Characterization and validation of somatic mutation spectrum to reveal heterogeneity in gastric cancer by single cell sequencing
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摘要
Gastric cancer(GC) is a highly heterogeneous disease with multiple cellular types and poor prognosis.However, the cellular evolution and molecular basis of GC at the individual intra-tumor level has not been well demonstrated. We performed single-cell whole exome sequencing to detect somatic singlenucleotide variants(SNVs) and significantly mutated genes(SMGs) among 34 tumor cells and 9 normal cells from a patient with GC. The Complete Prediction for Protein Conformation(CPPC) approach directly predicting the folding conformation of the protein 3D structure with Protein Folding Shape Code, combined with functional experiments were used to confirm the characterization of mutated SMGs in GC cells. We identified 201 somatic SNVs, including 117 non-synonymous mutations in GC cells. Further analysis identified 24 significant mutated genes(SMGs) in single cells, for which a single amino acid change might affect protein conformation. Among them, two genes(CDC27 and FLG) that were mutated only in single cells but not in the corresponding tumor tissue, were recurrently present in another GC tissue cohort, and may play a potential role to promote carcinogenesis, as confirmed by functional characterization. Our findings showed a mutational landscape of GC at intra-tumor level for the first time and provided opportunities for understanding the heterogeneity and individualized target therapy for this disease.
Gastric cancer(GC) is a highly heterogeneous disease with multiple cellular types and poor prognosis.However, the cellular evolution and molecular basis of GC at the individual intra-tumor level has not been well demonstrated. We performed single-cell whole exome sequencing to detect somatic singlenucleotide variants(SNVs) and significantly mutated genes(SMGs) among 34 tumor cells and 9 normal cells from a patient with GC. The Complete Prediction for Protein Conformation(CPPC) approach directly predicting the folding conformation of the protein 3 D structure with Protein Folding Shape Code, combined with functional experiments were used to confirm the characterization of mutated SMGs in GC cells. We identified 201 somatic SNVs, including 117 non-synonymous mutations in GC cells. Further analysis identified 24 significant mutated genes(SMGs) in single cells, for which a single amino acid change might affect protein conformation. Among them, two genes(CDC27 and FLG) that were mutated only in single cells but not in the corresponding tumor tissue, were recurrently present in another GC tissue cohort, and may play a potential role to promote carcinogenesis, as confirmed by functional characterization. Our findings showed a mutational landscape of GC at intra-tumor level for the first time and provided opportunities for understanding the heterogeneity and individualized target therapy for this disease.
Gastric cancer(GC) is a highly heterogeneous disease with multiple cellular types and poor prognosis.However, the cellular evolution and molecular basis of GC at the individual intra-tumor level has not been well demonstrated. We performed single-cell whole exome sequencing to detect somatic singlenucleotide variants(SNVs) and significantly mutated genes(SMGs) among 34 tumor cells and 9 normal cells from a patient with GC. The Complete Prediction for Protein Conformation(CPPC) approach directly predicting the folding conformation of the protein 3 D structure with Protein Folding Shape Code, combined with functional experiments were used to confirm the characterization of mutated SMGs in GC cells. We identified 201 somatic SNVs, including 117 non-synonymous mutations in GC cells. Further analysis identified 24 significant mutated genes(SMGs) in single cells, for which a single amino acid change might affect protein conformation. Among them, two genes(CDC27 and FLG) that were mutated only in single cells but not in the corresponding tumor tissue, were recurrently present in another GC tissue cohort, and may play a potential role to promote carcinogenesis, as confirmed by functional characterization. Our findings showed a mutational landscape of GC at intra-tumor level for the first time and provided opportunities for understanding the heterogeneity and individualized target therapy for this disease.
引文
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[2] Zang ZJ, Cutcutache I, Poon SL, et al. Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes. Nat Genet 2012;44:570–4.
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[13] Cibulskis K, Lawrence MS, Carter SL, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol2013;31:213–9.
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[17] Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013;499:214–8.
[18] Futreal PA, Coin L, Marshall M, et al. A census of human cancer genes. Nat Rev Cancer 2004;4:177–83.
[19] Lawrence MS, Stojanov P, Mermel CH, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 2014;505:495–501.
[20] Prager EM, Wilson AC. Construction of phylogenetic trees for proteins and nucleic acids:empirical evaluation of alternative matrix methods. J Mol Evol1978;11:129–42.
[21] Kumar S, Tamura K, Nei M. Mega:Molecular evolutionary genetics analysis software for microcomputers. Computer applications in the biosciences.CABIOS 1994;10:189–91.
[22] Yang J. Comprehensive description of protein structures using protein folding shape code. Proteins 2008;71:1497–518.
[23] Spits C, Le Caignec C, De Rycke M, et al. Whole-genome multiple displacement amplification from single cells. Nat Protoc 2006;1:1965–70.
[24] Uzozie A, Nanni P, Staiano T, et al. Sorbitol dehydrogenase overexpression and other aspects of dysregulated protein expression in human precancerous colorectal neoplasms:a quantitative proteomics study. Mol Cell Proteomics:MCP 2014;13:1198–218.
[25] Wen D, Xu Z, Xia L, et al. Important role of sumoylation of spliceosome factors in prostate cancer cells. J Proteome Res 2014;13:3571–82.
[26] Lee SJ, Langhans SA. Anaphase-promoting complex/cyclosome protein cdc27 is a target for curcumin-induced cell cycle arrest and apoptosis. BMC cancer2012;12:44.
[27] Lindberg J, Mills IG, Klevebring D, et al. The mitochondrial and autosomal mutation landscapes of prostate cancer. Eur Urol 2013;63:702–8.
[28] Talvinen K, Karra H, Pitkanen R, et al. Low cdc27 and high securin expression predict short survival for breast cancer patients. APMIS 2013;121:945–53.
[29] Gawad C, Koh W, Quake SR. Single-cell genome sequencing:current state of the science. Nat Rev Genet 2016;17:175–88.
[30] Grun D, van Oudenaarden A. Design and analysis of single-cell sequencing experiments. Cell 2015;163:799–810.
[31] McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution:past,present, and the future. Cell 2017;168:613–28.
[2] Zang ZJ, Cutcutache I, Poon SL, et al. Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes. Nat Genet 2012;44:570–4.
[3] Wang K, Yuen ST, Xu J, et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat Genet2014;46:573–82.
[4] Cancer Genome Atlas Research N. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 2014;513:202–9.
[5] Yu C, Yu J, Yao X, et al. Discovery of biclonal origin and a novel oncogene slc12a5 in colon cancer by single-cell sequencing. Cell Res 2014;24:701–12.
[6] Li Y, Xu X, Song L, et al. Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer.GigaScience 2012;1:12.
[7] Xu X, Hou Y, Yin X, et al. Single-cell exome sequencing reveals singlenucleotide mutation characteristics of a kidney tumor. Cell 2012;148:886–95.
[8] Hou Y, Song L, Zhu P, et al. Single-cell exome sequencing and monoclonal evolution of a jak2-negative myeloproliferative neoplasm. Cell2012;148:873–85.
[9] Demeulemeester J, Kumar P, Moller EK, et al. Tracing the origin of disseminated tumor cells in breast cancer using single-cell sequencing.Genome Biol 2016;17:250.
[10] Zhang X, Marjani SL, Hu Z, et al. Single-cell sequencing for precise cancer research:progress and prospects. Cancer Res 2016;76:1305–12.
[11] Navin NE. The first five years of single-cell cancer genomics and beyond.Genome Res 2015;25:1499–507.
[12] McKenna A, Hanna M, Banks E, et al. The genome analysis toolkit:a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res2010;20:1297–303.
[13] Cibulskis K, Lawrence MS, Carter SL, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol2013;31:213–9.
[14] Wang K, Li M, Hakonarson H. Annovar:Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38:e164.
[15] Imielinski M, Berger AH, Hammerman PS, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 2012;150:1107–20.
[16] Youn A, Simon R. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics 2011;27:175–81.
[17] Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013;499:214–8.
[18] Futreal PA, Coin L, Marshall M, et al. A census of human cancer genes. Nat Rev Cancer 2004;4:177–83.
[19] Lawrence MS, Stojanov P, Mermel CH, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 2014;505:495–501.
[20] Prager EM, Wilson AC. Construction of phylogenetic trees for proteins and nucleic acids:empirical evaluation of alternative matrix methods. J Mol Evol1978;11:129–42.
[21] Kumar S, Tamura K, Nei M. Mega:Molecular evolutionary genetics analysis software for microcomputers. Computer applications in the biosciences.CABIOS 1994;10:189–91.
[22] Yang J. Comprehensive description of protein structures using protein folding shape code. Proteins 2008;71:1497–518.
[23] Spits C, Le Caignec C, De Rycke M, et al. Whole-genome multiple displacement amplification from single cells. Nat Protoc 2006;1:1965–70.
[24] Uzozie A, Nanni P, Staiano T, et al. Sorbitol dehydrogenase overexpression and other aspects of dysregulated protein expression in human precancerous colorectal neoplasms:a quantitative proteomics study. Mol Cell Proteomics:MCP 2014;13:1198–218.
[25] Wen D, Xu Z, Xia L, et al. Important role of sumoylation of spliceosome factors in prostate cancer cells. J Proteome Res 2014;13:3571–82.
[26] Lee SJ, Langhans SA. Anaphase-promoting complex/cyclosome protein cdc27 is a target for curcumin-induced cell cycle arrest and apoptosis. BMC cancer2012;12:44.
[27] Lindberg J, Mills IG, Klevebring D, et al. The mitochondrial and autosomal mutation landscapes of prostate cancer. Eur Urol 2013;63:702–8.
[28] Talvinen K, Karra H, Pitkanen R, et al. Low cdc27 and high securin expression predict short survival for breast cancer patients. APMIS 2013;121:945–53.
[29] Gawad C, Koh W, Quake SR. Single-cell genome sequencing:current state of the science. Nat Rev Genet 2016;17:175–88.
[30] Grun D, van Oudenaarden A. Design and analysis of single-cell sequencing experiments. Cell 2015;163:799–810.
[31] McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution:past,present, and the future. Cell 2017;168:613–28.
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