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

在美国，癌症仍然是婴儿期后儿童死亡的主要原因。在 与具有许多基因突变的成人癌症相比，大多数儿科癌症几乎没有基因突变。相反地， 最近的研究表明，融合RNA及其编码的蛋白质可能导致儿童肿瘤的发生。 融合RNA由来自两个基因的外显子产生。随着儿童融合癌蛋白的推出， 癌症协会，更多的融合蛋白正在被发现和研究。然而，全面了解 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 研究集中于mRNA衍生的融合蛋白，并且尚未探索长的非编码RNA衍生的融合蛋白。 融合转录物（LNC-融合物）及其编码的蛋白质;尽管LNC-融合物已经被 据报道，在成人癌症中调节抗肿瘤免疫。长链非编码RNA（lncRNA）是长链转录物 至少有200个核苷酸不能编码蛋白质。lncRNA在很大程度上超过了mRNA， 在各种癌症中。因此，不可能对儿童癌症的驱动机制进行全面调查 而不将融合蛋白的研究扩展到包括lncRNA融合。第二，现有的LNC融合检测 这些方法不能探索来源于新lncRNA的lnc融合体。由于疾病特异性高，大多数 lncRNA尚未在儿科癌症中注释。第三，可以形成融合RNA，包括lnc-融合物， 通过替代机制，如染色体重排或异常剪接事件。这些替代 机制使对遗传机制的理解和治疗复杂化。 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。以前，我们已经开发了计算方法来识别和表征 lncRNA对人类疾病和发育的作用为了发现儿科癌症的分子驱动因素，我们将扩展 我们先前的lncRNA研究从RNA测序数据中鉴定lnc融合体（目的1）。我们将进一步 用综合方法确定lnc融合体的潜在功能和形成机制（目的2）。 机器学习算法将用于识别lnc融合作为推定的生物标志物和预后生物标志物 在儿科癌症中。 这项研究将集中在神经母细胞瘤和骨髓恶性肿瘤，因为这些儿科癌症有 Gabriella米勒儿童第一数据集中的大群RNA测序和全基因组测序数据。 总之，我们将在儿科癌症中发现lnc融合，开发计算方法， 这些框架广泛适用于现有和未来的RNA测序数据集。本研究将提高实用性 三个选定的共同基金数据集（Kids First、GTEx和4DNucleome）和两个外部数据库 (GEO 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).