Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles

Plain language summaryThere are various ways in which clinicians can predict the risk of disease progression in patients with leukemia, helping them to treat the patients accordingly. However, these approaches are usually designed by human experts and might not fully capture the complexity of a patient's disease. Here, with a large cohort of patients with acute myeloid leukemia, we design an unsupervised machine learning model - a type of computer model that learns from patterns in data without human input-to separate these patients into subgroups according to risk. We identify four distinct groups which differ with regards to patient genetics, laboratory values, and clinical characteristics. These groups have differences in response to treatment and patient survival, and we validate our findings in another dataset. Our approach might help clinicians to better predict outcomes in patients with leukemia and make decisions on treatment. Eckardt et al. identify four distinct risk clusters of patients with acute myeloid leukemia using unsupervised metaclustering, which is agnostic to current risk stratification models, in a large multicenter cohort. Clusters differ in terms of response to induction therapy and long-term survival, and are externally validated. BackgroundIncreasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven unsupervised learning can detect inherent patterns in such data sets.MethodsWhile unsupervised analysis in the medical literature commonly only utilizes a single clustering algorithm for a given data set, we developed a large-scale model with 605 different combinations of target dimensionalities as well as transformation and clustering algorithms and subsequent meta-clustering of individual results. With this model, we investigated a large cohort of 1383 patients from 59 centers in Germany with newly diagnosed acute myeloid leukemia for whom 212 clinical, laboratory, cytogenetic and molecular genetic parameters were available.ResultsUnsupervised learning identifies four distinct patient clusters, and statistical analysis shows significant differences in rate of complete remissions, event-free, relapse-free and overall survival between the four clusters. In comparison to the standard-of-care hypothesis-driven European Leukemia Net (ELN2017) risk stratification model, we find all three ELN2017 risk categories being represented in all four clusters in varying proportions indicating unappreciated complexity of AML biology in current established risk stratification models. Further, by using assigned clusters as labels we subsequently train a supervised model to validate cluster assignments on a large external multicenter cohort of 664 intensively treated AML patients.ConclusionsDynamic data-driven models are likely more suitable for risk stratification in the context of increasingly complex medical data than rigid hypothesis-driven models to allow for a more personalized treatment allocation and gain novel insights into disease biology.

Automated summary

Researchers use unsupervised machine learning to separate patients with acute myeloid leukemia into four distinct risk subgroups based on genetics, laboratory values, and clinical characteristics. These subgroups show differences in treatment response and patient survival, and the findings are validated in another dataset. This approach can improve outcome prediction and treatment decisions for leukemia patients.