There are a number of effective treatments for breast cancer in the neo-adjuvant, adjuvant and metastatic setting. These comprise combinations of radiotherapy, chemotherapy, hormonal treatments and targeted molecular therapies. However the benefit that individual patients derive from these treatments and the toxicity that they experience varies considerably. Differences in cancer patients responses to therapy can be associated with factors such as disease burden, drug interactions, age, gender and nutritional status amongst others. It is now also known that genetic variations in drug target genes, disease pathway genes and drug metabolizing enzymes can have important role and influence on the efficacy and toxicity of a particular therapy. These genetic variations may be due to variations in individuals germ line DNA or to somatic changes in the malignant cells. Understanding the genetic profile of the patient and the tumour will help to further refine therapies. Pharmacogenomics can be used to predict response to treatments that are known to have activity against breast cancer including anthracyclines, cyclophosphamide, methotrexate, fluorouracil, taxanes, tamoxifen, aromatase inhibitors and herceptin. The reliability and reproducibility of techniques needs to be validated in large randomized studies before they can be incorporated into routine clinical practice. Thus pharmacogenomics will help develop a profile to tailor therapies with minimal toxicity and maximum efficacy based on molecular signatures. This review discusses clinically relevant germ line mutations that can be used to predict response and toxicity to the above treatments as well as microarray based expression profile studies that may yield important information about prognosis, indication for treatment and response to treatment. The completion of the human genome project and advances in high through-put DNA sequencing and proteomic technology means that there is a real opportunity for pharmacogenomic assessment to become a clinically important part of the decision making process in determining the optimum adjuvant treatment regimen for patients with early stage breast cancer and aiding the management of advanced breast cancer, allowing clinicians to create an individual management plan for each breast cancer patient based on pharmacogenomic data.