Heterogeneity Of Acute Myeloid Leukemia Research Articles

Combinatorial antigen targeting strategies for acute leukemia: application in myeloid malignancy

Background aimsEfforts to safely and effectively treat acute myeloid leukemia (AML) by targeting a single leukemia-associated antigen with chimeric antigen receptor (CAR) T cells have met with limited success, due in part to heterogeneous expression of myeloid antigens. The authors hypothesized that T cells expressing CARs directed toward two different AML-associated antigens would eradicate tumors and prevent relapse. MethodsFor co-transduction with the authors’ previously optimized CLL-1 CAR currently in clinical study (NCT04219163), the authors generated two CARs targeting either CD123 or CD33. The authors then tested the anti-tumor activity of T cells expressing each of the three CARs either alone or after co-transduction. The authors analyzed CAR T-cell phenotype, expansion and transduction efficacy and assessed function by in vitro and in vivo activity against AML cell lines expressing high (MOLM-13: CD123 high, CD33 high, CLL-1 intermediate), intermediate (HL-60: CD123 low, CD33 intermediate, CLL-1 intermediate/high) or low (KG-1a: CD123 low, CD33 low, CLL-1 low) levels of the target antigens. ResultsThe in vitro benefit of dual expression was most evident when the target cell line expressed low antigen levels (KG-1a). Mechanistically, dual expression was associated with higher pCD3z levels in T cells compared with single CAR T cells on exposure to KG-1a (P < 0.0001). In vivo, combinatorial targeting with CD123 or CD33 and CLL-1 CAR T cells improved tumor control and animal survival for all lines (KG-1a, MOLM-13 and HL-60); no antigen escape was detected in residual tumors. ConclusionsOverall, these findings demonstrate that combinatorial targeting of CD33 or CD123 and CLL-1 with CAR T cells can control growth of heterogeneous AML tumors.

Assessment of Minimal/Measurable Residual Disease Testing in Acute Myeloid Leukaemia By Molecular Methods in an Interlaboratory Study

BackgroundMinimal/measurable residual disease (MRD) testing is increasingly utilised and accepted as standard of care to manage a range of different haematological malignancies. It's use as a surrogate outcome in clinical trials of new therapies is being explored, where it has the potential to accelerate drug assessment and approval. The phenotypic and genetic heterogeneity of acute myeloid leukaemia (AML) has limited the use of MRD in this context; however, the European LeukaemiaNet (ELN) MRD working group have recently published consensus guidelines to standardise both flow cytometric and molecular genetic MRD testing. To assess the accuracy of testing and concordance between laboratories, crucial to patient safety, external quality assessment (EQA)/proficiency testing (PT) is required.AimsTo determine the performance of molecular methods for measuring of MRD using the t(8;21)(q22:q22) RUNX1-RUNX1T1, inv(16)(p13q22) CBFB-MYH11, t(15;17)(q24.1;q21.2) PML-RARA and NPM1 Type A markers in an international interlaboratory study.MethodsA total of 12 batches of lyophilised EQA material were manufactured. These consisted of three batches of samples for each marker all containing 9x10^6 cells: an MRD ‘high’ sample; an MRD ‘low’ sample; and an MRD ‘negative’ sample. The t(8;21)(q22:q22) RUNX1-RUNX1T1 positive samples were manufactured using the KASUMI-1 cell line, the inv(16)(p13q22) CBFB-MYH11 positive samples using the ME-1 cell line; t(15;17)(q24.1;q21.2 PML-RARA positive samples using the NB4 cell line and the NPM1 Type A (NM_002520.6:c.860_863dup) positive samples using the OCI-AML3 cell line. MRD positive samples were diluted with HL60 cells to achieve the desired MRD level. MRD negative samples were manufactured using the HL60 cell line. The samples were shipped at ambient temperature to the 29 laboratories in 12 countries. Participants were asked to test the blinded samples with their in-house assay and report % normalised ratio of the relevant marker alongside additional methodological and technical data.ResultsFor t(8;21) RUNX1-RUNX1T1, all participants who returned results (n=23) classified the MRD ‘high’ and MRD ‘low’ samples as positive and the MRD ‘negative’ sample as negative. The robust mean log reduction between the MRD ‘high’ and MRD low sample was 2.7 (range 2.5-2.9). For inv(16) CBFB-MYH11, all participants who returned results (n=22) classified the MRD ‘high’ sample as positive, 21/22 (95.5%) classified the MRD ‘low’ sample as positive and 21/22 (95.5%) classified the MRD negative sample as negative. The robust mean log reduction between the MRD ‘high’ and MRD ‘low’ sample was 3.16 (range 2.8-4.2). For t(15;17) PML-RARA, all participants who returned results (n=22) classified the MRD ‘high’ sample as positive, 21/22 (95.5%) classified the MRD ‘low’ sample as positive and 21/22 (95.5%) classified the MRD negative sample as negative. The robust mean log reduction between the MRD ‘high’ and MRD ‘low’ sample was 2.1 (range 1.4-2.4). For NPM1, all participants who returned results (n=23) classified the MRD ‘high’ as positive, 21/23 (91.3%) classified the MRD ‘low’ sample as positive and, 17/23 (73.4%) classified the MRD negative sample as negative. The robust mean log reduction between the MRD ‘high’ and MRD ‘low’ sample was 3.8 (range 3.2-4.2).Summary/ConclusionThe majority of participants in this study were able to detect and accurately quantify MRD when assessing the t(8;21)(q22:q22) RUNX1-RUNX1T1, inv(16)(p13q22) CBFB-MYH11, t(15;17)(q24.1;q21.2) PML-RARA and NPM1 markers, at levels that would be expected within a clinical trial or standard of care setting. A high proportion of participants reported false positive results in the NPM1 marker negative sample. This would have significant consequences clinically, with NPM1 marker false-positivity potentially committing patients to unneeded additional chemotherapy and/or transplant with the attendant risk of morbidity and mortality which highlights the need for ongoing EQA in this area. UK NEQAS LI will work with laboratories advocating they undertake a root cause analysis process to identify the source(s) of error contributing to false positive NPM1 marker results and support their subsequent corrective actions; sharing any educational findings with all participants. [Display omitted] DisclosuresScott: Novartis: Research Funding; Biorad: Research Funding. Dillon: Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Research Support, Educational Events; Amgen: Other: Research support (paid to institution); Astellas: Consultancy, Other: Educational Events , Speakers Bureau; Menarini: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Other: Session chair (paid to institution), Speakers Bureau; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: educational events; Jazz: Other: Education events; Shattuck Labs: Membership on an entity's Board of Directors or advisory committees. Whitby: Roche: Membership on an entity's Board of Directors or advisory committees; Alexion: Honoraria, Other: Teaching.

Patient Heterogeneity in Acute Myeloid Leukemia: Leukemic Cell Communication by Release of Soluble Mediators and Its Effects on Mesenchymal Stem Cells.

Acute myeloid leukemia (AML) is an aggressive bone marrow malignancy, and non-leukemic stromal cells (including mesenchymal stem cells, MSCs) are involved in leukemogenesis and show AML-supporting effects. We investigated how constitutive extracellular mediator release by primary human AML cells alters proteomic profiles of normal bone marrow MSCs. An average of 6814 proteins (range 6493−6918 proteins) were quantified for 41 MSC cultures supplemented with AML-cell conditioned medium, whereas an average of 6715 proteins (range 6703−6722) were quantified for untreated control MSCs. The AML effect on global MSC proteomic profiles varied between patients. Hierarchical clustering analysis identified 10 patients (5/10 secondary AML) showing more extensive AML-effects on the MSC proteome, whereas the other 31 patients clustered together with the untreated control MSCs and showed less extensive AML-induced effects. These two patient subsets differed especially with regard to MSC levels of extracellular matrix and mitochondrial/metabolic regulatory proteins. Less than 10% of MSC proteins were significantly altered by the exposure to AML-conditioned media; 301 proteins could only be quantified after exposure to conditioned medium and 201 additional proteins were significantly altered compared with the levels in control samples (153 increased, 48 decreased). The AML-modulated MSC proteins formed several interacting networks mainly reflecting intracellular organellar structure/trafficking but also extracellular matrix/cytokine signaling, and a single small network reflecting altered DNA replication. Our results suggest that targeting of intracellular trafficking and/or intercellular communication is a possible therapeutic strategy in AML.

An Integer Weighted Genomic Mutation Score (GMS) Using Next Generation Sequencing Is Predictive of Prognosis in Intermediate Risk AML Patients

Introduction: The genomic heterogeneity in Acute Myeloid Leukemia (AML) has been shown to affect the overall prognosis and should be taken into account to decide the best course of treatment. Study of AML genomic landscape by The Cancer Genome Atlas (TCGA) research network by Ley, et.al, analyzed the genomes of 200 patients (pts) with de-novo AML and showed at least one potential driver mutation in nearly all AML samples and further classified these mutation in 9 functional categories: Myeloid transcription factor and fusion genes, Tumor suppressor genes, gene encoding Nucleophosmin, Activated signaling genes, DNA methylation related genes, Chromatin modifying genes, Cohesion complex genes and Spliceosome complex genes . The availability of Next Generation Sequencing (NGS) has allowed easy characterization of patient samples. However, this has now became increasingly important to prioritize the influence of different mutations clones so as to give actionable and prognostic information. We developed an Integer Weighted Genomic Mutation Score (GMS) to address this in the context of the TCGA data and applied this to a retrospective cohort of pts with AML. The goal was to meaningfully stratify the Intermediate risk AML patients per current ELN criteria based identified by NGS technique into low and high risk group and correlate this with clinical outcome.Study Method: We screened 1200 AML patients at our institution from 2010 to 2016 and identified 111 patients from our hospital database who had information on both cytogenetics and gene mutation detected by NGS. We queried for the diagnosis of AML and demographics, Cy, NGS and OS. We divided the patients into 3 groups according to their cytogenetic risks- Favorable (FCy), Intermediate (ICy),Poor (PCy). Mutations in 54 genes associated with myeloid disorders were tested in NGS by using the TruSight Myeloid Sequencing Panel (Illumina) (Table1). We also performed flow cytometric evaluation on a BD FACSCantos™ II 3-laser (405, 488, 630 nm excitation sources), 8-color flow cytometer (BD Biosciences, San Jose, CA, USA) and data analyzed using BD FACSDiva software. We then utilized the TCGA data by Leyet.al and developed a system of Integer Weights forthe Genomic Mutation Score (GMS) obtained by targeted NGS at Houston Methodist Hospital mentioned below. GMS was designed by assigning a score for each genomic mutation between the range of negative 2 (-2) for good risk to positive 2 (+2) for each of 9 TCGA mutation categories .The total IWGMS for each patient was calculated by the sum of mutation scores in each 9 categories. A score of 3 or greater than 3 was defined as high risk. We then performed a retrospective analysis of 111 AML patients to correlate the GMS with 2 year mortality and survival outcome. Statistical Analysis was done using multivariate logistic regression method.Results: Pts were grouped into 51 intermediate risk and 60 poor risk cytogenetics. Median age of the pts was 66 years and 57.6% were male. Immunophenotyping of CD34 and CD38, and CD117 did not emerge as significant predictors of survival when adjusting for covariates. 32% of patients underwent allogeneic stem cell transplant as consolidation therapy. Median overall survival for the entire study population was 24 months with a median follow up time was 20.01 months (95% CI; 15.77 - 25.17). Univariate analysis using cox proportional hazard method showed age and cytogenetics risk as a statistically significant predictor of overall survival as shown in Fig: 1. Overall survival in all AML patients and Intermediate risk patients showed a trend toward better OS with lower GMS but no such trend was seen in poor risk AML patients as shown in Fig: 2-4.Conclusion: We have developed an GMS system to further stratify Intermediate Risk AML patients into high and low risk groups. Age and cytogenetics risk per ELN criteria were independent prognostic factor for overall survival in our study cohort as expected. Patients with lower GMS in our study cohort had a trend towards better OS but patients with high GMS had a trend towards worse OS in intermediate risk AML patients. Larger size prospective study is required to validate this scoring system; it has a great potential to identify high risk patients among Intermediate risk group and plan different consolidation therapies to further improve their outcomes.Further treatment related analysis will be presented at ASH,Atlanta 2017. [Display omitted] DisclosuresIyer:Takeda: Research Funding; Genentech: Research Funding.

Comprehensive Methylation Analysis in Pediatric Patients with Acute Myeloid Leukemia —the JCCG Study, JPLSG AML-05-

IntroductionThe genetic heterogeneity of acute myeloid leukemia (AML) makes it difficult to predict an appropriate prognosis of patients with AML. Currently, several gene mutations associated with the pathogenesis of AML have been identified owing to recent advances in massively parallel sequencing technologies. However, despite incorporating these molecular markers, the prognosis of many patients remains uncertain. Aberrant DNA methylation has frequently been observed in myeloid cells of patients with AML. Reportedly, DNA methylation patterns are associated with molecular features. Furthermore, a previous report has demonstrated that the methylation levels of polycomb target genes are associated with the outcome, implying that the definition of DNA methylation patterns of AML is expected to reveal clinical and molecular features and survival in patients with AML. However, there are few reports of DNA methylation in pediatric patients with AML. Thus, we analyzed genome-wide DNA methylation in pediatric patients with AML to reveal its association with clinical features, genetic alterations, and prognostic impact. Besides, our previous study revealed that the high expression of MECOM (also known as EVI1 ) or PRDM16 (also known as MEL1 ) was associated with inferior outcomes in patients with AML. We also investigated the correlation between DNA methylation and gene expression in pediatric patients with AML.MethodsBetween 2006 and 2010, 443 pediatric patients with de novo AML (0-17 years) participated in the Japanese AML-05 trial conducted by the Japanese Pediatric Leukemia/Lymphoma Study Group. Of these, 369 samples were available, and 64 patients were enrolled in this study. The cytogenetic features of 64 patients were as follows: normal karyotype, 33; RUNX1-RUNX1T1 , 8; KMT2A rearrangement, 10; complex karyotype, 6; and other cytogenetics, 7. This cohort included 15 patients with FLT3 -internal tandem duplication (ITD), 8 with CEBPA biallelic mutations, 10 with high MECOM expression, and 30 with high PRDM16 expression. We performed comprehensive DNA methylation analysis using Infinium MethylationEPIC BeadChip (Illumina, Inc., San Diego, CA, USA) in 64 pediatric patients with AML to reveal the correlations among DNA methylation patterns, clinical features, genetic alterations, and prognostic impact in pediatric AML.Results and DiscussionUnsupervised hierarchical clustering based on methylation values of the 567 CpG sites, which showed most variable methylation values between 64 individuals, generated four clusters (clusters 1-4). The number of patients in clusters 1, 2, 3, and 4 was 23, 12, 13, and 16, respectively. Interestingly, these four clusters harbored their molecular features. Cluster 1 included patients with RUNX1/RUNX1T1 , normal karyotype with low PRDM16 expression, or KMT2A rearrangement with low MECOM expression. Cluster 2 included patients with FLT3 -ITD and/or normal karyotype with high PRDM16 expression. Cluster 3 included patients with normal karyotype with CEBPA biallelic mutations and cluster 4 patients with FLT3 -ITD, normal karyotype with high PRDM 16 expression, or KMT2A rearrangement with high MECOM expression. Differences in frequencies of common molecular aberrations among clusters were assessed with the Fisher-Freeman-Halton test. There was an unequal distribution of FLT3 -ITD and high PRDM16 expression (P = 0.003 for both), with concordantly high frequencies in clusters 2 and 4 and low in clusters 1 and 3. Clusters 2 and 4 tended to be correlated with older age, higher white blood cell counts at diagnosis, high relapse rate, and high mortality. These results suggest that DNA methylation can stratify the risk in pediatric AML. Conversely, no independent methylation marker was present to predict the adverse outcome, thereby indicating that an assortment of several methylation sites may be a useful marker for risk stratification.ConclusionWe investigated DNA methylation patterns in pediatric patients with AML and found four distinct clusters. Furthermore, our findings indicated that quantitative determination of DNA methylation percentage at specific CpG sites may be useful for risk stratification in pediatric AML. Considering the need of further analyses to reveal the significance of DNA methylation, we are analyzing the results currently and would like to discuss these data at the annual meeting. DisclosuresNo relevant conflicts of interest to declare.

RNA Sequencing Illustrates the Genetic Backgrounds of Pediatric Acute Myeloid Leukemia

BackgroundThe cytogenetic heterogeneity of acute myeloid leukemia (AML) has been recognized for more than three decades, and some chromosomal aberrations and gene mutations have been adopted for the risk stratification. However, approximately 50% of patients have disease relapse. Thus, novel and accurate prognostic markers are needed to improve the risk stratification. We have performed some targetting sequencing and gene expression array, and identified that PR DM16 and MECOM -overexpressing patients had significantly worse overall survival (OS) and event-free survival (EFS). Thisoverexpression was especially helpful for stratifying patients with FLT3 -ITD positive and/or normal karyotype. To establish a more appropriate risk classification, we investigated the correlations among genetic aberrations, gene fusions, and gene expression, which are significant prognostic markers for pediatric AML, and proposed a new risk stratification. In addition, we performed the whole-transcriptome analysis to reveal the comprehensive understanding of 57 patients whose aberrant genetic backgrounds were unclear.Patients and MethodsThis study enrolled 369 patients with de novo AML who had been registered with the Japan Children's Cancer Group study, JPLSG, AML-05 trial between November 2006 and December 2010. Patients diagnosed with acute promyelocytic leukemia or Down syndrome-associated AML were excluded from this study. We investigated gene mutations in KIT, CEBPA, NPM1, FLT3 -ITD, and MLL- PTD, gene fusions of RUNX1-RUNX1T1, CBFB-MYH11, KMT2A (MLL)- rearrangements, NUP98-NSD1, CBFA2T3-GLIS2, NUP98-KDM5A, FUS-ERG, and DEK-NUP214, and gene expression analyses of PRDM16 and MECOM . Although most of these patients were classified into five risk-dependent subgroups based on their genetic aberrations, the genetic background of 57 patients (15%), particularly in patients with complex karyotypes, was unclear. Thus, we additionally performed the whole-transcriptome analysis for these patients to reveal their genetic backgrounds.ResultsWe could re-stratify 369 patients into five new prognostic subgroups based on the genetic aberrations. Because genetic backgrounds of patients with acute megakaryocytic leukemia (AMKL) were different from non-AMKL patients, we proposed another stratification scheme for 34 patients with AMKL. For 335 patients with AML, as the first step, super low and extreme high groups were created. The super low group (5-year OS, 94%; 5-year EFS, 84%) consisted of CBFB-MYH11, KMT2A-R with low MECOM expression, NPM1 mutation, and CEBPA biallelic mutations. Conversely, the extremely high group (5-year OS, 24%; 5-year EFS, 16%) consisted of FUS-ERG, MLL-AF6, DEK-NUP214, CBFA2T3-GLIS2, MLL -PTD, NUP98-NSD1, MLL -R with MECOM and/or PRDM16 high, and FLT3 -ITD with high PRDM16 gene expression. Then, we chose low and intermediate groups as the second step. The low group (5-year OS, 86%; 5-year EFS, 76%) consisted of RUNX1-RUNX1T1 without KIT, MLL -R with low MECOM and PRDM16, and the intermediate group (5-year OS, 85%; 5-year EFS, 58%) consisted of RUNX1-RUNX1T1 with KIT, FLT3 -ITD with low MECOM, and PRDM16, according to the retrospective EFS and OS. For the remaining 57 patients (15%) whose genetic background was unclear, non-complete remission (CR) patients were assigned to the extremely high group, and patients who achieved CR were assigned to the high risk group (5-year OS, 53%; 5-year EFS, 44%). We performed the whole-transcriptome analysis for these 57 patients and identified gene fusions of PICALM-MLLT10 in 3, RPN1-MECOM in 2, CREBBP-KAT2A in 3, RUNX1 -rearrangements in 2, CBFA2T3 -rearrangement in 2, RARB -rearrangement in 1, and ETV6 -rearrangements in 3. The whole-transcriptome analysis was used to reveal the genetic background of these patients, particularly in patients with a complex karyotype. For the remaining 34 patients with AMKL, patients with CBFA2T3-GLIS2 or NUP98-KDM5A were assigned to the extremely high group and others to the intermediate group.ConclusionWe propose a new risk stratification comprising five prognostic subgroups based on the genetic aberrations. The whole-transcriptome analysis facilitated further understanding of the whole genetic background of pediatric patients with AML whose genetic background was unclear and was, thus, expected to improve more accurate risk stratification. DisclosuresNo relevant conflicts of interest to declare.

Development and validation of a prognostic model for adult patients with acute myeloid leukaemia.

BackgroundThe high heterogeneity of acute myeloid leukaemia (AML) reflected in the patient- and disease-related factors accounts for the unsatisfactory prognosis despite the introduction of novel therapeutic approaches and drugs in recent years.MethodsIn the development set (n = 412), parameters including age, hematopoietic cell transplantation-comorbidity index, white blood cell count, hemoglobin, biallelic CEBPA mutations, DNMT3A mutations, FLT3-ITD/NPM1 status, and ELN cytogenetic risk status were identified as independent prognostic factors for overall survival (OS) in the multivariable Cox regression analysis. A nomogram combining these predictors for individual risk estimation was established thereby.FindingsThe prognostic model demonstrated promising performance in the development cohort. The calibration plot, C-index (0.74), along with the 1-, 2- and 3-year area under the receiver operating characteristic curve (AUC, 0.76, 0.79, and 0.74, respectively) in the validation set (n = 238) substantiated the robustness of the model. In addition to stratifying young (age ≤ 60 years) and elderly patients (age > 60 years) into three and two risk groups with significant distinct outcomes, the prognostic model succeeded in distinguishing eligible candidates for hematopoietic stem cell transplantation.InterpretationThe prognostic model is capable of survival prediction, risk stratification and helping with therapeutic decision-making with the use of easily acquired variables in daily clinical routine.FundingThis work was supported in part by grants from the National Natural Science Foundation of China (81770141), the National Key R&D Program of China (2016YFE0202800), and Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (20161406).

The Application of Machine Learning to Improve the Subclassification and Prognostication of Acute Myeloid Leukemia

The Application of Machine Learning to Improve the Subclassification and Prognostication of Acute Myeloid Leukemia

AML-258: Next-Generation Sequencing Used to Detect Multiple Mutations in Acute Myeloid Leukemia Patients

Context New molecular tecniques allow the characterization of clonal heterogeneity in acute myeloid leukemia (AML) at diagnosis or during treatment. In the past decades, the mutational landscape identified in AML has become more diverse and is allowing identification of new prognostic factors. Objective To develop an in-house next-generation sequencing method that can be used to assess the mutational status of the patient at diagnosis and during treatment. Design The sequencing method screens for 8 regions in 5 genes involved in pathogenesis and progression of AML: FLT3, IDH1, IDH2, DNMT3A, and RUNX1. Setting Screening for these mutations will permit the stratification of patients into risk group at diagnosis, and using the method for minimal residual assesment will permit the development of personalised treatment programs. Patients or other participants A number of 51 patients diagnosed with AML in the Molecular Biology Laboratory of the Fundeni Clinical Institute between 2017 and 2019 were tested using an in-house sequencing method. Every patient included was tested at diagnosis for the presence of a reciprocal translocation and for presence of FLT3-ITD. Interventions For all patients, standard treatment was started after diagnosis. Bone marrow transplant was recommended and performed for only three patients. Main outcome measures To determine the functionality of the method for mutation detection in AML patients, as well as to familiarize the physicians with these mutations and to use them in treatment decisions. Results From the 51 patients included, 20 patients (39%) harboured one or more mutations in the genes tested. Mutations in DNMT3A and IDH2 were found to be more frequent. In 4 out of 20 patients (20%) mutations in DNMT3A and FLT3-ITD+ concurred. Survival was poor, and only 3 patients are still in follow-up: one patient harboring FLT3-ITD and RUNX1, one with mutations in RUNX1, and one with mutations in IDH2. Conclusions The method developed to test for somatic mutation has proven to be useful and has a lower cost than a commercial kit. Also, the method can be improved by adding new testing targets. Funding We gratefully acknowledge the funding from the project Competitiveness Operational Programme (COP), MyeloAL-EDiaProT, Contract 149/26.10.2016.

Application and clinical study of clonal heterogeneity analysis in acute myeloid leukemia

目的探讨急性髓系白血病（AML）的克隆异质性特征及其临床价值。方法采用高通量测序技术靶向检测2016年1月至2019年6月河南省肿瘤医院收治的465例AML患者的68种相关基因，将检出的基因突变位点根据变异等位基因频率（VAF）及同期流式细胞学结果进行克隆异质性分析，并分析其与预后的关系。结果338例（81.4%）初诊患者检出基因突变，其中携带DNMT3A、NRAS和RUNX1突变患者出现2个及以上克隆比例显著增加（DNMT3A：χ2=15.231, P<0.001；NRAS：χ2=19.866, P<0.001；RUNX1：χ2=23.647, P<0.001）。不同年龄组间克隆数差异有统计学意义（χ2=17.505, P=0.022），其中>60岁患者携带2个和≥3个克隆的比例增加。初诊患者与复发或继发AML患者间克隆分布差异具有统计学意义（χ2=11.302, P=0.010），且患者多克隆的比例随预后危险度增加而逐渐增加（χ2=17.505, P=0.022）。而主克隆分析中，RUNX1突变标志的主克隆比例较高（χ2=4.527, P=0.033）。克隆异质性与疗效相关分析显示，携带3个及以上克隆患者的总生存（OS）期和无进展生存（PFS）期均远低于其他患者（OS：χ2=13.533，P=0.004；PFS：χ2=9.817，P=0.020），而在中危组患者中，克隆数目多的患者其PFS期显著缩短（χ2=10.883，P=0.012）。Cox回归多因素分析显示，携带3个及以上克隆为影响预后的独立危险因素，其OS期和PFS期显著短于无克隆患者（OS：HR=3.296，95%CI 1.568~6.932，P=0.002；PFS：HR=3.241，95%CI 1.411~7.440，P=0.006）。结论克隆异质性可反映肿瘤的生物学特性，提示其耐药性、难治性及侵袭性，可进一步用来评估疗效和AML中危组患者的预后。

Targeted therapy in acute myeloid leukemia: current status and new insights from a proteomic perspective

ABSTRACTIntroduction: The biological heterogeneity of acute myeloid leukemia (AML) complicates personalized medicine. Individual prognosis is typically based on the presence of chromosomal and genetic lesions. Nevertheless, these classifications often lack a priori information about response to therapy. Since the protein expression landscape reflects the functional activity state of cells, we hypothesize that analyzing this can be used for the identification of protein activity markers to provide better risk stratification as well as may provide targeted therapeutic guidance in AML.Areas covered: Herein, we review recently new adopted drugs in the treatment for AML and discuss how quantitative proteomic techniques may contribute to better therapeutic selection in AML.Expert commentary: The net functional state of the cell is defined by the activity of protein within all the pathways that are active in the cell. Recognition of the proteomic profile of the leukemic blast could, therefore, complement current classification systems by providing a better a priori description of what pathways are important within a cell as a guide to the selection of therapy for the patient.

Single Cell Sequencing Reveals Evolution of Tumor Heterogeneity of Acute Myeloid Leukemia on Quizartinib

Single Cell Sequencing Reveals Evolution of Tumor Heterogeneity of Acute Myeloid Leukemia on Quizartinib

MiR-340 Expression As a Prognostic Marker to Guide Clinical Decision-Making in Acute Myeloid Leukemia

Background: Acute myeloid leukemia (AML) is an aggressive hematological disease. Allogeneic hematopoietic stem cell transplantation (Allo-HSCT) and chemotherapy are major treatment regimens for AML. However, prognostic markers cannot guide the decision for a specific treatment, as they are related with a various prognosis regardless of the given treatment. HOXA (homeobox A) genes cluster could promote tumor survival, proliferation, invasion, and increase the resistance of AML. The aim of this study was to screen potential miRNAs (microRNAs) that would target HOXA genes, and evaluate the utility of miRNAs in AML, help patients choose a better treatment between chemotherapy and allo-HCST. Methods: Clinical data and RNA-Seq expression data of selected cases were provided by The Cancer Genome Atlas (TCGA). Genome-wide screening was performed to identify miRNA in a heterogeneous AML population. Univariable Cox proportional hazards models and Multivariable Cox proportional hazards models were employed to identify whether OS and EFS would be affected by other variables. Results: In this study, totally 162 AML patients were recruited. All patients were firstly divided into the chemotherapy and allo-HSCT groups. Subsequently, according to median values of miR-340, patients were divided into miR-340high and miR-340low expressers, respectively. In chemotherapy group, no difference was found in clinical characteristics, such as the median age, FAB subtypes, karyotypes and genes mutation between miR-340high and miR-340low expressers. However, miR-340low expressers often accompanied with high first relapse rate or death rate in one year than high expressers (P=0.012; 82.2% vs 55.6%). To identify the independent prognostic role of miR-340 in chemotherapy group patients, Univariable and Multivariable Cox proportional hazards models were performed. We found that miR-340lowpatients showed shorter OS (P=0.0005; 5-year OS, 35.6% vs. 5.4%) and EFS (P=0.0005) compared with high expressers. In multivariable analysis, miR-340low patients showed reduced OS (P=0.004; HR: 2.07) and EFS (P=0.01; HR: 1.909) after adjusting other co-variates, such as age, WBC count and several genes mutation in chemotherapy group. Therefore, low miR-340 amounts could be an independent adverse bio-marker in AML patients undergoing chemotherapy. However, in the allo-HSCT group, miR-340 expression level was not associated with outcome in AML patients. To further explore the potential of allo-HSCT in overcoming the adverse characteristics of low miR-340 amounts, the whole 162 patients were regrouped into miR-340low and miR-340high groups. Then patients were divided into chemotherapy and allo-HSCT subgroups. Subgroup analysis revealed that miR-340low patients had significantly longer OS (P&lt;0.0001; HR: 0.316; 95%CI: 0.167-0.459) and EFS (P=0.002; HR: 0.391; 95%CI: 0.231-0.622) in allo-HSCT subgroup than in chemotherapy subgroup (Figure 1). However, in cases highly expressing miR-340, no difference in survival events was detected between the two treatment subgroups. These findings indicated, allo-HSCT may overcome the adverse prognostic effects of low mir-340 expression. Therefore, for low miR-340 cases, early allo-HSCT may be a better option. To explore underlying biological functions of miR-340, we examined gene expression signatures related to the miR-340 expression in AML patients. We observed 135 genes expression levels that associated with miR-340 expression, with 61 and 74 showing positive and negative correlations, respectively. Gene Ontology showed that these genes involved in cellular and developmental processes, transcription regulation, immune system process, cell apoptosis and proliferation, myeloid cell differentiation and hematopoietic organ development. Furthermore, miR-340 expression was negatively correlated with HOXA and HOXB cluster levels. Strikingly, HOXA10, HOXB2, MEIS1 and PRDM16 were predicted miR-340 targets according to in silico analysis. The results hint a prospective regulatory mechanism that links miR-340 to HOXA genes associated with AML. Conclusions: Our data indicate that decreased miR-340 expression predicts an adverse prognosis and allo-HSCT may overcome the potential adverse characteristics of low miR-340 expression. Therefore, lower miR-340 cases should be strongly considered for early allo-HSCT. Disclosures No relevant conflicts of interest to declare.

Clonal evolution of acute myeloid leukemia from diagnosis to relapse.

Based on the individual genetic profile, acute myeloid leukemia (AML) patients are classified into clinically meaningful molecular subtypes. However, the mutational profile within these groups is highly heterogeneous and multiple AML subclones may exist in a single patient in parallel. Distinct alterations of single cells may be key factors in providing the fitness to survive in this highly competitive environment. Although the majority of AML patients initially respond to induction chemotherapy and achieve a complete remission, most patients will eventually relapse. These points toward an evolutionary process transforming treatment‐sensitive cells into treatment‐resistant cells. As described by Charles Darwin, evolution by natural selection is the selection of individuals that are optimally adapted to their environment, based on the random acquisition of heritable changes. By changing their mutational profile, AML cell populations are able to adapt to the new environment defined by chemotherapy treatment, ultimately leading to cell survival and regrowth. In this review, we will summarize the current knowledge about clonal evolution in AML, describe different models of clonal evolution, and provide the methodological background that allows the detection of clonal evolution in individual AML patients. During the last years, numerous studies have focused on delineating the molecular patterns that are associated with AML relapse, each focusing on a particular genetic subgroup of AML. Finally, we will review the results of these studies in the light of Darwinian evolution and discuss open questions regarding the molecular background of relapse development.

Deconvolution of Clonal Heterogeneity in Acute Myeloid Leukemia using Single-Cell DNA Sequencing

Deconvolution of Clonal Heterogeneity in Acute Myeloid Leukemia using Single-Cell DNA Sequencing

TP53 deficiency permits chromosome abnormalities and karyotype heterogeneity in acute myeloid leukemia.

Abnormal karyotypes are common in cancer cells and frequently observed in acute myeloid leukemia (AML), in which complex karyotype aberrations are associated with poor prognosis. How exactly abnormal karyotypes arise and are propagated in AML is unclear. TP53 mutations and deletions are frequent in complex karyotype AML, suggesting a role of TP53 alterations in the development of chromosome abnormalities. Here, we generated isogenic TP53-knockout versions of the euploid AML cell line EEB to investigate the impact of TP53 on karyotype stability. We show that chromosome abnormalities spontaneously arise in TP53-deficient cells. Numerical aneuploidy could, to some extent, be propagated in a TP53-proficient setting, indicating that it does not necessarily trigger TP53 activation. In contrast, tolerance to structural chromosome aberrations was almost entirely restricted to TP53-knockout clones, all of which were able to continue proliferation in the presence of damaged DNA. Mechanistically, as a source of chromosome aberrations, limited numerical but not structural chromosomal instability was tolerated by TP53-wildtype cells. In contrast, structural instability was found only in TP53-knockout cells. Together, in myeloid cells TP53 loss allows for the development of complex karyotype aberrations and karyotype heterogeneity by perpetuation of chromosome segregation errors.

Tumor heterogeneity of acute myeloid leukemia: insights from single-cell sequencing.

Individual tumors comprise genetically and epigenetically heterogeneous subclones, each of which is presumably associated with a distinct function, such as self-renewal or drug sensitivity. The dissection of such intratumoral heterogeneity is crucial to understand how tumors evolve during disease progression and under the selection of therapeutic intervention. As a paradigm of cancer intratumoral heterogeneity and clonal evolution, acute myeloid leukemia (AML) has been shown to possess complex clonal architecture based on karyotype studies, as well as deep sequencing of mixed cellular populations using next-generation sequencing (NGS) technologies. The recent development of single-cell sequencing (SCS) methods provides a powerful tool to allow analysis of genomes, transcriptomes, proteomes, and epigenomes at an individual cell level. The technologies applied in AML have broadened our understanding of AML heterogeneity and provided new insights for the development of novel therapeutic strategies. In this review, we summarize the progress in the research of AML heterogeneity using SCS technology and discuss the limitations and future direction regarding how SCS can contribute to AML prognosis and treatment.

Ex vivo-model design and evaluation of the sensitivity of blast cells to chemotherapy as a way to personalize the treatment of acute myeloid leukemia

Despite continuous attempts to improve therapy, the outcomes of acute myeloid leukemia remain almost unchanged over last decades. Drugs made with a more complete understanding of the biology of acute myeloid leukemia do not equal the hopes for better prognosis. The best results are achieved only with high-dose chemotherapy, which is only possible for a limited number of patients. High phenotypic and genotypic heterogeneity of acute myeloid leukemia defines the relevance to develop personalized approaches to therapy, including those based on determination of individual drug sensitivity of blast cells.This article presents the results of developing an ex-vivo model of acute myeloid leukemia, as well as testing of two in vitro sensitivity assessment methods: evaluation of the genotoxicity of drugs in the micronucleus test and vitality and sensitivity to chemotherapy in sorted blast cells. Prospects of individualized therapy of acute myeloid leukemia were determined based on introduction into clinical practice and continuing the research.

Treatment patterns and healthcare resource utilization in patients with FLT3-mutated and wild-type acute myeloid leukemia: A medical chart study.

ObjectivesTo assess real‐world treatment patterns and healthcare resource utilization (HRU) among patients with FLT3–mutated (FLT3 mut) and FLT3–wild‐type (FLT3 wt) acute myeloid leukemia (AML).MethodsData were abstracted from medical charts of patients with AML from 10 countries. Patients were grouped based on their FLT3 mutation status, age (18‐64 or ≥65), and whether they were newly diagnosed (ND) or relapsed/refractory (R/R).ResultsCharts of 1027 AML patients were included (183 FLT3 mut 18‐64 ND; 136 FLT3 mut ≥65 ND; 181 FLT3 mut R/R; 186 FLT3 wt 18‐64 ND; 159 FLT3 wt ≥65 ND; 182 FLT3 wt R/R). Substantial heterogeneity was observed in treatment patterns for AML. Among ND patients 18‐64, the most common initial treatment was standard‐to‐intermediate dose cytarabine‐based therapies (43.2% for FLT3 mut and 55.9% for FLT3 wt); among ND patients ≥65, the most common initial treatment was hypomethylating agent‐based therapies (36.0% and 47.2%). Among R/R patients, the most common initial treatment after R/R was best supportive care only (39.8% and 24.7%). HRU was substantial across cohorts during both event‐free and post‐event periods.ConclusionsTreatment patterns of AML were heterogeneous and FLT3 mut AML was treated more aggressively than FLT3 wt disease. HRU was substantial for all cohorts, particularly after relapse or treatment failure.

Chromatin-Based Classification of Genetically Heterogeneous AMLs into Two Distinct Subtypes with Diverse Stemness Phenotypes

SummaryGlobal investigation of histone marks in acute myeloid leukemia (AML) remains limited. Analyses of 38 AML samples through integrated transcriptional and chromatin mark analysis exposes 2 major subtypes. One subtype is dominated by patients with NPM1 mutations or MLL-fusion genes, shows activation of the regulatory pathways involving HOX-family genes as targets, and displays high self-renewal capacity and stemness. The second subtype is enriched for RUNX1 or spliceosome mutations, suggesting potential interplay between the 2 aberrations, and mainly depends on IRF family regulators. Cellular consequences in prognosis predict a relatively worse outcome for the first subtype. Our integrated profiling establishes a rich resource to probe AML subtypes on the basis of expression and chromatin data.