The Shifting Prognosis of FLT3 Mutations in Acute Myeloid Leukemia in the Era of Targeted Therapy: A Real-World Study Using Large-Scale Electronic Health Record Data
Authors: Schwede Matthew , Rodriguez Gladys , Henry Solomon , Wood Douglas , Mannis Gabriel , Majeti Ravindra , Chen Jonathan , Bendavid Eran , Zhang Tian Y. 
Published: December 2023

Background:

Mutations in FLT3 occur in 20-30% of acute myeloid leukemia (AML), and their prognostic implications have varied over time with improved understanding of FLT3 (Döhner et al., Blood, 2022). However, since 2017, the US Food and Drug Administration (FDA) has approved three FLT3 inhibitors, and few studies have examined the prognosis of FLT3 mutations in the era of targeted therapy. Additionally, a real-world oncology data analysis suggested that novel therapies may have greater effects than clinical trials showed, due to the inclusion of previously ineligible patients (Liu et al., Nature, 2021). Therefore, we examined the relative prognosis of FLT3-mutated AML before and after the FDA approval date of the first FLT3-inhibitor, midostaurin.

Methods:

Patients were included if both FLT3-ITD (internal tandem duplication) and FLT3 D835 mutations were tested within 14 days of AML diagnosis at the Stanford Cancer Center. Data came from databases associated with Stanford's electronic health record (EHR), except that overall survival (OS) data was supplemented with external databases like the Social Security Death Index, and diagnosis dates came from the Stanford Cancer Registry. A FLT3 mutation was defined as either an ITD or D835 variant. The FDA approval of midostaurin served as a real-world, unplanned experiment illustrating the impact of FLT3 inhibitors' availability on the prognosis of FLT3-mutated AML. Thus, using a difference-in-differences approach, we built a logistic regression model predicting one-year overall survival using the variables FLT3 mutation, diagnosis before vs. after the midostaurin approval date, and an interaction between those terms. The model was adjusted for patient age at diagnosis, NPM1 mutation presence, and other interaction terms, and for that model, patients who were lost to follow-up before one year were excluded. Cox proportional hazards regression was used to make interpretation comparable to clinical trial hazard ratios, and Kaplan-Meier (KM) curves were used to explore age subgroups. Patients were excluded if they received gilteritinib, sorafenib, or midostaurin but were diagnosed before midostaurin's approval.

Results:

Between April 2008 and September 2022, 522 patients met inclusion criteria, and 120 (23%) had an ITD mutation only, 22 (4%) had a D835 mutation only, and 8 (2%) had both mutations at diagnosis, and the remainder were negative for FLT3 mutation. FLT3 mutation was not significantly associated with overall survival (p = 0.55), but the interaction between FLT3 and diagnosis after midostaurin's approval was associated with a lower chance of death in multiple logistic regression (Table 1, odds ratio [OR] 0.237, p = 0.0044), implying that a FLT3 mutation had a better prognosis after midostaurin's approval. This trend was present in patients both under age 60 (OR 0.153, p = 0.043; KM curves in Figure 1) and at least age 60 (OR 0.273, p = 0.04). In a Cox regression, the interaction term was similar (HR 0.54, p = 0.021), with a HR that compared favorably to 0.78, the HR seen in phase III clinical trials studying midostaurin or quizartinib at diagnosis (Stone et al., NEJM, 2017; Erba et al., Lancet, 2023). Younger patients with FLT3-mutated AML after midostaurin's approval were more likely to have a record of FLT3 inhibitor prescription (79% under age 60 vs. 56% at least age 60, p = 0.07).

Conclusions:

The relative prognosis of FLT3 mutations in AML has improved, including in older patients, likely due to the approval of FLT3 inhibitors, and the relative survival is more favorable than what was described in major clinical trials. Gathering real-world data to estimate prognosis of FLT3 mutations is crucial for accurate risk stratification, and this can be done more easily with EHR data.