DNA microarray technology is a powerful genomic tool that has the potential to elucidate the relationship between clinical features of cancers and their underlying biological alterations.
Methods
We performed a systemic search in PubMed and Medline databases for recently published articles. The search terms used included “genome-wide,” “microarrays,” “ovarian cancer,” “prognosis” “gene expression profiling,” “molecular marker,” and “molecular biomarker.”
Results
Genome-wide expression profiling using DNA microarray technology has enhanced our understanding of the genes that influence ovarian cancer development, histopathologic subtype, progression, response to therapy, and overall survival.
Conclusions
Gene expression profiling has demonstrated its utility in ovarian cancer research. It is hoped that with technologic, statistical, and bioinformatic advances, the reliability and reproducibility of this technique will increase, spawning clinical applications that may enhance our understanding of the disease and our ability to care for patients in the future.
References
1.
JemalA., SiegelR., XuJ.. Cancer statistics, 2010.CA Cancer J Clin.2010; 60(5): 277–300.
2.
HarriesM., KayeS.B.. Recent advances in the treatment of epithelial ovarian cancer.Expert Opin Investig Drugs.2001; 10(9): 1715–1724.
3.
KuleshD.A., CliveD.R., ZarlengaD.S.. Identification of interferon-modulated proliferation-related cDNA sequences.Proc Natl Acad Sci U S A.1987; 84(23): 8453–8457.
4.
LashkariD.A., DeRisiJ.L., McCuskerJ.H.. Yeast microarrays for genome wide parallel genetic and gene expression analysis.Proc Natl Acad Sci U S A.1997; 94(24): 13057–13062.
5.
GilksC.B., VanderhydenB.C., ZhuS.. Distinction between serous tumors of low malignant potential and serous carcinomas based on global mRNA expression profiling.Gynecol Oncol.2005; 96(3): 684–694.
BonomeT., LeeJ.Y., ParkD.C.. Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary.Cancer Res.2005; 65(22): 10602–10612.
8.
MokS.C., ChaoJ., SkatesS.. Prostasin, a potential serum marker for ovarian cancer: identification through microarray technology.J Natl Cancer Inst.2001; 93(19): 1458–1464.
9.
SpentzosD., LevineD.A., KoliaS.. Unique gene expression profile based on pathologic response in epithelial ovarian cancer.J Clin Oncol.2005; 23(31): 7911–7918.
10.
SpentzosD., LevineD.A., RamoniM.F.. Gene expression signature with independent prognostic significance in epithelial ovarian cancer.J Clin Oncol.2004; 22(23): 4700–4710.
11.
DufvaM.. Fabrication of DNA microarray.Methods Mol Biol.2009; 529: 63–79.
12.
PeaseA.C., SolasD., SullivanE.J.. Light-generated oligonucleotide arrays for rapid DNA sequence analysis.Proc Natl Acad Sci U S A.1994; 91(11): 5022–5026.
13.
KuhnK., BakerS.C., ChudinE.. A novel, high-performance random array platform for quantitative gene expression profiling.Genome Res.2004; 14(11): 2347–2356.
14.
HoshidaY., VillanuevaA., KobayashiM.. Gene expression in fixed tissues and outcome in hepatocellular carcinoma.N Engl J Med.2008; 359(19): 1995–2004.
15.
ChienJ., FanJ.B., BellD.A.. Analysis of gene expression in stage I serous tumors identifies critical pathways altered in ovarian cancer.Gynecol Oncol.2009; 114(1): 3–11.
16.
BrazmaA., HingampP., QuackenbushJ.. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.Nat Genet.2001; 29(4): 365–371.
17.
ShiL., ReidL.H., JonesW.D.. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.Nat Biotechnol.2006; 24(9): 1151–1161.
18.
GolubT.R., SlonimD.K., TamayoP.. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.Science.1999; 286(5439): 531–537.
19.
GiordanoT.J., SheddenK.A., SchwartzD.R.. Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles.Am J Pathol.2001; 159(4): 1231–1238.
20.
SchwartzD.R., KardiaS.L., SheddenK.A.. Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas.Cancer Res.2002; 62(16): 4722–4729.
21.
ZornK.K., BonomeT., GangiL.. Gene expression profiles of serous, endometrioid, and clear cell subtypes of ovarian and endometrial cancer.Clin Cancer Res.2005; 11(18): 6422–6430.
22.
MotzerR.J., AgarwalN., BeardC.. NCCN clinical practice guidelines in oncology: kidney cancer.J Natl Compr Canc Netw.2009; 7(6): 618–630.
23.
Rauh-HainJ.A., PensonR.T.. Potential benefit of Sunitinib in recurrent and refractory ovarian clear cell adenocarcinoma.Int J Gynecol Cancer.2008; 18(5): 934–936.
24.
SingerG., KurmanR.J., ChangH.W.. Diverse tumorigenic pathways in ovarian serous carcinoma.Am J Pathol.2002; 160(4): 1223–1228.
25.
AnglesioM.S., ArnoldJ.M., GeorgeJ.. Mutation of ERBB2 provides a novel alternative mechanism for the ubiquitous activation of RASMAPK in ovarian serous low malignant potential tumors.Mol Cancer Res.2008; 6(11): 1678–1690.
26.
TothillR.W., TinkerA.V., GeorgeJ.. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.Clin Cancer Res.2008; 14(16): 5198–5208.
27.
KimJ.H., SkatesS.J., UedeT.. Osteopontin as a potential diagnostic biomarker for ovarian cancer.JAMA.2002; 287(13): 1671–1679.
28.
WongK.K., ChengR.S., MokS.C.. Identification of differentially expressed genes from ovarian cancer cells by MICROMAX cDNA microarray system.Biotechniques.2001; 30(3): 670–675.
29.
Meinhold-HeerleinI., BauerschlagD., ZhouY.. An integrated clinical-genomics approach identifies a candidate multi-analyte blood test for serous ovarian carcinoma.Clin Cancer Res.2007; 13(2 Pt 1): 458–466.
30.
KimJ.H., HerlynD., WongK.K.. Identification of epithelial cell adhesion molecule autoantibody in patients with ovarian cancer.Clin Cancer Res.2003; 9(13): 4782–4791.
31.
ShvartsmanH.S., LuK.H., LeeJ.. Overexpression of kallikrein 10 in epithelial ovarian carcinomas.Gynecol Oncol.2003; 90(1): 44–50.
32.
HuddlestonH.G., WongK.K., WelchW.R.. Clinical applications of microarray technology: creatine kinase B is an up-regulated gene in epithelial ovarian cancer and shows promise as a serum marker.Gynecol Oncol.2005; 96(1): 77–83.
33.
MorG., VisintinI., LaiY.. Serum protein markers for early detection of ovarian cancer.Proc Natl Acad Sci U S A.2005; 102(21): 7677–7682.
34.
BerchuckA., IversenE.S., LuoJ.. Microarray analysis of early stage serous ovarian cancers shows profiles predictive of favorable outcome.Clin Cancer Res.2009; 15(7): 2448–2455.
35.
HartmannL.C., LuK.H., LinetteG.P.. Gene expression profiles predict early relapse in ovarian cancer after platinum-paclitaxel chemotherapy.Clin Cancer Res.2005; 11(6): 2149–2155.
BerchuckA., IversenE.S., LancasterJ.M.. Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers.Clin Cancer Res.2005; 11(10): 3686–3696.
38.
MossC., KayeS.B.. Ovarian cancer: progress and continuing controversies in management.Eur J Cancer.2002; 38(13): 1701–1707.
39.
von GeorgiR., FrankeF.E., MunstedtK.. Influence of surgery and postoperative therapy and tumor characteristics on patient prognosis in advanced ovarian carcinomas.Eur J Obstet Gynecol Reprod Biol.2003; 111(2): 189–196.
DressmanH.K., BerchuckA., ChanG.. An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer.J Clin Oncol.2007; 25(5): 517–525.
42.
PottiA., DressmanH.K., BildA.. Genomic signatures to guide the use of chemotherapeutics.Nat Med.2006; 12(11): 1294–1300.
43.
JazaeriA.A., AwtreyC.S., ChandramouliG.V.. Gene expression profiles associated with response to chemotherapy in epithelial ovarian cancers.Clin Cancer Res.2005; 11(17): 6300–6310.
44.
SelvanayagamZ.E., CheungT.H., WeiN.. Prediction of chemotherapeutic response in ovarian cancer with DNA microarray expression profiling.Cancer Genet Cytogenet.2004; 154(1): 63–66.
45.
RobertsD., SchickJ., ConwayS.. Identification of genes associated with platinum drug sensitivity and resistance in human ovarian cancer cells.Br J Cancer.2005; 92(6): 1149–1158.
