全外显子组和转录组测序发现了与转移和不良预后相关的基因表达模式

Shams Nassir, Miranda Yousif, Xing Li, Kevin Severson, Alysia Hughes, Jacob Kechter, Angelina Hwang, Blake Boudreaux, Puneet Bhullar, Nan Zhang, Duke Butterfield, Tao Ma, Ewoma Ogbaudu, Collin M Costello, Steven Nelson, David J DiCaudo, Aleksandar Sekulic, Christian Baum, Mark Pittelkow, Aaron R Mangold
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引用次数: 0

摘要

皮肤鳞状细胞癌(cSCC)是人类最常见的癌症之一,每年的致死人数与黑色素瘤不相上下。通过研究 cSCC 的突变和转录情况,发现了与疾病进展相关的驱动突变以及癌前病变进展过程中的关键通路激活。人们对转录变化与高危临床/组织病理学特征和预后的关系了解甚少。在这里,我们使用全外显子组和转录组测序技术对匹配样本进行了分期、结果分化的 cSCC 和相关临床病理风险因素的研究。外显子组分析确定了关键的驱动突变,包括TP53、CDKN2A、NOTCH1、SHC4、MIIP、CNOT1、C17orf66、LPHN22和TTC16,以及复制衰老、细胞对紫外线的反应、细胞-细胞粘附和细胞周期中驱动突变的通路富集。转录组分析确定了免疫信号/炎症、细胞周期通路、细胞外基质功能和染色质功能的通路富集。我们的综合分析确定了 183 个致癌关键基因,并将其用于开发 cSCC 的基因表达面板 (GEP) 模型。三个与结果相关的基因簇包括参与角质化、细胞分裂和新陈代谢的基因。我们发现 16 个基因可预测转移(风险评分≥ 9 Met & 风险评分 < 9 NoMet)。风险评分的 AUC 为 97.1%(95% CI:93.5% - 100%),灵敏度为 95.5%,特异度为 85.7%,总体准确率为 90%。我们选择了 11 个基因来生成总生存期(OS)风险评分。预测 OS 的 Harrell's C 统计量为 80.8%。在调整年龄、使用免疫抑制剂和转移状态后,风险评分每增加一个,死亡风险就增加 2.47(HR:2.47,95% CI:1.64-3.74;p<0.001)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Whole Exome and Transcriptome Sequencing of Stage-Matched, Outcome-Differentiated Cutaneous Squamous Cell Carcinoma Identifies Gene Expression Patterns Associated with Metastasis and Poor Outcomes
Cutaneous squamous cell carcinoma (cSCC) is one of the most common cancers in humans and kills as many people annually as melanoma. The mutational and transcriptional landscape of cSCC has identified driver mutations associated with disease progression as well as key pathway activation in the progression of pre-cancerous lesions. The understanding of the transcriptional changes with respect to high-risk clinical/histopathological features and outcome is poor. Here, we examine stage-matched, outcome-differentiated cSCC and associated clinicopathologic risk factors using whole exome and transcriptome sequencing on matched samples. Exome analysis identified key driver mutations including TP53, CDKN2A, NOTCH1, SHC4, MIIP, CNOT1, C17orf66, LPHN22, and TTC16 and pathway enrichment of driver mutations in replicative senescence, cellular response to UV, cell-cell adhesion, and cell cycle. Transcriptomic analysis identified pathway enrichment of immune signaling/inflammation, cell-cycle pathways, extracellular matrix function, and chromatin function. Our integrative analysis identified 183 critical genes in carcinogenesis and were used to develop a gene expression panel (GEP) model for cSCC. Three outcome-related gene clusters included those involved in keratinization, cell division, and metabolism. We found 16 genes were predictive of metastasis (Risk score ≥ 9 Met & Risk score < 9 NoMet). The Risk score has an AUC of 97.1% (95% CI: 93.5% - 100%), sensitivity 95.5%, specificity 85.7%, and overall accuracy of 90%. Eleven genes were chosen to generate the risk score for Overall Survival (OS). The Harrell’s C-statistic to predict OS is 80.8%. With each risk score increase, the risk of death increases by 2.47 (HR: 2.47, 95% CI: 1.64-3.74; p<0.001) after adjusting for age, immunosuppressant use, and metastasis status.
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