Identification of distinct tumor subpopulations in lung adenocarcinoma via single-cell RNA-seq

PLoS ONE

Volumn 10, Issue 8, 2015, Pages

  Min, Jae Woong ^a   Kim, Woo Jin ^a   Han, Jeong A ^a   Jung, Yu Jin ^a   Kim, Kyu Tae ^b   Park, Woong Yang ^b,c   Lee, Hae Ock ^b,c   Choi, Sun Shim ^a  

^a Kangwon National University   (South Korea)

^b Samsung Medical Center   (South Korea)

^c Sungkyunkwan University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTION FACTOR E2F1; TRANSCRIPTION FACTOR; TRANSCRIPTOME;

ADULT; ARTICLE; CANCER STAGING; CELL CULTURE; CELL SUBPOPULATION; CLUSTER ANALYSIS; CONTROLLED STUDY; FEASIBILITY STUDY; FEMALE; HUMAN; HUMAN CELL; HUMAN TISSUE; IN VITRO STUDY; LUNG ADENOCARCINOMA; MALE; MIDDLE AGED; MOUSE; NONHUMAN; RNA SEQUENCE; SINGLE CELL RNA SEQUENCING; SMOKING; TRANSCRIPTOMICS; TUMOR XENOGRAFT; ADENOCARCINOMA; CELL CYCLE; DOWN REGULATION; DRUG SCREENING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; KAPLAN MEIER METHOD; LUNG TUMOR; METABOLISM; MULTIVARIATE ANALYSIS; PATHOLOGY; PROCEDURES; PROGNOSIS; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; SOUTH KOREA; STATISTICAL MODEL; TUMOR GENE; UPREGULATION;

ADENOCARCINOMA; CELL CYCLE; CLUSTER ANALYSIS; DOWN-REGULATION; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; GENES, NEOPLASM; HUMANS; KAPLAN-MEIER ESTIMATE; LOGISTIC MODELS; LUNG NEOPLASMS; MALE; MIDDLE AGED; MULTIVARIATE ANALYSIS; PROGNOSIS; REPUBLIC OF KOREA; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TRANSCRIPTION FACTORS; TRANSCRIPTOME; UP-REGULATION; XENOGRAFT MODEL ANTITUMOR ASSAYS;

EID: 84942919186     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0135817     Document Type: Article

References (56)

1. Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature. 2013; 501 (7467):355-64. doi:10.1038/nature12627 PMID: 24048068
2. Lyng H, Vorren AO, Sundfør K, Taksdal I, Lien HH, Kaalhus O, et al. Intra-and intertumor heterogeneity in blood perfusion of human cervical cancer before treatment and after radiotherapy. International journal of cancer. 2001; 96(3):182-90.
3. Cusnir M, Cavalcante L. Inter-tumor heterogeneity. Human vaccines & immunotherapeutics. 2012; 8 (8):1143-5.
4. Pinto N, Dolan ME. Clinically relevant genetic variations in drug metabolizing enzymes. Curr Drug Metab. 2011 Jun; 12(5):487-97. PMID: 21453273
5. Yasuda S, Zhang L, Huang S. The role of ethnicity in variability in response to drugs: focus on clinical pharmacology studies. Clinical Pharmacology & Therapeutics. 2008; 84(3):417-23.
6. Jiang Y, Yuen RK, Jin X, Wang M, Chen N, Wu X, et al. Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. The American Journal of Human Genetics. 2013; 93(2):249-63. doi:10.1016/j.ajhg.2013.06.012 PMID: 23849776
7. Sottoriva A, Spiteri I, Piccirillo SG, Touloumis A, Collins VP, Marioni JC, et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A. 2013 Mar 5; 110(10):4009-14. doi:10.1073/pnas.1219747110 PMID: 23412337
8. Michor F, Polyak K. The origins and implications of intratumor heterogeneity. Cancer Prev Res (Phila). 2010 Nov; 3(11):1361-4.
9. 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(10):883-92. doi:10.1056/NEJMoa1113205 PMID: 22397650
10. Swanton C. Intratumor heterogeneity: evolution through space and time. Cancer Res. 2012 Oct 1; 72 (19):4875-82. doi:10.1158/0008-5472.CAN-12-2217 PMID: 23002210
11. Zhang J, Fujimoto J, Zhang J, Wedge DC, Song X, Zhang J, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science. 2014; 346(6206):256-9. doi:10.1126/science.1256930 PMID: 25301631
12. Hajirasouliha I, Mahmoody A, Raphael BJ. A combinatorial approach for analyzing intra-tumor heterogeneity from high-throughput sequencing data. Bioinformatics. 2014 Jun 15; 30(12):i78-86. doi:10.1093/bioinformatics/btu284 PMID: 24932008
13. de Bruin EC, Taylor TB, Swanton C. Intra-tumor heterogeneity: lessons from microbial evolution and clinical implications. Genome Med. 2013; 5(11):101,?. doi:10.1186/gm505 PMID: 24267946
14. Aparicio S, Mardis E. Tumor heterogeneity: next-generation sequencing enhances the view from the pathologist's microscope. Genome Biol. 2014 Oct 1; 15(9):463. PMID: 25315013
15. Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer. 2013; 108(3):479-85. doi:10.1038/bjc.2012.581 PMID: 23299535
16. Denisov EV, Litviakov NV, Zavyalova MV, Perelmuter VM, Vtorushin SV, Tsyganov MM, et al. Intratumoral morphological heterogeneity of breast cancer: neoadjuvant chemotherapy efficiency and multidrug resistance gene expression. Scientific reports. 2014; 4.
17. Heppner GH, Dexter DL, DeNucci T, Miller FR, Calabresi P. Heterogeneity in drug sensitivity among tumor cell subpopulations of a single mammary tumor. Cancer Res. 1978 Nov; 38(11 Pt 1):3758-63. PMID: 698935
18. Leedham SJ, Preston SL, McDonald SA, Elia G, Bhandari P, Poller D, et al. Individual crypt genetic heterogeneity and the origin of metaplastic glandular epithelium in human Barrett's oesophagus. Gut. 2008 Aug; 57(8):1041-8. doi:10.1136/gut.2007.143339 PMID: 18305067
19. Owonikoko T, Rees M, Gabbert HE, Sarbia M. Intratumoral genetic heterogeneity in Barrett adenocarcinoma. Am J Clin Pathol. 2002 Apr; 117(4):558-66. PMID: 11939730
20. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013; 502(7471):333-9. doi:10.1038/nature12634 PMID: 24132290
21. Roukos D, Batsis C, Baltogiannis G. Assessing tumor heterogeneity and emergence mutations using next-generation sequencing for overcoming cancer drugs resistance. 2012. doi:10.1586/era.12.105 PMID: 23176613
22. Timmermann B, Kerick M, Roehr C, Fischer A, Isau M, Boerno ST, et al. Somatic mutation profiles of MSI and MSS colorectal cancer identified by whole exome next generation sequencing and bioinformatics analysis. PloS one. 2010; 5(12):e15661. doi:10.1371/journal.pone.0015661 PMID: 21203531
23. Nik-Zainal S, Alexandrov LB, Wedge DC, Van Loo P, Greenman CD, Raine K, et al. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012; 149(5):979-93. doi:10.1016/j.cell.2012.04.024 PMID: 22608084
24. Hiley C, de Bruin EC, McGranahan N, Swanton C. Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine. Genome Biol. 2014; 15:453. doi:10.1186/s13059-014-0453-8 PMID: 25222836
25. Hou Y, Song L, Zhu P, Zhang B, Tao Y, Xu X, et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell. 2012; 148(5):873-85. doi:10.1016/j.cell.2012.02.028 PMID: 22385957
26. Wang Y, Waters J, Leung ML, Unruh A, Roh W, Shi X, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature. 2014; 512(7513):155-60. doi:10.1038/nature13600 PMID: 25079324
27. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014 Jun 20; 344(6190):1396-401. doi:10.1126/science.1254257 PMID: 24925914
28. Kim KT, Lee HW, Lee HO, Kim SC, Seo YJ, Chung W, et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 2015 Jun 19; 16(1):127.
29. Siolas D, Hannon GJ. Patient-derived tumor xenografts: transforming clinical samples into mouse models. Cancer Res. 2013 Sep 1; 73(17):5315-9. doi:10.1158/0008-5472.CAN-13-1069 PMID: 23733750
30. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014 Aug 1; 30(15):2114-20. doi:10.1093/bioinformatics/btu170 PMID: 24695404
31. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013 Apr 25; 14(4): R36,2013-14-4-r36. doi:10.1186/gb-2013-14-4-r36 PMID: 23618408
32. Tu Y, Stolovitzky G, Klein U. Quantitative noise analysis for gene expression microarray experiments. Proc Natl Acad Sci U S A. 2002 Oct 29; 99(22):14031-6. PMID: 12388780
33. Lundberg E, Fagerberg L, Klevebring D, Matic I, Geiger T, Cox J, et al. Defining the transcriptome and proteome in three functionally different human cell lines. Molecular systems biology. 2010; 6(1).
34. Whitfield ML, Sherlock G, Saldanha AJ, Murray JI, Ball CA, Alexander KE, et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell. 2002 Jun; 13(6):1977-2000. PMID: 12058064
35. Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 2011; 12(3):R22. doi:10.1186/gb-2011-12-3-r22 PMID: 21410973
36. Yang JH, Li JH, Jiang S, Zhou H, Qu LH. ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data. Nucleic Acids Res. 2013 Jan; 41(Database issue):D177-87. doi:10.1093/nar/gks1060 PMID: 23161675
37. Miyagi A, Takahashi H, Takahara K, Hirabayashi T, Nishimura Y, Tezuka T, et al. Principal component and hierarchical clustering analysis of metabolites in destructive weeds; polygonaceous plants. Metabolomics. 2010; 6(1):146-55.
38. Efron B. Logistic regression, survival analysis, and the Kaplan-Meier curve. Journal of the American Statistical Association. 1988; 83(402):414-25.
39. Cox DR. Models and life-tables regression. JR Stat.Soc.Ser.B. 1972; 34:187-220.
40. Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol. 2013 Mar 6; 13:33,2288-13-33. doi:10.1186/1471-2288-13-33 PMID: 23496923
41. West RB, Rubin BP, Miller MA, Subramanian S, Kaygusuz G, Montgomery K, et al. A landscape effect in tenosynovial giant-cell tumor from activation of CSF1 expression by a translocation in a minority of tumor cells. Proc Natl Acad Sci U S A. 2006 Jan 17; 103(3):690-5. PMID: 16407111
42. Shapiro E, Biezuner T, Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nature Reviews Genetics. 2013; 14(9):618-30. doi:10.1038/nrg3542 PMID: 23897237
43. Lee H, Han S, Rhee H, Park JH, Lee JS, Oh Y, et al. Differential expression of microRNAs and their target genes in non-small-cell lung cancer. Molecular medicine reports. 2015; 11(3):2034-40. doi:10.3892/mmr.2014.2890 PMID: 25376560
44. Goodwin G, Johns E, Lehn D, Landsman D, Wright J, Ferrari S, et al. A cell cycle regulator potentially involved in genesis of many tumor types. Biol Chem. 1986; 261:2274.
45. Pan H, Griep AE. Altered cell cycle regulation in the lens of HPV-16 E6 or E7 transgenic mice: implications for tumor suppressor gene function in development. Genes Dev. 1994 Jun 1; 8(11):1285-99. PMID: 7926731
46. Sherr CJ. Cancer cell cycles. Science. 1996 Dec 6; 274(5293):1672-7. PMID: 8939849
47. O'Connor PM, Jackman J, Jondle D, Bhatia K, Magrath I, Kohn KW. Role of the p53 tumor suppressor gene in cell cycle arrest and radiosensitivity of Burkitt's lymphoma cell lines. Cancer Res. 1993 Oct 15; 53(20):4776-80. PMID: 8402660
48. Toshiyuki M, Reed JC. Tumor suppressor p53 is a direct transcriptional activator of the human bax gene. Cell. 1995; 80(2):293-9. PMID: 7834749
49. Johnson DG, Ohtani K, Nevins JR. Autoregulatory control of E2F1 expression in response to positive and negative regulators of cell cycle progression. Genes Dev. 1994 Jul 1; 8(13):1514-25. PMID: 7958836
50. Lin SY, Black AR, Kostic D, Pajovic S, Hoover CN, Azizkhan JC. Cell cycle-regulated association of E2F1 and Sp1 is related to their functional interaction. Mol Cell Biol. 1996 Apr; 16(4):1668-75. PMID: 8657142
51. Ren B, Cam H, Takahashi Y, Volkert T, Terragni J, Young RA, et al. E2F integrates cell cycle progression with DNA repair, replication, and G(2)/M checkpoints. Genes Dev. 2002 Jan 15; 16(2):245-56. PMID: 11799067
52. Evan GI, Vousden KH. Proliferation, cell cycle and apoptosis in cancer. Nature. 2001; 411(6835):342-8. PMID: 11357141
53. Kelland L. "Of mice and men": values and liabilities of the athymic nude mouse model in anticancer drug development. Eur J Cancer. 2004; 40(6):827-36. PMID: 15120038
54. Sharpless NE, DePinho RA. The mighty mouse: genetically engineered mouse models in cancer drug development. Nature reviews Drug discovery. 2006; 5(9):741-54. PMID: 16915232
55. Prat A, Adamo B, Fan C, Peg V, Vidal M, Galván P, et al. Genomic analyses across six cancer types identify basal-like breast cancer as a unique molecular entity. Scientific reports. 2013;3.
56. Heller ER, Gor A, Wang D, Hu Q, Lucchese A, Kanduc D, et al. Molecular signatures of basal cell carcinoma susceptibility and pathogenesis: A genomic approach. Int J Oncol. 2013; 42(2):583-96. doi:10.3892/ijo.2012.1725 PMID: 23229765