Characterizing mechanisms and consequences of intergenic transcription in human cancers

摘要 解释基因间序列在肿瘤发展中的作用仍然是一个活跃的研究领域 对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。近期 研究表明，在不同类型的细胞中，转录和翻译位于表面上的非编码区，并且 我们自己对已公布的癌细胞系和患者肿瘤数据集的分析揭示了广泛的 基因间转录和翻译，特别是在结构变异(SVS)或其附近。鉴于SVS经常 将基因和基因间区域融合在一起，它们可能是几个基因间转录和 翻译事件，并确定这样的链接将有助于建立重大的下游后果 基因组不稳定，并表明基因间隔区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值 肿瘤基因组和转录本。这一目标将寻求解决两个突出的问题，在联系躯体 突变对基因表达的影响：(A)突变负荷和表达大小之间的非线性关系， 以及(B)与较小的体细胞变异相比，SVS相对罕见，这可能会使因果推断复杂化。 在目标2中，我们将探索无意义介导的衰变(NMD)在塑造观察到的 基因间转录本和融合转录本的表达水平，特别是如果融合转录本至少涉及一个 基因间伴侣。基于过去研究突变转录本如何逃避NMD的研究，我们将确定 这种预测是否能解释观察到的SV连锁或独立基因间表达的变化 样本中的成绩单。通过探索NMD的作用，我们将提供关于基因间和 SV生成的转录本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本的情况。 已公布的数据集中，患者接受PD-1或CTLA-4检查点抑制剂治疗。我们将决定 基因间转录负荷的增加是否与免疫渗透增加和 炎症途径激活和产生基因间转录本的SVS是否与改善有关 临床益处。我们还将测试预测逃脱NMD的转录本的表达水平是否也 与检查点抑制时免疫反应增强有关。通过分析免疫治疗数据， 我们可以确定SV连锁的或单独的基因间转录是否可以作为有用的相关性 改善了临床反应。 总而言之，这项研究将开发计算方法来测试关于基因间如何相互作用的假设 生成和维护成绩单，以及它们是否对免疫治疗有用。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.