Characterizing mechanisms and consequences of intergenic transcription in human cancers

摘要 解释基因间序列在肿瘤发生中的作用仍然是一个活跃的研究领域 这对于了解生殖系和体细胞突变对疾病进展的总体影响至关重要。最近 研究表明，在不同的细胞类型中，转录和翻译在表面上的非编码区， 我们自己对癌细胞系和患者肿瘤的已发表数据集的分析显示， 基因间转录和翻译，特别是在或接近结构变异体（SV）。由于SV通常 融合基因和基因间区域在一起，它们是几个基因间转录的可能原因， 翻译事件，并确定这种联系将有助于建立一个重要的下游后果， 基因组的不稳定性，并表明基因间区域如何塑造肿瘤生物学。 在目标1中，我们将开发一种机器学习方法来估计SV存在的因果效应 并在大型公共数据库中计算这样的估计， 肿瘤基因组和转录组。这一目标将寻求解决两个突出的问题，在连接体细胞 基因表达的突变：（a）突变负荷和表达量之间的非线性关系， 以及（B）与可能使因果推断复杂化的较小体细胞变异相比，SV的相对稀少。 在目标2中，我们将探讨无义介导的衰变（NMD）在塑造观察到的 基因间转录物和融合转录物的表达水平，特别是如果融合转录物涉及至少一个 基因间伴侣在过去研究突变体转录本如何逃避NMD的基础上，我们将确定 这种预测是否可以解释观察到的SV连锁或独立的基因间 不同样本的转录本。通过探讨NMD的作用，我们将提供新的见解，如何基因间和 SV产生的转录物持续存在于肿瘤转录组中。 最后，在目标3中，我们将分析SV连锁和独立的基因间转录物的景观， 已发表的数据集，其中患者接受PD-1或CTLA-4检查点抑制剂治疗。我们将确定 基因间转录负担的增加是否与免疫浸润的增加有关， 炎症途径激活以及产生基因间转录物的SV是否与改善的 临床获益。我们还将测试预测逃避NMD的转录本的表达水平是否也 与检查点抑制后增加的免疫应答相关。通过分析免疫治疗数据， 我们可以确定是否SV连锁或独立的基因间转录作为一个有用的相关性， 改善临床反应。 总之，本研究将开发计算方法来检验基因间 转录物的产生和维持以及它们是否可用于免疫治疗。这项研究将 对肿瘤基因组中基因间改变的原因和后果提供了新的见解。

Abstract Interpreting the role of intergenic sequences in tumor development remains an active area of research critical to understand the overall influence of germline and somatic mutations on disease progression. Recent studies have shown transcription and translation at ostensibly non-coding regions in different cell types, and our own analysis of published datasets across cancer cell lines and patient tumors have revealed extensive intergenic transcription and translation, particularly at or near structural variants (SVs). Given that SVs often fuse genic and intergenic regions together, they are a plausible cause for several intergenic transcription and translation events, and identifying such links would help establish a significant downstream consequence of genomic instability and indicate how intergenic regions could shape tumor biology. In Aim 1, we will develop a machine learning method to estimate the causal effect of SV presence within a locus upon local intergenic transcription and compute such estimates across large public databases of tumor genomes and transcriptomes. This aim will seek to address two outstanding problems in linking somatic mutations to gene expression: (a) non-linear relationships between mutational load and expression magnitude, and (b) the relative rarity of SVs compared to smaller somatic variation that can complicate causal inference. In Aim 2, we will explore the role of nonsense mediated decay (NMD) in shaping the observed expression levels of intergenic and fusion transcripts, particularly if the fusion transcript involves at least one intergenic partner. Building on past studies studying how mutant transcripts evade NMD, we will determine whether such predictions can explain observed variation in expression of SV-linked or standalone intergenic transcripts across samples. By exploring the role of NMD, we will provide new insights into how intergenic and SV-generated transcripts persist within the tumor transcriptome. Finally, in Aim 3, we will analyze the landscape of SV-linked and standalone intergenic transcripts from published datasets in which patients are treated with PD-1 or CTLA-4 checkpoint inhibitors. We will determine whether an increased burden of intergenic transcription is associated with elevated immune infiltration and inflammatory pathway activation and whether SVs that generate intergenic transcripts are linked to improved clinical benefit. We will also test whether expression levels of transcripts predicted to evade NMD are also associated with increased immune responses upon checkpoint inhibition. By analyzing immunotherapy data, we can establish whether SV-linked or standalone intergenic transcription serves as a useful correlate for improved clinical responses. In summary, this study will develop computational approaches to test hypotheses on how intergenic transcripts are generated and maintained and whether they are useful for immunotherapy. This research will contribute new insights into the causes and consequences of intergenic alterations in the tumor genome.