNP、INDEL、融合、基因表达变化和替代转录事件的组合检测的价值。在这里，我们将RNA-seq应用于49个T-ALL样本，并对生物信息学管道和过滤器进行严格评估，以识别每种类型的畸变。通过与外显子组重测序进行比较，并通过利用已知癌症驱动基因的目录，我们在T-ALL中鉴定了许多已知的和几种新的驱动基因。我们还确定了一种最佳的标准化策略，以获得准确的基因表达水平，并使用这些来识别表征不同T-ALL亚型的过表达转录因子。最后，通过PCR、克隆和体外细胞分析，我们发现了新的融合基因，这些基因在基因表达水平、致癌嵌合体和肿瘤抑制因子失活方面具有重要意义。总之，我们提出了T-ALL患者的第一个RNA-seq数据集，并确定了新的驱动事件。
RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations. We asked whether RNA-seq is suitable for the detection of driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). These leukemias are caused by a combination of gene fusions, over-expression of transcription factors and cooperative point mutations in oncogenes and tumor suppressor genes. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq. First, we optimized the detection of SNVs in RNA-seq data by comparing the results with exome re-sequencing data. We identified known driver genes with recurrent protein altering variations, as well as several new candidates including H3F3A, PTK2B, and STAT5B. Next, we determined accurate gene expression levels from the RNA-seq data through normalizations and batch effect removal, and used these to classify patients into T-ALL subtypes. Finally, we detected gene fusions, of which several can explain the over-expression of key driver genes such as TLX1, PLAG1, LMO1, or NKX2-1; and others result in novel fusion transcripts encoding activated kinases (SSBP2-FER and TPM3-JAK2) or involving MLLT10. In conclusion, we present novel analysis pipelines for variant calling, variant filtering, and expression normalization on RNA-seq data, and successfully applied these for the detection of translocations, point mutations, INDELs, exon-skipping events, and expression perturbations in T-ALL. The quest for somatic mutations underlying oncogenic processes is a central theme in today's cancer research. High-throughput genomics approaches including amplicon re-sequencing, exome re-sequencing, full genome re-sequencing, and SNP arrays have contributed to cataloguing driver genes across cancer types. Thus far transcriptome sequencing by RNA-seq has been mainly used for the detection of fusion genes, while few studies have assessed its value for the combined detection of SNPs, INDELs, fusions, gene expression changes, and alternative transcript events. Here we apply RNA-seq to 49 T-ALL samples and perform a critical assessment of the bioinformatics pipelines and filters to identify each type of aberration. By comparing to exome re-sequencing, and by exploiting the catalogues of known cancer drivers, we identified many known and several novel driver genes in T-ALL. We also determined an optimal normalization strategy to obtain accurate gene expression levels and used these to identify over-expressed transcription factors that characterize different T-ALL subtypes. Finally, by PCR, cloning, and in vitro cellular assays we uncover new fusion genes that have consequences at the level of gene expression, oncogenic chimaeras, and tumor suppressor inactivation. In conclusion, we present the first RNA-seq data set across T-ALL patients and identify new driver events.
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来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
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DOI：
10.1038/nrc1299
发表时间：
2004-03
期刊：
Nature reviews. Cancer
影响因子：
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作者：
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影响因子：
14.9
作者：
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通讯作者：
Feuk L
影响因子：
5.8
作者：
Gillis, Jesse;Pavlidis, Paul
通讯作者：
Pavlidis, Paul
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
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