Abstract
Introduction: Acute myeloid leukemia (AML) is characterized by dysregulated fibrinolysis, which leads to bleeding and thrombotic complications. While this is well characterized in acute promyelocytic leukemia (APL: a small sub-group of AML with distinct molecular pathogenesis), the mechanisms of fibrinolysis are not well characterized in non-APL AML. Early studies have focused on tissue factor mediated activation of coagulation as the primary cause of coagulopathy in AML, with recent studies indicating the role of tissue factor-independent mechanisms (PMID: 26251762). It is unclear how leukemia-driving clonal mutations contribute to dysregulated fibrinolysis in AML. In this study, we examined the associations between clonal mutations and biomarkers in the fibrinolytic pathway at the time of diagnosis in non-APL AML patients (referred hereafter as AML).
Methods: The study included prospectively enrolled adults (≥18 years) with newly diagnosed AML receiving care at the University of Alabama at Birmingham (UAB) between 2016 and 2021, and with available next generation sequencing data for clonal mutations procured as part of routine clinical care. Blood samples for the fibrinolytic biomarker analysis were collected in 10 mL EDTA tubes at the time of AML diagnosis before administration of chemotherapy. Biomarkers in the fibrinolytic pathway were measured in plasma samples using commercial assays and included the following: active plasminogen activator inhibitor-1 (PAI-1) (Cat#HPAIKT, Innovative Research), plasmin-antiplasmin complexes (PAP) (Cat#MBS2503292, MyBioSource, San Diego, CA, USA), and tissue plasminogen activator (tPA, Cat#IHUTPAKTT, Innovative Research, Novi, MI, USA). Descriptive statistics were used to describe the study population. Biomarkers were treated as continuous variables and observations were winsorized at four standard deviations above the mean. For clonal mutations, variant allele frequency of individual mutations was treated as a continuous variable. Classification trees and generalized additive models (GAM) were used to identify specific clonal mutations related to the biomarkers of interest. GAMs were used to test for statistical significance and nonlinearity, with estimated degrees of freedom >1 indicating a nonlinear association and p<0.05 indicating statistical significance. Models were adjusting for age, sex, race and total white count at AML diagnosis, using the mgcv package in R.
Results: The study included 242 participants; median age at AML diagnosis was 57.6 years, 58.3% were males and 76% were non-Hispanic White individuals. Multiple clonal mutations were common in the study population, and seen in 66.5% participants. The most common dominant mutations included DNMT3A (12%), TP53 (9.1%) and IDH2 (5.8%). GAM models identified significant non-linear relationship of PAI-1 with FLT3-ITD, FLT3-TKD (p<0.0001), TP53 (p=0.001) and CUX1 (p=0.0007) mutations. Significant non-linear relationships were observed between PAP levels and FLT3-ITD (p=0.004), NRAS (p=0.009), U2AF1 (p=0.009) and SF3B1 (p=0.04) mutations; and of tPA with NRAS (p=0.006), ASXL1 (p<0.001) and IDH2 mutations (p<0.001). A linear association was observed between tPA and BCOR mutation (p<0.001).
Conclusion: Clonal mutations show complex associations with fibrinolytic pathway biomarkers. Future research in understanding the mechanisms underlying these associations will help in the development of novel targeted therapies for the prevention and treatment of thrombo-hemorrhagic events in AML.
