Functional characterization of breast cancer using pathway profiles

BMC Medical Genomics

Zhenjun Hu¹  Michael Seiler¹  Yajie Wang²  Feng Tian¹ 

[1] Center for Advanced Genomic Technology, Boston University, Boston, MA 02215, USA;Department of Clinical Laboratory Diagnosis, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China

关键词: Breast cancer;    Enrichment analysis;    Pathway profile;    Pathway;    Signature genes;   

Others  :  1090774
DOI  :  10.1186/1755-8794-7-45

 received in 2013-12-24, accepted in 2014-07-09,  发布年份 2014

【 摘 要 】

Background

The molecular characteristics of human diseases are often represented by a list of genes termed “signature genes”. A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.

Methods

We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.

Results

Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.

Conclusions

Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.

【 授权许可】

   
2014 Tian et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 1.

【 参考文献 】

• [1]Loscalzo J, Kohane I, Barabasi AL: Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol Syst Biol 2007, 3:124.
• [2]Butte AJ: Medicine. The ultimate model organism. Science 2008, 320:325-327.
• [3]Itadani H, Mizuarai S, Kotani H: Can systems biology understand pathway activation? Gene expression signatures as surrogate markers for understanding the complexity of pathway activation. Curr Genomics 2008, 9:349-360.
• [4]Liu JG, Campen A, Huang SG, Peng SB, Ye X, Palakal M, Dunker AK, Xia YN, Li SY: Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data. BMC Med Genet 2008, 1:39.
• [5]Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D: Molecular portraits of human breast tumours. Nature 2000, 406:747-752.
• [6]Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lonning PE, Borresen-Dale AL: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001, 98:10869-10874.
• [7]van de Vijver MJ, He YD, van ’t Veer LJ, Dai H, Hart AAM, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002, 347:1999-2009.
• [8]Bandres E, Malumbres R, Cubedo E, Honorato B, Zarate R, Labarga A, Gabisu U, Sola JJ, Garcia-Foncillas J: A gene signature of 8 genes could identify the risk of recurrence and progression in Dukes’ B colon cancer patients. Oncol Rep 2007, 17:1089-1094.
• [9]Chen HY, Yu SL, Chen CH, Chang GC, Chen CY, Yuan A, Cheng CL, Wang CH, Terng HJ, Kao SF, Chan WK, Li HN, Liu CC, Singh S, Chen WJ, Chen JJ, Yang PC: A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med 2007, 356:11-20.
• [10]Huang H, Shiffman ML, Friedman S, Venkatesh R, Bzowej N, Abar OT, Rowland CM, Catanese JJ, Leong DU, Sninsky JJ, Layden TJ, Wright TL, White T, Cheung RC: A 7 gene signature identifies the risk of developing cirrhosis in patients with chronic hepatitis C. Hepatology 2007, 46:297-306.
• [11]Okahara S, Arimura Y, Yabana T, Kobayashi K, Gotoh A, Motoya S, Imamura A, Endo T, Imai K: Inflammatory gene signature in ulcerative colitis with cDNA macroarray analysis. Aliment Pharmacol Ther 2005, 21:1091-1097.
• [12]Koscielny S: Why most gene expression signatures of tumors have not been useful in the clinic. Sci Transl Med 2010, 2:14ps12.
• [13]Dowsett M, Sestak I, Lopez-Knowles E, Sidhu K, Dunbier AK, Cowens JW, Ferree S, Storhoff J, Schaper C, Cuzick J: Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol 2013, 31:2783-2790.
• [14]Ein-Dor L, Kela I, Getz G, Givol D, Domany E: Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 2005, 21:171-178.
• [15]Ein-Dor L, Zuk O, Domany E: Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci U S A 2006, 103:5923-5928.
• [16]Draghici S, Khatri P, Eklund AC, Szallasi Z: Reliability and reproducibility issues in DNA microarray measurements. Trends Genet 2006, 22:101-109.
• [17]Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004, 5:101-113.
• [18]van’t Veer LJ, Dai HY, van de Vijver MJ, He YDD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530-536.
• [19]Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 365:671-679.
• [20]Drier Y, Domany E: Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes? Plos One 2011, 6(3):e17795.
• [21]Oti M, Brunner HG: The modular nature of genetic diseases. Clin Genet 2007, 71:1-11.
• [22]Gonen M: Statistical aspects of gene signatures and molecular targets. Gastrointest Cancer Res 2009, 3:S19-S21.
• [23]Thomas DC, Baurley JW, Brown EE, Figueiredo JC, Goldstein A, Hazra A, Wilson RT, Rothman N: Approaches to complex pathways in molecular epidemiology: summary of a Special Conference of the American Association for Cancer Research. Cancer Res 2008, 68:10028-10030.
• [24]Chin L, Hahn WC, Getz G, Meyerson M: Making sense of cancer genomic data. Genes Dev 2011, 25:534-555.
• [25]Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BR: MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 2003, 4:R7.
• [26]Segal E, Friedman N, Koller D, Regev A: A module map showing conditional activity of expression modules in cancer. Nat Genet 2004, 36:1090-1098.
• [27]Bild AH, Yao G, Chang JT, Wang QL, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, Olson JA, Marks JR, Dressman HK, West M, Nevins JR: Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006, 439:353-357.
• [28]Emmert-Streib F, Glazko GV: Pathway Analysis of expression data: deciphering functional building blocks of complex diseases. Plos Comput Biol 2011, 7:e1002053.
• [29]Cary MP, Bader GD, Sander C: Pathway information for systems biology. FEBS Letters 2005, 579:1815-1820.
• [30]Kim S, Kon M, DeLisi C: Pathway-based classification of cancer subtypes. Biol Direct 2012, 7:21.
• [31]Hung JH, Whitfield TW, Yang TH, Hu Z, Weng Z, DeLisi C: Identification of functional modules that correlate with phenotypic difference: the influence of network topology. Genome Biol 2010, 11:R23.
• [32]Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102:15545-15550.
• [33]Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu JC, Haussler D, Stuart JM: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 2010, 26:i237-i245.
• [34]Drier Y, Sheffer M, Domany E: Pathway-based personalized analysis of cancer. Proc Natl Acad Sci U S A 2013, 110:6388-6393.
• [35]Koboldt DC, Fulton RS, McLellan MD, Schmidt H, Kalicki-Veizer J, McMichael JF, Fulton LL, Dooling DJ, Ding L, Mardis ER, Wilson RK, Ally A, Balasundaram M, Butterfield YSN, Carlsen R, Carter C, Chu A, Chuah E, Chun HJE, Coope RJN, Dhalla N, Guin R, Hirst C, Hirst M, Holt RA, Lee D, Li HYI, Mayo M, Moore RA, Mungall AJ, et al.: Comprehensive molecular portraits of human breast tumours. Nature 2012, 490:61-70.
• [36]Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, Graf S, Ha G, Haffari G, Bashashati A, Russell R, McKinney S, Langerod A, Green A, Provenzano E, Wishart G, Pinder S, Watson P, Markowetz F, Murphy L, Ellis I, Purushotham A, Borresen-Dale AL, Brenton JD, Tavare S, Caldas C, et al.: The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012, 486:346-352.
• [37]Bell D, Berchuck A, Birrer M, Chien J, Cramer DW, Dao F, Dhir R, DiSaia P, Gabra H, Glenn P, Godwin AK, Gross J, Hartmann L, Huang M, Huntsman DG, Iacocca M, Imielinski M, Kalloger S, Karlan BY, Levine DA, Mills GB, Morrison C, Mutch D, Olvera N, Orsulic S, Park K, Petrelli N, Rabeno B, Rader JS, Sikic BI, et al.: Integrated genomic analyses of ovarian carcinoma. Nature 2011, 474:609-615.
• [38]Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27-30.
• [39]Cline MS, Craft B, Swatloski T, Goldman M, Ma S, Haussler D, Zhu J: Exploring TCGA Pan-Cancer data at the UCSC Cancer Genomics Browser. Sci Rep 2013, 3:2652.
• [40]Hung JH, Yang TH, Hu ZJ, Weng ZP, DeLisi C: Gene set enrichment analysis: performance evaluation and usage guidelines. Brief Bioinform 2012, 13:281-291.
• [41]Alexe G, Dalgin GS, Scanfeld D, Tamayo P, Mesirov JP, DeLisi C, Harris L, Barnard N, Martel M, Levine AJ, Ganesan S, Bhanot G: High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. Cancer Res 2007, 67:10669-10676.
• [42]Eroles P, Bosch A, Perez-Fidalgo JA, Lluch A: Molecular biology in breast cancer: intrinsic subtypes and signaling pathways. Cancer Treat Rev 2012, 38:698-707.
• [43]Fan C, Oh DS, Wessels L, Weigelt B, Nuyten DSA, Nobel AB, van’t Veer LJ, Perou CM: Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006, 355:560-569.
• [44]Benjamini Y, Hochberg Y: Controlling the false discovery rate - a practical and powerful approach to multiple testing. J R Stat Soc Ser B-Methodological 1995, 57:289-300.
• [45]Li ZG, Su ZQ, Wen ZN, Shi LM, Chen T: Microarray platform consistency is revealed by biologically functional analysis of gene expression profiles. BMC Bioinformatics 2009, 10(Suppl 11):S12.
• [46]Chuang HY, Lee E, Liu YT, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Mol Syst Biol 2007, 3:140.
• [47]Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008, 18:1509-1517.
• [48]Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10:57-63.
• [49]Khatri P, Sirota M, Butte AJ: Ten Years of pathway analysis: current approaches and outstanding challenges. Plos Comput Biol 2012, 8:e1002375.
• [50]Dang CV: Links between metabolism and cancer. Genes Dev 2012, 26:877-890.
• [51]Fernadez V, Hartmann E, Ott G, Campo E, Rosenwald A: Pathogenesis of mantle-cell lymphoma: all oncogenic roads lead to dysregulation of cell cycle and DNA damage response pathways. J Clin Oncol 2005, 23:6364-6369.
• [52]Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Kamiyama H, Jimeno A, Hong SM, Fu B, Lin MT, Calhoun ES, Kamiyama M, Walter K, Nikolskaya T, Nikolsky Y, Hartigan J, Smith DR, Hidalgo M, Leach SD, Klein AP, Jaffee EM, Goggins M, Maitra A, Iacobuzio-Donahue C, Eshleman JR, Kern SE, Hruban RH, et al.: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 2008, 321:1801-1806.
• [53]Pham L, Christadore L, Schaus S, Kolaczyk ED: Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis. Proc Natl Acad Sci U S A 2011, 108:13347-13352.
• [54]Cully M, You H, Levine AJ, Mak TW: Beyond PTEN mutations: the PI3K pathway as an integrator of multiple inputs during tumorigenesis. Nat Rev Cancer 2006, 6:184-192.
• [55]Al-Hussaini H, Subramanyam D, Reedijk M, Sridhar SS: Notch signaling pathway as a therapeutic target in breast cancer. Mol Cancer Ther 2011, 10:9-15.
• [56]Osborne CK, Shou J, Massarweh S, Schiff R: Crosstalk between estrogen receptor and growth factor receptor pathways as a cause for endocrine therapy resistance in breast cancer. Clin Cancer Res 2005, 11:865s-870s.
• [57]Yu ZR, Wang LP, Wang CG, Ju XM, Wang M, Chen K, Loro E, Li ZP, Zhang YZ, Wu KM, Casimiro MC, Gormley M, Ertel A, Fortina P, Chen YH, Tozeren A, Liu ZM, Pestell RG: Cyclin D1 induction of Dicer governs microRNA processing and expression in breast cancer. Nat Commun 2013, 4:2812.
• [58]Pontano LL, Aggarwal P, Barbash O, Brown EJ, Bassing CH, Diehl JA: Genotoxic stress-induced cyclin D1 phosphorylation and proteolysis are required for genomic stability. Mol Cell Biol 2008, 28:7245-7258.
• [59]Graeser M, McCarthy A, Lord CJ, Savage K, Hills M, Salter J, Orr N, Parton M, Smith IE, Reis JS, Dowsett M, Ashworth A, Turner NC: A marker of homologous recombination predicts pathologic complete response to neoadjuvant chemotherapy in primary breast cancer. Clin Cancer Res 2010, 16:6159-6168.
• [60]Stefansson OA, Jonasson JG, Olafsdottir K, Bjarnason H, Th Johannsson O, Bodvarsdottir SK, Valgeirsdottir S, Eyfjord JE: Genomic and phenotypic analysis of BRCA2 mutated breast cancers reveals co-occurring changes linked to progression. Breast Cancer Res 2011, 13:R95.
• [61]Banda M, Speyer C, Gorski D: Metabotropic glutamate receptor-1 is oncogenic in triple-negative breast cancer. Ann Surg Oncol 2012, 19:23-23.
• [62]Budczies J, Denkert C, Muller BM, Brockmoller SF, Klauschen F, Gyorffy B, Dietel M, Richter-Ehrenstein C, Marten U, Salek RM, Griffin JL, Hilvo M, Oresic M, Wohlgemuth G, Fiehn O: Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue - a GC-TOFMS based metabolomics study. BMC Genomics 2012, 13:334.
• [63]Hynes NE, MacDonald G: ErbB receptors and signaling pathways in cancer. Curr Opin Cell Biol 2009, 21:177-184.
• [64]Chen ST, Choo KB, Hou MF, Yeh KT, Kuo SJ, Chang JG: Deregulated expression of the PER1, PER2 and PER3 genes in breast cancers. Carcinogenesis 2005, 26:1241-1246.
• [65]Wu J, Hu Z, DeLisi C: Gene annotation and network inference by phylogenetic profiling. BMC Bioinformatics 2006, 7:80.
• [66]Perez-Llamas C, Lopez-Bigas N: Gitools: analysis and visualisation of genomic data using interactive heat-maps. Plos One 2011, 6:e19541.
• [67]Hu Z, Mellor J, DeLisi C: Analyzing networks with VisANT. Curr Protoc Bioinformatics 2004., 8Chapter 8:Unit 8.8.1-24
• [68]Hu Z, Mellor J, Wu J, DeLisi C: VisANT: an online visualization and analysis tool for biological interaction data. BMC Bioinformatics 2004, 5:17.
• [69]Hu Z, Ng DM, Yamada T, Chen C, Kawashima S, Mellor J, Linghu B, Kanehisa M, Stuart JM, DeLisi C: VisANT 3.0: new modules for pathway visualization, editing, prediction and construction. Nucleic Acids Res 2007, 35:W625-W632.
• [70]Hu Z, Snitkin ES, DeLisi C: VisANT: an integrative framework for networks in systems biology. Brief Bioinform 2008, 9:317-325.
• [71]Hu Z, Chang YC, Wang Y, Huang CL, Liu Y, Tian F, Granger B, Delisi C: VisANT 4.0: Integrative network platform to connect genes, drugs, diseases and therapies. Nucleic Acids Res 2013, 41:W225-W231.
• [72]Hu Z, Mellor J, Wu J, Yamada T, Holloway D, Delisi C: VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res 2005, 33:W352-W357.
• [73]Hu Z, Mellor J, Wu J, Kanehisa M, Stuart JM, DeLisi C: Towards zoomable multidimensional maps of the cell. Nat Biotechnol 2007, 25:547-554.
• [74]Hu Z, Hung JH, Wang Y, Chang YC, Huang CL, Huyck M, DeLisi C: VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res 2009, 37:W115-W121.
• [75]Polyak K: Breast cancer: origins and evolution. J Clin Invest 2007, 117:3155-3163.
• [76]Cleary MP, Grossmann ME: Minireview: obesity and breast cancer: the estrogen connection. Endocrinology 2009, 150:2537-2542.
• [77]Piccirilli M, Salvati M, Bistazzoni S, Frati A, Brogna C, Giangaspero F, Frati R, Santoro A: Glioblastoma multiforme and breast cancer: report on 11 cases and clinico-pathological remarks. Tumori 2005, 91:256-260.
• [78]Leslie KK, Keefe D, Powell S, Naftolin F: Estrogen receptors are identified in the glioblastoma cell line U138MG. J Soc Gynecol Investig 1994, 1:238-244.
• [79]Mineo JF, Bordron A, Baroncini M, Maurage CA, Ramirez C, Siminski RM, Berthou C, Dam Hieu P: Low HER2-expressing glioblastomas are more often secondary to anaplastic transformation of low-grade glioma. J Neurooncol 2007, 85:281-287.
• [80]Lawrence JE, Cook NJ, Rovin RA, Winn RJ: Leptin promotes glioblastoma. Neurol Res Int 2012, 2012:870807.
• [81]Ulitsky I, Shamir R: Identification of functional modules using network topology and high-throughput data. BMC Syst Biol 2007, 1:8.
• [82]Gatenby RA, Gillies RJ: Why do cancers have high aerobic glycolysis? Nat Rev Cancer 2004, 4:891-899.
• [83]Ruckhaberle E, Rody A, Engels K, Gaetje R, von Minckwitz G, Schiffmann S, Grosch S, Geisslinger G, Holtrich U, Karn T, Kaufmann M: Microarray analysis of altered sphingolipid metabolism reveals prognostic significance of sphingosine kinase 1 in breast cancer. Breast Cancer Res Treat 2008, 112:41-52.
• [84]Schramm G, Surmann EM, Wiesberg S, Oswald M, Reinelt G, Eils R, Konig R: Analyzing the regulation of metabolic pathways in human breast cancer. BMC Med Genomics 2010, 3:39.
• [85]Miecznikowski JC, Wang D, Liu S, Sucheston L, Gold D: Comparative survival analysis of breast cancer microarray studies identifies important prognostic genetic pathways. BMC Cancer 2010, 10:573.
• [86]Singh R, Pervin S, Karimi A, Cederbaum S, Chaudhuri G: Arginase activity in human breast cancer cell lines: N (omega)-hydroxy-L-arginine selectively inhibits cell proliferation and induces apoptosis in MDA-MB-468 cells. Cancer Res 2000, 60:3305-3312.
• [87]Hitosugi T, Fan J, Chung TW, Lythgoe K, Wang X, Xie JX, Ge QY, Gu TL, Polakiewicz RD, Roesel JL, Chen GZ, Boggon TJ, Lonial S, Fu HA, Khuri FR, Kang SM, Chen J: Tyrosine phosphorylation of mitochondrial pyruvate dehydrogenase kinase 1 is important for cancer metabolism. Mol Cell 2011, 44:864-877.
• [88]Fong MY, McDunn J, Kakar SS: Identification of metabolites in the normal ovary and their transformation in primary and metastatic ovarian cancer. PLoS One 2011, 6:e19963.
• [89]Chen J, Gammon MD, Chan W, Palomeque C, Wetmur JG, Kabat GC, Teitelbaum SL, Britton JA, Terry MB, Neugut AI, Santella RM: One-carbon metabolism, MTHFR polymorphisms, and risk of breast cancer. Cancer Res 2005, 65:1606-1614.
• [90]Jacquemont C, Taniguchi T: The Fanconi anemia pathway and ubiquitin. BMC Biochem 2007, 8(Suppl 1):S10.
• [91]Hennessy BT, Smith DL, Ram PT, Lu Y, Mills GB: Exploiting the PI3K/AKT pathway for cancer drug discovery. Nat Rev Drug Discov 2005, 4:988-1004.
• [92]Yamaguchi H, Condeelis J: Regulation of the actin cytoskeleton in cancer cell migration and invasion. Biochim BiophysActa 2007, 1773:642-652.
• [93]Golubovskaya V, Beviglia L, Xu LH, Earp HS 3rd, Craven R, Cance W: Dual inhibition of focal adhesion kinase and epidermal growth factor receptor pathways cooperatively induces death receptor-mediated apoptosis in human breast cancer cells. J Biol Chem 2002, 277:38978-38987.
• [94]Housa D, Housova J, Vernerova Z, Haluzik M: Adipocytokines and cancer. Physiol Res 2006, 55:233-244.
• [95]Chedotal A, Kerjan G, Moreau-Fauvarque C: The brain within the tumor: new roles for axon guidance molecules in cancers. Cell Death Differ 2005, 12:1044-1056.