Predicting the clinical behavior of ovarian cancer from gene expression profiles

We investigated whether prognostic information is reflected in the expression patterns of ovarian carcinoma samples. RNA obtained from seven FIGO stage I without recurrence, seven platin-sensitive advanced-stage (III or IV), and six platin-resistant advanced-stage ovarian tumors was hybridized on a complementary DNA microarray with 21,372 spotted clones. The results revealed that a considerable number of genes exhibit nonaccidental differential expression between the different tumor classes. Principal component analysis reflected the differences between the three tumor classes and their order of transition. Using a leave-one-out approach together with least squares support vector machines, we obtained an estimated classification test accuracy of 100% for the distinction between stage I and advanced-stage disease and 76.92% for the distinction between platin-resistant versus platin-sensitive disease in FIGO stage III/IV. These results indicate that gene expression patterns could be useful in clinical management of ovarian cancer.

[1]  Johan A. K. Suykens,et al.  Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction , 2004, Bioinform..

[2]  László G Puskás,et al.  RNA amplification results in reproducible microarray data with slight ratio bias. , 2002, BioTechniques.

[3]  S. Rubin,et al.  Second-line platinum therapy in patients with ovarian cancer previously treated with cisplatin. , 1991, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  M. Radmacher,et al.  Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. , 2003, Journal of the National Cancer Institute.

[5]  Russ B. Altman,et al.  Nonparametric methods for identifying differentially expressed genes in microarray data , 2002, Bioinform..

[6]  Partha S. Vasisht.  Computational Analysis of Microarray Data , 2003 .

[7]  Beth Dawson,et al.  Basic & Clinical Biostatistics , 1990 .

[8]  Kathleen R. Cho,et al.  Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas. , 2002, Cancer research.

[9]  M. Franchi,et al.  Impact of adjuvant chemotherapy and surgical staging in early-stage ovarian carcinoma: European Organisation for Research and Treatment of Cancer-Adjuvant ChemoTherapy in Ovarian Neoplasm trial. , 2003, Journal of the National Cancer Institute.

[10]  D. Lockhart,et al.  Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[11]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[12]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Robert C. Bast,et al.  Selection of Potential Markers for Epithelial Ovarian Cancer with Gene Expression Arrays and Recursive Descent Partition Analysis , 2004, Clinical Cancer Research.

[14]  Jason E. Stewart,et al.  Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.

[15]  F De Smet,et al.  Balancing false positives and false negatives for the detection of differential expression in malignancies , 2004, British Journal of Cancer.

[16]  Christos Sotiriou,et al.  Gene expression profiles of BRCA1-linked, BRCA2-linked, and sporadic ovarian cancers. , 2002, Journal of the National Cancer Institute.

[17]  M. West,et al.  Prediction of optimal versus suboptimal cytoreduction of advanced-stage serous ovarian cancer with the use of microarrays. , 2004, American journal of obstetrics and gynecology.

[18]  Jeffrey R Marks,et al.  Gene Expression Patterns That Characterize Advanced Stage Serous Ovarian Cancers , 2004, The Journal of the Society for Gynecologic Investigation: JSGI.

[19]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.