Prediction of chemotherapeutic response in ovarian cancer with DNA microarray expression profiling

Zachariah E. Selvanayagam, Tak Hong Cheung, Nien Wei, Ragini Vittal, Keith Wing Kit Lo, Winnie Yeo, Tsunekazu Kita, Roald Ravatn, Tony Kwok Hung Chung, Yick Fu Wong, Khew Voon Chin

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

Ovarian carcinoma is a leading cause of gynecologic cancer death in women. Despite treatment, a large number of women with ovarian cancer eventually relapse and die of the disease. Hence, recurrent ovarian cancer continues to be a therapeutic dilemma, possibly a result of the emergence of drug resistance during relapse. Recent advances in expression genomics enable global transcript analysis that leads to molecular classification of cancers and prediction of outcome and treatment response. We did a cDNA microarray examination of the expression profiles of eight primary ovarian cancers stratified into two groups based on their chemotherapeutic response. We applied a voice-speech-pattern recognition algorithm for microarray data analysis and were able to model and predict the response of these patients to chemotherapy from their expression profiles. Hence, gene expression profiling by means of DNA microarray may be applied diagnostically for predicting treatment response in ovarian cancer.

Original languageEnglish (US)
Pages (from-to)63-66
Number of pages4
JournalCancer Genetics and Cytogenetics
Volume154
Issue number1
DOIs
StatePublished - Oct 1 2004

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Cancer Research

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    Selvanayagam, Z. E., Cheung, T. H., Wei, N., Vittal, R., Kit Lo, K. W., Yeo, W., Kita, T., Ravatn, R., Hung Chung, T. K., Wong, Y. F., & Chin, K. V. (2004). Prediction of chemotherapeutic response in ovarian cancer with DNA microarray expression profiling. Cancer Genetics and Cytogenetics, 154(1), 63-66. https://doi.org/10.1016/j.cancergencyto.2004.01.024