A Genome-Wide Systematic Analysis Reveals Different and Predictive Proliferation Expression Signatures of Cancerous vs. Non-Cancerous Cells

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Understanding cell proliferation mechanisms has been a long-lasting goal of the scientific community and specifically of cancer researchers. Previous genome-scale studies of cancer proliferation determinants have mainly relied on knockdown screens aimed to gauge their effects on cancer growth. This powerful approach has several limitations such as off-target effects, partial knockdown, and masking effects due to functional backups. Here we employ a complementary approach and assign each gene a cancer Proliferation Index (cPI) that quantifies the association between its expression levels and growth rate measurements across 60 cancer cell lines. Reassuringly, genes found essential in cancer gene knockdown screens exhibit significant positive cPI values, while tumor suppressors exhibit significant negative cPI values. Cell cycle, DNA replication, splicing and protein production related processes are positively associated with cancer proliferation, while cellular migration is negatively associated with it –⁠ in accordance with the well known “go or grow” dichotomy. A parallel analysis of genes' non-cancerous proliferation indices (nPI) across 224 lymphoblastoid cell lines reveals surprisingly marked differences between cancerous and non-cancerous proliferation. These differences highlight genes in the translation and spliceosome machineries as selective cancer proliferation-associated proteins. A cross species comparison reveals that cancer proliferation resembles that of microorganisms while non-cancerous proliferation does not. Furthermore, combining cancerous and non-cancerous proliferation signatures leads to enhanced prediction of patient outcome and gene essentiality in cancer. Overall, these results point to an inherent difference between cancerous and non-cancerous proliferation determinants, whose understanding may contribute to the future development of novel cancer-specific anti-proliferative drugs.

Vyšlo v časopise: A Genome-Wide Systematic Analysis Reveals Different and Predictive Proliferation Expression Signatures of Cancerous vs. Non-Cancerous Cells. PLoS Genet 9(9): e32767. doi:10.1371/journal.pgen.1003806
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003806

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1. FerlayJ, ShinHR, BrayF, FormanD, MathersC, et al. (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. International journal of cancer 127 : 2893–2917.

2. EvanGI, VousdenKH (2001) Proliferation, cell cycle and apoptosis in cancer. Nature 411 : 342–348.

3. HanahanD, WeinbergRA (2011) Hallmarks of cancer: The next generation. Cell 144 : 646–674.

4. KittlerR, PelletierL, HeningerAK, SlabickiM, TheisM, et al. (2007) Genome-scale RNAi profiling of cell division in human tissue culture cells. Nat Cell Biol 9 : 1401–1412.

5. SchlabachMR, LuoJ, SoliminiNL, HuG, XuQ, et al. (2008) Cancer proliferation gene discovery through functional genomics. Science 319 : 620–624.

6. LuoB, CheungHW, SubramanianA, SharifniaT, OkamotoM, et al. (2008) Highly parallel identification of essential genes in cancer cells. Proceedings of the National Academy of Sciences 105 : 20380–20385.

7. MarcotteR, BrownKR, SuarezF, SayadA, KaramboulasK, et al. (2012) Essential gene profiles in breast, pancreatic, and ovarian cancer cells. Cancer Discovery 2 : 172–189.

8. MohrS, BakalC, PerrimonN (2010) Genomic screening with RNAi: Results and challenges. Annu Rev Biochem 79 : 37–64.

9. PerrimonN, Mathey-PrevotB (2007) Applications of high-throughput RNA interference screens to problems in cell and developmental biology. Genetics 175 : 7–16.

10. SharmaS, RaoA (2009) RNAi screening: Tips and techniques. Nat Immunol 10 : 799–804.

11. RossDT, ScherfU, EisenMB, PerouCM, ReesC, et al. (2000) Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 24 : 227–235.

12. GaurA, JewellDA, LiangY, RidzonD, MooreJH, et al. (2007) Characterization of microRNA expression levels and their biological correlates in human cancer cell lines. Cancer Res 67 : 2456–2468.

13. MarkertE, LevineA, VazquezA (2012) Proliferation and tissue remodeling in cancer: The hallmarks revisited. Cell Death & Disease 3: e397.

14. ShoemakerRH (2006) The NCI60 human tumour cell line anticancer drug screen. Nature Reviews Cancer 6 : 813–823.

15. BrauerMJ, HuttenhowerC, AiroldiEM, RosensteinR, MateseJC, et al. (2008) Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Mol Biol Cell 19 : 352–367.

16. ZhaoM, SunJ, ZhaoZ (2013) TSGene: A web resource for tumor suppressor genes. Nucleic Acids Res 41: D970–D976.

17. SoliminiNL, XuQ, MermelCH, LiangAC, SchlabachMR, et al. (2012) Recurrent hemizygous deletions in cancers may optimize proliferative potential. Science 337 : 104–109.

18. HigginsME, ClaremontM, MajorJE, SanderC, LashAE (2007) CancerGenes: A gene selection resource for cancer genome projects. Nucleic Acids Res 35: D721–D726.

19. AshburnerM, BallCA, BlakeJA, BotsteinD, ButlerH, et al. (2000) Gene ontology: Tool for the unification of biology. Nat Genet 25 : 25–29.

20. DuarteNC, BeckerSA, JamshidiN, ThieleI, MoML, et al. (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Sciences 104 : 1777–1782.

21. SrebrowA, KornblihttAR (2006) The connection between splicing and cancer. J Cell Sci 119 : 2635–2641.

22. VenablesJP (2004) Aberrant and alternative splicing in cancer. Cancer Res 64 : 7647–7654.

23. GieseA, LooMA, TranN, HaskettD, CoonsSW, et al. (1998) Dichotomy of astrocytoma migration and proliferation. International journal of cancer 67 : 275–282.

24. GieseA, BjerkvigR, BerensM, WestphalM (2003) Cost of migration: Invasion of malignant gliomas and implications for treatment. Journal of clinical oncology 21 : 1624–1636.

25. GodlewskiJ, NowickiMO, BroniszA, NuovoG, PalatiniJ, et al. (2010) MicroRNA-451 regulates LKB1/AMPK signaling and allows adaptation to metabolic stress in glioma cells. Mol Cell 37 : 620–632.

26. HöringE, HarterPN, SeznecJ, SchittenhelmJ, BühringHJ, et al. (2012) The “go or grow” potential of gliomas is linked to the neuropeptide processing enzyme carboxypeptidase E and mediated by metabolic stress. Acta Neuropathol 1–15.

27. JerbyL, WolfL, DenkertC, SteinGY, HilvoM, et al. (2012) Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Res 72 : 5712–5720.

28. Zaidel-BarR, ItzkovitzS, Ma'ayanA, IyengarR, GeigerB (2007) Functional atlas of the integrin adhesome. Nat Cell Biol 9 : 858–867.

29. SimpsonKJ, SelforsLM, BuiJ, ReynoldsA, LeakeD, et al. (2008) Identification of genes that regulate epithelial cell migration using an siRNA screening approach. Nat Cell Biol 10 : 1027–1038.

30. van DuijnhovenFJB, Bueno-De-MesquitaHB, CalligaroM, JenabM, PischonT, et al. (2011) Blood lipid and lipoprotein concentrations and colorectal cancer risk in the european prospective investigation into cancer and nutrition. Gut 60 : 1094–1102.

31. ChoyE, YelenskyR, BonakdarS, PlengeRM, SaxenaR, et al. (2008) Genetic analysis of human traits in vitro: Drug response and gene expression in lymphoblastoid cell lines. PLoS genetics 4: e1000287.

32. Mizrachy-SchwartzS, Kravchenko-BalashaN, Ben-BassatH, KleinS, LevitzkiA (2007) Optimization of energy-consuming pathways towards rapid growth in HPV-transformed cells. PLoS One 2: e628.

33. Van De Vijver, MarcJ, HeYD, van't VeerLJ, DaiH, HartAA, et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347 : 1999–2009.

34. GarberME, TroyanskayaOG, SchluensK, PetersenS, ThaeslerZ, et al. (2001) Diversity of gene expression in adenocarcinoma of the lung. Proceedings of the National Academy of Sciences 98 : 13784–13789.

35. GodardS, GetzG, DelorenziM, FarmerP, KobayashiH, et al. (2003) Classification of human astrocytic gliomas on the basis of gene expression A correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes. Cancer Res 63 : 6613–6625.

36. RosenwaldA, WrightG, WiestnerA, ChanWC, ConnorsJM, et al. (2003) The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer cell 3 : 185–197.

37. VenetD, DumontJE, DetoursV (2011) Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS computational biology 7: e1002240.

38. DaiH, van't VeerL, LambJ, HeYD, MaoM, et al. (2005) A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res 65 : 4059–4066.

39. NagallaS, ChouJW, WillinghamMC, RuizJ, VaughnJP, et al. (2013) Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis. Genome Biol 14: R34.

40. DressaireC, RedonE, MilhemH, BesseP, LoubièreP, et al. (2008) Growth rate regulated genes and their wide involvement in the lactococcus lactis stress responses. BMC Genomics 9 : 343.

41. Ben-JacobE, S CoffeyD, LevineH (2012) Bacterial survival strategies suggest rethinking cancer cooperativity. Trends Microbiol 20 : 403–410.

42. ImHK, GamazonER, StarkAL, HuangRS, CoxNJ, et al. (2012) Mixed effects modeling of proliferation rates in cell-based models: Consequence for pharmacogenomics and cancer. PLoS genetics 8: e1002525.

43. HagnerPR, SchneiderA, GartenhausRB (2010) Targeting the translational machinery as a novel treatment strategy for hematologic malignancies. Blood 115 : 2127–2135.

44. MericF, HuntKK (2002) Translation initiation in cancer: A novel target for therapy. Molecular Cancer Therapeutics 1 : 971–979.

45. RobertF, CarrierM, RaweS, ChenS, LoweS, et al. (2009) Altering chemosensitivity by modulating translation elongation. PLoS One 4: e5428.

46. RuggeroD (2012) Revisiting the nucleolus: From marker to dynamic integrator of cancer signaling. Science Signalling 5: pe38.

47. BywaterMJ, PoortingaG, SanijE, HeinN, PeckA, et al. (2012) Inhibition of RNA polymerase I as a therapeutic strategy to promote cancer-specific activation of p53. Cancer Cell 22 : 51–65.

48. Van AlphenR, WiemerE, BurgerH, EskensF (2008) The spliceosome as target for anticancer treatment. Br J Cancer 100 : 228–232.

49. EdgarR, DomrachevM, LashAE (2002) Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30 : 207–210.

50. LeeJK, HavaleshkoDM, ChoHJ, WeinsteinJN, KaldjianEP, et al. (2007) A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery. Proceedings of the National Academy of Sciences 104 : 13086–13091.

51. WestraH, JansenRC, FehrmannRS, te MeermanGJ, van HeelD, et al. (2011) MixupMapper: Correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. Bioinformatics 27 : 2104–2111.

52. ÖstlundG, SchmittT, ForslundK, KöstlerT, MessinaDN, et al. (2010) InParanoid 7: New algorithms and tools for eukaryotic orthology analysis. Nucleic Acids Res 38: D196–D203.

53. HuangD, ShermanBT, LempickiRA (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols 4 : 44–57.

54. TibshiraniR (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society.Series B (Methodological) 267–288.

55. BlandJM, AltmanDG (2004) Statistics notes: The logrank test. BMJ: British Medical Journal 328 : 1073.

56. MillerLD, SmedsJ, GeorgeJ, VegaVB, VergaraL, et al. (2005) An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci U S A 102 : 13550–13555.

57. IvshinaAV, GeorgeJ, SenkoO, MowB, PuttiTC, et al. (2006) Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res 66 : 10292–10301.

58. PhillipsHS, KharbandaS, ChenR, ForrestWF, SorianoRH, et al. (2006) Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer cell 9 : 157–173.

59. ChuangH, RassentiL, SalcedoM, LiconK, KohlmannA, et al. (2012) Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. Blood 120 : 2639–2649.

60. BotlingJ, EdlundK, LohrM, HellwigB, HolmbergL, et al. (2013) Biomarker discovery in Non–Small cell lung cancer: Integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clinical Cancer Research 19 : 194–204.

61. KimDU, HaylesJ, KimD, WoodV, ParkHO, et al. (2010) Analysis of a genome-wide set of gene deletions in the fission yeast schizosaccharomyces pombe. Nat Biotechnol 28 : 617–623.

62. WinzelerEA, ShoemakerDD, AstromoffA, LiangH, AndersonK, et al. (1999) Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285 : 901–906.

63. SayersEW, BarrettT, BensonDA, BoltonE, BryantSH, et al. (2011) Database resources of the national center for biotechnology information. Nucleic Acids Res 39: D38–D51.

64. WaldmanYY, TullerT, ShlomiT, SharanR, RuppinE (2010) Translation efficiency in humans: Tissue specificity, global optimization and differences between developmental stages. Nucleic Acids Res 38 : 2964–2974.