Personalized Prediction Model to Risk Stratify Patients With Myelodysplastic Syndromes

PURPOSE Patients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies. METHODS A total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index. RESULTS The median age for the training cohort was 71 years. Commonly mutated genes included SF3B1, TET2, and ASXL1. The algorithm identified chromosomal karyotype, platelet, hemoglobin levels, bone marrow blast percentage, age, other clinical variables, seven discrete gene mutations, and mutation number as having prognostic impact on overall and leukemia-free survivals. The model was validated in an independent external cohort of 465 patients, a cohort of patients with MDS treated in a prospective clinical trial, a cohort of patients with paired samples at different time points during the disease course, and a cohort of patients who underwent hematopoietic stem-cell transplantation. CONCLUSION A personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.

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Therapeutic Outcomes and Prognostic Impact of Gene Mutations Including TP53 and SF3B1 in Patients with Del(5q) Myelodysplastic Syndromes (MDS)

Clinical Lymphoma Myeloma & Leukemia ◽

10.1016/j.clml.2022.01.002 ◽

2022 ◽

Author(s):

Onyee Chan ◽

Najla Al Ali ◽

David Sallman ◽

Eric Padron ◽

Jeffrey Lancet ◽

...

Keyword(s):

Myelodysplastic Syndromes ◽

Gene Mutations ◽

Prognostic Impact ◽

Therapeutic Outcomes

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Prognostic Impact of Bone Marrow B Lymphocyte Precursors (hematogones) Detection in 95 Acute Lymphoblastic Leukemia Cases

Blood ◽

10.1182/blood.v120.21.4791.4791 ◽

2012 ◽

Vol 120 (21) ◽

pp. 4791-4791 ◽

Cited By ~ 1

Author(s):

Sylvain P Chantepie ◽

Audrey Emmanuelle Dugué ◽

Patrice Chevallier ◽

Aline Schmidt-Tanguy ◽

Véronique Salaün ◽

...

Keyword(s):

Bone Marrow ◽

Overall Survival ◽

Prognostic Factors ◽

Acute Lymphoblastic Leukemia ◽

Lymphoblastic Leukemia ◽

Prognostic Impact ◽

Hematopoietic Stem ◽

Time Points ◽

Number Of Patients

Abstract Abstract 4791 Acute lymphoblastic leukemia (ALL) multiparameter flow cytometry (MFC) study of bone marrow aspiration after chemotherapy is crucial for determining minimal residual disease (MRD). Hematogones (HGs) have to be distinguishing from leukemic cells in B-cell subtypes and could be quantify during follow-up. To date, the incidence of Hgs in ALL and their prognostic significance have not been investigated. The aim of this multicenter study was to quantify Hgs after chemotherapy in ALL adult patients and to define its prognostic value. We retrospectively analyzed the incidence of HGs in 95 ALL patients, 71 with B-ALL (75%), 24 (25%) with T-ALL in first line treatment. The median age was 37 years [8–71], 20% had t(9;22) cytogenetic abnormality, and 70% had abnormal karyotype. 4/5-color MFC analysis MRD and HGs were performed at different time point (TP) after diagnosis: TP1 (post-induction, day 45 [41–61], n=78), TP2 (post-consolidation, day 111 [94–144] 25, n=42), TP3 (post-intensification/before hematopoietic stem cell transplantation (HSCT), day 179 [125–268], n=58), TP4 (n=11), TP5 (n=17), TP6 (n=9) after a median of 33, 91 and 167 days after HSCT, respectively. A total of 39 patients (41%) relapsed with a median of 26 months [7.7–47.9]. Forty seven patients (50%) received an HSCT in a complete (98%) or partial remission (2%). At TP1, TP2, TP3, TP4, TP5, TP6, the median HGs [range] were as followed: 0.00 [0.00–6.90]%, 0.30 [0.00–11.2]%, 0.98 [0.00–33.00]%, 0.52 [0.00;23.00]%, 5.50 [0.00;25.00]%, 4.60 [0.00;34.00]%, 5.90 [0.32;11.80]%, respectively. Figure 1 showed the percentage of patients with negative MRD (Figure 1A) and detectable HGs (figure 1B) during the follow up of ALL patients. There is a progressive increase of the percentage of patients with detectable HGs during the time of treatment and follow-up. Interestingly, there was no correlation between age and HGs level while in physiological situation the HGs rate decreases with increasing age. There was a negative correlation between positive MRD and detectable HGs at TP1 (p=0.022) but not at TP3 (p=0.88). In univariate analysis positive MRD at P1 and P3, age (/10), the presence of t(9;22) and absence of HGs at TP3 (figure 2) were bad prognostic factors for relapse free-survival (RFS) and overall survival (OS). The presence of HGs at other different time of evaluation was not associated with a significant decrease of relapse or death. However, patients who had a negative MRD at TP1 and detectable HGs in the bone marrow at TP3 exhibited a better RFS and OS (p=0.018 and p=0.065 respectively). Patients who had negative MRD at TP3 and had detectable HGs at TP3 had also a better RFS and OS (p=0.007 and p=0.011, respectively) compared to patients with negative MRD at TP3 and without HGs (figure 3). In patients who had a positive MRD at TP1, detectable HGs at TP3 identified a subgroup of patient with favorable OS compared to patient with positive MRD at TP1 and without detectable HGs (p=0.072). These results should be taken with cautious because of the decreasing number of patients evaluated at different time points. However, HGs analysis could represent a new area of investigation in search of new prognostic factors in the context of adult ALL. Figure 1. Percentage of patients with (A) negative MRD and (B) detectable HGs at different time points after starting of treatment. Figure 1. Percentage of patients with (A) negative MRD and (B) detectable HGs at different time points after starting of treatment. Figure 2. Overall survival according to HGs status at TP3. Figure 2. Overall survival according to HGs status at TP3. Figure 3. Overall survival in patients with negative MRD at TP3 according to HGs status. Figure 3. Overall survival in patients with negative MRD at TP3 according to HGs status. Disclosures: No relevant conflicts of interest to declare.

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Adding Molecular Data to Prognostic Models can Improve their Predictive Power in Treated Patients with Myelodysplastic Syndromes (MDS)

Leukemia Research ◽

10.1016/s0145-2126(17)30163-7 ◽

2017 ◽

Vol 55 ◽

pp. S33-S34

Author(s):

A. Nazha ◽

K. Al-Issa ◽

A. Zarzour ◽

T. Radivoyevitch ◽

B. Hamilton ◽

...

Keyword(s):

Myelodysplastic Syndromes ◽

Predictive Power ◽

Molecular Data ◽

Prognostic Models

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Prognostic significance of concurrent gene mutations in intensively treated patients with IDH-mutated AML, an ALFA study

Blood ◽

10.1182/blood.2020010165 ◽

2021 ◽

Author(s):

Matthieu Duchmann ◽

Jean-Baptiste Micol ◽

Nicolas Duployez ◽

Emmanuel Raffoux ◽

Xavier Thomas ◽

...

Keyword(s):

Prognostic Significance ◽

Gene Mutations ◽

Normal Karyotype ◽

Genetic Alterations ◽

Prognostic Impact ◽

Hematopoietic Stem ◽

Fully Integrated ◽

Mutational Status ◽

Trial Testing ◽

Prospective Trials

IDH inhibitors are effective in AML, and trials evaluating frontline combinations with intensive chemotherapy (IC) are ongoing. Data on the prognostic significance of co-occurring genetic alterations and allogeneic hematopoietic stem cell transplantation (HSCT) are conflicting in each IDH-mutated subgroup treated by IC, while this information is important for trial design and results interpretation. We retrospectively analyzed 127 IDH1, 135 IDH2R140 and 57 IDH2R172 newly diagnosed AML patients treated with IC in three Acute Leukemia French Association (ALFA) prospective trials. We addressed in each IDH subgroup the prognostic impact of clinical and genetic covariates, and the role of HSCT in eligible patients. In IDH1 patients, presence of NPM1 mutations was the only variable predicting improved OS in multivariate analysis (p < 0.0001). In IDH2R140, normal karyotype (p= 0.008) and NPM1 mutations (p = 0.01) predicted better OS. NPM1 mutations were associated with better DFS (p = 0.0009) whereas presence of DNMT3A mutations was associated with shorter DFS (p = 0.0006). In IDH2R172, platelet count was the only variable retained in the multivariate model for OS (p = 0.002). Among non-favorable ELN-2010 eligible patients, 71 (36%) achieved an HSCT in first complete remission (CR1) and had longer OS (p = 0.03) and DFS (p = 0.02) than not-transplanted patients. Future clinical trial testing frontline IDH inhibitors combined with IC may consider stratification on NPM1 mutational status, the main prognostic factor in IDH1 and IDH2R140 mutated AML. HSCT improve OS of non-favorable IDH1/2-mutated AML and should be fully integrated in the treatment strategy.

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Role of Gene Mutations in Adult Acute Myeloid Leukemia Patients Receiving Allogeneic Hematopoietic Stem Cell Transplantation.

Blood ◽

10.1182/blood.v114.22.3373.3373 ◽

2009 ◽

Vol 114 (22) ◽

pp. 3373-3373

Author(s):

Sheng-Chieh Chou ◽

Jih-Luh Tang ◽

Liang-In Lin ◽

Hsin-An Hou ◽

Chien-Yuan Chen ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Stem Cell ◽

Myeloid Leukemia ◽

Gene Mutations ◽

Genetic Alterations ◽

Prognostic Impact ◽

Hematopoietic Stem ◽

Wbc Count ◽

Acute Myeloid

Abstract Abstract 3373 Poster Board III-261 Purpose Several gene mutations had been found to have clinical implications in patients with acute myeloid leukemia (AML), especially in those with normal karyotype. However, the role of such gene mutations for AML patients receiving allogeneic hematopoietic stem cell transplantation (allo-HSCT) was unclear and inconclusive. We retrospectively evaluated the prognostic impact of 8 gene mutations in adult AML patients undergoing allo-HSCT. Materials & Methods From 1995 to 2007, a total of 463 consecutive adult patients with de novo non-M3 AML had comprehensive gene mutation analyses at the National Taiwan University Hospital. Three hundred and twenty five patients who received conventional induction chemotherapy were enrolled in this study. Those who received only low dose chemotherapy or palliative treatment were excluded. The genetic alterations analyzed included NPM1, FLT3/ITD, FLT3/TKD, CEBPA, AML1/RUNX1, RAS, MLL/PTD, and WT1. The clinical implication of these genetic alterations in the patients receiving allo-HSCT was analyzed, and the result was compared with that in patients without allo-HSCT. Results The clinical characteristics in the patients receiving allo-HSCT (n=100) and those without (n=225) were similar with the exception of age, being younger in the former group (35.4 years vs. 49.5 years p<0.001). In univariate analysis, older age (Age > 45 years), higher initial WBC count (WBC>50K/μL), elevated LDH level, unfavorable karyotype, FLT3/ITD, mutations of AML1/RUNX1 were significantly associated with poorer overall survival (OS) in patients not receiving allo-HSCT; While NPM1mut/FLT3ITDneg and CEBPA mutations served as significantly good prognostic indicators. In multivariate analysis, age, WBC count, karyotype, FLT3/ITD, AML1/RUNX1, CEBPA and NPM1mut/FLT3ITDneg remained to be independent prognostic factors in non-allo-HSCT patients. However, in patients receiving allo-HSCT, only unfavorable karyotype and disease status (refractory or remission) at the time of transplantation were associated with poorer OS both in univariate and multivariate analyses. The similar prognostic impact of FLT3/ITD, CEBPA, AML1/RUNX1 and NPM1 on OS was not seen in patients receiving allo-HSCT. Furthermore, in contrast to its poor prognostic impact in non-allo-HSCT patients, mutation of AML1/RUNX1 was a significant good prognostic factor for relapse free survival (p=0.046), although not for OS, in allo-HSCT group. Conclusion FLT3/ITD, mutations of AML1/RUNX1, CEBPA and NPM1 have great prognostic implication for OS in AML patients not receiving allo-HSCT. However, their impact on OS is ameliorated in patients receiving allo-HSCT. The results need to be confirmed by further studies on more patients. Disclosures: No relevant conflicts of interest to declare.

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Leukemogenesis in Seven Donor Cell Derived Myeloid Neoplasms Patients. Whole Exome Sequencing Reveals Clonal Dynamics

Blood ◽

10.1182/blood-2018-99-118894 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2752-2752

Author(s):

Julia Suárez González ◽

Juan Carlos Triviño ◽

Mi Kwon ◽

Angela Figuera Alvarez ◽

Guiomar Bautista ◽

...

Keyword(s):

Exome Sequencing ◽

Somatic Mutations ◽

Gene Mutations ◽

Donor Cell ◽

Hematopoietic Stem ◽

Time Points ◽

Post Transplant ◽

Myeloid Neoplasm ◽

Whole Exome ◽

The Moment

Abstract Introduction Donor cell derived myeloid neoplasm (DCMN), defined as the development of de novohematological malignancies from cells of donor origin,is a late complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). We report on seven cases of DCMN in which whole-exome sequencing (WES) at different time-points after allo-HSCT, as well as in a sample from each donor, was performed. The ultimate objective was to accurately described the clonal architecture, spatial heterogeneity and identify somatic mutations that are induced in the process of leukemogenesis and clonal evolution of myeloid neoplasm. Donors were also analyzed to detect underlying condition predisposing to the development of DCMN. Patient and Methods Seven patients with a confirmed diagnosis of DCMN and their donors were recruited from different Spanish institutions. This cohort included a total of 32 BM samples at different time points after allo-HSCTand one PB sample from each donor (one case received dual allo-HSCT, CB and PB from both donors were obtained), which provided a total of 40 samples. Genomic DNA samples were prepared according to Agilent SureSelect-XT Human exon 50Mb enrichment kit (Agilent Technlogies, Santa Clara, CA) preparation guide and libraries were sequenced on Illumina HiSeq platform (Illumina, San Diego, CA). DNA sequencing data from recipient post-transplant BM samples, were matched against their donor PB sample and previous post-transplant BM samples to identify the acquisition of mutations along the post allo-HSCT period.Germline variants in donors were studied in order to detect mutations that predisposed to the development of a myeloid neoplasm.The research protocol was approved by the Ethic Committee of Gregorio Marañón General University Hospital. Patients´ and donors´ information was collected from their medical records. Results Clinical and biological characteristics of the 7 patients with DCMN and their donors are shown in Table 1. Mutational profiles obtained from the follow-up samples at different time-points post-HSCT demonstrated high intra-tumor genetic heterogeneity and clonal dynamic for all cases. The number of variants are increased over time and at the moment of DCMN diagnosis, the median number of variants was 28, ranging from 18 to 92 variants (Figure 1). WES identified in DCMN patients gene mutations commonly seen in adult AML or MDS, such as in SETBP1, DNMT3A, TET2, RUNX1, CSF3R, EP300and IDH2.In addition, others non-silent variants were acquired in all cases. Among the additional novel alterated genes, we found 23 strong candidateswith oncogenic potential. LUC7L2, NOP14, LAMA5, SKOR2, EML1, SNX13, RHPN2, IRS1, MTG2, TENM2, MEFV, GSE1, NOTCH4, DTX1, CNOT4, PNKP, GRB7,SENP7,TAF1L, ZKSCAN2, ZBTB20, ZNF461 and MEGF10. Analysis of CNVs revealed numerical alterations across the post allo-HSCT samples in patients 1, 2, 3, 4, 5 and 7. The most common chromosomal alterations in DCMN were monosomy 7 and other chromosome 7 abnormalities, which detected in the 86% (6/7) of the patients. Although none of the donors developed a myeloid neoplasm at the moment of diagnosis of DCMN in recipient, donor 1 revealed an abnormal karyotype (45,XY,-7) at the moment of the allo-HSCT. All other donors harbored at least one pathogenic or probably-pathogenic variants, most probably of germline origin, in genes involved in hematological or solid tumor predisposition. Conclusions The development of DCMN involves dynamic genomic processes that begin months before the clinical onset. In this study, our integrated multi-step analysis revealed the intra-tumor heterogeneity and evolutionary history of seven DCMN. The present study reveals a process of sequential clonal expansions promoted by the acquisition of somatic mutations in donor hematopoietic cells. Detection of heritable or acquired gene mutations in donors associated with predisposition to haematological malignancies could have clinical implications for the patients undergoing to allo-HSCT. Leukemic transformation ofdonor hematopoietic stem cells provides a useful in vivomodel to study the mechanisms involved in leukemogenesis. Novel approaches based on high-depth next generation sequencing to study consecutive samples from post-transplant period in these patients, appear promising to discover new genes involved in myeloid neoplasm and to decipher the mechanisms of leukemogenesis. Disclosures No relevant conflicts of interest to declare.

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Adding molecular data to prognostic models can improve predictive power in treated patients with myelodysplastic syndromes

Leukemia ◽

10.1038/leu.2017.266 ◽

2017 ◽

Vol 31 (12) ◽

pp. 2848-2850 ◽

Cited By ~ 28

Author(s):

A Nazha ◽

K Al-Issa ◽

B K Hamilton ◽

T Radivoyevitch ◽

A T Gerds ◽

...

Keyword(s):

Myelodysplastic Syndromes ◽

Predictive Power ◽

Molecular Data ◽

Prognostic Models

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A Personalized Prediction Model to Risk Stratify Patients with Myelodysplastic Syndromes

Blood ◽

10.1182/blood-2018-99-114774 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 793-793 ◽

Cited By ~ 13

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.

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Myelodysplastic Syndromes-Associated Gene Mutations Lead to Pseudohypoxia Condition and Epigenome Hyper-Methylation in Mouse Genetic Models

Blood ◽

10.1182/blood-2019-126799 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 1696-1696

Author(s):

Asumi Yokota ◽

Lulu Zhang ◽

Jianhua Feng ◽

Xiaomin Feng ◽

Xiaomei Yan ◽

...

Keyword(s):

Myelodysplastic Syndromes ◽

Knockout Mice ◽

Macrocytic Anemia ◽

Gene Mutations ◽

Tca Cycle ◽

Conditional Knockout ◽

Hematopoietic Stem ◽

The Impact ◽

Mouse Genetic ◽

Novel Therapeutic

Myelodysplastic syndromes (MDS) are heterogeneous diseases caused by a complex combination of various gene mutations. Patients develop anemia, pancytopenia, and uni-/multi-lineage dysplasia, which represent ineffective hematopoiesis and also potential for leukemic transformation. Recent advances in next generation sequencing identified a variety of gene mutations involved in transcriptional and epigenetic regulation, RNA splicing, or metabolic enzymes. However, the underlying mechanism of those commonly shared MDS phenotypes caused by these heterogenous genetic mutations has not been fully elucidated yet. We recently have identified that HIF1A activation caused by metabolic rewiring and pathobiological pseudohypoxia are critical for the pathogenesis of MDS, and HIF1A is a potential novel therapeutic target in MDS (Cancer Discovery2018). This unique metabolic rewiring is associated with activation of glycolysis, suppression of mitochondrial TCA cycle and oxidative phosphorylation (OXPHOS), accumulation of the intermediate metabolites in TCA cycle, which suppress a-KG-dependent dioxygenases including prolyl hydroxylase domain (PHD), aspartate hydroxylase (FIH1), DNA demethylases (TETs), and RNA demethylases (FTO/ALKBH5), and lysine demethylases (KDMs). This pseudohypoxia condition also leads to HIF1A stabilization and hypermethylation of DNA/RNA/histones (epigenome). Interestingly, we found that mitochondrial complex II, succinate dehydrogenase (SDH), is downregulated in hematopoietic stem/progenitor cells (HSPCs) of Mll-PTD (partial tandem duplication) knocked-in (MllPTD/WT) mice, which is one of the MDS-associated mutations found in patients. We confirmed that MllPTD/WTmice indeed exhibited activated glycolysis and suppressed TCA cycle and OXPHOS in NMR-based Stable Isotope-Resolved Metabolomics (SIRM)analysis with labeled glucose (13C-glucose). Based on these early findings, we further investigated the impact of the major MDS-associated mutations on pseudohypoxia in several genetic mouse models in this study. We first established multiple mouse genetic MDS models mimicking co-occurrence of gene mutations found in MDS patients. MLL-PTDis not sufficient for MDS onset in humans and mice, and is frequently accompanied by RUNX1 mutations in MDS as well as AML. MLL-PTD has been identified in healthy populations, which reflects the clonal hematopoiesis of indeterminant potential (CHIP) condition. Indeed, HSPCs from MllPTD/WTknocked-in mice have a clonal advantage after bone marrow (BM) transplantation, but does not induce an MDS phenotype in mice (Blood2012). In contrast, Vav1-Cre-driven Runx1 knockout in MllPTD/WT background induces MDS phenotypes: pancytopenia, macrocytic anemia, thrombocytopenia, and multi-lineage dysplasia. We further examined HSPCs of MllPTD/WT/Vav1-Cre/Runx1Flox/Flox mice and confirmed that there are increases of methylation levels of DNA, RNA, and histones. HIF1A protein was also accumulated in HSPCs of these mice. Gene mutations in epigenetic modifiers, TET2, DNMT3A, and ASXL1, are often found in the CHIP condition as well as in MDS, and some patients have double or triple mutations of these genes. Thus, we developed Vav1-Cre-driven Tet2, Dnmt3a, and Asxl1 knockout mice. The triple conditional knockout mice exhibit anemia, thrombocytopenia, leukocytosis, and expansion of HSPCs. HSPCs from this strain exhibited elevated DNA, RNA, and histone methylation levels, which were more obvious compared to MllPTD/WT/Runx1Δ/Δ, and also high HIF1A protein expression. These results suggest that pseudohypoxia is commonly generated by MDS-associated gene mutations and may contribute to the pathophysiology of MDS. Collectively, in this study we found that the combinations of MDS-associated mutations are sufficient for generating pseudohypoxia condition, HIF1A activation and hypermethylation of epigenomes, which recapitulates the conditions in MDS patients. We are currently investigating the detailed mechanism of pseudohypoxia and the metabolic rewiring, which are caused by the individual or the multiple combinations of gene mutations found in MDS. We believe that our research on pseudohypoxia may provide a better understanding for the pathophysiology of MDS and can also help us develop novel therapeutic strategies targeting the vulnerabilities of these unique metabolic and epigenetic statuses in MDS. Disclosures No relevant conflicts of interest to declare.

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