LBPO.PS01 · 人群科学 · Late-Breaking

H3K27ac-HiChIP变异到基因映射证实了癌症驱动基因和致癌信号通路在黑色素瘤风险中的重要性

H3K27ac-HiChIP variant-to-gene mapping confirms the importance of cancer drivers and oncogenic signaling pathways in melanoma risk

编号 LB384 展板 14 时间 4/21 02:00–05:00 区域 Section 55 主讲 Rohit Thakur, BS;PhD

分会场 Late-Breaking Research: Population Sciences

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作者与单位 Authors & Affiliations

Rohit Thakur¹, G J M Shanika R Jayasinghe², Mai Xu¹, Linh Bui-Raborn¹, Jianxin Shi¹, Diptavo Dutta¹, Phuc H. Hoang¹, Mathias Seviiri², Christopher I. Amos³, Andrew Bakshi⁴, Anne E. Cust⁵, Florence Demenais⁶, David L. Duffy⁷, Lars G. Fritsche⁸, Jiali Han⁹, Nicholas K. Hayward¹⁰, Kiarash Khosrotehrani¹¹, Rajiv Kumar¹², John F. Thompson¹³, Stuart MacGregor¹⁴, Miguel Renteria¹⁵, Diane T. Smelser¹⁶, Sarah V. Ward¹⁷, Maria Concetta Fargnoli¹⁸, Paola Ghiorzo¹⁹, Alisa M. Goldstein¹, Chiara Menin²⁰, David Millan-Esteban²¹, Eduardo Nagore²², Cristina Pellegrini²³, Susana Puig²⁴, Alex Stratigos²⁵, David C. Whiteman², Melanoma Meta-Analysis Consortium, Mark M. Iles²⁶, Lee E. Whelees²⁷, Rebecca I. Hartman²⁸, Maria Teresa Landi¹, Matthew H. Law², Kevin M. Brown¹

¹National Cancer Institute, Bethesda, MD,²Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia,³Epidemiology & Population Science, Baylor College of Medicine, Houston, TX,⁴Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia,⁵The Daffodil Centre, Sydney School of Public Health, The University of Sydney, Sydney, Australia,⁶INSERM, Université Paris Diderot, Paris, France,⁷Genetic Epidemiology Laboratory, QIMR Berghofer Medical Research Institutee, Brisbane, Australia,⁸Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI,⁹IU Simon Cancer Center, Indiana University Indianapolis, Indianapolis, IN,¹⁰Oncogenomics, QIMR Berghofer Medical Research Institutenstitute, Brisbane, Australia,¹¹Frazer institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia,¹²German Cancer Research Center, Heidelberg, Germany,¹³Melanoma Institute Australia, The University of Sydney, Sydney, Australia,¹⁴Statistical Genetics, Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia,¹⁵QIMR Berghofer Medical Research Institute, Brisbane, Australia,¹⁶Department of Genomic Health, Geisinger Clinic, Geisinger Health System, Danville, PA,¹⁷School of Population and Global Health, The University of Western Australia, Perth, Australia,¹⁸Istituto Dermatologico San Gallicano - IRCCS, Rome, Italy,¹⁹IRCCS Ospedale Policlinico San Martino,University of Genoa, Genoa, Italy,²⁰Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy,²¹Department of Dermatology, Universidad Catolica de Valencia San Vicente Mártir; Fundación Instituto Valenciano de Oncología, Valencia, Spain,²²Department of Dermatology, Fundación Instituto Valenciano de Oncología; Universidad Católica de Valencia San Vicente Mártir, Valencia, Spain,²³Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Abruzzo, Italy,²⁴Dermatology Department, Universitat de Barcelona, Barcelona, Spain,²⁵Department of Dermatology-Venereology, National and Kapodistrian University of Athens, Athens, Greece,²⁶Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom,²⁷Tennessee Valley Healthcare System VA Medical Center, Nashville, TN,²⁸Dermatology Section, VA Boston Healthcare System, Boston, MA

摘要 Abstract

中文摘要

我们开展了一项新的黑色素瘤GWAS荟萃分析，与Landi及同事最近的研究相比，样本量大约翻倍至约70,000例病例。在这项新的荟萃分析中，我们识别了116个独立风险位点，复制了先前报告的52/54个位点。GWAS后的变异到基因映射对于功能性解释本分析发现的新位点仍然至关重要。大多数位点包含许多处于连锁不平衡的候选因果变异，其中极少改变蛋白质编码序列，提示大多数位点的基础是顺式调控功能。数量性状位点（QTL）共定位方法可有效地在易感位点优先确定靶基因，但许多位点仍未分配靶基因，这也许反映了QTL数据集中未充分反映的情境特异性变异效应。为补充QTL并更全面地识别潜在因果基因（包括超过1 Mb之外的基因），我们在人类原代黑色素细胞（黑色素瘤的起源细胞类型）中应用了H3K27ac-HiChIP。H3K27ac-HiChIP将Hi-C方法与H3K27ac ChIP步骤相结合，以检测风险位点的增强子-启动子相互作用。使用FitHiChIP流程识别具有统计学意义的染色质相互作用。我们随后在所有全基因组显著的黑色素瘤位点进行了变异到基因（V2G）映射，在精细定位变异与各自启动子重叠或物理相互作用处提名靶基因。基于HiChIP的V2G映射方法在94%的位点识别了靶基因（在109/116个位点提名了779个基因），优于其他基因提名方法（QTL共定位、蛋白质编码变异），后者在57%的风险位点（67/116个位点）提名了102个候选基因。在通过黑色素细胞或黑色素瘤eQTL共定位识别的基因中，大多数（32个中的19个）也被V2G映射提名。同样，5个剪接QTL基因中的3个和34个meQTL基因中的21个与V2G基因集重叠。接下来，我们将包含V2G映射识别候选基因的基因集与仅由QTL和蛋白质编码变异识别的基因集进行比较。纳入V2G候选基因的基因集显示出对致癌信号通路的显著富集，包括WNT/beta-catenin信号（FDR：含V2G基因集P = 6.3 × 10^-05对不含V2G基因集P = 0.2）、芳香烃受体信号（P= 1.6 × 10^-4对0.2）和NOTCH1信号（P=0.002对0.5）。值得注意的是，V2G映射在46%的位点（53/116；提名了76个癌症驱动基因）将风险相关变异与已知癌症驱动基因（如PIK3CA、NOTCH2、MDM4等）联系起来，其中若干位于>1Mb之外。引人注目的是，我们检测到一个高度显著的长程相互作用（约2 Mb），将8q24.21位点附近的精细定位变异与癌症驱动基因MYC连接起来。总体而言，基于H3K27ac-HiChIP的V2G映射通过识别远距离易感基因、突显已知癌症驱动基因作为潜在靶点，并揭示这些位点汇聚于关键致癌信号通路，极大改善了对黑色素瘤易感位点的解读。

查看英文原文 English abstract

We performed a new melanoma GWAS meta-analysis, roughly doubling the sample size to ~70,000 cases compared to the most recent study by Landi and colleagues. In the new meta-analysis, we identify 116 independent risk loci, replicating 52/54 loci previously reported. Post-GWAS variant-to-gene mapping remains critical to functionally interpret new loci discovered by this analysis. Most loci contain many candidate causal variants in linkage disequilibrium, with very few altering protein-coding sequence, suggesting cis -regulatory function underlying the majority of loci. Quantitative trait locus (QTL) colocalization methods can effectively prioritize target genes at susceptibility loci but many loci remain without assigned targets, perhaps reflecting context-specific variant effects not well-reflected in QTL datasets. To complement QTLs and more comprehensively identify potential causal genes, including those beyond 1 Mb, we applied H3K27ac-HiChIP in human primary melanocytes, the cell type of origin for melanoma. H3K27ac-HiChIP combines a Hi-C approach with a H3K27ac ChIP step to detect enhancer-promoter interactions at the risk loci. Statistically significant chromatin interactions were identified using the FitHiChIP pipeline. We subsequently performed variant-to-gene (V2G) mapping at all genome-wide significant melanoma loci, nominating target genes where fine-mapped variants overlapped or physically interacted with their respective promoters. HiChIP-based V2G mapping approach identified target genes at 94% loci (779 genes nominated at 109/116 loci), outperforming other gene nomination approaches (QTL colocalization, protein-coding variants), which nominated 102 candidate genes at 57% of risk loci (67/116 loci). Among genes identified by melanocyte or melanoma eQTL colocalization, a majority (19 of 32) were also nominated by V2G mapping. Likewise, 3 of 5 splice QTL genes and 21 of 34 meQTL genes overlapped with the V2G gene set. Next, we compared gene sets including the V2G mapping-identified candidates to those identified solely by QTLs and protein-coding variants. The gene set incorporating V2G candidates showed significant enrichment for oncogenic signaling pathways, including WNT/beta-catenin signaling (FDR: P with V2G gene set =  6.3 × 10 -05 vs P without V2G gene set = 0.2), aryl hydrocarbon receptor signaling (P= 1.6 × 10 -4 vs 0.2), and signaling by NOTCH1 (P=0.002 vs0.5). Notably, V2G mapping linked risk-associated variants to known cancer drivers (e.g. PIK3CA , NOTCH2 , MDM4 etc.) at 46% of loci (53/116; nominated 76 cancer drivers), with several located >1Mb away. Strikingly, we detected a highly significant long-range interaction (~2 Mb) connecting fine-mapped variants near the locus 8q24.21 to cancer driver MYC. Overall, H3K27ac-HiChIP-based V2G mapping greatly improves the interpretation of melanoma susceptibility loci by identifying distant susceptibility genes, highlighting known cancer drivers as potential targets, and revealing that these loci converge on key oncogenic signaling pathways.

利益披露 Disclosure

R. Thakur, None.. G. Jayasinghe, None.. M. Xu, None.. L. Bui-Raborn, None.. J. Shi, None.. D. Dutta, None.. P. H. Hoang, None.. M. Seviiri, None.. C. I. Amos, None.. A. Bakshi, None.. A. E. Cust, None.. F. Demenais, None.. D. L. Duffy, None.. L. G. Fritsche, None.. J. Han, None.. N. K. Hayward, None.. K. Khosrotehrani, None.. R. Kumar, None. J. F. Thompson, BMS Australia Received honoraria for advisory board participation.. MSD Australia Received honoraria for advisory board participation.. GSK Travel, Received honoraria for advisory board participation.. Provectus Biopharmaceuticals Travel, Received honoraria for advisory board participation.. Novartis Travel. S. MacGregor, None.. M. Renteria, None.. D. T. Smelser, None.. S. V. Ward, None.. M. C. Fargnoli, None.. P. Ghiorzo, None.. A. M. Goldstein, None.. C. Menin, None.. D. Millan-Esteban, None.. E. Nagore, None.. C. Pellegrini, None.. S. Puig, None.. A. Stratigos, None.. D. C. Whiteman, None.. M. M. Iles, None.. L. E. Whelees, None.. R. I. Hartman, None.. M. T. Landi, None.. M. H. Law, None.. K. M. Brown, None.

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