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Meng XH, Weng YT, Rao Y, Xu YQ, Sun H, Li C. Two genetic variants in the HIBCH and FTCDNL1 genes are associated with susceptibility to developmental dysplasia of the hips among the Han Chinese population of Southwest China. J Orthop Surg Res 2024; 19:464. [PMID: 39113043 PMCID: PMC11304665 DOI: 10.1186/s13018-024-04958-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 07/29/2024] [Indexed: 08/11/2024] Open
Abstract
BACKGROUND Developmental dysplasia of the hip (DDH) is a common cause of childhood disability, and the incidence of DDH shows significant familial aggregation. As the genetic factors of DDH remain unknown, the correlation between five candidate single nucleotide polymorphisms (SNPs) and DDH was evaluated in the Han Chinese population of Southwest China. METHODS A case‒control association study was conducted in 276 patients with DDH and 318 healthy controls. SNP genotyping in the case and control groups was performed by SNPshot and multiple PCR. SNPs were genotyped in the case and control groups by multiplex PCR. The relationship between DDH and candidate SNPs was evaluated using the χ2 test. RESULTS The genotype distributions of rs291412 in HIBCH and rs769956 in FTCDNL1 were different between the case and control groups (P < 0.05). After genetic model analysis, logistic regression analysis revealed that the C allele of rs291412 had a protective effect on DDH (OR = 0.605, P = 0.010) and that the G allele of rs769956 was a risk factor (OR = 2.939, P = 0.010).s. CONCLUSION These SNPs could be associated with susceptibility to DDH but larger population-based studies should confirm the current results.
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Affiliation(s)
- Xu-Han Meng
- Department of Orthopaedic, 920th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, 212 Daguan Road, Xishan District, Kunming, 650032, Yunnan, China
| | - Yu-Ting Weng
- Department of Urology, Enze Hospital, Taizhou Enze Medical Center (Group), Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Affiliate to Hangzhou Medical College, Taizhou, 318000, Zhejiang, China
| | - Yu Rao
- Department of Orthopaedic, 920th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, 212 Daguan Road, Xishan District, Kunming, 650032, Yunnan, China
| | - Yong-Qing Xu
- Department of Orthopaedic, 920th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, 212 Daguan Road, Xishan District, Kunming, 650032, Yunnan, China
| | - Hao Sun
- The Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China.
| | - Chuan Li
- Department of Orthopaedic, 920th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, 212 Daguan Road, Xishan District, Kunming, 650032, Yunnan, China.
- Key Laboratory of Bioactive Peptides of Yunnan Province, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), and Sino-African Joint Research Center, New Cornerstone Science Laboratory, Kunming Institute of Zoology, Engineering Laboratory of Peptides of Chinese Academy of Sciences, National Resource Center for Non-Human Primates, the Chinese Academy of Sciences, No.17 Longxin Road, Kunming, Yunnan, 650201, China.
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2
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Muyas F, Sauer CM, Valle-Inclán JE, Li R, Rahbari R, Mitchell TJ, Hormoz S, Cortés-Ciriano I. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat Biotechnol 2024; 42:758-767. [PMID: 37414936 PMCID: PMC11098751 DOI: 10.1038/s41587-023-01863-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/07/2023] [Indexed: 07/08/2023]
Abstract
Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.
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Affiliation(s)
- Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Carolin M Sauer
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Jose Espejo Valle-Inclán
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Ruoyan Li
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Raheleh Rahbari
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Thomas J Mitchell
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
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3
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Farin HF, Mosa MH, Ndreshkjana B, Grebbin BM, Ritter B, Menche C, Kennel KB, Ziegler PK, Szabó L, Bollrath J, Rieder D, Michels BE, Kress A, Bozlar M, Darvishi T, Stier S, Kur IM, Bankov K, Kesselring R, Fichtner-Feigl S, Brüne B, Goetze TO, Al-Batran SE, Brandts CH, Bechstein WO, Wild PJ, Weigert A, Müller S, Knapp S, Trajanoski Z, Greten FR. Colorectal Cancer Organoid-Stroma Biobank Allows Subtype-Specific Assessment of Individualized Therapy Responses. Cancer Discov 2023; 13:2192-2211. [PMID: 37489084 PMCID: PMC10551667 DOI: 10.1158/2159-8290.cd-23-0050] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 06/05/2023] [Accepted: 07/21/2023] [Indexed: 07/26/2023]
Abstract
In colorectal cancers, the tumor microenvironment plays a key role in prognosis and therapy efficacy. Patient-derived tumor organoids (PDTO) show enormous potential for preclinical testing; however, cultured tumor cells lose important characteristics, including the consensus molecular subtypes (CMS). To better reflect the cellular heterogeneity, we established the colorectal cancer organoid-stroma biobank of matched PDTOs and cancer-associated fibroblasts (CAF) from 30 patients. Context-specific phenotyping showed that xenotransplantation or coculture with CAFs improves the transcriptomic fidelity and instructs subtype-specific stromal gene expression. Furthermore, functional profiling in coculture exposed CMS4-specific therapeutic resistance to gefitinib and SN-38 and prognostic expression signatures. Chemogenomic library screening identified patient- and therapy-dependent mechanisms of stromal resistance including MET as a common target. Our results demonstrate that colorectal cancer phenotypes are encrypted in the cancer epithelium in a plastic fashion that strongly depends on the context. Consequently, CAFs are essential for a faithful representation of molecular subtypes and therapy responses ex vivo. SIGNIFICANCE Systematic characterization of the organoid-stroma biobank provides a resource for context dependency in colorectal cancer. We demonstrate a colorectal cancer subtype memory of PDTOs that is independent of specific driver mutations. Our data underscore the importance of functional profiling in cocultures for improved preclinical testing and identification of stromal resistance mechanisms. This article is featured in Selected Articles from This Issue, p. 2109.
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Affiliation(s)
- Henner F. Farin
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mohammed H. Mosa
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
| | - Benardina Ndreshkjana
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
| | - Britta M. Grebbin
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Birgit Ritter
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
| | - Constantin Menche
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
| | - Kilian B. Kennel
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
| | - Paul K. Ziegler
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Lili Szabó
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Julia Bollrath
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Dietmar Rieder
- Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Birgitta E. Michels
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Alena Kress
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
| | - Müge Bozlar
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
| | - Tahmineh Darvishi
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Sara Stier
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Ivan-Maximilano Kur
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- Institute of Biochemistry I, Goethe University, Frankfurt am Main, Germany
| | - Katrin Bankov
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Rebecca Kesselring
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of General and Visceral Surgery, University of Freiburg, Freiburg, Germany
| | - Stefan Fichtner-Feigl
- Department of General and Visceral Surgery, University of Freiburg, Freiburg, Germany
| | - Bernhard Brüne
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Biochemistry I, Goethe University, Frankfurt am Main, Germany
| | | | | | - Christian H. Brandts
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medicine, Goethe University, Frankfurt am Main, Germany
| | - Wolf O. Bechstein
- Department of General and Visceral Surgery, Goethe University, Frankfurt am Main, Germany
| | - Peter J. Wild
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany
| | - Andreas Weigert
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Biochemistry I, Goethe University, Frankfurt am Main, Germany
| | - Susanne Müller
- Institute of Pharmaceutical Chemistry, Goethe University, Frankfurt am Main, Germany
- Structural Genomics Consortium, Goethe University, Frankfurt am Main, Germany
| | - Stefan Knapp
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Pharmaceutical Chemistry, Goethe University, Frankfurt am Main, Germany
- Structural Genomics Consortium, Goethe University, Frankfurt am Main, Germany
| | - Zlatko Trajanoski
- Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Florian R. Greten
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
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4
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Bolivar AM, Duzagac F, Sinha KM, Vilar E. Advances in vaccine development for cancer prevention and treatment in Lynch Syndrome. Mol Aspects Med 2023; 93:101204. [PMID: 37478804 PMCID: PMC10528439 DOI: 10.1016/j.mam.2023.101204] [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: 04/13/2023] [Revised: 07/06/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Abstract
Lynch Syndrome (LS) is one of the most common hereditary cancer syndromes, and is caused by mutations in one of the four DNA mismatch repair (MMR) genes, namely MLH1, MSH2, MSH6 and PMS2. Tumors developed by LS carriers display high levels of microsatellite instability, which leads to the accumulation of large numbers of mutations, among which frameshift insertion/deletions (indels) within microsatellite (MS) loci are the most common. As a result, MMR-deficient (MMRd) cells generate increased rates of tumor-specific neoantigens (neoAgs) that can be recognized by the immune system to activate cancer cell killing. In this context, LS is an ideal disease to leverage immune-interception strategies. Therefore, the identification of these neoAgs is an ongoing effort for the development of LS cancer preventive vaccines. In this review, we summarize the computational methods used for in silico neoAg prediction, including their challenges, and the experimental techniques used for in vitro validation of their immunogenicity. In addition, we outline results from past and on-going vaccine clinical trials and highlight avenues for improvement and future directions.
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Affiliation(s)
- Ana M Bolivar
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fahriye Duzagac
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Krishna M Sinha
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eduardo Vilar
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Clinical Cancer Genetics Program, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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5
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Ueno S, Hasegawa Y, Kato S, Mori H, Tsukada H, Ohira H, Kaneko S. Rapid survey of de novo mutations in naturally growing tree species following the March 2011 disaster in Fukushima: The effect of low-dose-rate radiation. ENVIRONMENT INTERNATIONAL 2023; 174:107893. [PMID: 37058973 DOI: 10.1016/j.envint.2023.107893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 02/13/2023] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
The impact of low-dose-rate radiation on genetics is largely unknown, particularly in natural environments. The Fukushima Dai-ich Nuclear Power Plant disaster resulted in the creation of contaminated natural lands. In this study, de novo mutations (DNMs) in germ line cells were surveyed from double-digest RADseq fragments in Japanese cedar and flowering cherry trees exposed to ambient dose rates ranging from 0.08 to 6.86 μGy h-1. These two species are among the most widely cultivated Japanese gymnosperm and angiosperm trees for forestry and horticultural purpose, respectively. For Japanese flowering cherry, open crossings were performed to produce seedlings, and only two candidate DNMs were detected from uncontaminated area. For Japanese cedar, the haploid megagametophytes were used as next generation samples. The use of megagametophytes from open crossing for next generation mutation screening had many advantages such as reducing exposure to radiation in contaminated areas because artificial crossings are not needed and the ease of data analysis owing to the haploid nature of megagametophytes. A direct comparison of the nucleotide sequences of parents and megagametophytes revealed an average of 1.4 candidate DNMs per megagametophyte sample (range: 0-40) after filtering procedures were optimized based on the validation of DNMs via Sanger sequencing. There was no relationship between the observed mutations and the ambient dose rate in the growing area or the concentration of 137Cs in cedar branches. The present results also suggest that mutation rates differ among lineages and that the growing environment has a relatively large influence on these mutation rates. These results suggested there was no significant increase in the mutation rate of the germplasm of Japanese cedar and flowering cherry trees growing in the contaminated areas.
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Affiliation(s)
- Saneyoshi Ueno
- Department of Forest Molecular Genetics and Biotechnology, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan.
| | - Yoichi Hasegawa
- Department of Forest Molecular Genetics and Biotechnology, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan
| | - Shuri Kato
- Department of Forest Molecular Genetics and Biotechnology, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan; Tama Forest Science Garden, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 1833-81 Todori, Hachioji, Tokyo 193-0843, Japan
| | - Hideki Mori
- Department of Forest Molecular Genetics and Biotechnology, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan
| | - Hirofumi Tsukada
- Institute of Environmental Radioactivity, Fukushima University, 1 Kanayagawa, Fukushima, Fukushima 960-1296, Japan
| | - Hajime Ohira
- Faculty of Symbiotic Systems Science, Fukushima University, 1 Kanayagawa, Fukushima, Fukushima 960-1296, Japan
| | - Shingo Kaneko
- Faculty of Symbiotic Systems Science, Fukushima University, 1 Kanayagawa, Fukushima, Fukushima 960-1296, Japan.
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6
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Vornholz L, Isay SE, Kurgyis Z, Strobl DC, Loll P, Mosa MH, Luecken MD, Sterr M, Lickert H, Winter C, Greten FR, Farin HF, Theis FJ, Ruland J. Synthetic enforcement of STING signaling in cancer cells appropriates the immune microenvironment for checkpoint inhibitor therapy. SCIENCE ADVANCES 2023; 9:eadd8564. [PMID: 36921054 PMCID: PMC10017047 DOI: 10.1126/sciadv.add8564] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Immune checkpoint inhibitors (ICIs) enhance anticancer immunity by releasing repressive signals into tumor microenvironments (TMEs). To be effective, ICIs require preexisting immunologically "hot" niches for tumor antigen presentation and lymphocyte recruitment. How the mutational landscape of cancer cells shapes these immunological niches remains poorly defined. We found in human and murine colorectal cancer (CRC) models that the superior antitumor immune response of mismatch repair (MMR)-deficient CRC required tumor cell-intrinsic activation of cGAS-STING signaling triggered by genomic instability. Subsequently, we synthetically enforced STING signaling in CRC cells with intact MMR signaling using constitutively active STING variants. Even in MMR-proficient CRC, genetically encoded gain-of-function STING was sufficient to induce cancer cell-intrinsic interferon signaling, local activation of antigen-presenting cells, recruitment of effector lymphocytes, and sensitization of previously "cold" TMEs to ICI therapy in vivo. Thus, our results introduce a rational strategy for modulating cancer cell-intrinsic programs via engineered STING enforcement to sensitize resistant tumors to ICI responsiveness.
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Affiliation(s)
- Larsen Vornholz
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Sophie E. Isay
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Zsuzsanna Kurgyis
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Department of Dermatology and Allergology, Technical University of Munich, Munich, Germany
| | - Daniel C. Strobl
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Institute of Computational Biology, Department of Computational Health, Helmholtz Center Munich, Neuherberg, Germany
| | - Patricia Loll
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Mohammed H. Mosa
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt/Main, Germany
- Frankfurt Cancer Institute, Goethe University Frankfurt, Frankfurt/Main, Germany
| | - Malte D. Luecken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Center Munich, Neuherberg, Germany
- Institute of Lung Health and Immunity (LHI), Helmholtz Center Munich, Neuherberg, Germany
- Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Michael Sterr
- Institute of Diabetes and Regeneration Research, Helmholtz Center Munich, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Center Munich, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Stem Cell Research, Helmholtz Center Munich, Neuherberg, Germany
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Christof Winter
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich partner site, Germany
- German Cancer Consortium (DKTK), Frankfurt/Mainz partner site, Germany
| | - Florian R. Greten
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt/Main, Germany
- Frankfurt Cancer Institute, Goethe University Frankfurt, Frankfurt/Main, Germany
- German Cancer Consortium (DKTK), Frankfurt/Mainz partner site, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Henner F. Farin
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt/Main, Germany
- Frankfurt Cancer Institute, Goethe University Frankfurt, Frankfurt/Main, Germany
- German Cancer Consortium (DKTK), Frankfurt/Mainz partner site, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Center Munich, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Jürgen Ruland
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich partner site, Germany
- German Cancer Consortium (DKTK), Frankfurt/Mainz partner site, Germany
- German Center for Infection Research (DZIF), Munich partner site, Germany
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7
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Ba Q, Zhou J, Li J, Cheng S, Zhang X, Wang H. Mutagenic Characteristics of Six Heavy Metals in Escherichia coli: The Commonality and Specificity. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13867-13877. [PMID: 36121417 PMCID: PMC9536316 DOI: 10.1021/acs.est.2c04785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/27/2022] [Accepted: 08/31/2022] [Indexed: 06/15/2023]
Abstract
The history of long-term environmental exposure to heavy metals can be recorded in the genome as sporadic and specific mutations. Variable environments introduce diverse and adaptive mutations to organisms. To reveal the information hidden in genomes about environmental exposure to heavy metals, we performed long-term mutation accumulation (MA) experiments with Escherichia coli, analyzed genomes from 36 populations across 1650 generations with 6 heavy metal exposure regimes (arsenic, cadmium, chromium, copper, nickel, and lead), and inferred metal-specific evolution modes at the genomic level. All heavy metals induced genetic mutations with a mean rate of 3.459 × 10-9 per nucleotide per generation. The mutational spectrum exhibited distinct signatures; however, heavy metals also shared common mutation signatures prominently associated with all cancer types. The mutated genes showed an average similarity of 54.4% within the same exposure regime, whereas only 38.8% between exposure regimes. In terms of biological insights, mutated genes were enriched to fundamental cellular processes such as metabolism, motility, and transport. Our study elucidates the mutagenic commonality and specificity of environmental heavy metals, which are highly specific at mutational features and locus, but conserved at gene and functional levels, and may play crucial roles in the convergence of adaptation to heavy metals.
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Affiliation(s)
- Qian Ba
- State
Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell
Omics, School of Public Health, Shanghai
Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jingqi Zhou
- State
Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell
Omics, School of Public Health, Shanghai
Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jingquan Li
- State
Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell
Omics, School of Public Health, Shanghai
Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shujun Cheng
- State
Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell
Omics, School of Public Health, Shanghai
Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaokang Zhang
- School
of Public Health and Health Management, Gannan Medical University, Ganzhou 341000, China
| | - Hui Wang
- State
Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell
Omics, School of Public Health, Shanghai
Jiao Tong University School of Medicine, Shanghai 200025, China
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8
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A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers. Sci Data 2022; 9:587. [PMID: 36153392 PMCID: PMC9509351 DOI: 10.1038/s41597-022-01681-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/09/2022] [Indexed: 11/28/2022] Open
Abstract
Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research. Measurement(s) | RNA expression profiling • whole-exome sequencing (WES) • somatic mutations • copy number alterations (CNAs) | Technology Type(s) | RNA sequencing • DNA sequencing • OncoScan CNV assay | Factor Type(s) | Intervention or procedure | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | China |
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9
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Miao YR, Liu CJ, Hu H, Yang M, Guo AY. Biological Pathway-Derived TMB Robustly Predicts the Outcome of Immune Checkpoint Blockade Therapy. Cells 2022; 11:cells11182802. [PMID: 36139377 PMCID: PMC9496944 DOI: 10.3390/cells11182802] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/22/2022] [Accepted: 09/02/2022] [Indexed: 11/28/2022] Open
Abstract
Although immune checkpoint blockade (ICB) therapies have achieved great progress, the patient response varies among cancers. In this study, we analyzed the potential genomic indicators contributing to ICB therapy response. The results showed that high tumor mutation burden (TMB) failed to predict response in anti-PD1 treated melanoma. SERPINB3 was the most significant response-related gene in melanoma and mutations in either SERPINB3 or PEG3 can serve as an independent risk factor in melanoma. Some recurrent mutations in CSMD3 were only in responders or non-responders, indicating their diverse impacts on patient response. Enrichment scores (ES) of gene mutations in 12 biological pathways were significantly higher in responders or non-responders. Next, the P-TMB calculated from genes in these pathways was significantly related to patient response with prediction AUC 0.74–0.82 in all collected datasets. In conclusion, our work provides new insights into the application of TMB in predicting patient response, which will benefit to immunotherapy research.
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Affiliation(s)
- Ya-Ru Miao
- Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chun-Jie Liu
- Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Hu
- Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Mei Yang
- Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - An-Yuan Guo
- Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
- Research Institute of Huazhong University of Science and Technology in Shenzhen, Shenzhen 518000, China
- Correspondence:
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10
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Comprehensive profiling of 1015 patients' exomes reveals genomic-clinical associations in colorectal cancer. Nat Commun 2022; 13:2342. [PMID: 35487942 PMCID: PMC9055073 DOI: 10.1038/s41467-022-30062-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 04/14/2022] [Indexed: 01/12/2023] Open
Abstract
The genetic basis of colorectal cancer (CRC) and its clinical associations remain poorly understood due to limited samples or targeted genes in current studies. Here, we perform ultradeep whole-exome sequencing on 1015 patients with CRC as part of the ChangKang Project. We identify 46 high-confident significantly mutated genes, 8 of which mutate in 14.9% of patients: LYST, DAPK1, CR2, KIF16B, NPIPB15, SYTL2, ZNF91, and KIAA0586. With an unsupervised clustering algorithm, we propose a subtyping strategy that classisfies CRC patients into four genomic subtypes with distinct clinical characteristics, including hypermutated, chromosome instability with high risk, chromosome instability with low risk, and genome stability. Analysis of immunogenicity uncover the association of immunogenicity reduction with genomic subtypes and poor prognosis in CRC. Moreover, we find that mitochondrial DNA copy number is an independent factor for predicting the survival outcome of CRCs. Overall, our results provide CRC-related molecular features for clinical practice and a valuable resource for translational research.
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11
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Cheng Z, Vermeulen M, Rollins-Green M, Babak T, DeVeale B. Identifying tumorigenic non-coding mutations through altered cis-regulation. STAR Protoc 2021; 2:100934. [PMID: 34816127 PMCID: PMC8591365 DOI: 10.1016/j.xpro.2021.100934] [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] [Indexed: 11/29/2022] Open
Abstract
Identification of non-coding mutations driving tumorigenesis requires alternative approaches to coding mutations. Enriched associations between mutated regulatory elements and altered cis-regulation in tumors are a promising approach to stratify candidate non-coding driver mutations. Here we provide a bioinformatics pipeline to mine data from the Cancer Genomic Commons (GDC) for such associations. The pipeline integrates RNA and whole-genome sequencing with genotyping data to reveal putative non-coding driver mutations by cancer type. For complete information on the generation and use of this protocol, please refer to Cheng et al. (2021). Integration of expression, genotyping, and whole-genome sequencing modalities RNA-seq profiles resolved to gene-level allele-specific expression (ASE). Mutated cis-regulatory features associated with gene-level ASE by cancer type
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Affiliation(s)
- Zhongshan Cheng
- Department of Biology, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Michael Vermeulen
- Department of Biology, Queen's University, Kingston, ON K7L 3N6, Canada
| | | | - Tomas Babak
- Department of Biology, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Brian DeVeale
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
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12
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Ye L, Wang L, Yang J, Hu P, Zhang C, Tong S, Liu Z, Tian D. Identification of Tumor Antigens and Immune Landscape in Glioblastoma for mRNA Vaccine Development. Front Genet 2021; 12:701065. [PMID: 34527020 PMCID: PMC8435740 DOI: 10.3389/fgene.2021.701065] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/29/2021] [Indexed: 12/21/2022] Open
Abstract
Background: Clinical benefits from standard therapies against glioblastoma (GBM) are limited in part due to the intrinsic radio- and chemo-resistance. As an essential part of tumor immunotherapy for adjunct, therapeutic tumor vaccines have been effective against multiple solid cancers, while their efficacy against GBM remains undefined. Therefore, this study aims to find the possible tumor antigens of GBM and identify the suitable population for cancer vaccination through immunophenotyping. Method: The genomic and responding clinical data of 169 GBM samples and five normal brain samples were obtained from The Cancer Genome Atlas (TCGA). The mRNA_seq data of 940 normal brain tissue were downloaded from Genotype-Tissue Expression (GTEx). Potential GBM mRNA antigens were screened out by differential expression, copy number variant (CNV), and mutation analysis. K-M survival and Cox analysis were carried out to investigate the prognostic association of potential tumor antigens. Tumor Immune Estimation Resource (TIMER) was used to explore the association between the antigens and tumor immune infiltrating cells (TIICs). Immunophenotyping of 169 samples was performed through consensus clustering based on the abundance of 22 kinds of immune cells. The characteristics of the tumor immune microenvironment (TIME) in each cluster were explored through single-sample gene set enrichment analysis based on 29 kinds of immune-related hallmarks and pathways. Weighted gene co-expression network analysis (WGCNA) was performed to cluster the genes related to immune subtypes. Finally, pathway enrichment analyses were performed to annotate the potential function of modules screened through WGCNA. Results: Two potential tumor antigens selected were significantly positively associated with the antigen-presenting immune cells (APCs) in GBM. Furthermore, the expression of antigens was verified at the protein level by Immunohistochemistry. Two robust immune subtypes, immune subtype 1 (IS1) and immune subtype 2 (IS2), representing immune status "immune inhibition" and "immune inflamed", respectively, had distinct clinical outcomes in GBM. Conclusion: ARPC1B and HK3 were potential mRNA antigens for developing GBM mRNA vaccination, and the patients in IS2 were considered the most suitable population for vaccination in GBM.
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Affiliation(s)
- Liguo Ye
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Long Wang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ji'an Yang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ping Hu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chunyu Zhang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shi'ao Tong
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhennan Liu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Daofeng Tian
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
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13
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Ye L, Wang L, Yang J, Hu P, Zhang C, Tong S, Liu Z, Tian D. Identification of tumor antigens and immune subtypes in lower grade gliomas for mRNA vaccine development. J Transl Med 2021; 19:352. [PMID: 34404444 PMCID: PMC8369324 DOI: 10.1186/s12967-021-03014-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 07/26/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND As an important part of tumor immunotherapy for adjunct, therapeutic tumor vaccines have been effective against multiple solid cancers, while their efficacy against lower grade glioma (LGG) remains undefined. Immunophenotyping of tumors is an essential tool to evaluate the immune function of patients with immunodeficiency or autoimmunity. Therefore, this study aims to find the potential tumor antigen of LGG and identify the suitable population for cancer vaccination based on the immune landscape. METHOD The genomic and clinical data of 529 patients with LGG were obtained from TCGA, the mRNA_seq data of normal brain tissue were downloaded from GTEx. Differential expression gene and mutation analysis were performed to screen out potential antigens, K-M curves were carried out to investigate the correlation between the level of potential antigens and OS and DFS of patients. TIMER dataset was used to explore the correlation between genes and immune infiltrating cells. Immunophenotyping of 529 tumor samples was based on the single-sample gene sets enrichment analysis. Cibersort and Estimate algorithm were used to explore the tumor immune microenvironment characteristics in each immune subtype. Weighted gene co-expression network analysis (WGCNA) clustered immune-related genes and screened the hub genes, and pathway enrichment analyses were performed on the hub modules related to immune subtype in the WGCNA. RESULTS Selecting for the mutated, up-regulated, prognosis- and immune-related genes, four potential tumor antigens were identified in LGG. They were also significantly positively associated with the antigen-presenting immune cells (APCs). Three robust immune subtypes, IS1, IS2 and IS3, represented immune status "desert", "immune inhibition", and "inflamed" respectively, which might serve as a predictive parameter. Subsequently, clinicopathological features, including the codeletion status of 1p19q, IDH mutation status, tumor mutation burden, tumor stemness, etc., were significantly different among subtypes. CONCLUSION FCGBP, FLNC, TLR7, and CSF2RA were potential antigens for developing cancer vaccination, and the patients in IS3 were considered the most suitable for vaccination in LGG.
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Affiliation(s)
- Liguo Ye
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, P.R. China
| | - Long Wang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, P.R. China
| | - Ji'an Yang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, P.R. China
| | - Ping Hu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, P.R. China
| | - Chunyu Zhang
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, P.R. China
| | - Shi'ao Tong
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, P.R. China
| | - Zhennan Liu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, P.R. China
| | - Daofeng Tian
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, P.R. China.
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Chu S, Skidmore ZL, Kunisaki J, Walker JR, Griffith M, Griffith OL, Bryan JN. Unraveling the chaotic genomic landscape of primary and metastatic canine appendicular osteosarcoma with current sequencing technologies and bioinformatic approaches. PLoS One 2021; 16:e0246443. [PMID: 33556121 PMCID: PMC7870011 DOI: 10.1371/journal.pone.0246443] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/19/2021] [Indexed: 12/03/2022] Open
Abstract
Osteosarcoma is a rare disease in children but is one of the most common cancers in adult large breed dogs. The mutational landscape of both the primary and pulmonary metastatic tumor in two dogs with appendicular osteosarcoma (OSA) was comprehensively evaluated using an automated whole genome sequencing, exome and RNA-seq pipeline that was adapted for this study for use in dogs. Chromosomal lesions were the most common type of mutation. The mutational landscape varied substantially between dogs but the lesions within the same patient were similar. Copy number neutral loss of heterozygosity in mutant TP53 was the most significant driver mutation and involved a large region in the middle of chromosome 5. Canine and human OSA is characterized by loss of cell cycle checkpoint integrity and DNA damage response pathways. Mutational profiling of individual patients with canine OSA would be recommended prior to targeted therapy, given the heterogeneity seen in our study and previous studies.
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Affiliation(s)
- Shirley Chu
- Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, MO, United States of America
- * E-mail:
| | - Zachary L. Skidmore
- McDonnell Genome Institute, Washington University, St. Louis, MO, United States of America
| | - Jason Kunisaki
- McDonnell Genome Institute, Washington University, St. Louis, MO, United States of America
| | - Jason R. Walker
- McDonnell Genome Institute, Washington University, St. Louis, MO, United States of America
| | - Malachi Griffith
- McDonnell Genome Institute, Washington University, St. Louis, MO, United States of America
- Department of Medicine, Washington University, St. Louis, MO, United States of America
| | - Obi L. Griffith
- McDonnell Genome Institute, Washington University, St. Louis, MO, United States of America
- Department of Medicine, Washington University, St. Louis, MO, United States of America
| | - Jeffrey N. Bryan
- Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, MO, United States of America
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15
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Abstract
Advances in next-generation sequencing technology have enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data. However, there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using the Genome in a Bottle Consortium, "synthetic-diploid" and simulated WGS datasets. DRAGEN and DeepVariant show better accuracy in SNP and indel calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient execution speed, and therefore superior performance in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also suggests a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical applications.
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16
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Zhao S, Agafonov O, Azab A, Stokowy T, Hovig E. Accuracy and efficiency of germline variant calling pipelines for human genome data. Sci Rep 2020; 10:20222. [PMID: 33214604 PMCID: PMC7678823 DOI: 10.1038/s41598-020-77218-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 11/02/2020] [Indexed: 12/30/2022] Open
Abstract
Advances in next-generation sequencing technology have enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data. However, there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using the Genome in a Bottle Consortium, "synthetic-diploid" and simulated WGS datasets. DRAGEN and DeepVariant show better accuracy in SNP and indel calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient execution speed, and therefore superior performance in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also suggests a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical applications.
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Affiliation(s)
- Sen Zhao
- Department of Tumor Biology, Institute of Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0310, Oslo, Norway
| | | | - Abdulrahman Azab
- Center for Bioinformatics, Department of Informatics, University of Oslo, 0316, Oslo, Norway
- Division of Research Computing, University Center for Information Technology (USIT), University of Oslo, 0316, Oslo, Norway
| | - Tomasz Stokowy
- Computational Biology Unit, Institute of Informatics, University of Bergen, 5008, Bergen, Norway
- Department of Clinical Science, University of Bergen, 5021, Bergen, Norway
| | - Eivind Hovig
- Department of Tumor Biology, Institute of Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0310, Oslo, Norway.
- Center for Bioinformatics, Department of Informatics, University of Oslo, 0316, Oslo, Norway.
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17
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SoRelle JA, Wachsmann M, Cantarel BL. Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays. Arch Pathol Lab Med 2020; 144:1118-1130. [PMID: 32045276 DOI: 10.5858/arpa.2019-0476-ra] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2019] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories. OBJECTIVE.— To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay. DATA SOURCES.— This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation. CONCLUSIONS.— This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.
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Affiliation(s)
- Jeffrey A SoRelle
- Department of Pathology (SoRelle, Wachsmann), University of Texas Southwestern Medical Center, Dallas
| | - Megan Wachsmann
- Department of Pathology (SoRelle, Wachsmann), University of Texas Southwestern Medical Center, Dallas
| | - Brandi L Cantarel
- Bioinformatics Core Facility (Cantarel), University of Texas Southwestern Medical Center, Dallas.,Department of Bioinformatics (Cantarel), University of Texas Southwestern Medical Center, Dallas.,University of Texas Southwestern Medical Center, Dallas
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18
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Chanwigoon S, Piwluang S, Wichadakul D. inCNV: An Integrated Analysis Tool for Copy Number Variation on Whole Exome Sequencing. Evol Bioinform Online 2020; 16:1176934320956577. [PMID: 33029071 PMCID: PMC7520931 DOI: 10.1177/1176934320956577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 08/13/2020] [Indexed: 12/13/2022] Open
Abstract
The detection of copy number variations (CNVs) on whole-exome sequencing (WES) represents a cost-effective technique for the study of genetic variants. This approach, however, has encountered an obstacle with high false-positive rates due to biases from exome sequencing capture kits and GC contents. Although plenty of CNV detection tools have been developed, they do not perform well with all types of CNVs. In addition, most tools lack features of genetic annotation, CNV visualization, and flexible installation, requiring users to put much effort into CNV interpretation. Here, we present "inCNV," a web-based application that can accept multiple CNV-tool results, then integrate and prioritize them with user-friendly interfaces. This application helps users analyze the importance of called CNVs by generating CNV annotations from Ensembl, Database of Genomic Variants (DGV), ClinVar, and Online Mendelian Inheritance in Man (OMIM). Moreover, users can select and export CNVs of interest including their flanking sequences for primer design and experimental verification. We demonstrated how inCNV could help users filter and narrow down the called CNVs to a potentially novel CNV, a common CNV within a group of samples of the same disease, or a de novo CNV of a sample within the same family. Besides, we have provided in CNV as a docker image for ease of installation (https://github.com/saowwapark/inCNV).
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Affiliation(s)
- Saowwapark Chanwigoon
- Software Engineering Program, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Sakkayaphab Piwluang
- Software Engineering Program, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Duangdao Wichadakul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
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Zhang W, Williams TA, Bhagwath AS, Hiermann JS, Peacock CD, Watkins DN, Ding P, Park JY, Montgomery EA, Forastiere AA, Jie C, Cantarel BL, Pham TH, Wang DH. GEAMP, a novel gastroesophageal junction carcinoma cell line derived from a malignant pleural effusion. J Transl Med 2020; 100:16-26. [PMID: 31292541 PMCID: PMC6920545 DOI: 10.1038/s41374-019-0278-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 05/06/2019] [Accepted: 05/06/2019] [Indexed: 02/06/2023] Open
Abstract
Gastroesophageal junction (GEJ) cancer remains a clinically significant disease in Western countries due to its increasing incidence, which mirrors that of esophageal cancer, and poor prognosis. To develop novel and effective approaches for prevention, early detection, and treatment of patients with GEJ cancer, a better understanding of the mechanisms driving pathogenesis and malignant progression of this disease is required. These efforts have been limited by the small number of available cell lines and appropriate preclinical animal models for in vitro and in vivo studies. We have established and characterized a novel GEJ cancer cell line, GEAMP, derived from the malignant pleural effusion of a previously treated GEJ cancer patient. Comprehensive genetic analyses confirmed a clonal relationship between GEAMP cells and the primary tumor. Targeted next-generation sequencing identified 56 nonsynonymous alterations in 51 genes including TP53 and APC, which are commonly altered in GEJ cancer. In addition, multiple copy-number alterations were found including EGFR and K-RAS gene amplifications and loss of CDKN2A and CDKN2B. Histological examination of subcutaneous flank xenografts in nude and NOD-SCID mice showed a carcinoma with mixed squamous and glandular differentiation, suggesting GEAMP cells contain a subpopulation with multipotent potential. Finally, pharmacologic inhibition of the EGFR signaling pathway led to downregulation of key downstream kinases and inhibition of cell proliferation in vitro. Thus, GEAMP represents a valuable addition to the limited number of bona fide GEJ cancer cell lines.
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Affiliation(s)
- Wei Zhang
- Esophageal Diseases Center and Division of Hematology-Oncology, Department of Internal Medicine and the Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Taylor A. Williams
- Esophageal Diseases Center and Division of Hematology-Oncology, Department of Internal Medicine and the Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ankur S. Bhagwath
- Esophageal Diseases Center and Division of Hematology-Oncology, Department of Internal Medicine and the Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jared S. Hiermann
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Craig D. Peacock
- Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA
| | - D. Neil Watkins
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Peiguo Ding
- Esophageal Diseases Center and Division of Hematology-Oncology, Department of Internal Medicine and the Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jason Y. Park
- Department of Pathology and the Eugene McDermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA
| | - Elizabeth A. Montgomery
- Division of Gastrointestinal and Liver Pathology, Department of Pathology, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Arlene A. Forastiere
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chunfa Jie
- Department of Biochemistry and Nutrition, Des Moines University, Des Moines, IA, USA
| | - Brandi L. Cantarel
- Bioinformatics Core Facility, Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Thai H. Pham
- Esophageal Diseases Center and Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA,VA North Texas Health Care System, Dallas, TX, USA
| | - David H. Wang
- Esophageal Diseases Center and Division of Hematology-Oncology, Department of Internal Medicine and the Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA,VA North Texas Health Care System, Dallas, TX, USA
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20
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Wood DE, White JR, Georgiadis A, Van Emburgh B, Parpart-Li S, Mitchell J, Anagnostou V, Niknafs N, Karchin R, Papp E, McCord C, LoVerso P, Riley D, Diaz LA, Jones S, Sausen M, Velculescu VE, Angiuoli SV. A machine learning approach for somatic mutation discovery. Sci Transl Med 2019; 10:10/457/eaar7939. [PMID: 30185652 DOI: 10.1126/scitranslmed.aar7939] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 05/26/2018] [Accepted: 08/16/2018] [Indexed: 12/19/2022]
Abstract
Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load-based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.
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Affiliation(s)
| | - James R White
- Personal Genome Diagnostics, Baltimore, MD 21224, USA
| | | | | | | | | | - Valsamo Anagnostou
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Noushin Niknafs
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Rachel Karchin
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.,Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Eniko Papp
- Personal Genome Diagnostics, Baltimore, MD 21224, USA
| | | | - Peter LoVerso
- Personal Genome Diagnostics, Baltimore, MD 21224, USA
| | - David Riley
- Personal Genome Diagnostics, Baltimore, MD 21224, USA
| | - Luis A Diaz
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Siân Jones
- Personal Genome Diagnostics, Baltimore, MD 21224, USA
| | - Mark Sausen
- Personal Genome Diagnostics, Baltimore, MD 21224, USA
| | - Victor E Velculescu
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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21
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Huang M, Tailor J, Zhen Q, Gillmor AH, Miller ML, Weishaupt H, Chen J, Zheng T, Nash EK, McHenry LK, An Z, Ye F, Takashima Y, Clarke J, Ayetey H, Cavalli FMG, Luu B, Moriarity BS, Ilkhanizadeh S, Chavez L, Yu C, Kurian KM, Magnaldo T, Sevenet N, Koch P, Pollard SM, Dirks P, Snyder MP, Largaespada DA, Cho YJ, Phillips JJ, Swartling FJ, Morrissy AS, Kool M, Pfister SM, Taylor MD, Smith A, Weiss WA. Engineering Genetic Predisposition in Human Neuroepithelial Stem Cells Recapitulates Medulloblastoma Tumorigenesis. Cell Stem Cell 2019; 25:433-446.e7. [PMID: 31204176 PMCID: PMC6731167 DOI: 10.1016/j.stem.2019.05.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 03/15/2019] [Accepted: 05/13/2019] [Indexed: 12/11/2022]
Abstract
Human neural stem cell cultures provide progenitor cells that are potential cells of origin for brain cancers. However, the extent to which genetic predisposition to tumor formation can be faithfully captured in stem cell lines is uncertain. Here, we evaluated neuroepithelial stem (NES) cells, representative of cerebellar progenitors. We transduced NES cells with MYCN, observing medulloblastoma upon orthotopic implantation in mice. Significantly, transcriptomes and patterns of DNA methylation from xenograft tumors were globally more representative of human medulloblastoma compared to a MYCN-driven genetically engineered mouse model. Orthotopic transplantation of NES cells generated from Gorlin syndrome patients, who are predisposed to medulloblastoma due to germline-mutated PTCH1, also generated medulloblastoma. We engineered candidate cooperating mutations in Gorlin NES cells, with mutation of DDX3X or loss of GSE1 both accelerating tumorigenesis. These findings demonstrate that human NES cells provide a potent experimental resource for dissecting genetic causation in medulloblastoma.
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Affiliation(s)
- Miller Huang
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jignesh Tailor
- Wellcome Trust-MRC Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK; Institute of Cancer Research, Sutton, London SM2 5NG, UK; Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada; Division of Neurosurgery, The Hospital for Sick Children, Toronto, ON, Canada
| | - Qiqi Zhen
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Aaron H Gillmor
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada; Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Calgary, AB, Canada
| | - Matthew L Miller
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Holger Weishaupt
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 751 85 Uppsala, Sweden
| | - Justin Chen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tina Zheng
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Emily K Nash
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Lauren K McHenry
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Zhenyi An
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Fubaiyang Ye
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Yasuhiro Takashima
- Wellcome Trust-MRC Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
| | - James Clarke
- Wellcome Trust-MRC Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
| | - Harold Ayetey
- Wellcome Trust-MRC Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
| | - Florence M G Cavalli
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Betty Luu
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Branden S Moriarity
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA; Center for Genome Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA
| | - Shirin Ilkhanizadeh
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Lukas Chavez
- Hopp-Children's Cancer Center (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Chunying Yu
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Kathreena M Kurian
- Institute of Clinical Neurosciences, Level 1, Learning and Research Building, Southmead Hospital, University of Bristol, Bristol BS10 5NB, UK
| | - Thierry Magnaldo
- Institute for Research on Cancer and Aging, Nice UMR CNRS 7284 INSERM U1081 UNS/UCA, Nice, France
| | - Nicolas Sevenet
- Institut Bergonie & INSERM U1218, Universite de Bordeaux, 229 cours de l'Argonne, 33076 Bordeaux Cedex, France
| | - Philipp Koch
- Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim and Hector Institut for Translational Brain Research (HITBR gGmbH), Mannheim, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Steven M Pollard
- MRC Centre for Regenerative Medicine and Cancer Research UK Edinburgh Centre, University of Edinburgh, Edinburgh, UK
| | - Peter Dirks
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada; Division of Neurosurgery, The Hospital for Sick Children, Toronto, ON, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David A Largaespada
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA; Center for Genome Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yoon Jae Cho
- Division of Pediatric Neurology, Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA; Papé Family Pediatric Research Institute, Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Joanna J Phillips
- Departments of Neurological Surgery and Pathology, University of California, San Francisco, CA 94158, USA
| | - Fredrik J Swartling
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 751 85 Uppsala, Sweden
| | - A Sorana Morrissy
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada; Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada; Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Calgary, AB, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Marcel Kool
- Hopp-Children's Cancer Center (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Stefan M Pfister
- Hopp-Children's Cancer Center (KiTZ), Heidelberg, Germany; Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany; Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael D Taylor
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada; Division of Neurosurgery, The Hospital for Sick Children, Toronto, ON, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Austin Smith
- Wellcome Trust-MRC Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
| | - William A Weiss
- Department of Neurology and the Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158, USA; Departments of Pediatrics, Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, CA 94158, USA.
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22
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Zhang P, Kang B, Xie G, Li S, Gu Y, Shen Y, Zhao X, Ma Y, Li F, Si J, Wang J, Chen J, Yang H, Xu X, Yang Y. Genomic sequencing and editing revealed the GRM8 signaling pathway as potential therapeutic targets of squamous cell lung cancer. Cancer Lett 2018; 442:53-67. [PMID: 30391781 DOI: 10.1016/j.canlet.2018.10.035] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 10/18/2018] [Accepted: 10/24/2018] [Indexed: 02/06/2023]
Abstract
The study sought to explore novel genetic aberration driving squamous cell lung carcinoma (LUSC). The whole exome (WES), whole genome (WGS) and target region (TS) sequencings and CRISPR-Cas9 genome editing techniques were integrated to explore and validate novel targeting candidates from LUSC primary tumors and corresponding patient-derived xenografts (PDXs). Seven genes (FGFR2, GRM1,PIK3CG, PIK3CA,ZFHX4, CSMD3, GRM8) with high frequencies of both single nucleotide variants (SNVs) and copy number variants (CNVs), and two genes (CLDN1 and RIT1) only with CNVs were identified by bioinformatics analysis. The functions of these candidates were validated through CRISPR-Cas9 system in primary PDX cells. Furthermore, we focused on the genetic and functional analysis of Metabotropic glutamate receptor 8 (GRM8), whose transcriptional activation was elucidated to promote the survival of LUSC tumor cell through inhibiting cAMP pathway and activating MAPK pathway. The SNV identified in GRM8, A112G, activated downstream signaling pathway and induced cell proliferation, which could be reversed by cAMP stimulator and MEK inhibitor. In conclusion, the components of GRM8 signaling pathway could serve as potential targets of squamous cell lung cancer carrying GRM8 activating variants.
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MESH Headings
- Animals
- Antineoplastic Agents/therapeutic use
- CRISPR-Associated Protein 9/genetics
- CRISPR-Associated Protein 9/metabolism
- CRISPR-Cas Systems
- Carcinoma, Squamous Cell/drug therapy
- Carcinoma, Squamous Cell/genetics
- Carcinoma, Squamous Cell/metabolism
- Carcinoma, Squamous Cell/pathology
- Cell Line, Tumor
- Cell Proliferation
- Cell Survival
- Clustered Regularly Interspaced Short Palindromic Repeats
- Cyclic AMP/metabolism
- Gene Editing/methods
- Gene Expression Regulation, Neoplastic
- Humans
- Lung Neoplasms/drug therapy
- Lung Neoplasms/genetics
- Lung Neoplasms/metabolism
- Lung Neoplasms/pathology
- Mice, Inbred NOD
- Mice, SCID
- Mitogen-Activated Protein Kinases/metabolism
- Molecular Targeted Therapy
- Mutation
- Receptors, Metabotropic Glutamate/genetics
- Receptors, Metabotropic Glutamate/metabolism
- Signal Transduction
- Transcriptional Activation
- Tumor Burden
- Tumor Cells, Cultured
- Exome Sequencing
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Affiliation(s)
- Panpan Zhang
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education/Beijing, Peking University Cancer Hospital and Institute, Beijing, China
| | - Bin Kang
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Jinsha Road, Shenzhen, 518120, China
| | - Guoyun Xie
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China
| | - Shaolei Li
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education/Beijing, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ying Gu
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Jinsha Road, Shenzhen, 518120, China
| | - Yue Shen
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Jinsha Road, Shenzhen, 518120, China
| | - Xin Zhao
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China
| | - Yuanyuan Ma
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education/Beijing, Peking University Cancer Hospital and Institute, Beijing, China
| | - Fuqiang Li
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China
| | - Jiahui Si
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education/Beijing, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jian Wang
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China
| | - Jinfeng Chen
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education/Beijing, Peking University Cancer Hospital and Institute, Beijing, China
| | - Huanming Yang
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Jinsha Road, Shenzhen, 518120, China
| | - Xun Xu
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China; China National GeneBank, BGI-Shenzhen, Jinsha Road, Shenzhen, 518120, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Jinsha Road, Shenzhen, 518120, China.
| | - Yue Yang
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education/Beijing, Peking University Cancer Hospital and Institute, Beijing, China.
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