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Meng M, Wang J, Yang J, Zhang Y, Tu X, Hu P. PRR13 expression as a prognostic biomarker in breast cancer: correlations with immune infiltration and clinical outcomes. Front Mol Biosci 2025; 12:1518031. [PMID: 40099041 PMCID: PMC11911201 DOI: 10.3389/fmolb.2025.1518031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2024] [Accepted: 01/08/2025] [Indexed: 03/19/2025] Open
Abstract
Introduction Breast cancer continues to be a primary cause of cancer-related mortality among women globally. Identifying novel biomarkers is essential for enhancing patient prognosis and informing therapeutic decisions. The PRR13 gene, associated with taxol resistance and the progression of various cancers, remains under-characterized in breast cancer. This study aimed to investigate the role of PRR13 in breast cancer and its potential as a prognostic biomarker. Methods We performed a comparative analysis of PRR13 gene expression utilizing the TCGA database against non-cancerous tissues and employed STRING to evaluate PRR13's protein-protein interactions and associated pathways. Additionally, we investigated the relationship between PRR13 mRNA expression and immune cell infiltration in breast cancer (BRCA) using two methodologies. Furthermore, a retrospective analysis of 160 patients was conducted, wherein clinical data were collected and PRR13 expression was evaluated through immunohistochemistry and qRT-PCR to determine its association with clinicopathological features and patient survival. Results Analysis of the TCGA database revealed significant upregulation of PRR13 expression across 12 different cancer types, including breast cancer. High PRR13 expression was positively correlated with various immune cells, including NK cells, eosinophils, Th17 cells, and mast cells, whereas a negative correlation was observed with B cells, macrophages, and other immune subsets. Enrichment analysis of PRR13 and its 50 interacting proteins revealed significant associations with biological processes such as cell adhesion and migration, and pathways including ECMreceptor interaction and PI3K-Akt signaling. Single-cell analysis demonstrated associations between PRR13 and pathways pertinent to inflammation and apoptosis. Validation studies confirmed elevated PRR13 expression in tumor tissue compared to adjacent non-cancerous tissue. Immunohistochemistry demonstrated high PRR13 expression in 55.6% of cancer cases, particularly associated with advanced clinical stage and lymph node metastasis. Moreover, high PRR13 expression significantly correlated with shorter overall survival and served as an independent prognostic factor. Subgroup analysis underscored the prognostic significance of PRR13 in aggressive tumor subtypes, with particularly strong associations observed in T3, N1-3, and moderately to poorly differentiated tumors. Discussion In conclusion, PRR13 expression is upregulated in breast cancer tissues and may serve as a valuable prognostic indicator for breast cancer patients, potentially impacting patient survival and therapeutic strategies.
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Affiliation(s)
- Mingjing Meng
- Department of Research and Foreign Affairs, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China
| | - Jiani Wang
- Breast Cancer Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiumei Yang
- Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China
| | - Yangming Zhang
- Equipment Department, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xusheng Tu
- Emergency Department, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Pan Hu
- Breast Cancer Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Sonehara K, Okada Y. Leveraging genome-wide association studies to better understand the etiology of cancers. Cancer Sci 2025; 116:288-296. [PMID: 39561785 PMCID: PMC11786324 DOI: 10.1111/cas.16402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Revised: 10/21/2024] [Accepted: 11/05/2024] [Indexed: 11/21/2024] Open
Abstract
Genome-wide association studies (GWAS) statistically assess the association between tens of millions of genetic variants in the whole genome and a phenotype of interest. Genome-wide association studies enable the elucidation of polygenic inheritance of cancer, in which myriad low-penetrance genetic variants collectively contribute to a substantial proportion of the heritable susceptibility. In addition to the robust genotype-phenotype associations provided by GWAS, combining GWAS data with functional genomic datasets or sophisticated statistical genetic methods unlocks deeper insights. Integrating genotype and molecular phenotyping data facilitates functional characterization of GWAS association signals through molecular quantitative trait loci mapping and transcriptome-wide association studies. Furthermore, aggregating genome-wide polygenic signals, including subthreshold associations, enables one to estimate genetic correlations across diverse phenotypes and helps in clinical risk predictions by evaluating polygenic risk scores. In this review, we begin by summarizing the rationale for GWAS of cancer, introduce recent methodological updates in the GWAS-derived downstream analyses, and demonstrate their applications to GWAS of cancers.
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Affiliation(s)
- Kyuto Sonehara
- Department of Genome Informatics, Graduate School of MedicineThe University of TokyoTokyoJapan
- Department of Statistical GeneticsOsaka University Graduate School of MedicineSuitaJapan
- Laboratory for Systems GeneticsRIKEN Center for Integrative Medical SciencesYokohamaJapan
| | - Yukinori Okada
- Department of Genome Informatics, Graduate School of MedicineThe University of TokyoTokyoJapan
- Department of Statistical GeneticsOsaka University Graduate School of MedicineSuitaJapan
- Laboratory for Systems GeneticsRIKEN Center for Integrative Medical SciencesYokohamaJapan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI‐IFReC)Osaka UniversitySuitaJapan
- Premium Research Institute for Human Metaverse Medicine (WPI‐PRIMe)Osaka UniversitySuitaJapan
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Geraghty S, Boyer JA, Fazel-Zarandi M, Arzouni N, Ryseck RP, McBride MJ, Parsons LR, Rabinowitz JD, Singh M. Integrative Computational Framework, Dyscovr, Links Mutated Driver Genes to Expression Dysregulation Across 19 Cancer Types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.20.624509. [PMID: 39605479 PMCID: PMC11601522 DOI: 10.1101/2024.11.20.624509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Though somatic mutations play a critical role in driving cancer initiation and progression, the systems-level functional impacts of these mutations-particularly, how they alter expression across the genome and give rise to cancer hallmarks-are not yet well-understood, even for well-studied cancer driver genes. To address this, we designed an integrative machine learning model, Dyscovr, that leverages mutation, gene expression, copy number alteration (CNA), methylation, and clinical data to uncover putative relationships between nonsynonymous mutations in key cancer driver genes and transcriptional changes across the genome. We applied Dyscovr pan-cancer and within 19 individual cancer types, finding both broadly relevant and cancer type-specific links between driver genes and putative targets, including a subset we further identify as exhibiting negative genetic relationships. Our work newly implicates-and validates in cell lines-KBTBD2 and mutant PIK3CA as putative synthetic lethals in breast cancer, suggesting a novel combinatorial treatment approach.
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Affiliation(s)
- Sara Geraghty
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Jacob A. Boyer
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
- Ludwig Cancer Institute, Princeton Branch, Princeton University, Princeton, NJ 08554
| | - Mahya Fazel-Zarandi
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Nibal Arzouni
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Rolf-Peter Ryseck
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Matthew J. McBride
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854
| | - Lance R. Parsons
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
| | - Joshua D. Rabinowitz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
- Ludwig Cancer Institute, Princeton Branch, Princeton University, Princeton, NJ 08554
- Department of Chemistry, Princeton University, Princeton, NJ 08544
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
- Department of Computer Science, Princeton University, Princeton, NJ 08544
- Lead Contact
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Tian Y, Wu L, Huang CC, Wang L. Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599704. [PMID: 38948854 PMCID: PMC11213234 DOI: 10.1101/2024.06.19.599704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations (p-value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants (p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856-RUVBL1 (multiome p-value = 0.0099 in BPH1) and rs7247241-SPINT2 (multiome p-value = 0.0002- 0.0004 in 22Rv1). We also functionally validated a new regulatory SNP and its target gene rs2474694-VPS53 (multiome p-value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.
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Affiliation(s)
- Yijun Tian
- Department of Tumor Biology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, United States
| | - Lang Wu
- Population Sciences in the Pacific Program, University of Hawai i Cancer Center, University of Hawai i at Mānoa, Honolulu, HI 96813, USA
| | - Chang-Ching Huang
- Zilber College of Public Health, University of Wisconsin, Milwaukee, WI 53226, United States
| | - Liang Wang
- Department of Tumor Biology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, United States
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Li H, Zhou Y, Zhao N, Wang Y, Lai Y, Zeng F, Yang F. ISMI-VAE: A deep learning model for classifying disease cells using gene expression and SNV data. Comput Biol Med 2024; 175:108485. [PMID: 38653063 DOI: 10.1016/j.compbiomed.2024.108485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 04/03/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
Various studies have linked several diseases, including cancer and COVID-19, to single nucleotide variations (SNV). Although single-cell RNA sequencing (scRNA-seq) technology can provide SNV and gene expression data, few studies have integrated and analyzed these multimodal data. To address this issue, we introduce Interpretable Single-cell Multimodal Data Integration Based on Variational Autoencoder (ISMI-VAE). ISMI-VAE leverages latent variable models that utilize the characteristics of SNV and gene expression data to overcome high noise levels and uses deep learning techniques to integrate multimodal information, map them to a low-dimensional space, and classify disease cells. Moreover, ISMI-VAE introduces an attention mechanism to reflect feature importance and analyze genetic features that could potentially cause disease. Experimental results on three cancer data sets and one COVID-19 data set demonstrate that ISMI-VAE surpasses the baseline method in terms of both effectiveness and interpretability and can effectively identify disease-causing gene features.
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Affiliation(s)
- Han Li
- Department of Automation, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China; Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Making, Xiamen university, Xiamen, 361000, China
| | - Yitao Zhou
- Department of Automation, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China; Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Making, Xiamen university, Xiamen, 361000, China
| | - Ningyuan Zhao
- Department of Automation, Xiamen University, Xiamen, China
| | - Ying Wang
- Department of Automation, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China; Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Making, Xiamen university, Xiamen, 361000, China
| | - Yongxuan Lai
- School of Informatics, Xiamen University, Xiamen, China
| | - Feng Zeng
- Department of Automation, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China; Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Making, Xiamen university, Xiamen, 361000, China; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, China; Research Unit of Cellular Stress of CAMS, Cancer Research Center, School of Medicine, Xiamen University, China.
| | - Fan Yang
- Department of Automation, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China; Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Making, Xiamen university, Xiamen, 361000, China.
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Zhu Z, Chen X, Zhang S, Yu R, Qi C, Cheng L, Zhang X. Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective. Hum Genet 2023; 142:1543-1560. [PMID: 37755483 DOI: 10.1007/s00439-023-02602-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xinyu Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China.
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
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Zhong C, Wu C, Lin Y, Lin D. Refined expression quantitative trait locus analysis on adenocarcinoma at the gastroesophageal junction reveals susceptibility and prognostic markers. Front Genet 2023; 14:1180500. [PMID: 37265963 PMCID: PMC10230079 DOI: 10.3389/fgene.2023.1180500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/03/2023] [Indexed: 06/03/2023] Open
Abstract
Objectives: This study aimed to explore cell type level expression quantitative trait loci (eQTL) in adenocarcinoma at the gastroesophageal junction (ACGEJ) and identify susceptibility and prognosis markers. Methods: Whole-genome sequencing (WGS) was performed on 120 paired samples from Chinese ACGEJ patients. Germline mutations were detected by GATK tools. RNA sequencing (RNA-seq) data on ACGEJ samples were taken from our previous studies. Public single-cell RNA sequencing (scRNA-seq) data were used to produce the proportion of epithelial cells. Matrix eQTL and a linear mixed model were used to identify condition-specific cis-eQTLs. The R package coloc was used to perform co-localization analysis with the public data of genome-wide association studies (GWASs). Log-rank and Cox regression tests were used to identify survival-associated eQTL and genes. Functions of candidate risk loci were explored by experimental validation. Results: Refined eQTL analyses of paired ACGEJ samples were performed and 2,036 potential ACGEJ-specific eQTLs with East Asian specificity were identified in total. ACGEJ-gain eQTLs were enriched at promoter regions more than ACGEJ-loss eQTLs. rs658524 was identified as the top eQTL close to the transcription start site of its paired gene (CTSW). rs2240191-RASAL1, rs4236599-FOXP2, rs4947311-PSORS1C1, rs13134812-LOC391674, and rs17508585-CDK13-DT were identified as ACGEJ-specific susceptibility eQTLs. rs309483-LINC01355 was associated with the overall survival of ACGEJ patients. We explored functions of candidate eQTLs such as rs658524, rs309483, rs2240191, and rs4947311 by experimental validation. Conclusion: This study provides new risk loci for ACGEJ susceptibility and effective disease prognosis biomarkers.
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Affiliation(s)
- Ce Zhong
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chen Wu
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Lin
- Beijing Advanced Innovation Center for Genomics (ICG), Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
| | - Dongxin Lin
- Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Pagadala M, Sears TJ, Wu VH, Pérez-Guijarro E, Kim H, Castro A, Talwar JV, Gonzalez-Colin C, Cao S, Schmiedel BJ, Goudarzi S, Kirani D, Au J, Zhang T, Landi T, Salem RM, Morris GP, Harismendy O, Patel SP, Alexandrov LB, Mesirov JP, Zanetti M, Day CP, Fan CC, Thompson WK, Merlino G, Gutkind JS, Vijayanand P, Carter H. Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response. Nat Commun 2023; 14:2744. [PMID: 37173324 PMCID: PMC10182072 DOI: 10.1038/s41467-023-38271-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.
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Affiliation(s)
- Meghana Pagadala
- Biomedical Sciences Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Timothy J Sears
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Victoria H Wu
- Department of Pharmacology, UCSD Moores Cancer Center, La Jolla, CA, 92093, USA
| | - Eva Pérez-Guijarro
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Hyo Kim
- Undergraduate Bioengineering Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Andrea Castro
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - James V Talwar
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - Steven Cao
- Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | | | | | - Divya Kirani
- Undergraduate Biology and Bioinformatics Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jessica Au
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Rany M Salem
- Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | - Gerald P Morris
- Department of Pathology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Olivier Harismendy
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego School of Medicine, La Jolla, CA, 92093, USA
| | - Sandip Pravin Patel
- Center for Personalized Cancer Therapy, Division of Hematology and Oncology, UC San Diego Moores Cancer Center, San Diego, CA, 92037, USA
| | - Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jill P Mesirov
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Maurizio Zanetti
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA
- The Laboratory of Immunology and Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Chi-Ping Day
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Chun Chieh Fan
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74136, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Wesley K Thompson
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | - Glenn Merlino
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - J Silvio Gutkind
- Department of Pharmacology, UCSD Moores Cancer Center, La Jolla, CA, 92093, USA
| | | | - Hannah Carter
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA, 92093, USA.
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Rey-Vargas L, Bejarano-Rivera LM, Mejia-Henao JC, Sua LF, Bastidas-Andrade JF, Ossa CA, Gutiérrez-Castañeda LD, Fejerman L, Sanabria-Salas MC, Serrano-Gómez SJ. Association of genetic ancestry with HER2, GRB7 AND estrogen receptor expression among Colombian women with breast cancer. Front Oncol 2022; 12:989761. [PMID: 36620598 PMCID: PMC9815522 DOI: 10.3389/fonc.2022.989761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Background Our previous study reported higher mRNA levels of the human epidermal growth factor receptor 2 (HER2)-amplicon genes ERBB2 and GRB7 in estrogen receptor (ER)-positive breast cancer patients with relatively high Indigenous American (IA) ancestry from Colombia. Even though the protein expression of HER2 and GRB7 is highly correlated, they may also express independently, an event that could change the patients' prognosis. In this study, we aimed to explore the differences in ER, HER2 and GRB7 protein expression according to genetic ancestry, to further assess the clinical implications of this association. Methods We estimated genetic ancestry from non-tumoral breast tissue DNA and assessed tumoral protein expression of ER, HER2, and GRB7 by immunohistochemistry in a cohort of Colombian patients from different health institutions. We used binomial and multinomial logistic regression models to test the association between genetic ancestry and protein expression. Kaplan-Meier and log-rank tests were used to evaluate the effect of HER2/GRB7 co-expression on patients' survival. Results Our results show that patients with higher IA ancestry have higher odds of having HER2+/GRB7- breast tumors, compared to the HER2-/GRB7- subtype, and this association seems to be stronger among ER-positive tumors (ER+/HER2+/GRB7-: OR=3.04, 95% CI, 1.47-6.37, p<0.05). However, in the multivariate model this association was attenuated (OR=1.80, 95% CI, 0.72-4.44, p=0.19). On the other hand, it was observed that having a higher European ancestry patients presented lower odds of ER+/HER2+/GRB7- breast tumors, this association remained significant in the multivariate model (OR=0.36, 95% CI, 0.13 - 0.93, p= 0.0395). The survival analysis according to HER2/GRB7 co-expression did not show statistically significant differences in the overall survival and recurrence-free survival. Conclusions Our results suggest that Colombian patients with higher IA ancestry and a lower European fraction have higher odds of ER+/HER2+/GRB7- tumors compared to ER+/HER2-/GRB7- disease. However, this association does not seem to be associated with patients' overall or recurrence-free survival.
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Affiliation(s)
- Laura Rey-Vargas
- Cancer Biology Research Group, National Cancer Institute of Colombia, Bogotá, Colombia,Doctoral Program in Biological Sciences, Pontificia Universidad Javeriana, Bogotá, Colombia
| | | | - Juan Carlos Mejia-Henao
- Oncological Pathology Research Group, National Cancer Institute of Colombia, Bogotá, Colombia
| | - Luz F. Sua
- Department of Pathology and Laboratory Medicine, Fundación Valle del Lili, and Faculty of Health Sciences, Universidad ICESI, Cali, Colombia
| | | | | | - Luz Dary Gutiérrez-Castañeda
- Research Institute, Group of Basic Sciences in Health (CBS), Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Laura Fejerman
- Department of Public Health Sciences and Comprehensive Cancer Center, University of California Davis, Davis, CA, United States
| | | | - Silvia J. Serrano-Gómez
- Cancer Biology Research Group, National Cancer Institute of Colombia, Bogotá, Colombia,Research support and follow-up group, National Cancer Institute of Colombia, Bogotá, Colombia,*Correspondence: Silvia J. Serrano-Gómez,
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10
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Kalita CA, Gusev A. DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk. Genome Biol 2022; 23:152. [PMID: 35804456 PMCID: PMC9264694 DOI: 10.1186/s13059-022-02708-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 06/15/2022] [Indexed: 01/09/2023] Open
Abstract
Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identify 3664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63× increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell-type-specific eQTL data are more enriched for cancer risk than conventional eQTLs. Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies.
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Affiliation(s)
- Cynthia A. Kalita
- Division of Population Sciences, Dana–Farber Cancer Institute & Harvard Medical School, Boston, USA
| | - Alexander Gusev
- Division of Population Sciences, Dana–Farber Cancer Institute & Harvard Medical School, Boston, USA
- The Broad Institute, Boston, USA
- Division of Genetics, Brigham & Women’s Hospital, Boston, USA
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11
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Pereira B, Labrot E, Durand E, Korn JM, Kauffmann A, Campbell CD. Contribution and clinical relevance of germline variation to the cancer transcriptome. BMC Cancer 2022; 22:675. [PMID: 35725412 PMCID: PMC9208227 DOI: 10.1186/s12885-022-09757-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 06/10/2022] [Indexed: 11/20/2022] Open
Abstract
Background Somatic alterations in the cancer genome, some of which are associated with changes in gene expression, have been characterized in multiple studies across diverse cancer types. However, less is known about germline variants that influence tumor biology by shaping the cancer transcriptome. Methods We performed expression quantitative trait loci (eQTL) analyses using multi-dimensional data from The Cancer Genome Atlas to explore the role of germline variation in mediating the cancer transcriptome. After accounting for associations between somatic alterations and gene expression, we determined the contribution of inherited variants to the cancer transcriptome relative to that of somatic variants. Finally, we performed an interaction analysis using estimates of tumor cellularity to identify cell type-restricted eQTLs. Results The proportion of genes with at least one eQTL varied between cancer types, ranging between 0.8% in melanoma to 28.5% in thyroid cancer and was correlated more strongly with intratumor heterogeneity than with somatic alteration rates. Although contributions to variance in gene expression was low for most genes, some eQTLs accounted for more than 30% of expression of proximal genes. We identified cell type-restricted eQTLs in genes known to be cancer drivers including LPP and EZH2 that were associated with disease-specific mortality in TCGA but not associated with disease risk in published GWAS. Together, our results highlight the need to consider germline variation in interpreting cancer biology beyond risk prediction. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09757-0.
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Affiliation(s)
- Bernard Pereira
- Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Emma Labrot
- Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Eric Durand
- Novartis Institutes for Biomedical Research, Novartis Campus, Fabrikstrasse 2, CH-4056, Basel, Switzerland
| | - Joshua M Korn
- Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Audrey Kauffmann
- Novartis Institutes for Biomedical Research, Novartis Campus, Fabrikstrasse 2, CH-4056, Basel, Switzerland
| | - Catarina D Campbell
- Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA.
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12
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Grishin D, Gusev A. Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms. Nat Genet 2022; 54:837-849. [PMID: 35697866 PMCID: PMC9886437 DOI: 10.1038/s41588-022-01075-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 04/08/2022] [Indexed: 02/02/2023]
Abstract
While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain largely unknown. Here we used 406 cancer ATAC-Seq samples across 23 cancer types to identify 7,262 germline allele-specific accessibility QTLs (as-aQTLs). Cancer as-aQTLs had stronger enrichment for cancer risk heritability (up to 145 fold) than any other functional annotation across seven cancer GWAS. Most cancer as-aQTLs directly altered transcription factor (TF) motifs and exhibited differential TF binding and gene expression in functional screens. To connect as-aQTLs to putative risk mechanisms, we introduced the regulome-wide associations study (RWAS). RWAS identified genetically associated accessible peaks at >70% of known breast and prostate loci and discovered new risk loci in all examined cancer types. Integrating as-aQTL discovery, motif analysis and RWAS identified candidate causal regulatory elements and their probable upstream regulators. Our work establishes cancer as-aQTLs and RWAS analysis as powerful tools to study the genetic architecture of cancer risk.
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Affiliation(s)
- Dennis Grishin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. .,The Eli and Edythe L. Broad Institute, Cambridge, MA, USA. .,Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
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13
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The Expression Quantitative Trait Loci in Immune Response Genes Impact the Characteristics and Survival of Colorectal Cancer. Diagnostics (Basel) 2022; 12:diagnostics12020315. [PMID: 35204406 PMCID: PMC8871427 DOI: 10.3390/diagnostics12020315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/24/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
The impact of germline variants on the regulation of the expression of tumor microenvironment (TME)-based immune response genes remains unclear. Expression quantitative trait loci (eQTL) provide insight into the effect of downstream target genes (eGenes) regulated by germline-associated variants (eVariants). Through eQTL analyses, we illustrated the relationships between germline eVariants, TME-based immune response eGenes, and clinical outcomes. In this study, both RNA sequencing data from primary tumor and germline whole-genome sequencing data were collected from patients with stage III colorectal cancer (CRC). Ninety-nine high-risk subjects were subjected to immune response gene expression analyses. Seventy-seven subjects remained for further analysis after quality control, of which twenty-two patients (28.5%) experienced tumor recurrence. We found that 65 eQTL, including 60 germline eVariants and 22 TME-based eGenes, impacted the survival of cancer patients. For the recurrence prediction model, 41 differentially expressed genes (DEGs) achieved the best area under the receiver operating characteristic curve of 0.93. In total, 19 survival-associated eGenes were identified among the DEGs. Most of these genes were related to the regulation of lymphocytes and cytokines. A high expression of HGF, CCR5, IL18, FCER1G, TDO2, IFITM2, and LAPTM5 was significantly associated with a poor prognosis. In addition, the FCER1G eGene was associated with tumor invasion, tumor nodal stage, and tumor site. The eVariants that regulate the TME-based expression of FCER1G, including rs2118867 and rs12124509, were determined to influence survival and chromatin binding preferences. We also demonstrated that FCER1G and co-expressed genes in TME were related to the aggregation of leukocytes via pathway analysis. By analyzing the eQTL from the cancer genome using germline variants and TME-based RNA sequencing, we identified the eQTL in immune response genes that impact colorectal cancer characteristics and survival.
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14
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Shetty A, Seo JH, Bell CA, O’Connor EP, Pomerantz MM, Freedman ML, Gusev A. Allele-specific epigenetic activity in prostate cancer and normal prostate tissue implicates prostate cancer risk mechanisms. Am J Hum Genet 2021; 108:2071-2085. [PMID: 34699744 DOI: 10.1016/j.ajhg.2021.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/15/2021] [Indexed: 11/26/2022] Open
Abstract
Genome-wide association studies (GWASs) of prostate cancer have identified >250 significant risk loci, but the causal variants and mechanisms for these loci remain largely unknown. Here, we sought to identify and characterize risk-harboring regulatory elements by integrating epigenomes from primary prostate tumor and normal tissues of 27 individuals across the H3K27ac, H3K4me3, and H3K4me2 histone marks and FOXA1 and HOXB13 transcription factors. We identified 7,371 peaks with significant allele specificity (allele-specific quantitative trait locus [asQTL] peaks). Showcasing their relevance to prostate cancer risk, H3K27ac T-asQTL peaks were the single annotation most enriched for prostate cancer GWAS heritability (40×), significantly higher than corresponding non-asQTL H3K27ac peaks (14×) or coding regions (14×). Surprisingly, fine-mapped GWAS risk variants were most significantly enriched for asQTL peaks observed in tumors, including asQTL peaks that were differentially imbalanced with respect to tumor-normal states. These data pinpointed putative causal regulatory elements at 20 GWAS loci, of which 11 were detected only in the tumor samples. More broadly, tumor-specific asQTLs were enriched for expression QTLs in benign tissues as well as accessible regions found in stem cells, supporting a hypothesis where some germline variants become reactivated during or after transformation and can be captured by epigenomic profiling of the tumor. Our study demonstrates the power of allele specificity in chromatin signals to uncover GWAS mechanisms, highlights the relevance of tumor-specific regulation in the context of cancer risk, and prioritizes multiple loci for experimental follow-up.
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15
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Bhattacharya A, Hamilton AM, Troester MA, Love MI. DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing. Nucleic Acids Res 2021; 49:e48. [PMID: 33524140 PMCID: PMC8096278 DOI: 10.1093/nar/gkab031] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/21/2020] [Accepted: 01/12/2021] [Indexed: 12/13/2022] Open
Abstract
Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA
| | - Alina M Hamilton
- Department of Pathology and Laboratory Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
| | - Melissa A Troester
- Department of Pathology and Laboratory Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
- Department of Epidemiology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA
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16
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Li W, Xu C, Guo J, Liu K, Hu Y, Wu D, Fang H, Zou Y, Wei Z, Wang Z, Zhou Y, Li Q. Cis- and Trans-Acting Expression Quantitative Trait Loci of Long Non-Coding RNA in 2,549 Cancers With Potential Clinical and Therapeutic Implications. Front Oncol 2020; 10:602104. [PMID: 33194770 PMCID: PMC7604522 DOI: 10.3389/fonc.2020.602104] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 09/28/2020] [Indexed: 11/13/2022] Open
Abstract
Many cancer risk loci act as expression quantitative trait loci (eQTLs) of transcripts including non-coding RNA. Long non-coding RNAs (lncRNAs) are implicated in various human cancers. However, the pathological and clinical impacts of the genetic determinants of lncRNAs in cancers remain largely unknown. In this study, we performed eQTL mapping of lncRNA expression (elncRNA) in 11 TCGA cancer types and characterized the biological processes of elncRNAs in the setting of genomic location, cancer treatment responses, and immune microenvironment. As a result, 10.86% of the cis-eQTLs and 1.67% of the trans-eQTLs of lncRNA were related to known genome-wide association studies (GWAS) cancer risk loci. The elncRNAs are significantly enriched for those which are previously annotated as predictive of drug sensitivities in cancer cell lines. We further revealed the downstream transcriptomic effectors of eQTL-elncRNA pairs. Our data specifically suggested that the genes affected by eQTL-elncRNA associations are enriched in the immune system processes and eQTL-elncRNA associations influence the constitution of tumor infiltrating lymphocytes. In ovarian cancer, the "rs34631313-AC092580.4" pair was associated with increased fraction of CD8+ T cells and M1 Macrophage; whereas in KIRC, the "rs9546285-LINC00426" pair was associated with increased fraction of CD8+ T cells and a decreased fraction of M2 macrophages. Our findings provide a systematic view of the transcriptomic impacts of the eQTL landscape of lncRNA in human cancers and suggest its strong potential relevance to cancer immunity and treatment.
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Affiliation(s)
- Wenzhi Li
- Department of Urology, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chaoqun Xu
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jintao Guo
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Ke Liu
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yudi Hu
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Dan Wu
- Department of Oncology, Xiamen the Fifth Hospital, Xiamen, China
| | - Hongkun Fang
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yun Zou
- Department of Urology, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ziwei Wei
- Department of Urology, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhong Wang
- Department of Urology, Shanghai Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ying Zhou
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Qiyuan Li
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
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17
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Beesley J, Sivakumaran H, Moradi Marjaneh M, Shi W, Hillman KM, Kaufmann S, Hussein N, Kar S, Lima LG, Ham S, Möller A, Chenevix-Trench G, Edwards SL, French JD. eQTL Colocalization Analyses Identify NTN4 as a Candidate Breast Cancer Risk Gene. Am J Hum Genet 2020; 107:778-787. [PMID: 32871102 PMCID: PMC7536644 DOI: 10.1016/j.ajhg.2020.08.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 08/10/2020] [Indexed: 12/13/2022] Open
Abstract
Breast cancer genome-wide association studies (GWASs) have identified 150 genomic risk regions containing more than 13,000 credible causal variants (CCVs). The CCVs are predominantly noncoding and enriched in regulatory elements. However, the genes underlying breast cancer risk associations are largely unknown. Here, we used genetic colocalization analysis to identify loci at which gene expression could potentially explain breast cancer risk phenotypes. Using data from the Breast Cancer Association Consortium (BCAC) and quantitative trait loci (QTL) from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Project (TCGA), we identify shared genetic relationships and reveal novel associations between cancer phenotypes and effector genes. Seventeen genes, including NTN4, were identified as potential mediators of breast cancer risk. For NTN4, we showed the rs61938093 CCV at this region was located within an enhancer element that physically interacts with the NTN4 promoter, and the risk allele reduced NTN4 promoter activity. Furthermore, knockdown of NTN4 in breast cells increased cell proliferation in vitro and tumor growth in vivo. These data provide evidence linking risk-associated variation to genes that may contribute to breast cancer predisposition.
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Affiliation(s)
- Jonathan Beesley
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia.
| | - Haran Sivakumaran
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Mahdi Moradi Marjaneh
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Wei Shi
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Kristine M Hillman
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Susanne Kaufmann
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Nehal Hussein
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia; Faculty of Medicine, The University of Queensland, Brisbane, QLD 4006, Australia
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Luize G Lima
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Sunyoung Ham
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Andreas Möller
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia; Faculty of Medicine, The University of Queensland, Brisbane, QLD 4006, Australia
| | | | - Stacey L Edwards
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia.
| | - Juliet D French
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
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18
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The Role of Noncoding Variants in Heritable Disease. Trends Genet 2020; 36:880-891. [PMID: 32741549 DOI: 10.1016/j.tig.2020.07.004] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/30/2020] [Accepted: 07/02/2020] [Indexed: 12/26/2022]
Abstract
The genetic basis of disease has largely focused on coding regions. However, it has become clear that a large proportion of the noncoding genome is functional and harbors genetic variants that contribute to disease etiology. Here, we review recent examples of inherited noncoding alterations that are responsible for Mendelian disorders or act to influence complex traits. We explore both rare and common genetic variants and discuss the wide range of mechanisms by which they affect gene regulation to promote disease. We also debate the challenges and progress associated with identifying and interpreting the functional and clinical significance of genetic variation in the context of the noncoding regulatory landscape.
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19
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Aguirre-Gamboa R, de Klein N, di Tommaso J, Claringbould A, van der Wijst MG, de Vries D, Brugge H, Oelen R, Võsa U, Zorro MM, Chu X, Bakker OB, Borek Z, Ricaño-Ponce I, Deelen P, Xu CJ, Swertz M, Jonkers I, Withoff S, Joosten I, Sanna S, Kumar V, Koenen HJPM, Joosten LAB, Netea MG, Wijmenga C, Franke L, Li Y. Deconvolution of bulk blood eQTL effects into immune cell subpopulations. BMC Bioinformatics 2020; 21:243. [PMID: 32532224 PMCID: PMC7291428 DOI: 10.1186/s12859-020-03576-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 06/01/2020] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). RESULTS The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. CONCLUSIONS Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution).
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Affiliation(s)
- Raúl Aguirre-Gamboa
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Niek de Klein
- Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jennifer di Tommaso
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Annique Claringbould
- Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Monique Gp van der Wijst
- Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Dylan de Vries
- Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Harm Brugge
- Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Roy Oelen
- Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Urmo Võsa
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Maria M Zorro
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Xiaojin Chu
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Centre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Feodor-Lynen-Str. 7, 30625, Hannover, Germany
| | - Olivier B Bakker
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Zuzanna Borek
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Isis Ricaño-Ponce
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Patrick Deelen
- Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Cheng-Jiang Xu
- Centre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Feodor-Lynen-Str. 7, 30625, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Morris Swertz
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Groningen, the Netherlands
| | - Iris Jonkers
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Sebo Withoff
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Irma Joosten
- Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Serena Sanna
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Vinod Kumar
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Hans J P M Koenen
- Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Leo A B Joosten
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Yang Li
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Centre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Feodor-Lynen-Str. 7, 30625, Hannover, Germany.
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
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20
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Pattee J, Zhan X, Xiao G, Pan W. Integrating germline and somatic genetics to identify genes associated with lung cancer. Genet Epidemiol 2019; 44:233-247. [PMID: 31821614 DOI: 10.1002/gepi.22275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 10/31/2019] [Accepted: 11/25/2019] [Indexed: 12/22/2022]
Abstract
Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex traits. However, GWAS experience power issues, resulting in the failure to detect certain associated variants. Additionally, GWAS are often unable to parse the biological mechanisms of driving associations. An existing gene-based association test framework, Transcriptome-Wide Association Studies (TWAS), leverages expression quantitative trait loci data to increase the power of association tests and illuminate the biological mechanisms by which genetic variants modulate complex traits. We extend the TWAS methodology to incorporate somatic information from tumors. By integrating germline and somatic data we are able to leverage information from the nuanced somatic landscape of tumors. Thus we can augment the power of TWAS-type tests to detect germline genetic variants associated with cancer phenotypes. We use somatic and germline data on lung adenocarcinomas from The Cancer Genome Atlas in conjunction with a meta-analyzed lung cancer GWAS to identify novel genes associated with lung cancer.
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Affiliation(s)
- Jack Pattee
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
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21
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Zhang Y, Manjunath M, Yan J, Baur BA, Zhang S, Roy S, Song JS. The Cancer-Associated Genetic Variant Rs3903072 Modulates Immune Cells in the Tumor Microenvironment. Front Genet 2019; 10:754. [PMID: 31507631 PMCID: PMC6715770 DOI: 10.3389/fgene.2019.00754] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 07/17/2019] [Indexed: 01/02/2023] Open
Abstract
Genome-wide association studies (GWAS) have hitherto identified several germline variants associated with cancer susceptibility, but the molecular functions of these risk modulators remain largely uncharacterized. Recent studies have begun to uncover the regulatory potential of noncoding GWAS SNPs using epigenetic information in corresponding cancer cell types and matched normal tissues. However, this approach does not explore the potential effect of risk germline variants on other important cell types that constitute the microenvironment of tumor or its precursor. This paper presents evidence that the breast-cancer-associated variant rs3903072 may regulate the expression of CTSW in tumor-infiltrating lymphocytes. CTSW is a candidate tumor-suppressor gene, with expression highly specific to immune cells and also positively correlated with breast cancer patient survival. Integrative analyses suggest a putative causative variant in a GWAS-linked enhancer in lymphocytes that loops to the 3' end of CTSW through three-dimensional chromatin interaction. Our work thus poses the possibility that a cancer-associated genetic variant could regulate a gene not only in the cell of cancer origin but also in immune cells in the microenvironment, thereby modulating the immune surveillance by T lymphocytes and natural killer cells and affecting the clearing of early cancer initiating cells.
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Affiliation(s)
- Yi Zhang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Mohith Manjunath
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Jialu Yan
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States.,Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Brittany A Baur
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States
| | - Shilu Zhang
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States
| | - Jun S Song
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States.,Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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22
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Wei Y, Dong S, Zhu Y, Zhao Y, Wu C, Zhu Y, Li K, Xu Y. DNA co-methylation analysis of lincRNAs across nine cancer types reveals novel potential epigenetic biomarkers in cancer. Epigenomics 2019; 11:1177-1190. [PMID: 31347388 DOI: 10.2217/epi-2018-0138] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Aim: The potential functions and prognostic value of lincRNAs with co-methylation events are explored in 9 cancer types. Materials & methods: Here, we evaluated the co-methylation events in promoter and gene-body regions between two lincRNAs across 9 cancer types by constructing a systematic biological framework. Results: The co-methylation events in both promoter and gene-body regions tended to be highly cancer specific. Patient samples could be separated by tumor and normal types according to the eigengenes of universal co-methylation clusters. Functional enrichment results revealed the lincRNAs that brought promoter and gene-body co-methylation events that affected cancer progress through participating in different pathways and could serve as potential prognostic biomarkers. Conclusion: The study provides new insight into the epigenetic regulation in cancer and leads to a potential new direction for epigenetic biomarker discovery.
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Affiliation(s)
- Yunzhen Wei
- College of Bioinformatics Science & Technology, Harbin Medical University, Harbin 150081, PR China.,School of Life Science, Faculty of Science, The Chinese University of Hong Kong, PR China
| | - Siyao Dong
- College of Bioinformatics Science & Technology, Harbin Medical University, Harbin 150081, PR China
| | - Yanjiao Zhu
- College of Bioinformatics Science & Technology, Harbin Medical University, Harbin 150081, PR China
| | - Yichuan Zhao
- College of Bioinformatics Science & Technology, Harbin Medical University, Harbin 150081, PR China
| | - Cheng Wu
- College of Bioinformatics Science & Technology, Harbin Medical University, Harbin 150081, PR China
| | - Yinling Zhu
- College of Bioinformatics Science & Technology, Harbin Medical University, Harbin 150081, PR China
| | - Kun Li
- College of Bioinformatics Science & Technology, Harbin Medical University, Harbin 150081, PR China
| | - Yan Xu
- College of Bioinformatics Science & Technology, Harbin Medical University, Harbin 150081, PR China
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23
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Ng JCF, Quist J, Grigoriadis A, Malim MH, Fraternali F. Pan-cancer transcriptomic analysis dissects immune and proliferative functions of APOBEC3 cytidine deaminases. Nucleic Acids Res 2019; 47:1178-1194. [PMID: 30624727 PMCID: PMC6379723 DOI: 10.1093/nar/gky1316] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 12/19/2018] [Accepted: 01/04/2019] [Indexed: 12/25/2022] Open
Abstract
APOBEC3 cytidine deaminases are largely known for their innate immune protection from viral infections. Recently, members of the family have been associated with a distinct mutational activity in some cancer types. We report a pan-tissue, pan-cancer analysis of RNA-seq data specific to the APOBEC3 genes in 8,951 tumours, 786 cancer cell lines and 6,119 normal tissues. By deconvolution of levels of different cell types in tumour admixtures, we demonstrate that APOBEC3B (A3B), the primary candidate as a cancer mutagen, shows little association with immune cell types compared to its paralogues. We present a pipeline called RESPECTEx (REconstituting SPecific Cell-Type Expression) and use it to deconvolute cell-type specific expression levels in a given cohort of tumour samples. We functionally annotate APOBEC3 co-expressing genes, and create an interactive visualization tool which 'barcodes' the functional enrichment (http://fraternalilab.kcl.ac.uk/apobec-barcodes/). These analyses reveal that A3B expression correlates with cell cycle and DNA repair genes, whereas the other APOBEC3 members display specificity for immune processes and immune cell populations. We offer molecular insights into the functions of individual APOBEC3 proteins in antiviral and proliferative contexts, and demonstrate the diversification this family of enzymes displays at the transcriptomic level, despite their high similarity in protein sequences and structures.
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Affiliation(s)
- Joseph C F Ng
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
| | - Jelmar Quist
- Cancer Bioinformatics, School of Cancer and Pharmaceutical Sciences, CRUK King's Health Partners Centre, Breast Cancer Now Research Unit, King's College London, London, UK
| | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer and Pharmaceutical Sciences, CRUK King's Health Partners Centre, Breast Cancer Now Research Unit, King's College London, London, UK
| | - Michael H Malim
- Department of Infectious Diseases, School of Immunology and Microbial Sciences, King's College London, London, UK
| | - Franca Fraternali
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
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