Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the Western world. The most active drug against this malignancy is 5-Fluorouracil (5-FU), which produces response rates of only 20-25% in advanced CRC. Efforts to improve efficacy have led to combination of 5-FU with newer agents such as Irinotecan and Oxaliplatin, which have significantly improved response rates to 40-50%. Despite these improvements, less than half of patients who undergo chemotherapy will receive any benefit from treatment. A major factor limiting the effectiveness of chemotherapy against CRC is inherent or acquired drug resistance. We have previously generated a panel of HCT116 CRC cell lines resistant to 5-FU and Oxaliplatin by repeated exposure to stepwise increasing concentrations of drug over a period of months. We have used these model cell lines in conjunction with DNA microarray technology to identify novel determinants of response to 5-FU and Oxaliplatin. Using complex bioinformatic analysis, we have identified panels of genes whose expression is constitutively altered in drug-resistant cells relative to sensitive parental cells and induced following acute exposure of parental cells to drug. In total, 119 genes and 37 genes were identified that correlate with 5-FU and Oxaliplatin sensitivity respectively. These data suggest that cancer cells may recruit and activate the expression of a distinct set of genes in transient induction or when selected for resistance to drug. We propose that this set of genes may represent a distinct molecular signature indicative of drug resistance. Using real-time RT-PCR, we have successfully validated a representative subset of these genes and calculated R2 values to examine the correlation between our microarray and real-time data. This has allowed us to assign an arbitrary p-value cut-off, whereby gene targets with p>N will be assumed to have come through the microarray screen by chance and omitted from further analyses. Ultimately, we will use custom-built oligonucleotide arrays to measure the mRNA expression levels our combined “classifier” gene set in tumour biopsies from patients with metastatic CRC receiving first-line 5-FU and Oxaliplatin as part of a UK-based Phase III clinical study and use bioinformatic analysis to identify gene sets that most accurately predict response. Furthermore, we will compare the predictive power of our “classifier” set with previously established determinants of 5-FU and oxaliplatin response, which may also be combined with our best predictive gene set to assess whether they are able to further increase their predictive power. Ultimately, the data from these analyses will be used to design a prospective trial, in which patients will receive chemotherapy based on the molecular profiles of their tumours.

[Proc Amer Assoc Cancer Res, Volume 46, 2005]