Refractoriness to induction chemotherapy and relapse after achievement of remission are the main obstacles to cure in acute myeloid leukaemia (AML). After standard induction chemotherapy, patients are assigned to different post-remission strategies on the basis of cytogenetic and molecular abnormalities that broadly define adverse, intermediate and favourable risk categories. However, some patients do not respond to induction therapy and another subset will eventually relapse despite the lack of adverse risk factors. There is an urgent need for better biomarkers to identify these high-risk patients before starting induction chemotherapy, to enable testing of alternative induction strategies in clinical trials. The high rate of relapse in AML has been attributed to the persistence of leukaemia stem cells (LSCs), which possess a number of stem cell properties, including quiescence, that are linked to therapy resistance. Here, to develop predictive and/or prognostic biomarkers related to stemness, we generated a list of genes that are differentially expressed between 138 LSC and 89 LSC cell fractions from 78 AML patients validated by xenotransplantation. To extract the core transcriptional components of stemness relevant to clinical outcomes, we performed sparse regression analysis of LSC gene expression against survival in a large training cohort, generating a 17-gene LSC score (LSC17). The LSC17 score was highly prognostic in five independent cohorts comprising patients of diverse AML subtypes (n = 908) and contributed greatly to accurate prediction of initial therapy resistance. Patients with high LSC17 scores had poor outcomes with current treatments including allogeneic stem cell transplantation. The LSC17 score provides clinicians with a rapid and powerful tool to identify AML patients who do not benefit from standard therapy and who should be enrolled in trials evaluating novel upfront or post-remission strategies.

Background: Coronavirus disease 2019 (COVID-19) can manifest as a viral-induced hyperinflammation with multiorgan involvement. Such patients often experience rapid deterioration and need for mechanical ventilation. Currently, no prospectively validated biomarker of impending respiratory failure is available. Objective: We aimed to identify and prospectively validate biomarkers that allow the identification of patients in need of impending mechanical ventilation. Methods: Patients with COVID-19 who were hospitalized from February 29 to April 9, 2020, were analyzed for baseline clinical and laboratory findings at admission and during the disease. Data from 89 evaluable patients were available for the purpose of analysis comprising an initial evaluation cohort (n 5 40) followed by a temporally separated validation cohort (n 5 49). Results: We identified markers of inflammation, lactate dehydrogenase, and creatinine as the variables most predictive of respiratory failure in the evaluation cohort. Maximal IL-6 level before intubation showed the strongest association with the need for mechanical ventilation, followed by maximal CRP level. The respective AUC values for IL-6 and CRP levels in the evaluation cohort were 0.97 and 0.86, and they were similar in the validation cohort (0.90 and 0.83, respectively). The calculated optimal cutoff values during the course of disease from the evaluation cohort (IL-6 level > 80 pg/mL and CRP level > 97 mg/L) both correctly classified 80% of patients in the validation cohort regarding their risk of respiratory failure. Conclusion: The maximal level of IL-6, followed by CRP level, was highly predictive of the need for mechanical ventilation. This suggests the possibility of using IL-6 or CRP level to guide escalation of treatment in patients with COVID-19-related hyperinflammatory syndrome.