Integrative analysis of multi-omics data to identify and characterize long noncoding RNA-derived fusions in pediatric cancer

癌症仍然是美国儿童因婴儿期疾病死亡的主要原因。在……里面 与有许多基因突变的成人癌症相比，大多数儿童癌症的基因突变很少。相反， 最近的研究表明，融合RNA及其编码的蛋白质可能会导致儿童肿瘤的发生。 融合RNA是由两个基因的外显子产生的。随着儿童时期融合癌蛋白的推出 癌症联盟，更多的融合蛋白正在被发现和研究。然而，完全理解 儿科癌症的机制仍然难以捉摸，主要是由于三个未解决的挑战。第一，当前 研究主要集中在信使核糖核酸衍生的融合蛋白上，还没有探索由长非编码的核糖核酸衍生的融合蛋白 儿童癌症中的融合转录本(LNC-Fusion)及其编码蛋白；尽管LNC-Fusion已被 据报道，在成人癌症中调节抗肿瘤免疫。长非编码RNA(LncRNAs)是一种长转录本 至少有200个核苷酸不能编码蛋白质。LncRNAs的数量远远超过mRNAs，并发挥着关键作用 在各种癌症中。因此，全面研究儿童癌症的发病机制是不可能的。 而不会将融合蛋白的研究扩大到包括lncRNA-融合。第二，现有的LNC-融合检测 方法不能探索从新的lncRNA衍生的lnc融合。由于疾病的高度特异性，大多数 儿科癌症中的lncRNAs还没有被注解。第三，可以形成融合RNA，包括lnc-融合。 通过其他机制，如染色体重排或异常剪接事件。这些替代方案 机制使对遗传机制的理解复杂化，从而使治疗复杂化。 来自各种共同基金来源的大量多组学数据使我们能够解决这些问题 儿科癌症的挑战。在此之前，我们已经开发了计算方法来识别和表征 与人类疾病和发育有关的IncRNA。为了发现儿童癌症的分子驱动因素，我们将扩展 我们以前对lncRNAs的研究，以从RNA测序数据中识别lnc融合(目标1)。我们将进一步 使用综合方法确定lnc融合的潜在功能和形成机制(目标2)。 机器学习算法将被用来识别LNC融合作为假定的生物标记和预后生物标记 在儿科癌症方面。 这项研究将集中在神经母细胞瘤和髓系恶性肿瘤上，因为这些儿童癌症 Gabriella Miller Kids First数据集中的大量RNA测序和全基因组测序数据。 总而言之，我们将发现儿童癌症中的融合，开发计算方法，并 框架广泛适用于现有和未来的RNA测序数据集。这项研究将提高实用性。 三个选定的共同基金数据集(儿童第一、GTEx和4DNucleome)和两个外部数据库 (Geo和TCGA)。

Cancer remains the leading cause of death by disease past infancy among children in the United States. In contrast to adult cancer with many genetic mutations, most pediatric cancers have few genetic mutations. Instead, recent studies have shown that fusion RNAs and their encoded proteins may drive tumorigenesis in children. Fusion RNAs are generated by exons from two genes. With the launch of the Fusion Oncoproteins in Childhood Cancers Consortium, more fusion proteins are being found and studied. However, a complete understanding of the mechanisms in pediatric cancer remains elusive, mainly due to three unsolved challenges. First, current studies have focused on mRNA-derived fusion proteins and have not explored long noncoding RNA-derived fusion transcripts (lnc-fusions) and their encoded proteins in pediatric cancer; although lnc-fusions have been reported in adult cancer to regulate anti-tumor immunity. long noncoding RNAs (lncRNAs) are long transcripts of at least 200 nucleotides that cannot encode protein. lncRNAs largely outnumber mRNAs and play critical roles in various cancers. Therefore, a complete investigation of mechanisms driving pediatric cancer is not possible without expanding the study of fusion proteins to include lncRNA-fusions. Second, existing lnc-fusion detection methods cannot explore lnc-fusions that are derived from novel lncRNAs. Due to high disease-specificity, most lncRNAs have not been annotated in pediatric cancer. Third, fusion RNAs, including lnc-fusions, may be formed by alternative mechanisms, such as chromosome rearrangement or aberrant splicing events. These alternative mechanisms complicate the understanding of genetic mechanisms and thus treatment. The large amount of multi-omics data from various Common Fund sources enables us to address these challenges in pediatric cancer. Previously, we had developed computational methods to identify and characterize lncRNAs for human diseases and development. To discover molecular drivers in pediatric cancer, we will extend our previous studies of lncRNAs to identify lnc-fusions from RNA sequencing data (Aim 1). We will further determine the potential functions and formation mechanisms of lnc-fusions using integrative methods (Aim 2). Machine learning algorithms will be used to identify lnc-fusions as putative biomarkers and prognostic biomarkers in pediatric cancers. This study will focus on neuroblastoma and myeloid malignancies since these pediatric cancers have large cohorts of RNA sequencing and whole-genome sequencing data in the Gabriella Miller Kids First Dataset. In summary, we will discover lnc-fusions in pediatric cancers, develop computational methods and frameworks broadly applicable to existing and future RNA sequencing datasets. This study will improve the utility of three selected Common Fund datasets (Kids First, GTEx and 4DNucleome), and two external databases (GEO and TCGA).