Background: The five year overall survival rate for colorectal cancer (CRC) patients varies between 38.8% and 59.9%. Selecting patients who are likely to respond to therapy remains a clinical and pathological challenge, hence the need for predictive and prognostic biomarkers. The objectives of this study were: 1) to establish which genes were differentially expressed with respect to sensitivity to treatment, 2) to integrate the list of differentially expressed genes with copy number to systematically identify predictive biomarkers, and 3) to establish which genes are commonly gained in the panel of CRC cell lines. As proof of concept of the approach the copy number variations of the identified genes were assessed in a cohort of Dukes’ A and B cancers, in order to analyse the likelihood of these genes acting as useful biomarkers. Methods: Cell viability assays were carried out on a panel 15 CRC cell lines. IC50s were measured for 5-fluoruracil (5-FU), oxaliplatin (L-OHP), and BEZ-235, a PI3K/mTOR inhibitor. We carried out a systematic array-based survey of gene expression and copy number variation in CRC cell lines, and compared these to responses to different treatments. Cell lines were profiled using array comparative genomic hybridisation (aCGH; NimbleGen 135k), Illumina gene expression analysis, reverse phase protein arrays (RPPA), and targeted sequencing of KRAS hotspot mutations. The associations between the biological variables and drug sensitivity were assessed using correlation coefficients, chi-square analysis, and the Mann Whitney-U test. Tissue microarrays (TMA) were constructed for a cohort of CRC patients (n=118) and TRIB1 and MYC amplifications were measured using fluorescence in situ hybridisation (FISH). The protein expression for trib1 and 14 associated biomarkers were investigated using Automated Quantitative Analysis (AQUA) and analysed using the Pearson’s correlation coefficient. Results: Twenty-three regions were frequently gained, and fourteen regions were lost across the cell line panel. Gains were observed at 2p, 3q, 5p, 7p, 7q, 8q, 12p, 13q, 14q, and 17q, and losses at 2q, 3p, 5q, 8p, 9p, 9q, 14q, 18q, and 20p. Frequently gained regions contained EGFR, PIK3CA, MYC, SMO, TRIB1, FZD1, and BRCA2, while frequently lost regions contained FHIT and MACROD2. Gene enrichment analysis showed that differentially expressed genes with respect to treatment response were involved in Wnt signalling, EGF receptor signalling, apoptosis, cell cycle, and angiogenesis. Stepwise integration of copy number and gene expression data yielded 47 candidate genes that were significantly correlated (corrected p-value ≤0.05). Differentially expressed genes common to all three treatment responses included AEBP2, DDX56, MRPL32, MRPS17, MYC, NSMCE2, and TBRG4. TRIB1 (n=76) and MYC (n=81) were amplified (FISH score ≥1.8) in 14.5% and 7.4% of the CRC cohort, respectively. TRIB1 and MYC amplifications were significantly correlated (corrected p-value ≤ 0.0001). Trib1 protein expression in the patient cohort was significantly correlated (corrected p-value ≤ 0.01) with protein expression of pErk, Akt, and Caspase 3. Conclusions: The CRC in-vitro model was used effectively in this study for discovery of both predictive and prognostic biomarkers. A set of candidate predictive biomarkers for 5-FU, L-OHP, and BEZ235 have been described, worthy of further study. Amplification of the putative oncogene TRIB1 has been assessed for the first time in a cohort of CRC patients. Inhibition of TRIB1 may be a synthetic lethal approach when MYC amplifications are present, which requires further clinical and experimental validation.