Refractoriness to induction therapy and relapse after complete remission are the leading causes of death in patients with acute myeloid leukaemia (AML). This study focussed on the prediction of response to standard induction therapy and outcome of patients with AML using a combined strategy of mutational profiling by next-generation sequencing (NGS, n = 190) and ex vivo PharmaFlow testing (n = 74) for the 10 most widely used drugs for AML induction therapy, in a cohort of adult patients uniformly treated according to Spanish PETHEMA guidelines. We identified an adverse mutational profile (EZH2, KMT2A, U2AF1 and/or TP53 mutations) that carries a greater risk of death [hazard ratio (HR): 3Á29, P < 0Á0001]. A high correlation was found between the ex vivo PharmaFlow results and clinical induction response (69%). Clinical correlation analysis showed that the pattern of multiresistance revealed by ex vivo PharmaFlow identified patients with a high risk of death (HR: 2Á58). Patients with mutation status also ran a high risk (HR 4Á19), and the risk was increased further in patients with both adverse profiles (HR 4Á82). We have developed a new score based on NGS and ex vivo drug testing for AML patients that improves upon current prognostic risk stratification and allows clinicians to tailor treatments to minimise drug resistance.Keywords: acute myeloid leukaemia, sequencing, ex vivo sensitivity test.Refractoriness to induction therapy and relapse after achieving complete remission (CR) is a common cause of death among patients with acute myeloid leukaemia (AML). Cytogenetic and molecular alterations at diagnosis and response to therapy are the best predictors of the relative risk of AML relapse and are helpful in deciding between chemotherapy and haematopoietic stem cell transplantation (HSCT) at first CR (Onecha et al., 2018). In this respect, next-generation sequencing (NGS)-based studies have yielded important insights into the molecular pathogenesis of AML, and the mutational profile of AML is now better defined. For example, a single patient can carry up to 400 genomic variants, of