An Application of Cluster Analysis in Identifying and Evaluating Prognostic Subgroups for Therapy-Related Acute Myeloid Leukemia

Sammanfattning: Treatment for lymphoma with alkylating therapy is known to increase the risk of secondary malignancies such as Acute Myeloid Leukemia (AML), although the risk is not fully understood. This study investigates the characteristics of AML that arise after lymphoma treatment in contrastto AML cases without a prior lymphoma. The study population consists of 115 individuals identified from the Swedish lymphoma register (SLR) with a diagnosis in the quality register for AML between 2000-2019, matched 1:1 to lymphoma-free comparators. A hierarchical clusteranalysis with Gower’s similarity measure and the k-prototypes clustering algorithm are employed to separately identify subgroups of those with a lymphoma history and the matched comparators. The survival of lymphoma patients is compared between subgroups in a Cox regression model. The findings suggests a two-cluster partition achieved by the hierarchical method for patients with a lymphoma history as well as for lymphoma-free patients (average Silhouette 0.853 and0.842, respectively). Both partitions completely separates patients with genetic information from those without. For AML patients with a preceding lymphoma, a subgroup defined by the hierarchical two-cluster partition is associated with an increased mortality rate (HR 2.40). A three-cluster partition achieved by the k-prototypes algorithm could be more clinically relevant, however only one subgroup is associated with increased mortality (HR 2.73).