Deciphering cellular states of innate tumor drug responses

Clinical drug resistance is one major concern in the treatment of human cancers, most of them being found resistant to therapy at the time of drug presentation. Deciphering the molecular mechanisms that contribute to such innate drug resistance should improve both the prediction of treatment failure and the development of new strategies to overcome resistance. Our study reports for the first time a systems approach toward understanding the in vivo cellular states of clinical samples collected from colorectal cancer (CRC) patients prior to their exposure to combined chemotherapy of folinic acid (FA), 5-fluorouracil (5-FU) and irinotecan (CPT-11). Vigilant experimental design, power simulations and robust statistics were used to restrain the rates of false negative and false positive hybridizations, allowing successful discrimination between drug resistance and sensitivity states with restricted sampling. A list of 679 genes was established which intrinsically differentiate, for the first time prior to drug exposure, subsequently diagnosed chemo-sensitive and resistant patients. Independent biological validation performed through quantitative-PCR confirmed the expression pattern on two additional patients. Careful annotation of interconnected functional networks provided a unique representation of the cellular states underlying drug responses. Molecular interaction networks were described that provide a solid foundation on which to anchor working hypotheses about mechanisms underlying in vivo innate tumor drug responses. These broad-spectrum cellular signatures represent a starting point from which by-pass chemotherapy schemes, targeting simultaneously several of the molecular mechanisms involved, may be developed for critical therapeutic intervention in CRC patients. The demonstrated power of this research strategy makes it generally applicable to other physiological and pathological situations. Keywords: Drug response Microarray analyses were applied to tumor biopsies (including 9 tumor colons, 13 liver metastases, 6 adjacent non tumoral colons) from 13 individuals following a randomized and blinded unbalanced design. RNA labelling, hybridization and analysis of fluorescence was carried out, considering that uneven numbers of samples were randomly allocated to each of the engineers who were not aware of sample phenotypes. To assess data reproducibility and minimize dye bias effects, each of the samples was measured four times, twice with Cy3 and twice with Cy5. To ensure robustness and flexibility in data analysis, a reference design was used with a universal reference sample (Stratagene, USA) serving as a baseline for the comparisons of tumor samples. Hybridizations were performed onto an 11K human array (GPL3282), which provides a genome-wide coverage of functional pathways. Raw data were obtained using the ArrayVision™ 7.0 software (Imaging Research Inc., USA); the resulting 3.2x106 hybridization data points collected from 70 arrays were stored in a database and pre-processed for normalization and filtering. Statistical comparison was done considering that biopsies collected prior to drug exposure may be subsequently categorized in two groups of chemo-sensitive or resistant samples in view of the initial response rates of individual patients to combined chemotherapy. The initial response rate to combined chemotherapy (complete or partial regression, stabilization or progression of disease) was evaluated after two treatment cycles as described in [Ychou, et al. (2004) J of Clinical Oncology. 22 (Supp 14S):3601, Douillard, et al. (2000) Lancet. 355:1041-7], according to the complete guideline to evaluate the response to treatment described in [Therasse, et al. (2000) J Natl Cancer Inst. 92:205-16].