Perkins, Cecelia

Background. Some patients who present with clinical features consistent with idiopathic hypereosinophilic syndrome (HES; absolute eosinophil count (AEC) ≥1.5 x 10 9/L with related end organ damage) are ultimately diagnosed with a myeloid neoplasm. Although FIP1L1::PDGFRA is the most common molecular abnormality in this setting, many other fusion genes, point mutations and cytogenetic abnormalities have been implicated in driving primary (neoplastic) eosinophilia. Given the poor prognosis of these disorders in the absence of treatment and the availability of targeted therapies for many of the driver mutations, it is becoming increasingly important to rapidly identify the underlying genetic alteration. In this regard, the expense and variable sensitivity of molecular diagnostic tests remains problematic. Although clinical features have been reported to distinguish between PDGFRA-associated myeloid neoplasms and D816V KIT-positive systemic mastocytosis with hypereosinophilia, little is known about the clinical phenotypes of other subsets of patients. This multicenter retrospective study aimed to assemble a large cohort of patients with HES secondary to a myeloid neoplasm to explore the utility of clinical and laboratory features in predicting the underlying molecular abnormality. Methods. Retrospective, de-identified data from 16 centers were collected via an online Research Electronic Data Capture repository. Patients and associated data were obtained from in-house research databases and/or electronic medical record searches at each site. Clinical and laboratory data were entered in accordance with local Institutional Review Boards. Inclusion criteria were peripheral blood hypereosinophilia (AEC >1.5 x 10 9/L) on at least two occasions and one of the following: ≥1 confirmed genetic abnormality recurrently associated with primary myeloid neoplasms, other evidence of clonal eosinophilia, or increased blasts (≥2% in the blood or >5% but <20% in the marrow). Patients with acute myeloid leukemia were excluded. Results. A total of 277 patients followed between 1990 and 2022 were included in the analysis. Median length of follow up was 31 months (range <1-356), and 217/277 (78%) were alive at last follow up. The most common genetic abnormality was FIP1L1::PDGFRA (n=131) followed by D816V KIT mutations (n=39) and alterations involving JAK2 (n=22) (Figure 1). Five patients had genetic abnormalities involving two distinct genes recurrently associated with eosinophilic myeloid neoplasms. The overall median age at diagnosis was 55 years (range 12-89) and 79% of the patients were male. When patients with abnormalities involving PDGFRA and PDGFRB were excluded from the analysis, the male predominance was significantly lower at 62% (p<0.001, Fisher's exact test as compared to the data set as a whole). The overall pattern of organ system involvement was similar irrespective of the underlying genetic abnormality with a few notable exceptions. Cardiac involvement was most common in patients with FIP1L1:: PDGFRA, abnormalities involving JAK2 or without pathologic alterations identified (reported in 22%, 32% and 30%, respectively); whereas skin and gastrointestinal involvement, lymphadenopathy and splenomegaly were most frequent in patients with KIT D816V. Laboratory abnormalities also differed between groups. Whereas anemia was the most common blood abnormality (reported in >50% of patients), erythrocytosis was noted primarily in patients with abnormalities involving JAK2. In contrast, circulating blasts were identified in 70% of patient with BCR::ABL1 and 29% of patients with chromosomal abnormalities or KIT D816V, but in <15% of patients in any of the other groups. Whereas peak AEC alone did not discriminate between the different patient groups, the AEC/tryptase ratio was increased in patients with PDGFRA-associated myeloid neoplasms, molecular abnormalities involving JAK2, or chromosomal abnormalities as compared to those with KIT mutations (Figure 2). Conclusions. Despite the limitations of retrospective data analysis, the data from this large multicenter cohort of patients with HES due to a chronic myeloid neoplasm suggest that routine demographic, clinical and laboratory features vary depending on the underlying molecular abnormality. This approach could ultimately help guide empiric therapy when molecular studies are negative or unavailable.

Abstract Background: JAK2 inhibition improves splenomegaly and symptoms in patients with myelofibrosis (MF), and the JAK2 inhibitor ruxolitinib has been shown to improve both bone marrow fibrosis and survival in MF patients (Cervantes et al., 2013; Verstovsek et al., 2013). Current prognostic models such as DIPSS plus (Gangat et al., 2011) predict survival in MF based on clinical, laboratory, and cytogenetic information, but predate the era of JAK2 inhibitor use in MF. As such, their value in predicting clinical response or survival during treatment with JAK2 inhibitors remains unknown. We hypothesized that features not considered by DIPSS plus, including bone marrow fibrosis and splenomegaly, have independent effects on therapy response. We therefore conducted a retrospective analysis to create a new model to risk stratify patients with respect to their likelihood of achieving a > 50% reduction in splenic size by palpation with JAK2-inhibitor therapy. Methods: We studied a cohort of 418 patients with bone marrow biopsy-proven MF who were treated with ruxolitinib or one of four other JAK2 inhibitors. An initial cohort of 203 patients seen at University of Michigan, Stanford University, and Mayo Clinic in Scottsdale, AZ was used identify factors that would predict a clinical response to therapy. Response to therapy was evaluated at an early time point (3-4 mos) in 167 patients and at a late time point (5-12 mos) in 138 patients, with 127 patients overlapping between the two. We constructed a multivariable logistic regression model, with Bayesian variable selection, for predicting the probability of early or late splenic response to JAK2 inhibitor therapy. The model was developed with this initial group of 203 patients. The model was then validated with a cohort of 215 patients from the PERSIST1 trial (data supplied by CTI Biopharma). Results: Conclusion: We identified several factors that play a role in predicting whether or not patients with MF are likely to respond to JAK2 inhibitor therapy. The interaction between spleen size and WBC at the time of therapy initiation plays the strongest role in predicting which patients will have an early response to therapy. Additionally, our model suggests that patients who do not have a response to JAK2 inhibitor therapy by about 4 months of treatment are unlikely to develop a later response. Our model is able to discriminate between those likely to benefit after several months of therapy and those for whom a change in therapy should be considered early in their treatment course. Figure 1 The following characteristics were selected for inclusion in the prognostic model based on the data collected from our 418-patient cohort. Spleen size and white blood cell count at the time of therapy initiation were the strongest predictors of early response to JAK2 inhibitor therapy. Achievement of an early response was used to further enhance the model for the late time point (third column) and was the strongest predictor of late response. Interaction terms looked at the interdependence of variables on splenic response. Log odds ratios are provided with 95% credible intervals in parentheses. Figure 1. The following characteristics were selected for inclusion in the prognostic model based on the data collected from our 418-patient cohort. Spleen size and white blood cell count at the time of therapy initiation were the strongest predictors of early response to JAK2 inhibitor therapy. Achievement of an early response was used to further enhance the model for the late time point (third column) and was the strongest predictor of late response. Interaction terms looked at the interdependence of variables on splenic response. Log odds ratios are provided with 95% credible intervals in parentheses. Figure 2 An example of the predicted response for a hypothetical patient. The curves show the interaction of a high initial WBC (red) or a low initial WBC (green) with baseline spleen size (depicted along the X-axis) in predicting whether or not the patient will have a late response to therapy (depicted along the Y-axis). The solid lines show the probability that the patient will respond at the later time point if they had an early response. The dashed lines show the probably of a late response if the patient had not achieved a response by the early time point. Overall, a patient with a higher initial WBC and a larger baseline spleen size is less likely to respond to JAK2 inhibitor therapy. Additionally, the curves clearly separate to show that, for a given baseline WBC and spleen size, a patient who has an early response to therapy is much more likely to have a late response to therapy, whereas a patient who fails to respond early in their treatment course is much less likely to respond with further treatment. Figure 2. An example of the predicted response for a hypothetical patient. The curves show the interaction of a high initial WBC (red) or a low initial WBC (green) with baseline spleen size (depicted along the X-axis) in predicting whether or not the patient will have a late response to therapy (depicted along the Y-axis). The solid lines show the probability that the patient will respond at the later time point if they had an early response. The dashed lines show the probably of a late response if the patient had not achieved a response by the early time point. Overall, a patient with a higher initial WBC and a larger baseline spleen size is less likely to respond to JAK2 inhibitor therapy. Additionally, the curves clearly separate to show that, for a given baseline WBC and spleen size, a patient who has an early response to therapy is much more likely to have a late response to therapy, whereas a patient who fails to respond early in their treatment course is much less likely to respond with further treatment. Disclosures Boonstra: CTI Biopharma: Research Funding. Gowin:Incyte: Membership on an entity's Board of Directors or advisory committees. Mesa:Novartis: Consultancy; Ariad: Consultancy; Promedior: Research Funding; Celgene: Research Funding; Galena: Consultancy; Incyte: Research Funding; CTI Biopharma: Research Funding; Gilead: Research Funding. Talpaz:Ariad: Other: Expense reimbursement, travel accomodation expenses, Research Funding; Novartis: Research Funding; Incyte Corporation: Other: Travel expense reimbursement, Research Funding; Pfizer: Consultancy, Other: travel accomodation expenses, Research Funding.

Abstract Background: JAK2 inhibitors have been shown to improve symptoms and produce durable reductions in splenomegaly in patients with myelofibrosis (MF), and ruxolitinib has been shown to improve survival in MF patients (Cervantes et al., 2013; Verstovsek et al., 2013). Current prognostic models such as DIPSS plus (Gangat et al., 2011) predict survival in MF based on clinical, laboratory, and cytogenetic information, but their value in predicting clinical response or survival during treatment with JAK2 inhibitors remains unknown. We hypothesized that clinical features such as bone marrow fibrosis and splenomegaly may have independent effects on therapy response. Therefore, we conducted a retrospective analysis to create a new model to risk stratify patients with respect to their likelihood of responding to oral JAK2-inhibitor therapy. Methods: We studied a cohort of 203 patients with bone marrow biopsy-proven MF seen at University of Michigan, Stanford University, and Mayo Clinic in Scottsdale, AZ. These patients were all treated with ruxolitinib or an experimental JAK2 inhibitor. Our primary endpoint was defined as IWG-MRT criteria for splenic response by palpation (Tefferi et al., 2013). Response in patients with spleen size of less than 5 cm was defined as complete resolution of splenomegaly. Of the 203 patients studied, splenic response was evaluated after 3 months of therapy in 167 patients and after 6 months of therapy in 138 patients; 127 patients were in both groups. A logistic regression was performed to identify factors that would predict clinical response. Results: The following characteristics were significantly associated with spleen response at 3 and 6 months: initial spleen size, European consensus criteria grading of MF on bone marrow biopsy, initial DIPSS plus score, and initial WBC count. Cellularity on marrow biopsy was not significant. We enriched a baseline logistic model of initial dose of oral JAK2 therapy and DIPSS plus score with additional prognostic factors. We found the following clinical characteristics to be jointly associated with splenic response: normalized initial dose of oral JAK2 inhibitor, initial spleen size, DIPSS plus score, degree of fibrosis by European consensus criteria. Duration of disease from time of diagnosis to time of treatment initiation was not prognostic for splenic response. We used this model to calculate the probability of splenic response based on a risk score: Risk score = –1.18(Dose) + 0.09(Initial Spleen Size in cm) + 0.20(DIPSS-plus Points) +0.92 (if fibrosis is MF-3). The probability at 6 months can then be calculated from the risk score as follows: [1/(1+e(risk score-2.6))]. The figure demonstrates the prognostic gain of our model over a model based on DIPSS plus alone. Conclusion: With this observational study, we propose a predictive model which may serve as a clinical tool to identify which patients are most likely to benefit from JAK2 inhibitor-based therapies. Further validation in independent data sets will be required before this model can be more widely applied. Disclosures Mesa: Incyte Corporation, CTI, NPS Pharma, Inc., Gilead Science Inc., Celgene: Research Funding. Gotlib:Incyte: Consultancy, Honoraria, Research Funding, Travel Reimbursement Other; Gilead: Research Funding; Sanofi: Research Funding; Novartis: Research Funding, Travel Reimbursement, Travel Reimbursement Other. Talpaz:ARIAD Pharmaceuticals, Inc., BMS, Sanofi, Incyte, Pfizer: Research Funding.