Risk Stratification of Patients with RUNX1-mutated Acute Myeloid Leukemia

Mutations in RUNX1 (RUNX1mut) occur in ~15% of intensively treated AML cases. RUNX1mut have no specific hotspot and various types of alteration are observed. The European LeukemiaNet (ELN) risk stratification assigns adverse prognosis to RUNX1mut if they do not co-occur with favorable-risk genotypes. Considering the biological complexity of RUNX1 it seems implausible that all alterations have similar consequences.

Using clinical and genetic variables, we developed a prognostic risk stratification model for ELN adverse-risk RUNX1mut AML patients.

We combined data from five groups, totaling 609 patients with intensively treated RUNX1mut AML, to develop the model. Our training set included 448 patients treated on trials of the AML Cooperative Group (AMLCG; Herold et al, Leukemia, 2020; (n=178)), AML Study Group (Gerstung et al, NEJM, 2016; (n=116)) and Study Alliance Leukemia (n=154). Patients from the Munich Leukemia Laboratory (MLL; (n=107)) and of the Alliance group (trials NCT00048958, NCT00899223, NCT00900224; Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org; (n=54)) served as independent validation cohorts. Additionally, 955 patients without RUNX1mut treated on AMLCG trials served as controls. Patients with t(15;17), prior treatment, or RUNX1mut with co-occurring favorable-risk genotypes according to ELN 2017 were excluded.

Differences between RUNX1mut patients and controls were investigated using univariate logistic regression. Testing was performed using likelihood ratio tests and adjusted for study group. Univariate analyses were adjusted for multiple testing using the Benjamini-Hochberg procedure. We obtained risk prediction models using multivariate Cox regression. Missing values were imputed using the missForest approach. Model selection was performed using forward selection based on the Bayesian information criterion. Cut-offs were based on the 25^th- 50^th- and 75^th-percentile score values obtained in the training data. Performance was evaluated using Kaplan-Meier curves. For internal validation Harrell’s C index was estimated using cross-study validation. For external validation, the final risk prediction models were separately applied to the external datasets.

RUNX1mut were more common in older, male patients and sAML (table). White blood cells, lactate dehydrogenase and bone marrow blasts were lower in RUNX1mut patients. Mutations in several myelodysplasia-related genes were enriched in RUNX1mut patients (ASXL1, BCOR, BCORL1, EZH2, KMT2A, PHF6, and STAG2, SF3B1, SRSF2, and U2AF1), whereas DNMT3A, NPM1 and FLT3 were more frequently altered in controls. A strong association with mutations of the splicing factor complex was identified (49% vs. 13%, p<0.0001). Contradicting previous reports, we found no association with IDH mutations.

The risk prediction score we obtained for OS is as follows: 0.03054 x age (y) + 0.74996 x adverse MRC + 0.43779 x FLT3-ITD + 0.00317 x WBC count (10^9/L) - 0.00158 x platelet count (10^9/L) + 0.37401 x NRAS-mutation, where the obtained cut-off values are: <1.592 (low risk); 1.592 to 2.303 (moderate risk) >2.303 (high risk). Binary variables are coded as 0 or 1. Harrell's C index estimated using internal validation was 0.6. Kaplan-Meier curves estimated using the training set suggest strong differences in survival between the risk categories. Median overall survival (OS) was 2.5, 1.0 and 0.6 years for low-, intermediate-, and high-risk. External validation using the Alliance cohort shows similar results (Figure). Results obtained for the MLL cohort show smaller differences. However, we still observe clear separation of risk groups with a significant difference between low- and high-risk groups (adjusted p-value: 0.0266). Scores for relapse-free survival (RFS) as well as for OS and RFS censored for allogeneic transplant show similar results.

We analyzed a large collection of intensively treated RUNX1mut AML patients and observed heterogenous outcomes that could be predicted by applying few variables (age, MRC-score, FLT3/NRAS mutation-status, and WBC/platelet count). The OS of RUNX1mut high-risk patients is discouraging, highlighting the unmet need of these patients. In addition, our work demonstrates that ELN risk groups can be further stratified and that integrated approaches using routinely available variables can further advance risk prediction.