scholarly journals Easix for Prediction of Survival in Myelodysplastic Syndromes

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1722-1722
Author(s):  
Almuth Maria Anni Merz ◽  
Ulrich Germing ◽  
Guido Kobbe ◽  
Jennifer Kaivers ◽  
Anna Jauch ◽  
...  
Keyword(s):  
Endothelial Dysfunction ◽  
Board Of Directors ◽  
Lower Risk ◽  
Research Funding ◽  
Validation Cohort ◽  
P Value ◽  
Advisory Committees ◽  
Training Cohort ◽  
Risk Patients ◽  
Support Research

Introduction Patients with myelodysplastic syndromes (MDS) are at risk of early death from cardiovascular complications due to the link between clonal hematopoiesis and endothelial dysfunction. EASIX (Endothelial Activation and Stress Index) has been established to predict endothelial complications after allogeneic stem cell transplantation (alloSCT). EASIX is an endothelial dysfunction related biomarker that can be easily obtained with routine laboratory markers (creatinine× LDH/thrombocytes). We analyzed whether EASIX can predict mortality in higher-risk and in lower-risk MDS patients. Since alloSCT can alter the natural history of MDS and may be an independent contributor to endothelial complications, this analysis was restricted to patients who did not undergo alloSCT during the course of their disease. Patients and Methods We investigated the impact of EASIX measured at first diagnosis on survival of patients with lower- and higher-risk MDS (no allogeneic transplantation) in two independent institutions: n=192 (training cohort) and n=333 (validation cohort). For the purposes of this study, patients with low and intermediate-1 according to IPSS and very low and low-risk according to IPSS-R, respectively, were considered to have lower-risk MDS, while patients with intermediate-2 and high-risk according to IPSS and intermediate, high and very-high according to IPSS-R, respectively, were considered to have higher-risk MDS. Serum markers of endothelial cell distress were measured and correlated to EASIX. Results While no effects of EASIX on survival were observed in higher-risk patients, EASIX was associated with shorter survival in patients with lower-risk MDS in both cohorts (univariate: training cohort: hazard ratio (HR): 1.46; 95% confidence interval (CI) 1.24-1.71; p-value < 0.001 / validation cohort: HR 1.31 [1.17-1.48]; p-value < 0.001). We visualized this continuous effect of EASIX in lower-risk disease by grouping patients into quartiles according to EASIX (Figure 1 A and B). In both cohorts, patients in the highest quartile had a shorter survival compared to patients in lower quartiles. Confining our analysis to lower-risk patients who did not develop AML within the observation period, EASIX similarly predicted OS in both cohorts (training: n=78, no. of events n=17, HR per log2 increase 1.43 [1.05-1.94]; p=0.02; validation: n=267, no. of events n=93, HR per log2 increase 1.33 [1.21-1.47]; p<0.00). Multivariate Cox regression analysis and prediction error analyses confirmed that EASIX remained a significant predictor of survival after adjustment for age, sex, cytogenetic abnormalities and bone marrow blasts in lower-risk patients. The model of the training cohort could be validated. No effect of EASIX on survival was found in higher-risk patients from both cohorts in univariate and multivariate analysis. Serum levels of Angiopioetin-2 correlated significantly with EASIX whereas S100A9 or IL-1b did not. However, we observed a strong intra-pathway correlation of S100A9 with IL1b, IL18, IL37, and HMGB1. Conclusion We introduce EASIX as an easily accessible and independent predictor for survival in patients with lower-risk MDS. The strong correlation of EASIX and ANG2 underlines the endothelial nature of this biomarker. Disclosures Germing: Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Jazz Pharmaceuticals: Honoraria; Amgen: Honoraria. Kobbe:Neovii: Honoraria, Other: Travel support; Pfizer: Honoraria, Other: Travel support; Medac: Honoraria, Other: Travel support; Abbvie: Honoraria, Other: Travel support; Biotest: Honoraria, Other: Travel support; Jazz: Honoraria, Other: Travel support; Celgene: Honoraria, Other: Travel support, Research Funding; Roche: Honoraria, Other: Travel support; Amgen: Honoraria, Other: Travel support, Research Funding; MSD: Honoraria, Other: Travel support; Novartis: Honoraria, Other: Travel support; Takeda: Honoraria, Other: Travel support. Kaivers:Jazz Pharmaceuticals: Other: Travel Support. Merz:Janssen: Other: Travel grants; Amgen: Membership on an entity's Board of Directors or advisory committees, Other: Travel grants; Abbvie: Other: Travel grants; Celgene: Other: Travel grants; Takeda Vertrieb GmbH: Other: Travel grants, Research Funding. Dreger:MSD: Membership on an entity's Board of Directors or advisory committees, Other: Sponsoring of Symposia; Neovii, Riemser: Research Funding; AbbVie, Gilead, Novartis, Riemser, Roche: Speakers Bureau; AbbVie, AstraZeneca, Gilead, Janssen, Novartis, Riemser, Roche: Consultancy.

scholarly journals External Validation and Revision of Thrombosis Lymphoma /Throly/ Score

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 140-140 ◽  
Author(s):  
Darko Antic ◽  
Natasa Milic ◽  
Biljana Mihaljevic ◽  
Bruce Cheson ◽  
Mayur Narkhede ◽  
...  
Keyword(s):  
At Risk ◽  
Board Of Directors ◽  
Predictive Value ◽  
Research Funding ◽  
Validation Cohort ◽  
External Validation ◽  
Advisory Committees ◽  
Final Model ◽  
Risk Patients ◽  
Reduced Mobility

Abstract Introduction Lymphoma patients are at increased risk of thromboembolic events (TE), however, thromboprophylaxis in these patients is largely under utilized. Actual guidelines recommend different models for thromboembolic risk estimation in cancer patients. Proposed models are of limited use in lymphoma patients as their development is not based on specific characteristics for this patient population. Previously, we developed and internally validated a simple model, based on individual clinical and laboratory patient characteristics that would classify lymphoma patients at risk for a TE. The variables independently associated with the risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m2, reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. For patients classified at risk in derivation cohort (n=1236), the model revealed positive predictive value of 25.1%, negative predictive value of 98.5%, sensitivity of 75.4%, and specificity of 87.5%. The diagnostic performance measures retained similar values in the internal validation cohort (n=584). The aim of this study was to perform external validation of the previously developed thrombosis lymphoma (Throly) score. Methods The study population included patients with a confirmed diagnosis of non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), and chronic lymphocytic leukemia (CLL)/ small lymphocytic lymphoma (SLL) from 8 lymphoma centers from USA, France, Spain, Croatia, Austria, Switzerland, Macedonia, and Jordan. During 2015 to 2016, data were prospectively collected for venous TE events from time of diagnosis to 3 months after the last cycle of therapy for newly diagnosed and relapsed patients who had completed a minimum of one chemotherapy cycle. The score development and validation were done according to TRIPOD suggested guidelines. Sensitivity analyses were carried out to test the model robustness to possible different settings, according to in/out patient settings and according to different countries included. Results External validation cohort included 1723 patients, similar to the developed group and consisted of 467 indolent NHL, 647 aggressive NHL, 235 CLL/SLL and 366 HL patients, out of which 121 (7%) patients developed venous thromboembolic events. For patients classified at risk in external validation cohort, the model resulted in positive and negative predictive values of 17% and 93%, respectively. Based on new available information from this large prospective cohort study this model was revised to include the following variables: diagnosis/clinical stage, previous VTE, reduced mobility, hemoglobin level < 100g/L and presence of vascular devices. In the new score we divided patients in two groups: low risk patients, score value ≤ 2; and high risk patients, score value > 2. For patients classified at risk by the revised model, the model produced positive predictive value of 22%, negative predictive value of 96%, sensitivity of 51%, and specificity of 72%. In sensitivity analysis, the final model proved its robustness in different settings of major importance for lymphoma patients. The final model presented good discrimination and calibration performance. Concordance C statistics was 0.794 (95% CI 0.750-0.837). Conclusions Revised Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis risk in solid cancer patients. We included biological characteristic of lymphoma, indolent vs aggressive, as well as data about dissemination of disease, localized vs advanced stage, reflecting specificity of lymphomas comparing to other types of cancer. Also, we pointed out significance of central vascular devices as risk factor having considered the role of vascular damage during insertion as a potential trigger for activation of the clotting cascade. This score is user friendly for daily clinical practice and provides a very good predictive power to identify patients who are candidates for pharmacological thromboprophylaxis. Disclosures Cheson: AbbVie, Roche/Genentech, Pharmacyclics, Acerta, TG Therapeutics: Consultancy. Ghielmini:Roche: Consultancy, Honoraria, Research Funding, Speakers Bureau. Jaeger:Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; AOP Orphan: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; MSD: Research Funding; Bioverativ: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Honoraria; Mundipharma: Membership on an entity's Board of Directors or advisory committees; Takeda-Millenium: Membership on an entity's Board of Directors or advisory committees; Takeda-Millenium: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Infinity: Membership on an entity's Board of Directors or advisory committees.

Impact of Hypomethylating Agent Therapy in Myelodysplastic Syndromes with Chromosome 3 Abnormalities

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1705-1705
Author(s):  
David Sallman ◽  
Guillermo Garcia-Manero ◽  
Elias Jabbour ◽  
Mikkael A. Sekeres ◽  
Amy E. DeZern ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Board Of Directors ◽  
Myelodysplastic Syndromes ◽  
Lower Risk ◽  
Research Funding ◽  
Chromosome 3 ◽  
Advisory Committees ◽  
Monosomy 3 ◽  
Risk Patients

Abstract Background In myelodysplastic syndromes (MDS), abnormalities of chromosome 3 (i.e. inversion 3 (inv(3)), translocation 3q (t(3q)), or deletion 3q (del(3q)) represent a poor-risk karyotype in the Revised International Prognostic Scoring System (IPSS-R). In acute myeloid leukemia (AML) patients with 3q abnormalities, patients with inv(3)/t3;3 represented the most unfavorable group with a median overall survival (OS) of 10.3 months (Lugthart et al., 2010). We previously presented a single institution experience regarding outcomes of MDS patients with chromosome 3 abnormalities. Here, we sought to further define outcomes of chromosome 3 abnormalities in MDS and address the impact of hypomethylating agents (HMA) on outcome in multiple institutions. Patients and Methods Patients were identified through the MDS Clinical Research Consortium and were included if they had a WHO diagnosis of MDS, MDS/myeloproliferative neoplasm (MPN), therapy related MDS (t-MDS), or AML (20-30% myeloblasts) and had any karyotypic abnormality involving chromosome 3. Data analyzed included baseline demographics, disease characteristics, IPSS/IPSS-R scores, treatment and outcome. Responses to HMA therapy were evaluated using International Working Group (IWG) 2006 criteria. Kaplan-Meier estimates were used for overall survival. Results A total of 413 patients were identified with a median age at diagnosis of 67 years. WHO classification was as follows: 9% RA/RARS, 12% RCMD, 26% RAEB-1, 31% RAEB-2, 2% MDS/MPN, 7% MDS Unclassified, 13% AML; 34% had t-MDS. Overall, 97% of patients were higher risk by IPSS-R (i.e., intermediate to very high risk) with a median blast % in bone marrow of 8%. Distribution of cytogenetic abnormalities were inv(3) (10%), del(3q) (12%), t(3q) (18%), monosomy 3 (22%), 3p abnormalities (22%), and other chromosome 3 changes (17%). Median OS for the cohort was 12.0 months (95% C.I. 10.8 to 13.9 months) and 31% of patients without AML transformed to AML. IPSS-R was predictive of median OS across subgroups (P < 0.00001). The specific cytogenetic abnormality was predictive for survival (P < 0.00001) with median OS for t(3q) 19 months, inv(3) 13 months, del(3q) 13 months, 3p 10 months, monosomy 3 9 months, and other 3 abnormalities 11 months. There was no survival difference between patients with translocations of 3q21 versus 3q26 (median OS 18 months versus 18.6 months, P = 0.96). Patients with an isolated chromosome 3 abnormality had significantly improved OS (25.1 months versus 10.9 months (P < 0.00001). Complex karyotype (>/= 3 abnormalities) was observed in 74% of patients and was associated with decreased OS (11 months versus 21 months, P < 0.00001). Of patients who received HMA therapy (48%), the overall response rate was 46% (17% hematological improvement (HI), 7% PR, 20% CR, 2% marrow CR (CRm) with stable disease in 23%). Median OS with and without HMA was 15.5 months versus 8.4 months (p=0.038). In int-2/high risk patients by IPSS, HMA treated patient had a median OS of 14.0 months versus 7.6 months for patients not treated with HMAs (P = 0.005) with no benefit for HMAs in lower-risk patients (median OS 24.5 months with HMA versus 38.7 months without; P =0.41). Cox regression modeling with HMA therapy, IPSS and clinical site confirmed the HMA OS benefit in higher-risk patients (HR 0.69; 95% CI 0.53-0.89; P = 0.005), but showed decreased OS in lower-risk patients (HR 2.0; 95% CI 1.03-3.92; P = 0.04). Allogeneic transplantation was performed in 18% (n=75) of patients, with median OS of 18 months versus 10 months in non-transplanted patients (P < 0.00001). Conclusion In this large cohort of patients with MDS and oligoblastic AML associated with chromosome 3 abnormalities, survival was heterogeneous but overall poor, with isolated chromosome 3 abnormality and t(3q) patients having a more favorable OS than patients with other chromosome 3 anomalies. MDS patients with 3p changes have poor outcomes. Although some patients with chromosome 3 respond to HMA therapy, the overall survival remains poor and novel approaches are needed. Disclosures Sekeres: Amgen: Membership on an entity's Board of Directors or advisory committees; TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees. Steensma:Amgen: Consultancy; Celgene: Consultancy; Incyte: Consultancy; Onconova: Consultancy. Lancet:Boehringer-Ingelheim: Consultancy; Kalo-Bios: Consultancy; Pfizer: Consultancy; Seattle Genetics: Consultancy; Celgene: Consultancy, Research Funding; Amgen: Consultancy. List:Celgene Corporation: Honoraria, Research Funding. Komrokji:Incyte: Consultancy; Celgene: Consultancy, Research Funding; Novartis: Research Funding, Speakers Bureau; Pharmacylics: Speakers Bureau.

scholarly journals Early Prediction of Treatment Response in Newly Diagnosed Multiple Myeloma

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3159-3159
Author(s):  
Moritz Binder ◽  
S. Vincent Rajkumar ◽  
Martha Q. Lacy ◽  
Morie A. Gertz ◽  
Francis K. Buadi ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Board Of Directors ◽  
Research Funding ◽  
Treatment Cycle ◽  
Validation Cohort ◽  
Newly Diagnosed ◽  
First Line ◽  
Advisory Committees ◽  
Training Cohort ◽  
M Spike

Abstract Background: The introduction of several novel agents has led to an improvement in response rates and survival outcomes in patients with newly diagnosed multiple myeloma. Despite significant therapeutic advances a subset of patients with multiple myeloma do not achieve a deep response to first-line treatment and biomarkers are needed to identify those at risk. Methods: We studied 472 patients with newly diagnosed multiple myeloma who were treated in clinical practice (n = 280, training cohort) or on prospective clinical trials (n = 192, validation cohort) at Mayo Clinic between 12/2003 and 12/2015. All patients had measurable disease (M-spike ≥ 1.0 g/dL) and received treatment with novel agents. Serum M-spike and free light chains (FLC) were measured before and after the first treatment cycle. The outcome of interest was the best response to first-line treatment (evaluated using the International Myeloma Working Group Uniform Response Criteria). Failure to achieve a deep response was defined as not achieving a very good partial response or better. The serum parameters of interest were the relative decrease in M-spike and absolute free light chain difference (ΔFLC). The Wilcoxon signed-rank test was used to compare the serum parameters before and after the first treatment cycle. Logistic regression models were used to assess the associations between the change in serum parameters after the first treatment cycle and best response to first-line treatment. P-values below 0.05 were considered statistically significant. Results: The median age at diagnosis in the training and the validation cohort were 67 (32 - 94) and 66 years (41 - 86), respectively. One hundred eighty one (65%) and 103 patients (54%) were male, respectively. The three most common regimens in the training cohort were lenalidomide + dexamethasone, lenalidomide + cyclophosphamide + dexamethasone, and bortezomib + lenalidomide + dexamethasone. In the validation cohort, the three most common regimens were carfilzomib + thalidomide + cyclophosphamide, lenalidomide + cyclophosphamide + dexamethasone, and ixazomib + cyclophosphamide + dexamethasone. In the two cohorts, the median baseline M-spike decreased from 3.1 g/dL (1.0 - 10.0) to 1.7 g/dL (0.0 - 6.6) and 3.2 g/dL (1.0 - 8.5) to 1.4 g/dL (0.0 - 4.5) after the first treatment cycle, respectively (p < 0.001 for both comparisons). The median baseline ΔFLC decreased from 31.1 mg/dL (0.0 - 2269.7) to 5.4 mg/dL (0.0 - 785.8) and from 23.3 mg/dL (0.1 - 3579.6) to 4.3 mg/dL (0.0 - 1139.3) after the first treatment cycle, respectively (p < 0.001 for both comparisons). M-spike reduction < 50% during the first treatment cycle was associated with failure to achieve a deep response: OR 4.54 (95% CI 2.72 - 7.58, p < 0.001) in the training cohort and OR 6.68 (95% CI 3.42 - 13.03, p < 0.001) in the validation cohort. Patients with reduction in M-spike < 10% during the first treatment cycle experienced failure to achieve a deep response in 86% (25/29) and 100% (10/10), respectively. ΔFLC reduction < 50% during the first treatment cycle was associated with treatment failure to achieve a deep response: OR 4.83 (95% CI 2.83 - 8.25, p < 0.001) in the training cohort and OR 5.09 (95% CI 2.37 - 10.92, p < 0.001) in the validation cohort. Patients with reduction in ΔFLC < 10% during the first treatment cycle experienced failure to achieve a deep response in 83% (34/41) and 91% (19/21), respectively. Conclusions: Early changes in M-spike and ΔFLC are strong predictors of response to treatment. We established and prospectively validated two readily available biomarkers that can identify patients at risk for treatment failure and may inform treatment decisions in newly diagnosed multiple myeloma. Disclosures Lacy: Celgene: Research Funding. Gertz:Physicians Education Resource: Consultancy; janssen: Consultancy; Teva: Consultancy; Alnylam: Honoraria; Research to Practice: Consultancy; Apellis: Consultancy; Medscape: Consultancy; annexon: Consultancy; Abbvie: Consultancy; celgene: Consultancy; Prothena: Honoraria; Ionis: Honoraria; Amgen: Consultancy; spectrum: Consultancy, Honoraria. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Russell:Vyriad: Equity Ownership. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding.

scholarly journals A Personalized Prediction Model to Risk Stratify Patients with Myelodysplastic Syndromes

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 793-793 ◽  
Author(s):  
Aziz Nazha ◽  
Rami S. Komrokji ◽  
Manja Meggendorfer ◽  
Sudipto Mukherjee ◽  
Najla Al Ali ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Myelodysplastic Syndromes ◽  
Research Funding ◽  
Validation Cohort ◽  
Employment Equity ◽  
Advisory Committees ◽  
Training Cohort ◽  
New Model ◽  
Time Points ◽  
Equity Ownership

Abstract Background Patients (pts) with myelodysplastic syndromes (MDS) have heterogeneous outcomes that can range from months for some pts to decades for others. Although several prognostic scoring systems have been developed to risk stratify MDS pts, survival varies even within discrete categories, which may lead to over- or under-treatment. Deficits in discriminatory power likely derive from analytic approaches or lack of incorporation of molecular data. Here, we developed a model that uses a machine learning approach to analyze genomic and clinical data to provide a personalized overall outcome that is patient-specific. Method Clinical and mutational data from MDS pts diagnosed according to 2008 WHO criteria were analyzed. The model was developed in a combined cohort from the Cleveland Clinic and Munich Leukemia Laboratory and validated in a separate cohort from the Moffitt Cancer Center. Next generation targeted deep sequencing of 40 gene mutations commonly found in myeloid malignancies was performed. Pts who underwent hematopoietic cell transplant (HCT) were censored at the time of transplant. A random survival forest (RSF) algorithm was used to build the model, in which clinical and molecular variables are randomly selected for inclusion in determining survival, thereby avoiding the shortcomings of traditional Cox step-wise regression in accounting for variable interactions. Survival prediction is thus specific to each pt's particular clinical and molecular characteristics. The accuracy of the proposed model, compared to other models, was assessed by concordance (c-) index. Results Of 2302 pts, 1471 were included in the training cohort and 831 in the validation cohort. In the training cohort, the median age was 71 years (range, 19-99), 230 pts (16%) progressed to AML, 156 (11%) had secondary/therapy-related MDS, and 130(9%) underwent HCT. Risk stratification by IPSS: 391 (27%) low, 626 (43%) intermediate-1, 280 (19%) intermediate-2, 104 (7%) high, 104 (7%) missing, and by IPSS-R: 749 (51%) very low/ low, 336 (23%) intermediate, 190 (13%) high, 92 (6%) very high, and 104 (7%) missing. Cytogenetic analysis by IPSS-R criteria: 65 (4%) very good, 1060 (72%) good, 193 (13%) intermediate, 60 (4%) poor, and 93 (6%) very poor. The most commonly mutated genes were: SF3B1 (26%), TET2 (25%), ASXL1 (20%), SRSF2 (15%), DNMT3A (12%), STAG2 (8%), RUNX1 (8%), and TP53 (8%). All clinical variables and mutations were included in the RSF algorithm. To identify the most important variables that impacted the outcome and the least number of variables that produced the best prediction, we conducted several feature extraction analyses which identified the following variables that impacted OS (ranked from the most important to the least): cytogenetic risk categories by IPSS-R, platelets, mutation number, hemoglobin, bone marrow blasts %, 2008 WHO diagnosis, WBC, age, ANC, absolute lymphocyte count (ALC), TP53, RUNX1, STAG2, ASXL1, absolute monocyte counts (AMC), SF3B1, SRSF2, RAD21, secondary vs. de novo MDS, NRAS, NPM1, TET2, and EZH2. The clinical and mutational variables can be entered into a web application that can run the trained model and provide OS and AML transformation probabilities at different time points that are specific for a pt, Figure 1. The C-index for the new model was .74 for OS and .81 for AML transformation. The new model outperformed IPSS (c-index .66, .73) and IPSS-R (.67, .73) for OS and AML transformation, respectively. The geno-clinical model outperformed mutations only (c-index .64, .72), mutations + cytogenetics (c-index .68, .74), and mutations + cytogenetics +age (c-index .69, .75) for OS and AML transformation, respectively. Addition of mutational variant allelic frequency did not significantly improve prediction accuracy. When applying the new model to the validation cohort, the c-index for OS and AML transformation were .80, and .78, respectively. Conclusion We built a personalized prediction model based on clinical and genomic data that outperformed IPSS and IPSS-R in predicting OS and AML transformation. The new model gives survival probabilities at different time points that are unique for a given pt. Incorporating clinical and mutational data outperformed a mutations only model even when cytogenetics and age were added. Disclosures Nazha: MEI: Consultancy. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Sallman:Celgene: Research Funding, Speakers Bureau. Roboz:Otsuka: Consultancy; Orsenix: Consultancy; Celgene Corporation: Consultancy; Daiichi Sankyo: Consultancy; Pfizer: Consultancy; Cellectis: Research Funding; Argenx: Consultancy; Roche/Genentech: Consultancy; Celltrion: Consultancy; Sandoz: Consultancy; Aphivena Therapeutics: Consultancy; Bayer: Consultancy; Pfizer: Consultancy; Aphivena Therapeutics: Consultancy; Eisai: Consultancy; Sandoz: Consultancy; Eisai: Consultancy; Roche/Genentech: Consultancy; AbbVie: Consultancy; Novartis: Consultancy; Janssen Pharmaceuticals: Consultancy; Bayer: Consultancy; Celltrion: Consultancy; Novartis: Consultancy; Janssen Pharmaceuticals: Consultancy; Astex Pharmaceuticals: Consultancy; Daiichi Sankyo: Consultancy; Celgene Corporation: Consultancy; Jazz Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Cellectis: Research Funding; Otsuka: Consultancy; Orsenix: Consultancy; Argenx: Consultancy; Astex Pharmaceuticals: Consultancy; AbbVie: Consultancy. List:Celgene: Research Funding. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Apellis Pharmaceuticals: Consultancy. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Sekeres:Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees.

scholarly journals To Treat or Not to Treat? Understanding Treatment Patterns in Patients with Lower-Risk Myelofibrosis at the Time of Enrollment in the MOST Study

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 49-51
Author(s):  
Rami S. Komrokji ◽  
Brady L. Stein ◽  
Robyn M. Scherber ◽  
Patricia Kalafut ◽  
Haobo Ren ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Lower Risk ◽  
Research Funding ◽  
Symptom Burden ◽  
Risk Groups ◽  
Treatment Patterns ◽  
Low Risk ◽  
Publicly Traded ◽  
Advisory Committees ◽  
Therapy Initiation

Background: Myelofibrosis (MF) is a chronic Philadelphia chromosome-negative myeloproliferative neoplasm (MPN) characterized by extramedullary hematopoiesis, bone marrow fibrosis, splenomegaly, constitutional symptoms, and diminished quality of life. Treatment decisions may involve a variety of factors including prognosis and symptomatology. Data regarding real-world disease and demographic factors that contribute to therapy initiation and choice in pts with lower risk MF are limited. This analysis of data from the ongoing Myelofibrosis and Essential Thrombocythemia Observational STudy (MOST; NCT02953704) assessed whether these factors differ for lower risk pts who were treated vs untreated at enrollment. Methods: MOST is a longitudinal, noninterventional, prospective, observational study in pts with MF or essential thrombocythemia enrolled at clinical practices within the US. Pts included in the analysis (≥18 y), had low risk MF by the Dynamic International Prognostic Scoring System (DIPSS; Blood. 2010;115:1703), or intermediate-1 (INT-1) risk by age &gt;65 y alone. Pt data were entered into an electronic case report form during usual-care visits over a planned 36-month observation period. Pt-reported symptom burden was assessed using the MPN-Symptom Assessment Form (MPN-SAF); Total Symptom Score (TSS) was calculated (0 [absent] to 100 [worst imaginable]; J Clin Oncol. 2012;30:4098). Data were analyzed with basic descriptive and inferential statistics. Results: Of 233 pts with MF enrolled at 124 sites between 11/29/2016 and 03/29/2019, 205 were included in this analysis; 28 were excluded for being INT-1 risk for reasons other than age. Of the 205 pts, 85 (41.5%) were low- and 120 (58.5%) were INT-1 risk; 56.5% (48/85) and 59.2% (71/120), respectively, were being treated at enrollment. Pt characteristics are listed in Table 1A. Fewer low- vs INT-1 risk pts were JAK2 V617F or MPL positive, and more were CALR positive. The proportion of pts with palpable splenomegaly was similar for treated low- and INT-1 risk pts. In low risk pts, the proportion of pts with palpable splenomegaly was higher in untreated vs treated pts; whereas, in INT-1 risk pts, the opposite was observed (ie, lower proportion in untreated vs treated pts). Blood counts were generally similar across cohorts, except median leukocytes were lower for low risk treated pts and platelet counts were elevated in low- vs INT-1 risk pts. The proportion of pts with comorbidities was similar across cohorts, except for fewer cardiovascular comorbidities in low- vs INT-1 risk pts. Mean TSS was lower in low- vs INT-1 risk pts, but the proportion of pts with TSS ≥20 was greater in treated vs untreated pts in both low- and INT-1 risk groups. Fatigue was the most severe pt-reported symptom in all cohorts. Differences in mean TSS and individual symptom scores between risk groups were not significant (P &gt; 0.05), except itching was worse among INT-1 risk pts (P=0.03). Physician-reported signs and symptoms were generally more frequent for untreated vs treated pts, irrespective of risk (all P &gt; 0.05). Most low risk (69.4%) and INT-1 risk pts (61.2%) who were currently untreated at enrollment had not received any prior MF-directed treatment (Table 1B); the most common prior treatment among currently untreated pts was hydroxyurea (HU) in both risk groups. Of currently treated pts, HU was the most common MF-directed monotherapy at enrollment in low-risk pts, and ruxolitinib was most common in INT-1 risk pts. No low risk pts and few INT-1 risk pts were currently receiving &gt;1 MF-directed therapy at enrollment. Conclusion: These real-world data from pts with MF enrolled in MOST show that a substantial proportion of both low- and INT-1 risk pts who had received treatment before enrollment were not being treated at the time of enrollment. Although watch-and-wait is a therapeutic option, the finding that many of these lower risk pts had in fact received prior therapies suggests an unmet need for effective and tolerable second-line treatment options. Treated pts had greater pt-reported symptom burden vs untreated pts, which suggests that high symptom burden may contribute to the decision for treatment. Prospective studies are needed to evaluate symptom burden change with therapy initiation. In this regard, future analyses of data from MOST are planned to assess the longitudinal evolution of the clinical characteristics, treatment patterns, and management of pts with MF. Disclosures Komrokji: Geron: Honoraria; Agios: Honoraria, Speakers Bureau; AbbVie: Honoraria; Incyte: Honoraria; Novartis: Honoraria; BMS: Honoraria, Speakers Bureau; JAZZ: Honoraria, Speakers Bureau; Acceleron: Honoraria. Stein:Incyte: Research Funding; Kartos: Other: educational content presented; Constellation Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Pharmaessentia: Membership on an entity's Board of Directors or advisory committees. Scherber:Incyte Corporation: Current Employment, Current equity holder in publicly-traded company. Kalafut:Incyte: Current Employment, Current equity holder in publicly-traded company. Ren:Incyte: Current Employment, Current equity holder in publicly-traded company. Verstovsek:Incyte Corporation: Consultancy, Research Funding; Roche: Research Funding; Genentech: Research Funding; Blueprint Medicines Corp: Research Funding; CTI Biopharma Corp: Research Funding; NS Pharma: Research Funding; ItalPharma: Research Funding; Celgene: Consultancy, Research Funding; Gilead: Research Funding; Protagonist Therapeutics: Research Funding; Novartis: Consultancy, Research Funding; Sierra Oncology: Consultancy, Research Funding; PharmaEssentia: Research Funding; AstraZeneca: Research Funding; Promedior: Research Funding.

scholarly journals Profiling T-Cell Receptor Diversity and Dynamics during Lymphoma Immunotherapy Using Cell-Free DNA (cfDNA)

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 49-50
Author(s):  
Navika D Shukla ◽  
Alexander F. M. Craig ◽  
Brian Sworder ◽  
David M. Kurtz ◽  
Charles Macaulay ◽  
...  
Keyword(s):  
T Cell ◽  
Cell Therapy ◽  
Board Of Directors ◽  
Research Funding ◽  
Advisory Committees ◽  
T Cell Therapy ◽  
Car T Cell ◽  
Car T ◽  
Tcr Repertoire ◽  
Support Research

Background: Characterization of T-cell receptor (TCR) diversity and dynamics is increasingly critical to understanding therapeutic immune responses targeting tumors. Current TCR profiling methods generally require invasive tissue biopsies that capture a single snapshot of immune activity or are limited by the sheer diversity of the circulating TCR repertoire. In theory, T-cells with the greatest turnover could best reflect pivotal immune dynamics from both circulating and tissue-derived compartments, including non-circulating tissue-resident memory T-cells (Trm). To noninvasively capture such responses in the blood, we developed and benchmarked a high-throughput TCR profiling approach using plasma, optimized for the fragmented nature of cfDNA and the non-templated nature of rearranged TCRs. We then applied this method for residual disease monitoring in mature T-cell lymphomas (TCL) without circulating disease and for characterizing immune dynamics after anti-CD19 chimeric antigen receptor (CAR19) T-cell therapy of B-cell lymphomas with axicabtagene ciloleucel. Methods: We developed SABER (Sequence Affinity capture & analysis By Enumeration of cell-free Receptors) as a technique for TCR enrichment and analysis of fragmented rearrangements shed in cfDNA and applied this method using Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq). We used SABER to profile a total of 381 samples (300 cfDNA and 81 PBMC samples) from 75 lymphoma patients and 18 healthy controls. After mapping sequencing reads (hg38) to identify candidate rearrangements within TCR loci, unique cfDNA fragments were resolved by a novel strategy to define consensus of unique molecular identifiers clustered by Levenshtein distances, followed by CDR3-anchoring for enumeration of final receptor clonotypes. SABER thus leverages information from fragmented TCRs, a critical requirement for cfDNA, to make V gene, CDR3, and J gene assignments after deduplication-mediated error-correction. We benchmarked SABER against established amplicon-based TCR-β targeted sequencing (LymphoTrack, Invivoscribe) and repertoire analysis methods (MiXCR; Bolotin et al, 2015 Nature Methods) when considering both cfDNA and PBMC samples from healthy adults and TCL patients. We assessed SABER performance for tracking clonal molecular disease in patients with mature TCLs from both cellular and cell-free circulating compartments (n=9). Malignant TCL clonotypes were identified in tumor specimens using clonoSEQ (Adaptive Biotechnologies). Finally, we evaluated TCR repertoire dynamics over time in 66 DLBCL patients after CAR19 T-cell therapy. Results: SABER demonstrated superior recovery of TCR clonotypes from cfDNA compared to both amplicon sequencing (LymphoTrack, Invivoscribe) and hybrid-capture methods when enumerating receptors using MiXCR (Fig. 1A). When applied to blood samples from TCL patients, SABER identified the malignant clonal TCR-β rearrangement in 8/9 (88.9%) cases, with significantly improved detection in cfDNA (p=0.015, Fig. 1B). Specifically, tumoral TCR clonotype was detectable only in cfDNA in 6 cases (75%), cfDNA-enriched in 1 case (12.5%), and detectable only in PBMCs in 1 case (12.5%). We applied SABER to monitor TCR repertoire dynamics in cfDNA after CAR T-cell therapy of patients with relapsed/refractory DLBCL and observed increased T-cell turnover and repertoire expansion (greater total TCR-β clonotypes) (Fig. 1C). As early as 1-week after CAR19 infusion, TCR repertoire size was significantly correlated both with cellular CAR19 T-cell levels by flow cytometry (p=0.008) as well as with retroviral CAR19 levels in cfDNA (p=2.20e-07) suggesting faithful monitoring of CAR T-cell activity (Fig. 1D). TCR repertoire size one month after infusion was significantly associated with longer progression-free survival (HR 0.246, 95% CI 0.080-0.754, p=0.014). Conclusions: SABER has a favorable profile for cfDNA TCR repertoire capture when compared to existing methods and could thus have potential broad applicability to diverse disease contexts. Given the higher abundance of lymphoma-derived TCRs in cfDNA than intact circulating leukocytes, SABER holds promise for monitoring minimal residual disease in T-cell lymphomas. This approach also holds promise for monitoring T-cell repertoire changes including after CAR T-cell therapy and for predicting therapeutic responses. Disclosures Kurtz: Genentech: Consultancy; Foresight Diagnostics: Other: Ownership; Roche: Consultancy. Kim:Corvus: Research Funding; Eisai: Membership on an entity's Board of Directors or advisory committees, Research Funding; Elorac: Research Funding; Forty Seven Inc: Research Funding; Galderma: Membership on an entity's Board of Directors or advisory committees, Research Funding; Horizon Pharma: Consultancy, Research Funding; Innate Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kyowa-Kirin Pharma: Research Funding; Medivir: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; miRagen: Research Funding; Neumedicine: Consultancy, Research Funding; Portola: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Solingenix: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Trillium: Research Funding. Mackall:Lyell Immunopharma: Consultancy, Current equity holder in private company; BMS: Consultancy; Allogene: Current equity holder in publicly-traded company; Apricity Health: Consultancy, Current equity holder in private company; Nektar Therapeutics: Consultancy; NeoImmune Tech: Consultancy. Miklos:Kite-Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Research Funding; Adaptive Biotech: Consultancy, Other: Travel support, Research Funding; Juno-Celgene-Bristol-Myers Squibb: Consultancy, Other: Travel support, Research Funding; Novartis: Consultancy, Other: Travel support, Research Funding; Allogene Therapeutics Inc.: Research Funding; Pharmacyclics: Consultancy, Other: Travel support, Patents & Royalties, Research Funding; Janssen: Consultancy, Other: Travel support; Miltenyi Biotec: Research Funding. Diehn:Varian Medical Systems: Research Funding; Illumina: Research Funding; Roche: Consultancy; AstraZeneca: Consultancy; RefleXion: Consultancy; BioNTech: Consultancy. Khodadoust:Seattle Genetics: Consultancy; Kyowa Kirin: Consultancy. Alizadeh:Janssen: Consultancy; Genentech: Consultancy; Pharmacyclics: Consultancy; Chugai: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Roche: Consultancy; Pfizer: Research Funding.

ABVD Versus Escalated Beacopp in Advanced Stage Hodgkin's Lymphoma: Results from a Retrospective, Multicenter European Study

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1565-1565 ◽  
Author(s):  
Patrizia Mondello ◽  
Irene Dogliotti ◽  
Jan-Paul Bohn ◽  
Federica Cavallo ◽  
Simone Ferrero ◽  
...  
Keyword(s):  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Advanced Stage ◽  
Advisory Committees ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Patients

Purpose: Hodgkin's lymphoma (HL) is a highly curable disease even in advanced-stage, with &gt;90% of long-term survivors. Currently, the standard of care is ABVD (doxorubicin, etoposide, vinblastine and dacarbazine), as it is less toxic and as effective as other more intensive chemotherapy regimens. Alternatively, BEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine and prednisone) has been proposed as front-line intensified regimen with a better initial disease control and prolonged time to relapse when compared to ABVD. However, this advantage is associated with higher rates of severe hematologic toxicity, treatment-related deaths, secondary neoplasms and infertility. To date, the debate regarding which regimen should be preferred as first line for advanced-stage HL is still ongoing. To shed some light on this open question we compared efficacy and safety of both regimens in clinical practice. Patients and Methods: From October 2009 to October 2018, patients with HL stage III-IV treated with either ABVD or BEACOPP escalated (BEACOPPesc) were retrospectively assessed in 7 European cancer centers. Results: A total of 372 consecutive patients were included in the study. One-hundred and ten patients were treated with BEACOPPesc and 262 with ABVD. The baseline characteristics of the two groups did not differ significantly, except for a higher rate of high-risk patients in the BEACOPPesc group in contrast to the ABVD one (47% vs 18%; p= 0.003). Complete response rate (CR) assessed by PET imaging at the end of the second cycle was 67% and 78% for the ABVD and BEACOPPesc group (p= 0.003), respectively. Thirteen patients of the ABVD group achieved stable disease (SD) and 6 had a progression disease (PD). On the other hand, 4 of the patients in the BEACOPPesc group progressed, another 2 interrupted therapy because life-threatening toxicity. At the end of the therapy, CR was 76% in the ABVD group and 85% in the BEACOPPesc group (p= 0.01). A total of 20% patients in the ABVD group and 14% patients in the BEACOPPesc group received consolidation radiotherapy on the mediastinal mass at the dose of 30Gy. After radiotherapy, the number of patients with CR increased to 79% and 87% in the two groups (p= 0.041), respectively. Thirty-nine patients (35%) in the BEACOPPesc group required dose reduction of chemotherapy due to toxicity compared to 12 patients (5%; p= &lt;0.001) in the ABVD group. Overall, the rate of severe toxicities was higher in the BEACOPPesc group in comparison with the ABVD cohort. In particular, there was a significant increased frequency of acute grade 3-4 hematologic adverse events (neutropenia 61% vs 24%; anemia 29% vs 4%; thrombocytopenia 29% vs 3%), febrile neutropenia (29% vs 3%), severe infections (18% vs 3%). Myeloid growth factors were administered to 85% and 59% of patients in the BEACOPPesc group compared to the ABVD group. Blood transfusions were required in 51% and 6% of patients in the BEACOPPesc group compared to the ABVD cohort. Progression during or shortly after treatment occurred in 5 patients in the BEACOPPesc group (4%) and in 16 patients in the ABVD group (6%; p= 0.62). Among the 96 patients who achieved a CR after BEACOPPesc and radiotherapy, 8 relapsed (8%), compared to 29 of 208 patients in the ABVD group (14%; p= 0.04). At a median follow-up period of 5 years, no statistical difference in progression free survival (PFS; p=0.11) and event-free survival (EFS; p=0.22) was observed between the BEACOPPesc and ABVD cohorts. Similarly, overall survival (OS) did not differ between the two groups (p=0.14). The baseline international prognostic score (IPS &lt;3 vs ≥ 3) significantly influenced the EFS with an advantage for the high-risk group treated with BEACOPPesc (Figure 1A; p=0.03), but not the PFS (Figure 1B; p=0.06) and OS (Figure 1C; p=0.14). During the follow-up period, in the BEACOPPesc group one patient developed myelodysplasia and one acute leukemia. Second solid tumors developed in one patient in the ABVD group (lung cancer) and one in BEACOPPesc group (breast cancer). Conclusion: We confirm that the ABVD regimen is an effective and less toxic therapeutic option for advanced-stage HL. Although BEACOPP results in better initial tumor control especially in high-risk patients, the long-term outcome remains similar between the two regimens. Disclosures Ferrero: EUSA Pharma: Membership on an entity's Board of Directors or advisory committees; Servier: Speakers Bureau; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Speakers Bureau. Martinelli:BMS: Consultancy; Pfizer: Consultancy; ARIAD: Consultancy; Roche: Consultancy; Novartis: Consultancy. Willenbacher:European Commission: Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Myelom- und Lymphomselbsthilfe Österreich: Consultancy, Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead Science: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; IQVIA: Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; oncotyrol: Employment, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Fujimoto: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tirol Program: Research Funding; Abbvie: Consultancy, Honoraria; Sandoz: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees.

Hypogammaglobulinemia during Rituximab Maintenance after Transplantation Is a Surrogate Marker for Disease Control in Patients with Mantle-Cell Lymphoma, an Analysis from the LyMa Trial

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 42-42
Author(s):  
Louise Bouard ◽  
Catherine Thieblemont ◽  
Krimo Bouabdallah ◽  
Thomas Gastinne ◽  
Anne Moreau ◽  
...  
Keyword(s):  
Immune Deficiency ◽  
Disease Control ◽  
Board Of Directors ◽  
Research Funding ◽  
P Value ◽  
Immune Recovery ◽  
Advisory Committees ◽  
Post Transplant ◽  
Rituximab Maintenance ◽  
Mantle Cell

Introduction Rituximab maintenance (RM) (375mg/m2 per infusion every 2 months for 3 years) in transplanted patients with mantle-cell lymphomas (MCL) prolongs disease control (LyMa trial, Le Gouill et al NEJM; NCT00921414). However, post-transplant RM might also induce long-term immune deficiency and thus increases risk of infection. To address these issues, we performed an ancillary pre-planned study based on the LyMa trial, a phase III trial that compared RM versus observation (Obs) after ASCT in MCL patients. We compared post-transplant immune-deficiency and its impact on PFS and OS in the RM vs Obs groups. Method All transplanted and randomized patients enrolled in the LyMa trial were eligible for the present study. The following data were collected during the post-ASCT period and monitored according to protocol procedure: febrile event, clinically documented infection, hospitalization for infection, neutropenia, hypogammaglobulinemia and T CD4 lymphocytes count. We also retrospectively collected the use of immune globulin (Ig) substitution. In the LyMa trial, patients were randomized between RM vs Obs after transplantation. To decipher the implication of ASCT or RM in immune recovery, treatment periods were divided in 4: &lt; 6 months after randomization, from 6 to 12 months after randomization, from one to two year after randomization, and from 2 to 3 years after randomization (respectively periods A, B, C and D). Chi-square or Fisher's exact tests were used as appropriate to investigate differences between arms in each treatment period. For all tests, a two-sided p-value&lt;0.05 was considered statistically significant Results 240 patients were eligible, 120 in each arm. Patients' characteristics at diagnosis and inclusion were similar in the two arms. Number of hospitalizations due to infections was not statistically different in RM vs Obs in all periods. As previously shown, grade 3/4 infections incidence did not differ in the 2 arms. However, febrile events were more frequent in the RM arm (32 pts vs 11; 38 events vs 12) but this was statistically significant only in C and D periods; p=0,03 for the 2 periods. In all, 51 infections in 44 pts were reported in Obs vs 127 events in 82 pts in RM arm. This difference was also only statistically significant during the C period, p=0,001. Grade 4 neutropenia incidence and T CD4 count did not differ between the two arms in all tested periods. Hypogammaglobulinemia was statistically more frequent in RM during C and D periods (p=0,0001 and p&lt; 0,0001, respectively). Mean level of gammaglobulinemia on D period was 6,50 g/L (range 0,6-11,7) in obs arm versus 4,99 (range 1,0-9,5) in RM arm (p&lt; 0,0001). 36 pts in RM arm vs 10 pts in obs arm were substituted with Ig and the difference was statistically significant only in period D, p&lt;0,0001. Febrile and infectious episodes; neutropenia and T CD 4 lymphopenia did not modify PFS and OS. Patients with gammaglobulinemia &lt; 6g/L in RM arm and in the whole cohort had longer PFS compared to pts who did not present hypogammaglobulinemia : 3-years PFS 93,2% vs 63,5% in RM arm HR = 0,294, 95% CI (0,113-0,767 and), p=0,01 and 3-years PFS 85,6% vs 63,6% in the whole cohort, HR adjusted on treatment arm=0,488 95% CI (0,287-0,830), p=0,008 . PFS was not modified by gammaglobulin level in the Obs arm and it did not modified OS in both arms. We performed a multivariate analysis to determine which data were predictive of infectious events and delayed immune recovery (neutropenia, hypogamma, T CD 4 lymphopenia). This included all univariate parameters with p value &lt; 0,2, among clinical and biological characteristics at diagnosis, response after induction and number of rituximab injections. Interestingly, among others expected parameters, complete response assessed by TDM was predictive of hypogamma with Odd Ratio 2,972 (1,263-6,994) p=0,0126. No value was predictive of neutropenia or T CD4 cytopenia. Conclusion As compared to observation, the use of post-transplant RM does not increase risk of neutropenia and T CD4 lymphopenia. However febrile and infectious events, hypogammaglobulinemia and Ig substitution are more frequent after one year post transplantation. Hypogamma &lt; 6g/L is associated with longer PFS and complete morphologic response. This suggests that hypogammaglobulinemia could be a surrogate for disease response quality and duration. Our findings deserve to be confirmed. Disclosures Thieblemont: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Hospira: Research Funding; Cellectis: Speakers Bureau; Janssen: Honoraria; University Employement: Current Employment. Bouabdallah:Gilead Sciences: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Oberic:Roche, Janssen: Other: Travel, Accommodations, Expenses; Roche: Honoraria; Roche, Janssen: Consultancy. Hermine:AB Science: Consultancy, Current equity holder in publicly-traded company, Honoraria, Patents & Royalties, Research Funding; Celgene BMS: Consultancy, Research Funding; Novartis: Research Funding; Alexion: Research Funding; Roche: Consultancy. Le Gouill:Loxo Oncology at Lilly: Consultancy; Roche Genentech, Janssen-Cilag and Abbvie, Celgene, Jazz pharmaceutical, Gilead-kite, Loxo, Daiichi-Sankyo and Servier: Honoraria.

Mobilization with Plerixafor (Mozobil ®)Plus G-CSF Results in Superior Day 1 Collection of CD34+ Cells Compared to Placebo Plus G-CSF: Results From Two Randomized Placebo-Controlled Trials in Patients with Multiple Myeloma or Non-Hodgkin's Lymphoma.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3224-3224 ◽  
Author(s):  
Brian J. Bolwell ◽  
Auayporn P. Nademanee ◽  
Patrick Stiff ◽  
Edward Stadtmauer ◽  
Richard T. Maziarz ◽  
...  
Keyword(s):  
Placebo Group ◽  
Board Of Directors ◽  
Research Funding ◽  
Cell Yield ◽  
P Value ◽  
Employment Equity ◽  
Advisory Committees ◽  
Cell Dose ◽  
Cd34 Cells ◽  
Equity Ownership

Abstract Abstract 3224 Poster Board III-161 Background While most centers use 2 × 106 CD34+ cells/kg as the minimal cell dose for autologous hematopoietic stem cell (HSC) transplantation (auto-HSCT), infusion of higher CD34+ cell dose is associated with better outcomes in patients with multiple myeloma (MM) or non-Hodgkin's lymphoma (NHL). Recent evidence suggests a correlation between CD34+ cell yield on Day 1 of collection and total CD34+ cell yield as well as post-transplant outcomes. This analysis was designed to: 1) compare Day 1 collection between patients with NHL or MM mobilized with plerixafor plus G-CSF or placebo plus G-CSF; and 2) determine whether Day 1 CD34+ cell yields correlated with the total mobilization yield and number of apheresis days. Methods Data were obtained from two prospective, randomized, double-blind, placebo-controlled, phase 3 clinical trials that compared the safety and efficacy of plerixafor (0.24 mg/kg/day SQ) plus G-CSF (10 μg/kg/day) with placebo plus G-CSF for mobilization of HSC for auto-HSCT in patients with NHL (3101 Study) or MM (3102 Study). Pearson correlation coefficient was used to evaluate the association of day 1 CD34+ cell collection with total CD34+ cell yield and the number of days of apheresis. Results In the NHL trial, 150 patients were mobilized with plerixafor plus G-CSF and 148 patients underwent mobilization with placebo plus G-CSF. More than half the patients (55.3%) in the plerixafor group collected ≥2 × 106 CD34+ cells/kg on Day 1 of apheresis (Figure 1A). In contrast, 19.6% patients in the placebo group collected ≥ 2 × 106 CD34+ cells/kg on Day 1 of apheresis (p< 0.001). In the MM study, 148 patients were mobilized with plerixafor plus G-CSF and 154 patients were mobilized with placebo plus G-CSF. More than half the patients (52.7%) in the plerixafor group collected ≥6 × 106 CD34+ cells/kg on the first day of collection compared to only 16.9% patients in the placebo group (p<0.001; Figure 1B). There was a strong positive correlation between day 1 collection and the total CD34+ cell yield in patients with NHL (r= 0.86, p-value= <0.0001) or MM (r= 0.87, p-value= <0.0001) in both the plerixafor and placebo groups. For NHL patients, the median Day 1 collection was higher in the plerixafor group compared to the placebo group: 2.66 × 106 vs. 0.77 × 106 CD34+ cells/kg (p<0.001) and this translated into higher total CD34+ cell yields in the two groups respectively: 5.69 × 106 vs. 1.98 × 106 CD34+ cells/kg (p<0.001). Similarly, for MM patients, the median CD34+ cells/kg collected on Day 1 was higher in the plerixafor group compared to the placebo group: 7.01 × 106 vs. 2.29 × 106 CD34+ cells/kg (p<0.001) and this translated into better overall collection in the plerixafor vs. placebo groups: 10.96 × 106 vs. 6.18 × 106 CD34+ cells/kg (p<0.001). A negative correlation was observed between CD34+ cells collected on Day 1 and the number of days of apheresis performed in patients with NHL (r= -0.67, p-value=<0.0001) or MM (r= -0.50, p-value= <0.0001) in both the plerixafor and placebo groups. Consequently, better Day 1 collection in plerixafor-treated NHL or MM patients translated into significantly fewer apheresis days to achieve the target collection compared to placebo treated patients. Conclusions These data support previous reports demonstrating a strong correlation between day 1 CD34+ cell collection and total CD34+ cell yield and apheresis days. These data also demonstrate that addition of plerixafor to G-CSF allows significantly more patients to achieve the target cell collection within 1 day of apheresis compared to G-CSF alone. These findings support the observation that mobilization with plerixafor plus G-CSF reduces the number of apheresis days required to achieve the minimal or optimal cell dose to proceed to transplantation. Disclosures Bolwell: Genzyme Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Nademanee:Genzyme Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Stiff:Genzyme Corp.: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Stadtmauer:Genzyme Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Maziarz:Genzyme Corp.: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Micallef:Genzyme Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding. Marulkar:Genzyme Corporation: Employment, Equity Ownership. Gandhi:Genzyme Corporation: Employment, Equity Ownership. DiPersio:Genzyme: Honoraria.

Global DNA Methylation Analysis Identifies Key Pathway Differences Between Poor (Low OCT-1 Activity) and Standard Risk CP-CML Patients At Diagnosis

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3730-3730
Author(s):  
Dale B. Watkins ◽  
Chung Hoow Kok ◽  
Richard J. D'Andrea ◽  
Timothy P. Hughes ◽  
Deborah L. White
Keyword(s):  
Dna Methylation ◽  
Board Of Directors ◽  
Research Funding ◽  
Global Dna Methylation ◽  
Advisory Committees ◽  
Poor Risk ◽  
Normal Individuals ◽  
Risk Patients ◽  
Dna Methylation Analysis ◽  
Standard Risk

Abstract Abstract 3730 Background: DNA methylation, specifically CpG methylation, is an essential mediator of epigenetic gene expression which is of vital importance to many biological processes and human malignancies. DNA hypermethylation has been previously described in a small number of genes in chronic myeloid leukemia (CML); however, current published studies have only examined the methylation status of selected genes, often based on the results of studies in other malignancies. Therefore, the global DNA methylation profile of chronic phase-CML (CP-CML) remains poorly understood, as does the impact of the epigenome on patient response to tyrosine kinase inhibitors (TKIs) including imatinib. The organic cation transport-1 (OCT-1) protein is the major active protein involved in imatinib transport, and we have previously demonstrated that measuring its function in leukemic mononuclear cells, expressed as OCT-1 activity (OA), in patient cells prior to imatinib therapy, provides a strong prognostic indicator. Notably, very low OA (poor risk cohort) is associated with patients at significant risk for poor molecular response, mutation development and leukemic transformation on imatinib therapy. Therefore, it is of particular interest to ascertain whether epigenetic changes are distinct and potentially biologically relevant in these poor risk patients. Aim: To investigate the global DNA methylation profile in CP-CML patients with a particular focus on poor risk patients (very low OCT-1 activity), and to ascertain whether aberrant epigenetic programming may underlie their poor response. Method: Cells were isolated from the blood of 55 CP-CML patients at diagnosis and 5 normal individuals. CP-CML patients were classified according to their OA values, with 29 classified as poor (very low OA) and 26 standard risk (high OA). Whole genome DNA methylation analysis was performed using the Illumina Infinium® HumanMethylation450 BeadChip. Analysis of array data was performed with R v2.15.0, using the minfiBioconductor package. Results: The methylation profile of CP-CML was significantly different to that of normal individuals, as shown in Table 1. GeneGo enrichment analysis revealed a significant enrichment in CML for genes known to be involved in other leukemias (p=4.92e−26) particularly AML and CLL, suggesting similar pathways may be under epigenetic control in CML. A significant number of polycomb group (BMI1 and EZH2) target genes were also identified, suggesting the likely involvement of this pathway in CML. Table 1: Summary of significant CpGs and corresponding genes when comparisons of CP-CML to normal individuals, and poor to standard risk patients, are made using methylation profiles. A significant difference was also observed when the methylation profiles of poor and standard risk CP-CML patients were analysed (Table 1). GeneGo analysis again identified polycomb group (SUZ12 and EZH2) target enrichment and significant enrichment of NOTCH, Hedgehog and WNT signalling (p=7.93e−9, p=2.42e−5 and p=3.66e−5 respectively) in poor risk patients, indicating these pathways may play a significant role in the unfavourable responses observed in many of these patients. Of particular interest were the ten CpGs where a fold change >4 was observed between the methylation profiles of poor and standard risk patients. Using the Prediction Analysis of Microarrays supervised learning algorithm, a classifier for patient OA prediction based on this 10 CpG signature was evaluated. This classifier had an overall accuracy of 94% (sensitivity: 95%, specificity: 93%). Conclusion: We present a comprehensive global DNA methylation analysis of CP-CML that indicates significant and widespread changes to the CML epigenome, compared with that of normal individuals. Importantly, we have generated a classifier, which identifies the poor risk patient subgroup (very low OA) with 94% accuracy. Validation of this classifier is currently in progress. The epigenetic changes identified here may contribute to CML pathogenesis, and also to the response heterogeneity observed between CP-CML patients treated with TKI therapy. Disclosures: Hughes: Novartis Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. White:Novartis Oncology: Honoraria, Research Funding; BMS: Research Funding; CSL: Research Funding.

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