DOI QR코드

DOI QR Code

Perspectives of Integrative Cancer Genomics in Next Generation Sequencing Era

  • Kwon, So-Mee (Department of Physiology, Ajou University School of Medicine) ;
  • Cho, Hyun-Woo (Department of Physiology, Ajou University School of Medicine) ;
  • Choi, Ji-Hye (Department of Physiology, Ajou University School of Medicine) ;
  • Jee, Byul-A (Department of Physiology, Ajou University School of Medicine) ;
  • Jo, Yun-A (Department of Physiology, Ajou University School of Medicine) ;
  • Woo, Hyun-Goo (Department of Physiology, Ajou University School of Medicine)
  • 투고 : 2012.04.28
  • 심사 : 2012.05.23
  • 발행 : 2012.06.30

초록

The explosive development of genomics technologies including microarrays and next generation sequencing (NGS) has provided comprehensive maps of cancer genomes, including the expression of mRNAs and microRNAs, DNA copy numbers, sequence variations, and epigenetic changes. These genome-wide profiles of the genetic aberrations could reveal the candidates for diagnostic and/or prognostic biomarkers as well as mechanistic insights into tumor development and progression. Recent efforts to establish the huge cancer genome compendium and integrative omics analyses, so-called "integromics", have extended our understanding on the cancer genome, showing its daunting complexity and heterogeneity. However, the challenges of the structured integration, sharing, and interpretation of the big omics data still remain to be resolved. Here, we review several issues raised in cancer omics data analysis, including NGS, focusing particularly on the study design and analysis strategies. This might be helpful to understand the current trends and strategies of the rapidly evolving cancer genomics research.

키워드

참고문헌

  1. Mardis ER. A decade's perspective on DNA sequencing technology. Nature 2011;470:198-203. https://doi.org/10.1038/nature09796
  2. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 2010;11:685-696. https://doi.org/10.1038/nrg2841
  3. Schadt EE, Turner S, Kasarskis A. A window into third-generation sequencing. Hum Mol Genet 2010;19:R227-R240. https://doi.org/10.1093/hmg/ddq416
  4. Mardis ER. The $1,000 genome, the $100,000 analysis? Genome Med 2010;2:84. https://doi.org/10.1186/gm205
  5. Pasqualucci L, Trifonov V, Fabbri G, Ma J, Rossi D, Chiarenza A, et al. Analysis of the coding genome of diffuse large B-cell lymphoma. Nat Genet 2011;43:830-837. https://doi.org/10.1038/ng.892
  6. Guichard C, Amaddeo G, Imbeaud S, Ladeiro Y, Pelletier L, Maad IB, et al. Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma. Nat Genet 2012;44:694-698. https://doi.org/10.1038/ng.2256
  7. Fujimoto A, Totoki Y, Abe T, Boroevich KA, Hosoda F, Nguyen HH, et al. Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators. Nat Genet 2012 May 27 [Epub]. http://dx.doi.org/10.1038/ng.2291.
  8. Wang K, Kan J, Yuen ST, Shi ST, Chu KM, Law S, et al. Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer. Nat Genet 2011;43:1219-1223. https://doi.org/10.1038/ng.982
  9. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008;321:956-960. https://doi.org/10.1126/science.1160342
  10. David CJ, Manley JL. Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged. Genes Dev 2010;24:2343-2364. https://doi.org/10.1101/gad.1973010
  11. Ju YS, Kim JI, Kim S, Hong D, Park H, Shin JY, et al. Extensive genomic and transcriptional diversity identified through massively parallel DNA and RNA sequencing of eighteen Korean individuals. Nat Genet 2011;43:745-752. https://doi.org/10.1038/ng.872
  12. Fu Y, Sun Y, Li Y, Li J, Rao X, Chen C, et al. Differential genome- wide profiling of tandem 3' UTRs among human breast cancer and normal cells by high-throughput sequencing. Genome Res 2011;21:741-747. https://doi.org/10.1101/gr.115295.110
  13. Mayr C, Bartel DP. Widespread shortening of 3'UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell 2009;138:673-684. https://doi.org/10.1016/j.cell.2009.06.016
  14. Maher CA, Kumar-Sinha C, Cao X, Kalyana-Sundaram S, Han B, Jing X, et al. Transcriptome sequencing to detect gene fusions in cancer. Nature 2009;458:97-101. https://doi.org/10.1038/nature07638
  15. Kohno T, Ichikawa H, Totoki Y, Yasuda K, Hiramoto M, Nammo T, et al. KIF5B-RET fusions in lung adenocarcinoma. Nat Med 2012;18:375-377. https://doi.org/10.1038/nm.2644
  16. Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings LA, et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 2010;467:1109-1113. https://doi.org/10.1038/nature09460
  17. Stephens PJ, Greenman CD, Fu B, Yang F, Bignell GR, Mudie LJ, et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 2011;144:27-40. https://doi.org/10.1016/j.cell.2010.11.055
  18. Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 2009;41:178-186. https://doi.org/10.1038/ng.298
  19. Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, et al. Increased methylation variation in epigenetic domains across cancer types. Nat Genet 2011;43:768-775. https://doi.org/10.1038/ng.865
  20. Sandoval J, Esteller M. Cancer epigenomics: beyond genomics. Curr Opin Genet Dev 2012;22:50-55. https://doi.org/10.1016/j.gde.2012.02.008
  21. Chiarle R, Zhang Y, Frock RL, Lewis SM, Molinie B, Ho YJ, et al. Genome-wide translocation sequencing reveals mechanisms of chromosome breaks and rearrangements in B cells. Cell 2011;147:107-119. https://doi.org/10.1016/j.cell.2011.07.049
  22. Klein IA, Resch W, Jankovic M, Oliveira T, Yamane A, Nakahashi H, et al. Translocation-capture sequencing reveals the extent and nature of chromosomal rearrangements in B lymphocytes. Cell 2011;147:95-106. https://doi.org/10.1016/j.cell.2011.07.048
  23. Prensner JR, Iyer MK, Balbin OA, Dhanasekaran SM, Cao Q, Brenner JC, et al. Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression. Nat Biotechnol 2011;29:742-749. https://doi.org/10.1038/nbt.1914
  24. Shibata Y, Kumar P, Layer R, Willcox S, Gagan JR, Griffith JD, et al. Extrachromosomal microDNAs and chromosomal microdeletions in normal tissues. Science 2012;336:82-86. https://doi.org/10.1126/science.1213307
  25. Woo HG, Park ES, Thorgeirsson SS, Kim YJ. Exploring genomic profiles of hepatocellular carcinoma. Mol Carcinog 2011;50:235-243. https://doi.org/10.1002/mc.20691
  26. Subramanian J, Simon R. Gene expression-based prognostic signatures in lung cancer: ready for clinical use? J Natl Cancer Inst 2010;102:464-474. https://doi.org/10.1093/jnci/djq025
  27. Ioannidis JP, Panagiotou OA. Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses. JAMA 2011;305:2200-2210. https://doi.org/10.1001/jama.2011.713
  28. Brooks JD. Translational genomics: the challenge of developing cancer biomarkers. Genome Res 2012;22:183-187. https://doi.org/10.1101/gr.124347.111
  29. Hu Z, Chen X, Zhao Y, Tian T, Jin G, Shu Y, et al. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-smallcell lung cancer. J Clin Oncol 2010;28:1721-1726. https://doi.org/10.1200/JCO.2009.24.9342
  30. Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer 2011;11:426-437. https://doi.org/10.1038/nrc3066
  31. Hoshida Y, Villanueva A, Kobayashi M, Peix J, Chiang DY, Camargo A, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med 2008;359:1995-2004. https://doi.org/10.1056/NEJMoa0804525
  32. Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW, Regev A, et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat Genet 2008;40:499-507. https://doi.org/10.1038/ng.127
  33. Lee JS, Heo J, Libbrecht L, Chu IS, Kaposi-Novak P, Calvisi DF, et al. A novel prognostic subtype of human hepatocellular carcinoma derived from hepatic progenitor cells. Nat Med 2006;12:410-416. https://doi.org/10.1038/nm1377
  34. Woo HG, Lee JH, Yoon JH, Kim CY, Lee HS, Jang JJ, et al. Identification of a cholangiocarcinoma-like gene expression trait in hepatocellular carcinoma. Cancer Res 2010;70:3034-3041. https://doi.org/10.1158/0008-5472.CAN-09-2823
  35. Woo HG, Wang XW, Budhu A, Kim YH, Kwon SM, Tang ZY, et al. Association of TP53 mutations with stem cell-like gene expression and survival of patients with hepatocellular carcinoma. Gastroenterology 2011;140:1063-1070. https://doi.org/10.1053/j.gastro.2010.11.034
  36. Katz SF, Lechel A, Obenauf AC, Begus-Nahrmann Y, Kraus JM, Hoffmann EM, et al. Disruption of Trp53 in livers of mice induces formation of carcinomas with bilineal differentiation. Gastroenterology 2012;142:1229-1239.e3.
  37. Chaffer CL, Weinberg RA. A perspective on cancer cell metastasis. Science 2011;331:1559-1564. https://doi.org/10.1126/science.1203543
  38. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883-892. https://doi.org/10.1056/NEJMoa1113205
  39. Ding L, Ellis MJ, Li S, Larson DE, Chen K, Wallis JW, et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 2010;464:999-1005. https://doi.org/10.1038/nature08989
  40. Tao Y, Ruan J, Yeh SH, Lu X, Wang Y, Zhai W, et al. Rapid growth of a hepatocellular carcinoma and the driving mutations revealed by cell-population genetic analysis of whole-genome data. Proc Natl Acad Sci U S A 2011;108:12042-12047. https://doi.org/10.1073/pnas.1108715108
  41. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature 2011;472:90-94. https://doi.org/10.1038/nature09807
  42. Shah SP, Morin RD, Khattra J, Prentice L, Pugh T, Burleigh A, et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 2009;461:809-813. https://doi.org/10.1038/nature08489
  43. Quackenbush J. Microarray analysis and tumor classification. N Engl J Med 2006;354:2463-2472. https://doi.org/10.1056/NEJMra042342
  44. Cancer Genome Atlas Research Network, Bell D, Berchuck A, Birrer M, Chien J, Cramer D, et al. Integrated genomic analyses of ovarian carcinoma. Nature 2011;474:609-615. https://doi.org/10.1038/nature10166
  45. Rhodes DR, Chinnaiyan AM. Integrative analysis of the cancer transcriptome. Nat Genet 2005;37 Suppl:S31-S37. https://doi.org/10.1038/ng1570
  46. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012;483:603-607. https://doi.org/10.1038/nature11003
  47. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 2012;483:570-575. https://doi.org/10.1038/nature11005
  48. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006;313:1929-1935. https://doi.org/10.1126/science.1132939

피인용 문헌

  1. Genetic Risk Prediction for Normal-Karyotype Acute Myeloid Leukemia Using Whole-Exome Sequencing vol.11, pp.1, 2013, https://doi.org/10.5808/GI.2013.11.1.46
  2. Recent Progress of Genome Study for Anaplastic Thyroid Cancer vol.11, pp.2, 2013, https://doi.org/10.5808/GI.2013.11.2.68
  3. The importance of information technology for clinical and basic researches on the field of oral and maxillofacial surgery vol.39, pp.4, 2013, https://doi.org/10.5125/jkaoms.2013.39.4.149
  4. Research ethics in the post-genomic era vol.54, pp.7, 2013, https://doi.org/10.1002/em.21804
  5. Molecular classification of basal cell carcinoma of skin by gene expression profiling vol.54, pp.12, 2014, https://doi.org/10.1002/mc.22233
  6. Profiling of Exome Mutations Associated with Progression of HBV-Related Hepatocellular Carcinoma vol.9, pp.12, 2014, https://doi.org/10.1371/journal.pone.0115152
  7. Genetics and Genomics of Coronary Artery Disease vol.18, pp.10, 2016, https://doi.org/10.1007/s11886-016-0777-y
  8. A three-caller pipeline for variant analysis of cancer whole-exome sequencing data vol.15, pp.5, 2017, https://doi.org/10.3892/mmr.2017.6336
  9. Testing personalized medicine: patient and physician expectations of next-generation genomic sequencing in late-stage cancer care vol.22, pp.3, 2014, https://doi.org/10.1038/ejhg.2013.158
  10. Web-based tools for microRNAs involved in human cancer vol.11, pp.6, 2016, https://doi.org/10.3892/ol.2016.4446
  11. SEQprocess: a modularized and customizable pipeline framework for NGS processing in R package vol.20, pp.1, 2019, https://doi.org/10.1186/s12859-019-2676-x