PDF(Pubmed)
Abstract：
Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging.
To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes.
We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities.
Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.
To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes.
We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities.
Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.
摘要：
背景：罕见的致癌驱动事件，特别是影响驱动基因的表达或剪接，被怀疑在很大程度上导致了血液系统恶性肿瘤的巨大异质性。然而,他们的身份仍然具有挑战性。
方法：要解决此问题，我们收集了迄今为止最大的数据集,对来自3760例患者的24个疾病实体的血液系统恶性肿瘤进行了匹配的全基因组测序和总RNA测序.利用我们的数据集大小，我们专注于发现罕见的监管异常。因此,我们使用工作流程DROP（RNA异常值检测管道）和AbSplice的扩展来调用表达和剪接异常值，一种变异效应预测因子，可识别导致异常剪接的遗传变异。接下来，我们训练了一个整合这些结果的机器学习模型，以优先考虑新的候选疾病特异性驱动基因。
结果：我们发现了七个异常表达基因的中位数，两个剪接离群基因，和每个样本两个罕见的影响剪接的变体。每个类别都显示出对已经充分表征的驱动基因的显着富集，在超过五个样本的基因中，比值比超过三个。根据保留的数据，我们的综合建模显著优于仅基于基因组数据的建模,并揭示了有前景的新型候选驱动基因.值得注意的是,我们发现低密度脂蛋白受体LRP1B转录物的截短形式在大约一半的毛细胞白血病变体（HCL-V）样品中异常过表达，在较小程度上，密切相关的B细胞肿瘤。这个观察,这在一个独立的队列中得到了证实，提示LRP1B是HCL-V亚类的新标记物，LRP1B在这些稀有实体中的功能作用尚未报道。
结论：总而言之，我们对24个血液恶性肿瘤实体的表达和剪接异常值的普查以及伴随的计算工作流程构成了独特的资源,可以加深我们对血液肿瘤中罕见致癌事件的理解.
方法：要解决此问题，我们收集了迄今为止最大的数据集,对来自3760例患者的24个疾病实体的血液系统恶性肿瘤进行了匹配的全基因组测序和总RNA测序.利用我们的数据集大小，我们专注于发现罕见的监管异常。因此,我们使用工作流程DROP（RNA异常值检测管道）和AbSplice的扩展来调用表达和剪接异常值，一种变异效应预测因子，可识别导致异常剪接的遗传变异。接下来，我们训练了一个整合这些结果的机器学习模型，以优先考虑新的候选疾病特异性驱动基因。
结果：我们发现了七个异常表达基因的中位数，两个剪接离群基因，和每个样本两个罕见的影响剪接的变体。每个类别都显示出对已经充分表征的驱动基因的显着富集，在超过五个样本的基因中，比值比超过三个。根据保留的数据，我们的综合建模显著优于仅基于基因组数据的建模,并揭示了有前景的新型候选驱动基因.值得注意的是,我们发现低密度脂蛋白受体LRP1B转录物的截短形式在大约一半的毛细胞白血病变体（HCL-V）样品中异常过表达，在较小程度上，密切相关的B细胞肿瘤。这个观察,这在一个独立的队列中得到了证实，提示LRP1B是HCL-V亚类的新标记物，LRP1B在这些稀有实体中的功能作用尚未报道。
结论：总而言之，我们对24个血液恶性肿瘤实体的表达和剪接异常值的普查以及伴随的计算工作流程构成了独特的资源,可以加深我们对血液肿瘤中罕见致癌事件的理解.
