Somatic variants in the cancer genome influence gene expression through diverse mechanisms depending on their specific locations.However,a systematic evaluation of the effects of somatic variants located in 3'untr...Somatic variants in the cancer genome influence gene expression through diverse mechanisms depending on their specific locations.However,a systematic evaluation of the effects of somatic variants located in 3'untranslated regions(3'UTRs)on alternative polyadenylation(APA)of m RNA remains lacking.In this study,we analyze 10,199 tumor samples across 32 cancer types and identify 1333 somatic single nucleotide variants(SNVs)associated with abnormal 3'UTR APA.Mechanistically,these 3'UTR SNVs can alter cisregulatory elements,such as the poly(A)signal and UGUA motif,leading to changes in APA.Minigene assays confirm that 3'UTR SNVs in multiple genes,including RPS23 and CHTOP,induce aberrant APA.Among affected genes,62 exhibit differential stability between tandem 3'UTR isoforms,including HSPA4and UCK2,validated by experimental assays.Finally,we establish that SNV-related abnormal APA usage serves as an additional layer of expression regulation for tumor-suppressor gene HMGN2 in breast cancer.Collectively,this study reveals 3'UTR APA as a critical mechanism mediating the functional impact of somatic noncoding variants in human cancers.展开更多
Importance:Postzygotic mutations in the GNAQ/GNA11 genes,which encode the G-protein nucleotide binding protein alpha subunits,have been identified in patients with phakomatosis pigmentovascularis(PPV).However,little i...Importance:Postzygotic mutations in the GNAQ/GNA11 genes,which encode the G-protein nucleotide binding protein alpha subunits,have been identified in patients with phakomatosis pigmentovascularis(PPV).However,little is known about the Chinese population.Objective:To identify pathogenic mutations in pediatric patients with PPV within the Chinese population.Methods:We performed whole-exome sequencing(WES)using skin lesion tissues from pediatric patients diagnosed with PPV.Additionally,ultradeep-targeted sequencing was conducted to validate the somatic mutations.A genotype-phenotype correlation was analyzed by integrating data from previous reports with the findings of the present study.Results:Thirteen patients were enrolled,all diagnosed with the cesioflammea type of PPV,except for one patient with an unclassifiable type.We identified somatic GNA11 c.547C>T(p.R183C)variant in seven patients and GNAQ c.548G>A(p.R183Q)in four patients,with low allelic fractions ranging from 2.1%to 8.6%through ultradeep sequencing.Besides,a GNAQ c.548G>A(p.R183Q)variant was detected through targeted sequencing in one of two patients who did not exhibit detectable variants via WES.The genotype-phenotype correlation analysis,involving 15 patients with a GNA11 variant and 10 with a GNAQ variant,revealed that facial capillary malformation(87%vs.50%,P=0.075)and ocular melanocytosis(80%vs.40%,P=0.087)appeared to be more frequent in patients with GNA11 mutation compared to those with GNAQ mutations.All four patients diagnosed with cesiomarmorata type or overlapping cesioflammea and cesiomarmorata type PPV carried the GNA11 variant.Interpretation:Our study demonstrated that the majority of PPV patients in the Chinese population carried a postzygotic variant of GNAQ/GNA11,thus further confirming the pathogenic role of GNAQ/GNA11 mosaicism in the development of PPV cesioflammea type.展开更多
Clonal hematopoiesis(CH)of indeterminate potential(CHIP),driven by somatic mutations in leukemia-associated genes,confers increased risk of hematologic malignancies,cardiovascular disease,and all-cause mortality.In bl...Clonal hematopoiesis(CH)of indeterminate potential(CHIP),driven by somatic mutations in leukemia-associated genes,confers increased risk of hematologic malignancies,cardiovascular disease,and all-cause mortality.In blood of healthy individuals,small CH clones can expand over time to reach 2% variant allele frequency(VAF),the current threshold for CHIP.Nevertheless,reliable detection of low-VAF CHIP mutations is challenging,often relying on deep targeted sequencing.Here,we present UNISOM,a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered,especially for low VAFs.UNISOM utilizes a meta-caller for variant detection,in couple with machine learning models which classify variants into CHIP,germline,and artifact.In whole-exome sequencing data,UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort.Applied to whole-genome sequencing data from Mayo Clinic Biobank,it recapitulated the patterns previously established in much larger cohorts,including the most frequently mutated CHIP genes and predominant mutation types and signatures,as well as strong associations of CHIP with age and smoking status.Notably,30% of the identified CHIP mutations had<5%VAFs,demonstrating its high sensitivity toward small mutant clones.This workflow is applicable to CHIP screening in population genomic studies.The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.展开更多
With the development of sequencing technologies,somatic mutation analysis has become an important component in cancer research and treatment.VarDict is a commonly used somatic variant caller for this task.Although the...With the development of sequencing technologies,somatic mutation analysis has become an important component in cancer research and treatment.VarDict is a commonly used somatic variant caller for this task.Although the heuristic-based VarDict algorithm exhibits high sensitivity and versatility,it may detect higher amounts of false positive variants than callers,limiting its clinical practicality.To address this problem,we propose DeepFilter,a deep-learning based filter for VarDict,which can filter out the false positive variants detected by VarDict effectively.Our approach trains two models for insertion-deletion mutations(InDels)and single nucleotide variants(SNVs),respectively.Experiments show that DeepFilter can filter at least 98.5%of false positive variants and retain 93.5%of true positive variants for InDels and SNVs in the commonly used tumor-normal paired mode.Source code and pre-trained models are available at https://github.com/LeiHaoa/DeepFilter.展开更多
基金financially supported by the National Key R&D Program of China(2023YFC3603300,2021YFA0909300)National Natural Science Foundation of China(92249302,32370592,32400437)China Postdoctoral Science Foundation(BX20230073 and 2023M740709)。
文摘Somatic variants in the cancer genome influence gene expression through diverse mechanisms depending on their specific locations.However,a systematic evaluation of the effects of somatic variants located in 3'untranslated regions(3'UTRs)on alternative polyadenylation(APA)of m RNA remains lacking.In this study,we analyze 10,199 tumor samples across 32 cancer types and identify 1333 somatic single nucleotide variants(SNVs)associated with abnormal 3'UTR APA.Mechanistically,these 3'UTR SNVs can alter cisregulatory elements,such as the poly(A)signal and UGUA motif,leading to changes in APA.Minigene assays confirm that 3'UTR SNVs in multiple genes,including RPS23 and CHTOP,induce aberrant APA.Among affected genes,62 exhibit differential stability between tandem 3'UTR isoforms,including HSPA4and UCK2,validated by experimental assays.Finally,we establish that SNV-related abnormal APA usage serves as an additional layer of expression regulation for tumor-suppressor gene HMGN2 in breast cancer.Collectively,this study reveals 3'UTR APA as a critical mechanism mediating the functional impact of somatic noncoding variants in human cancers.
基金Beijing Natural Science Foundation:Grant/Award Number:7222058The Open Project of Henan Clinical Research Center of Childhood Diseases:Grant/Award Number:YJZX202209+2 种基金National Regional Medical Center Opening Project:Grant/Award Number:NRMC0101Investigator Initiated Project:Grant/Award Number:[2022]-E-028-YBCH Young Investigator Program(BCHYIP):Grant/Award Number:3-1-014-01-36。
文摘Importance:Postzygotic mutations in the GNAQ/GNA11 genes,which encode the G-protein nucleotide binding protein alpha subunits,have been identified in patients with phakomatosis pigmentovascularis(PPV).However,little is known about the Chinese population.Objective:To identify pathogenic mutations in pediatric patients with PPV within the Chinese population.Methods:We performed whole-exome sequencing(WES)using skin lesion tissues from pediatric patients diagnosed with PPV.Additionally,ultradeep-targeted sequencing was conducted to validate the somatic mutations.A genotype-phenotype correlation was analyzed by integrating data from previous reports with the findings of the present study.Results:Thirteen patients were enrolled,all diagnosed with the cesioflammea type of PPV,except for one patient with an unclassifiable type.We identified somatic GNA11 c.547C>T(p.R183C)variant in seven patients and GNAQ c.548G>A(p.R183Q)in four patients,with low allelic fractions ranging from 2.1%to 8.6%through ultradeep sequencing.Besides,a GNAQ c.548G>A(p.R183Q)variant was detected through targeted sequencing in one of two patients who did not exhibit detectable variants via WES.The genotype-phenotype correlation analysis,involving 15 patients with a GNA11 variant and 10 with a GNAQ variant,revealed that facial capillary malformation(87%vs.50%,P=0.075)and ocular melanocytosis(80%vs.40%,P=0.087)appeared to be more frequent in patients with GNA11 mutation compared to those with GNAQ mutations.All four patients diagnosed with cesiomarmorata type or overlapping cesioflammea and cesiomarmorata type PPV carried the GNA11 variant.Interpretation:Our study demonstrated that the majority of PPV patients in the Chinese population carried a postzygotic variant of GNAQ/GNA11,thus further confirming the pathogenic role of GNAQ/GNA11 mosaicism in the development of PPV cesioflammea type.
基金supported by the Mayo Clinic Center for Individualized Medicine,USA.
文摘Clonal hematopoiesis(CH)of indeterminate potential(CHIP),driven by somatic mutations in leukemia-associated genes,confers increased risk of hematologic malignancies,cardiovascular disease,and all-cause mortality.In blood of healthy individuals,small CH clones can expand over time to reach 2% variant allele frequency(VAF),the current threshold for CHIP.Nevertheless,reliable detection of low-VAF CHIP mutations is challenging,often relying on deep targeted sequencing.Here,we present UNISOM,a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered,especially for low VAFs.UNISOM utilizes a meta-caller for variant detection,in couple with machine learning models which classify variants into CHIP,germline,and artifact.In whole-exome sequencing data,UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort.Applied to whole-genome sequencing data from Mayo Clinic Biobank,it recapitulated the patterns previously established in much larger cohorts,including the most frequently mutated CHIP genes and predominant mutation types and signatures,as well as strong associations of CHIP with age and smoking status.Notably,30% of the identified CHIP mutations had<5%VAFs,demonstrating its high sensitivity toward small mutant clones.This workflow is applicable to CHIP screening in population genomic studies.The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.
基金This work was partially supported by the National Natural Science Foundation of China(NSFC)(Nos.62102231 and 61972231)the Shenzhen Basic Research Fund(No.JCYJ20180507182818013)+3 种基金the Key Project of Joint Fund of Shandong Province(No.ZR2019LZH007)Shandong Provincial Natural Science Foundation(No.ZR2021QF089)the PPP project from CSC and DAADEngineering Research Center of Digital Media Technology,Ministry of Education,China.
文摘With the development of sequencing technologies,somatic mutation analysis has become an important component in cancer research and treatment.VarDict is a commonly used somatic variant caller for this task.Although the heuristic-based VarDict algorithm exhibits high sensitivity and versatility,it may detect higher amounts of false positive variants than callers,limiting its clinical practicality.To address this problem,we propose DeepFilter,a deep-learning based filter for VarDict,which can filter out the false positive variants detected by VarDict effectively.Our approach trains two models for insertion-deletion mutations(InDels)and single nucleotide variants(SNVs),respectively.Experiments show that DeepFilter can filter at least 98.5%of false positive variants and retain 93.5%of true positive variants for InDels and SNVs in the commonly used tumor-normal paired mode.Source code and pre-trained models are available at https://github.com/LeiHaoa/DeepFilter.
