Individuals with acute myeloid leukemia (AML) harboring an internal tandem duplication (ITD) in the gene encoding Fms-related tyrosine kinase 3 (FLT3) who relapse after allogeneic hematopoietic cell transplantation (allo-HCT) have a 1-year survival rate below 20%. We observed that sorafenib, a multitargeted tyrosine kinase inhibitor, increased IL-15 production by FLT3-ITD leukemia cells. This synergized with the allogeneic CD8 T cell response, leading to long-term survival in six mouse models of FLT3-ITD AML. Sorafenib-related IL-15 production caused an increase in CD8CD107aIFN-γ T cells with features of longevity (high levels of Bcl-2 and reduced PD-1 levels), which eradicated leukemia in secondary recipients. Mechanistically, sorafenib reduced expression of the transcription factor ATF4, thereby blocking negative regulation of interferon regulatory factor 7 (IRF7) activation, which enhanced IL-15 transcription. Both IRF7 knockdown and ATF4 overexpression in leukemia cells antagonized sorafenib-induced IL-15 production in vitro. Human FLT3-ITD AML cells obtained from sorafenib responders following sorafenib therapy showed increased levels of IL-15, phosphorylated IRF7, and a transcriptionally active IRF7 chromatin state. The mitochondrial spare respiratory capacity and glycolytic capacity of CD8 T cells increased upon sorafenib treatment in sorafenib responders but not in nonresponders. Our findings indicate that the synergism of T cells and sorafenib is mediated via reduced ATF4 expression, causing activation of the IRF7-IL-15 axis in leukemia cells and thereby leading to metabolic reprogramming of leukemia-reactive T cells in humans. Therefore, sorafenib treatment has the potential to contribute to an immune-mediated cure of FLT3-ITD-mutant AML relapse, an otherwise fatal complication after allo-HCT.
Acute graft-versus-host disease (GVHD) is treated with systemic corticosteroid immunosuppression. Clinical response after 1 week of therapy often guides further treatment decisions, but long-term outcomes vary widely among centers, and more accurate predictive tests are urgently needed. We analyzed clinical data and blood samples taken 1 week after systemic treatment of GVHD from 507 patients from 17 centers of the Mount Sinai Acute GVHD International Consortium (MAGIC), dividing them into a test cohort (n = 236) and 2 validation cohorts separated in time (n = 142 and n = 129). Initial response to systemic steroids correlated with response at 4 weeks, 1-year nonrelapse mortality (NRM), and overall survival (OS). A previously validated algorithm of 2 MAGIC biomarkers (ST2 and REG3α) consistently separated steroid-resistant patients into 2 groups with dramatically different NRM and OS ( < .001 for all 3 cohorts). High biomarker probability, resistance to steroids, and GVHD severity (Minnesota risk) were all significant predictors of NRM in multivariate analysis. A direct comparison of receiver operating characteristic curves showed that the area under the curve for biomarker probability (0.82) was significantly greater than that for steroid response (0.68, = .004) and for Minnesota risk (0.72, = .005). In conclusion, MAGIC biomarker probabilities generated after 1 week of systemic treatment of GVHD predict long-term outcomes in steroid-resistant GVHD better than clinical criteria and should prove useful in developing better treatment strategies.
In the statistics section, the equation to generate a final prediction model from the training set was incorrect. The correct sentence is below. We then created a training set at random and repeated the entire process to generate a final model: log[-log(1-p̂)] =-11.263 + 1.844(log10ST2) + 0.577(log10REG3α), where p̂ = predicted probability of 6-month NRM.
No laboratory test can predict the risk of nonrelapse mortality (NRM) or severe graft-versus-host disease (GVHD) after hematopoietic cellular transplantation (HCT) prior to the onset of GVHD symptoms. Patient blood samples on day 7 after HCT were obtained from a multicenter set of 1,287 patients, and 620 samples were assigned to a training set. We measured the concentrations of 4 GVHD biomarkers (ST2, REG3α, TNFR1, and IL-2Rα) and used them to model 6-month NRM using rigorous cross-validation strategies to identify the best algorithm that defined 2 distinct risk groups. We then applied the final algorithm in an independent test set ( = 309) and validation set ( = 358). A 2-biomarker model using ST2 and REG3α concentrations identified patients with a cumulative incidence of 6-month NRM of 28% in the high-risk group and 7% in the low-risk group ( < 0.001). The algorithm performed equally well in the test set (33% vs. 7%, < 0.001) and the multicenter validation set (26% vs. 10%, < 0.001). Sixteen percent, 17%, and 20% of patients were at high risk in the training, test, and validation sets, respectively. GVHD-related mortality was greater in high-risk patients (18% vs. 4%, < 0.001), as was severe gastrointestinal GVHD (17% vs. 8%, < 0.001). The same algorithm can be successfully adapted to define 3 distinct risk groups at GVHD onset. A biomarker algorithm based on a blood sample taken 7 days after HCT can consistently identify a group of patients at high risk for lethal GVHD and NRM. The National Cancer Institute, American Cancer Society, and the Doris Duke Charitable Foundation.
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