Cancer is the second leading cause of death globally and in the EU, and was responsible for an estimated one in six deaths in 2018. Early diagnosis plays an important role in determining cancer treatment outcomes, but the diagnosis of ovarian cancer poses significant challenges; the routine screening of asymptomatic average-risk women is not recommended by any professional society. Chemotherapy using Pt-based compounds (e.g. carboplatin, cisplatin) is a standard treatment, but tumours that are initially responsive may develop chemoresistance, resulting in treatment failure. The ability to predict patient response towards postoperative treatments would allow for ineffective treatments and harmful side effects to be avoided and alternative treatments to be commenced at earlier stages. Several mechanisms underlying tumour cell resistance have been identified so far, including the altered expression of ion channels (Na, Mg, K, and Ca) and Fe-, Cu-, and Zn-binding proteins, but these remain poorly understood and it is unclear whether they can be exploited as markers of resistance. To that end, I will apply high-precision isotopic analysis by multicollector-inductively coupled plasma-mass spectrometry to Fe, K, and Cu to understand processes and determine whether changes in the natural abundances of these isotopes are associated with the development of resistance to Pt-based chemotherapeutic drugs in high-grade serous ovarian cancer (HGSOC), the deadliest gynaecological cancer, which accounts for ~70% of ovarian malignancies. Deviations in natural isotopic abundances signal biochemical changes (affecting the extent of isotopic fractionation accompanying some biochemical processes) occurring as a result of a disease in a systematic and reliable way. Through ovarian cancer cell line, tumour spheroid, and HGSOC patient plasma experiments, I aim to develop a revolutionary, minimallyinvasive prognostic marker of Pt-based chemotherapeutic drug resistance.