{"title":"NRDE2 deficiency impairs homologous recombination repair and sensitizes hepatocellular carcinoma to PARP inhibitors.","authors":"Yahui Wang, Xinyi Liu, Xianbo Zuo, Cuiling Wang, Zheng Zhang, Haitao Zhang, Tao Zeng, Shunqi Chen, Mengyu Liu, Hongxia Chen, Qingfeng Song, Qi Li, Chenning Yang, Yi Le, Jinliang Xing, Hongxin Zhang, Jiaze An, Weihua Jia, Longli Kang, Hongxing Zhang, Hui Xie, Jiazhou Ye, Tianzhun Wu, Fuchu He, Xuejun Zhang, Yuanfeng Li, Gangqiao Zhou","doi":"10.1016/j.xgen.2024.100550","DOIUrl":"10.1016/j.xgen.2024.100550","url":null,"abstract":"<p><p>To identify novel susceptibility genes for hepatocellular carcinoma (HCC), we performed a rare-variant association study in Chinese populations consisting of 2,750 cases and 4,153 controls. We identified four HCC-associated genes, including NRDE2, RANBP17, RTEL1, and STEAP3. Using NRDE2 (index rs199890497 [p.N377I], p = 1.19 × 10<sup>-9</sup>) as an exemplary candidate, we demonstrated that it promotes homologous recombination (HR) repair and suppresses HCC. Mechanistically, NRDE2 binds to the subunits of casein kinase 2 (CK2) and facilitates the assembly and activity of the CK2 holoenzyme. This NRDE2-mediated enhancement of CK2 activity increases the phosphorylation of MDC1 and then facilitates the HR repair. These functions are eliminated almost completely by the NRDE2-p.N377I variant, which sensitizes the HCC cells to poly(ADP-ribose) polymerase (PARP) inhibitors, especially when combined with chemotherapy. Collectively, our findings highlight the relevance of the rare variants to genetic susceptibility to HCC, which would be helpful for the precise treatment of this malignancy.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11099347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140872393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-05-08Epub Date: 2024-05-01DOI: 10.1016/j.xgen.2024.100554
Roshni A Patel, Rachel A Ungar, Alanna L Pyke, Alvina Adimoelja, Meenakshi Chakraborty, Daniel J Cotter, Malika Freund, Pagé Goddard, Justin Gomez-Stafford, Emily Greenwald, Emily Higgs, Naiomi Hunter, Tim M G MacKenzie, Anjali Narain, Tamara Gjorgjieva, Daphne O Martschenko
{"title":"Increasing equity in science requires better ethics training: A course by trainees, for trainees.","authors":"Roshni A Patel, Rachel A Ungar, Alanna L Pyke, Alvina Adimoelja, Meenakshi Chakraborty, Daniel J Cotter, Malika Freund, Pagé Goddard, Justin Gomez-Stafford, Emily Greenwald, Emily Higgs, Naiomi Hunter, Tim M G MacKenzie, Anjali Narain, Tamara Gjorgjieva, Daphne O Martschenko","doi":"10.1016/j.xgen.2024.100554","DOIUrl":"10.1016/j.xgen.2024.100554","url":null,"abstract":"<p><p>Despite the profound impacts of scientific research, few scientists have received the necessary training to productively discuss the ethical and societal implications of their work. To address this critical gap, we-a group of predominantly human genetics trainees-developed a course on genetics, ethics, and society. We intend for this course to serve as a template for other institutions and scientific disciplines. Our curriculum positions human genetics within its historical and societal context and encourages students to evaluate how societal norms and structures impact the conduct of scientific research. We demonstrate the utility of this course via surveys of enrolled students and provide resources and strategies for others hoping to teach a similar course. We conclude by arguing that if we are to work toward rectifying the inequities and injustices produced by our field, we must first learn to view our own research as impacting and being impacted by society.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11099339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-05-08Epub Date: 2024-05-01DOI: 10.1016/j.xgen.2024.100556
Sheridan H Littleton, Khanh B Trang, Christina M Volpe, Kieona Cook, Nicole DeBruyne, Jean Ann Maguire, Mary Ann Weidekamp, Kenyaita M Hodge, Keith Boehm, Sumei Lu, Alessandra Chesi, Jonathan P Bradfield, James A Pippin, Stewart A Anderson, Andrew D Wells, Matthew C Pahl, Struan F A Grant
{"title":"Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3' UTR of FAIM2.","authors":"Sheridan H Littleton, Khanh B Trang, Christina M Volpe, Kieona Cook, Nicole DeBruyne, Jean Ann Maguire, Mary Ann Weidekamp, Kenyaita M Hodge, Keith Boehm, Sumei Lu, Alessandra Chesi, Jonathan P Bradfield, James A Pippin, Stewart A Anderson, Andrew D Wells, Matthew C Pahl, Struan F A Grant","doi":"10.1016/j.xgen.2024.100556","DOIUrl":"10.1016/j.xgen.2024.100556","url":null,"abstract":"<p><p>The ch12q13 locus is among the most significant childhood obesity loci identified in genome-wide association studies. This locus resides in a non-coding region within FAIM2; thus, the underlying causal variant(s) presumably influence disease susceptibility via cis-regulation. We implicated rs7132908 as a putative causal variant by leveraging our in-house 3D genomic data and public domain datasets. Using a luciferase reporter assay, we observed allele-specific cis-regulatory activity of the immediate region harboring rs7132908. We generated isogenic human embryonic stem cell lines homozygous for either rs7132908 allele to assess changes in gene expression and chromatin accessibility throughout a differentiation to hypothalamic neurons, a key cell type known to regulate feeding behavior. The rs7132908 obesity risk allele influenced expression of FAIM2 and other genes and decreased the proportion of neurons produced by differentiation. We have functionally validated rs7132908 as a causal obesity variant that temporally regulates nearby effector genes and influences neurodevelopment and survival.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11099382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-05-08Epub Date: 2024-04-29DOI: 10.1016/j.xgen.2024.100553
Yicheng Gao, Kejing Dong, Yuli Gao, Xuan Jin, Jingya Yang, Gang Yan, Qi Liu
{"title":"Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning.","authors":"Yicheng Gao, Kejing Dong, Yuli Gao, Xuan Jin, Jingya Yang, Gang Yan, Qi Liu","doi":"10.1016/j.xgen.2024.100553","DOIUrl":"10.1016/j.xgen.2024.100553","url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11099349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-05-01DOI: 10.1016/j.xgen.2024.100564
Zhangyu Wang, Benjamin Gregg, Li Du
{"title":"Regulatory barriers to US-China collaboration for generative AI development in genomic research","authors":"Zhangyu Wang, Benjamin Gregg, Li Du","doi":"10.1016/j.xgen.2024.100564","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100564","url":null,"abstract":"","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141136769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-04-10DOI: 10.1016/j.xgen.2024.100539
Jin Jin, Jianan Zhan, Jingning Zhang, Ruzhang Zhao, Jared O'Connell, Yunxuan Jiang, Steven Buyske, Christopher Gignoux, Christopher Haiman, Eimear E Kenny, Charles Kooperberg, Kari North, Bertram L Koelsch, Genevieve Wojcik, Haoyu Zhang, Nilanjan Chatterjee
{"title":"MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.","authors":"Jin Jin, Jianan Zhan, Jingning Zhang, Ruzhang Zhao, Jared O'Connell, Yunxuan Jiang, Steven Buyske, Christopher Gignoux, Christopher Haiman, Eimear E Kenny, Charles Kooperberg, Kari North, Bertram L Koelsch, Genevieve Wojcik, Haoyu Zhang, Nilanjan Chatterjee","doi":"10.1016/j.xgen.2024.100539","DOIUrl":"https://doi.org/10.1016/j.xgen.2024.100539","url":null,"abstract":"<p><p>Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R<sup>2</sup> across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140874035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-04-10Epub Date: 2024-03-19DOI: 10.1016/j.xgen.2024.100523
Buu Truong, Leland E Hull, Yunfeng Ruan, Qin Qin Huang, Whitney Hornsby, Hilary Martin, David A van Heel, Ying Wang, Alicia R Martin, S Hong Lee, Pradeep Natarajan
{"title":"Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases.","authors":"Buu Truong, Leland E Hull, Yunfeng Ruan, Qin Qin Huang, Whitney Hornsby, Hilary Martin, David A van Heel, Ying Wang, Alicia R Martin, S Hong Lee, Pradeep Natarajan","doi":"10.1016/j.xgen.2024.100523","DOIUrl":"10.1016/j.xgen.2024.100523","url":null,"abstract":"<p><p>Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10<sup>-5</sup>) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10<sup>-6</sup>), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10<sup>-6</sup>) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10<sup>-7</sup>) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10<sup>-4</sup>). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140178046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-04-10Epub Date: 2024-03-26DOI: 10.1016/j.xgen.2024.100526
Adam J de Smith, Lara Wahlster, Soyoung Jeon, Linda Kachuri, Susan Black, Jalen Langie, Liam D Cato, Nathan Nakatsuka, Tsz-Fung Chan, Guangze Xia, Soumyaa Mazumder, Wenjian Yang, Steven Gazal, Celeste Eng, Donglei Hu, Esteban González Burchard, Elad Ziv, Catherine Metayer, Nicholas Mancuso, Jun J Yang, Xiaomei Ma, Joseph L Wiemels, Fulong Yu, Charleston W K Chiang, Vijay G Sankaran
{"title":"A noncoding regulatory variant in IKZF1 increases acute lymphoblastic leukemia risk in Hispanic/Latino children.","authors":"Adam J de Smith, Lara Wahlster, Soyoung Jeon, Linda Kachuri, Susan Black, Jalen Langie, Liam D Cato, Nathan Nakatsuka, Tsz-Fung Chan, Guangze Xia, Soumyaa Mazumder, Wenjian Yang, Steven Gazal, Celeste Eng, Donglei Hu, Esteban González Burchard, Elad Ziv, Catherine Metayer, Nicholas Mancuso, Jun J Yang, Xiaomei Ma, Joseph L Wiemels, Fulong Yu, Charleston W K Chiang, Vijay G Sankaran","doi":"10.1016/j.xgen.2024.100526","DOIUrl":"10.1016/j.xgen.2024.100526","url":null,"abstract":"<p><p>Hispanic/Latino children have the highest risk of acute lymphoblastic leukemia (ALL) in the US compared to other racial/ethnic groups, yet the basis of this remains incompletely understood. Through genetic fine-mapping analyses, we identified a new independent childhood ALL risk signal near IKZF1 in self-reported Hispanic/Latino individuals, but not in non-Hispanic White individuals, with an effect size of ∼1.44 (95% confidence interval = 1.33-1.55) and a risk allele frequency of ∼18% in Hispanic/Latino populations and <0.5% in European populations. This risk allele was positively associated with Indigenous American ancestry, showed evidence of selection in human history, and was associated with reduced IKZF1 expression. We identified a putative causal variant in a downstream enhancer that is most active in pro-B cells and interacts with the IKZF1 promoter. This variant disrupts IKZF1 autoregulation at this enhancer and results in reduced enhancer activity in B cell progenitors. Our study reveals a genetic basis for the increased ALL risk in Hispanic/Latino children.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-04-10Epub Date: 2024-03-26DOI: 10.1016/j.xgen.2024.100527
Tristan V de Jong, Yanchao Pan, Pasi Rastas, Daniel Munro, Monika Tutaj, Huda Akil, Chris Benner, Denghui Chen, Apurva S Chitre, William Chow, Vincenza Colonna, Clifton L Dalgard, Wendy M Demos, Peter A Doris, Erik Garrison, Aron M Geurts, Hakan M Gunturkun, Victor Guryev, Thibaut Hourlier, Kerstin Howe, Jun Huang, Ted Kalbfleisch, Panjun Kim, Ling Li, Spencer Mahaffey, Fergal J Martin, Pejman Mohammadi, Ayse Bilge Ozel, Oksana Polesskaya, Michal Pravenec, Pjotr Prins, Jonathan Sebat, Jennifer R Smith, Leah C Solberg Woods, Boris Tabakoff, Alan Tracey, Marcela Uliano-Silva, Flavia Villani, Hongyang Wang, Burt M Sharp, Francesca Telese, Zhihua Jiang, Laura Saba, Xusheng Wang, Terence D Murphy, Abraham A Palmer, Anne E Kwitek, Melinda R Dwinell, Robert W Williams, Jun Z Li, Hao Chen
{"title":"A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats.","authors":"Tristan V de Jong, Yanchao Pan, Pasi Rastas, Daniel Munro, Monika Tutaj, Huda Akil, Chris Benner, Denghui Chen, Apurva S Chitre, William Chow, Vincenza Colonna, Clifton L Dalgard, Wendy M Demos, Peter A Doris, Erik Garrison, Aron M Geurts, Hakan M Gunturkun, Victor Guryev, Thibaut Hourlier, Kerstin Howe, Jun Huang, Ted Kalbfleisch, Panjun Kim, Ling Li, Spencer Mahaffey, Fergal J Martin, Pejman Mohammadi, Ayse Bilge Ozel, Oksana Polesskaya, Michal Pravenec, Pjotr Prins, Jonathan Sebat, Jennifer R Smith, Leah C Solberg Woods, Boris Tabakoff, Alan Tracey, Marcela Uliano-Silva, Flavia Villani, Hongyang Wang, Burt M Sharp, Francesca Telese, Zhihua Jiang, Laura Saba, Xusheng Wang, Terence D Murphy, Abraham A Palmer, Anne E Kwitek, Melinda R Dwinell, Robert W Williams, Jun Z Li, Hao Chen","doi":"10.1016/j.xgen.2024.100527","DOIUrl":"10.1016/j.xgen.2024.100527","url":null,"abstract":"<p><p>The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell genomicsPub Date : 2024-04-10Epub Date: 2024-03-28DOI: 10.1016/j.xgen.2024.100528
J Alberto Nakauma-González, Maud Rijnders, Minouk T W Noordsij, John W M Martens, Astrid A M van der Veldt, Martijn P J Lolkema, Joost L Boormans, Harmen J G van de Werken
{"title":"Whole-genome mapping of APOBEC mutagenesis in metastatic urothelial carcinoma identifies driver hotspot mutations and a novel mutational signature.","authors":"J Alberto Nakauma-González, Maud Rijnders, Minouk T W Noordsij, John W M Martens, Astrid A M van der Veldt, Martijn P J Lolkema, Joost L Boormans, Harmen J G van de Werken","doi":"10.1016/j.xgen.2024.100528","DOIUrl":"10.1016/j.xgen.2024.100528","url":null,"abstract":"<p><p>Apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like (APOBEC) enzymes mutate specific DNA sequences and hairpin-loop structures, challenging the distinction between passenger and driver hotspot mutations. Here, we characterized 115 whole genomes of metastatic urothelial carcinoma (mUC) to identify APOBEC mutagenic hotspot drivers. APOBEC-associated mutations were detected in 92% of mUCs and were equally distributed across the genome, while APOBEC hotspot mutations (ApoHMs) were enriched in open chromatin. Hairpin loops were frequent targets of didymi (twins in Greek), two hotspot mutations characterized by the APOBEC SBS2 signature, in conjunction with an uncharacterized mutational context (Ap[C>T]). Next, we developed a statistical framework that identified ApoHMs as drivers in coding and non-coding genomic regions of mUCs. Our results and statistical framework were validated in independent cohorts of 23 non-metastatic UCs and 3,744 samples of 17 metastatic cancers, identifying cancer-type-specific drivers. Our study highlights the role of APOBEC in cancer development and may contribute to developing novel targeted therapy options for APOBEC-driven cancers.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140327503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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