Background: Pain is common in cancer patients, particularly in the advanced stage of the disease when the prevalence is estimated to be more than 70%, contributing to poor physical and emotional well‐being. Despite the presence of increased attention regarding cancer research and pain management the ability of clinicians to predict and manage cancer patients’ pain has not changed significantly. A recent evidence‐based review into the pain associated polymorphisms and opioids stated that the clinical benefits of opioid analgesics are dependent on substantial variations in the responses to opioids, insufficient drug dosing and/or a high rate of adverse events. This wide interindividual variability in sensitivity to opioids leads to unpredictable clinical responses to opioid treatment and adverse events. This is troublesome to clinicians given the narrow therapeutic window in which treatment may be beneficial or harmful to the patient. Method: The aim of this study was to investigate how selected SNPs in KCNJ6, ARRB2 and BDNF affect fentanyl dose requirements and response. This was a sub‐study of a prospective, open label, dose individualisation study on the use of fentanyl for pain management in advanced cancer. Adult in‐patients or outpatients of the oncology and palliative care services of Mater Adults Hospital in Brisbane were eligible for inclusion. Fentanyl was administered via the transdermal route with the dose titrated according to clinical need by the palliative care specialists. Participant characteristics including age, height, weight, type of cancer, liver and renal function, and fentanyl doses were recorded. Participants were required to rate their pain on a numerical rating scale from 0 to 10, with a score of 0 representing “no pain” and 10 representing “pain as bad as you can imagine” using the Brief Pain Inventory questionnaire. Pain scores were recorded each time blood and saliva was collected for the dose individualisation study. Genotyping was conducted using pyrosequencing for KCNJ6 and BDNF, and TaqMan assays were used for ARRB2. Regression analysis was used to examine whether outcomes were dependent on nongenetic patient characteristics. Deviation of Hardy‐Weinberg equilibrium was determined by comparing the observed genotype frequencies with the expected values using the chisquare test. The Kruskal‐Wallis H test was used to determine whether genotypes were associated with fentanyl dose or pain score. 2 analysis was used to determine significant associations for low (<3.0/10) or high (>3.0/10) pain score and low (<50 mcg/hr) or high fentanyl dose (>50 mcg/hr), when outcomes were categorised. Results: Fifty‐six adults with advanced cancer receiving transdermal fentanyl were recruited to the study. The median fentanyl dose was 50 mcg/hr, with a range of 12 to 300 mcg/hr, and the mean pain score was 3.0/10.0 with a standard deviation of 2.3. No association was found between patient characteristics, fentanyl dose, and pain score. As each of the P values for age, gender, cancer diagnosis, BMI, BSA, weight, liver function, kidney function, and blood cell counts were above that of 0.05. KCNJ6 rs2070995, ARRB2 (rs34230287, rs3786047, rs1045280, rs2036657), and BDNF (rs7934165, rs10835210, rs1491850) were determined to have no association in relation to dose and pain score. Conclusion: There is so little understanding in the effect of genetics on the pharmacodynamic effect of medications in advanced cancer pain. While clinical studies in palliative care are challenging, we encourage continued research in the area to provide evidence to support clinicians in achieving better treatment outcomes and quality of life for their patients. In the case of this study, no association of SNPs in KCNJ6, ARRB2 and BDNF on fentanyl dose requirements or response was identified. However, any contribution to the evidence base will allow for continued development of gene‐drug dosing guidelines provided by the Clinical Pharmacogenetics Implementation Consortium (CPIC), thereby expediting the translation of research findings to clinical practice. This coupled with increasing technological advancement of low‐cost genetic mapping that is accessible to the general public, and the accessibility of patient electronic records, will allow gene‐based prescription of medications to become a reality.