Molecular profiling of platinum resistant ovarian cancer: Use of the model in clinical practice

✍ Scribed by Olivier Gevaert; Nathalie Pochet; Frank De Smet; Toon Van Gorp; Bart De Moor; Dirk Timmerman; Frédéric Amant; Ignace Vergote

• Publisher: John Wiley and Sons
• Year: 2006
• Tongue: French
• Weight: 39 KB
• Volume: 119
• Category: Article
• ISSN: 0020-7136
• DOI: 10.1002/ijc.21985

✦ Synopsis

With great interest we read the article by Helleman et al., 1 investigating whether a gene set identified using microarrays could be used to predict platin resistance in ovarian cancer. The authors studied a training set obtained from 24 tumours that were analysed using cDNA microarrays. This set contained 5 women who were platin-resistant (the nonresponders) and 19 women who were platin-sensitive (the responders). The authors concluded that 69 genes were differentially expressed between the responders and the nonresponders. An algorithm based on clustering was used to identify the most predictive genes among these 69 genes in the training set. This resulted in 9 genes (the differential expression of these genes was later confirmed with qRT-PCR) that could significantly discriminate between the responders and the nonresponders in the training set. Subsequently, this 9-gene set was used to predict platin resistance in an independent test set of 72 tumours (9 nonresponders and 63 responders) using expression levels measured with qRT-PCR. This resulted in a sensitivity of 89% and a specificity of 59%.

However, when examining the independent test set performance, we are not convinced that the approach described