Abstract 2706: Discovery of predictive biomarkers based on differential chemosensitivity of colorectal cell lines

Chemoresistance is the main reason for therapeutic ineffectiveness and recurrence in colorectal cancer. Furthermore, the choice of optimal chemotherapy is complicated by an interpatient variability in drug response caused by the individual geno- and phenotype. To promote a more individualized therapy, predictive biomarkers which indicate individual drug response of patients are urgently needed. In this study, using a combination of HPLC and mass spectroscopy, we discovered differentially expressed proteins which might be linked to specific phenotypes of intrinsic chemoresistance in colorectal cancer cell lines. Several commercially available cell lines as well as primary mixed cultures and clonal cell lines established from colorectal cancer patients were treated with different drugs, such as 5-Fluorouracil, Oxaliplatin and drug combinations e.g. FOLFOX. Two different assays, the ATPliteTM-(Perkin Elmer) and FMC-assay (according to Larsson et al.) were used in parallel to measure cytotoxic and cytostatic drug effects. Both assays showed time and dose dependent drug responses which differed significantly among the 18 colorectal cell cultures. The analysis of obtained in vitro chemosensitivity data allowed us to cluster sensitive versus resistant cell lines by determining IC50-values. From all 18 cell cultures, differential protein expression was analyzed using a combination of reversed phase high performance liquid chromatography and matrix assisted laser desorption/ionization mass spectrometry (RP-HPLC MALDI MS) workflow. Up to 3500 m/z ion signals in the molecular mass range between 2500-35000 m/z were detected reflecting a complex pattern of small proteins and peptides. For protein identification, RP-HPLC fractionated samples were digested by trypsin and fragments of interest were subjected to MS/MS analysis in the TOF/TOF mode. The corresponding MS/MS spectra were used to search the NCBInr database using MASCOT software. Based on these data, we hope to gain more understanding of the biological processes of chemoresistance concerning important cellular drug responses (e.g. signal transduction pathways) and thereby obtain insight into new drug targets for future therapeutic intervention. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 20