Molecular classification and prediction in gastric cancer
Chemotherapy is the standard treatment for patients with advanced gastric cancer; however, it has been difficult to predict chemotherapy response. In the current study, we attempted to develop a prediction model for individual response to doxorubicin chemotherapy in gastric cancer patients based on the hypothesis that expression analysis of a set of key drug sensitivity genes for doxorubicin could allow us to predict therapeutic response. From literature and our previous microarray data, the genes correlative in the expression levels with doxorubicin response were chosen. We selected seven reliable prediction markers for doxorubicin from 90 candidate sequences. Using expression data of genes quantified by real-time reverse transcription-PCR in 20 specimens, we fixed a linear model by multiple regressions, which converted the quantified expression data into a calculated inhibition rate of doxorubicin. Using the same set of genes, we then validated the formula in an independent set of 19 specimens. Our results suggest that the response of gastric cancer to doxorubicin can be predicted by expression patterns in this set of genes. The response prediction model will be of practical use to evaluate patient before chemotherapy.