Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Clin Chem. 2020 Dec 1;66(12):1521-1530. doi: 10.1093/clinchem/hvaa221.
Patients with hematological malignancies (HMs) carry a wide range of chromosomal and molecular abnormalities that impact their prognosis and treatment. Since no current technique can detect all relevant abnormalities, technique(s) are chosen depending on the reason for referral, and abnormalities can be missed. We tested targeted transcriptome sequencing as a single platform to detect all relevant abnormalities and compared it to current techniques.
We performed RNA-sequencing of 1385 genes (TruSight RNA Pan-Cancer, Illumina) in bone marrow from 136 patients with a primary diagnosis of HM. We then applied machine learning to expression profile data to perform leukemia classification, a method we named RANKING. Gene fusions for all the genes in the panel were detected, and overexpression of the genes EVI1, CCND1, and BCL2 was quantified. Single nucleotide variants/indels were analyzed in acute myeloid leukemia (AML), myelodysplastic syndrome and patients with acute lymphoblastic leukemia (ALL) using a virtual myeloid (54 genes) or lymphoid panel (72 genes).
RANKING correctly predicted the leukemia classification of all AML and ALL samples and improved classification in 3 patients. Compared to current methods, only one variant was missed, c.2447A>T in KIT (RT-PCR at 10-4), and BCL2 overexpression was not seen due to a t(14; 18)(q32; q21) in 2% of the cells. Our RNA-sequencing method also identified 6 additional fusion genes and overexpression of CCND1 due to a t(11; 14)(q13; q32) in 2 samples.
Our combination of targeted RNA-sequencing and data analysis workflow can improve the detection of relevant variants, and expression patterns can assist in establishing HM classification.
患有血液系统恶性肿瘤（HMs）的患者存在广泛的染色体和分子异常，这些异常会影响他们的预后和治疗。由于目前没有一种技术可以检测到所有相关的异常，因此会根据转诊的原因选择技术，并且可能会遗漏异常。我们测试了靶向转录组测序作为一种单一平台来检测所有相关异常，并将其与当前技术进行了比较。
我们对 136 名初诊为 HM 患者的骨髓进行了 1385 个基因的 RNA 测序（Illumina 的 TruSight RNA Pan-Cancer）。然后，我们应用机器学习对表达谱数据进行白血病分类，我们将这种方法命名为 RANKING。对面板中的所有基因进行基因融合检测，并对 EVI1、CCND1 和 BCL2 基因的过表达进行定量。在急性髓系白血病（AML）、骨髓增生异常综合征和急性淋巴细胞白血病（ALL）患者中，使用虚拟髓系（54 个基因）或淋巴系（72 个基因）面板分析单核苷酸变异/插入缺失。
RANKING 正确预测了所有 AML 和 ALL 样本的白血病分类，并在 3 名患者中改善了分类。与当前方法相比，仅遗漏了一个变异，即 KIT 的 c.2447A>T（10-4 的 RT-PCR），由于 2%的细胞中存在 c.14; 18（q32; q21）的 BCL2 过表达，因此未观察到。我们的 RNA 测序方法还在 2 个样本中发现了 6 个额外的融合基因和 CCND1 的过表达，原因是 c.11; 14（q13; q32）的存在。
我们的靶向 RNA 测序和数据分析工作流程的组合可以提高相关变异的检测率，表达模式有助于建立 HM 分类。
