Background: Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression profiling to delineate major deregulated pathways and biomarkers associated with the development of intrinsic chemotherapy resistance upon exposure to standard first-line therapy for ovarian cancer. Methods: The study cohort comprised 28 patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. All 28 patients had advanced stage, high-grade serous ovarian cancer, and were treated with the same standard platinum-based chemotherapy. Twelve patient tumors demonstrating relative resistance to platinum chemotherapy corresponding to shorter PFS (< eight months) were compared to sixteen tumors from platinum-sensitive patients (PFS > eighteen months). Whole transcriptome profiling was performed using a Affymetrix high-resolution microarray platform to permit global comparisons of gene expression profiles between tumors from the resistant group and the sensitive group. Results: Microarray data analysis revealed a set of 204 discriminating genes possessing expression levels, which could influence differential chemotherapy response between the two groups. Robust statistical testing was then performed which eliminated a dependence on the normalization algorithm employed, producing a restricted list of differentially regulated genes, and which found IGF1 to be the most strongly differentially expressed gene. Pathway analysis, based on the list of 204 genes, revealed enrichment in genes primarily involved in the IGF1/PI3K/NFB/ERK gene signalling networks. Conclusions: This study has identified pathway specific prognostic biomarkers possibly underlying a differential chemotherapy response in patients undergoing standard platinum-based treatment of serous epithelial ovarian cancer. Future studies to validate these markers are necessary to apply this knowledge to biomarker-based clinical trials.