Evolution Of Acute Myeloid Leukemia Research Articles

Comprehensive insights into AML relapse: genetic mutations, clonal evolution, and clinical outcomes.

Acute myeloid leukemia (AML) is a complex hematologic malignancy characterized by uncontrolled proliferation of myeloid precursor cells within bone marrow. Despite advances in understanding of its molecular underpinnings, AML remains a therapeutic challenge due to its high relapse rate and clonal evolution. In this retrospective study, we analyzed data from 24 AML patients diagnosed at a single institution between January 2017 and August 2023. Comprehensive genetic analyses, including chromosomal karyotyping, next-generation sequencing, and gene fusion assays, were performed on bone marrow samples obtained at initial diagnosis and relapse. Clinical data, treatment regimens, and patient outcomes were also documented. Mutations in core genes of FLT3, NPM1, DNMT3A, and IDH2 were frequently discovered in diagnostic sample and remained in relapse sample. FLT3-ITD, TP53, KIT, RUNX1, and WT1 mutation were acquired at relapse in one patient each. Gene fusion assays revealed stable patterns, while chromosomal karyotype analyses indicated a greater diversity of mutations in relapsed patients. Clonal evolution patterns varied, with some cases showing linear or branching evolution and others exhibiting no substantial change in core mutations between diagnosis and relapse. Our study integrates karyotype, gene rearrangements, and gene mutation results to provide a further understanding of AML heterogeneity and evolution. We demonstrate the clinical relevance of specific mutations and clonal evolution patterns, emphasizing the need for personalized therapies and measurable residual disease monitoring in AML management. By bridging the gap between genetics and clinical outcome, we move closer to tailored AML therapies and improved patient prognoses.

Convergent epigenetic evolution drives relapse in acute myeloid leukemia.

Relapse of acute myeloid leukemia (AML) is highly aggressive and often treatment refractory. We analyzed previously published AML relapse cohorts and found that 40% of relapses occur without changes in driver mutations, suggesting that non-genetic mechanisms drive relapse in a large proportion of cases. We therefore characterized epigenetic patterns of AML relapse using 26 matched diagnosis-relapse samples with ATAC-seq. This analysis identified a relapse-specific chromatin accessibility signature for mutationally stable AML, suggesting that AML undergoes epigenetic evolution at relapse independent of mutational changes. Analysis of leukemia stem cell (LSC) chromatin changes at relapse indicated that this leukemic compartment underwent significantly less epigenetic evolution than non-LSCs, while epigenetic changes in non-LSCs reflected overall evolution of the bulk leukemia. Finally, we used single-cell ATAC-seq paired with mitochondrial sequencing (mtscATAC) to map clones from diagnosis into relapse along with their epigenetic features. We found that distinct mitochondrially-defined clones exhibit more similar chromatin accessibility at relapse relative to diagnosis, demonstrating convergent epigenetic evolution in relapsed AML. These results demonstrate that epigenetic evolution is a feature of relapsed AML and that convergent epigenetic evolution can occur following treatment with induction chemotherapy.

Abstract A037: Investigating the role of the somatic DDX41 variant in the context of DDX41 hereditary myeloid malignancy syndromes (HMMS)

Abstract Heterozygous germline variants in DDX41 are identified in ~5% of patients with Myelodysplastic Syndrome (MDS) or Acute Myeloid Leukemia (AML). Recognition of the hereditary nature of such cancers and a better understanding of the factors that affect progression to malignancy has deep implications for health surveillance of patients and relatives that share a pathogenic DNA variant. DDX41 has a role in ribosome biogenesis and in opposing R-loop accumulation at promoters, but the mechanisms through which DDX41 loss drives cancer development are poorly understood. In a collaboration between Cambridge, UK and Mayo Clinic, USA we identified 85 patients with germline DDX41 variants [73/85 with germline pathogenic variants (GPV) and 12 with variants of uncertain significance (VUS)]. The majority were identified through targeted myeloid NGS upon presentation with AML (30/85) or MDS (44/85). Average age at diagnosis was 65 (35-93) with male predominance (57/85). The most common GPV was the NM_016222.4(DDX41):c.415_418dup; p.D140Gfs*2 (15/85). The majority (53.4% 39/73), also acquired a somatic variants in the other DDX41 allele (referred to as GPV+Som). The most common somatic variant (82% 32/39) was the R525H, a variant predicted to reduce the ATPase function of the helicase domain. We hypothesise that the markedly reduced DDX41 activity in cells with biallelic variants, is a key cause of progression to overt malignancy. In our cohort, we observed that somatic variants occurred at a similar frequency in association with start loss, truncating, splice, in-frame deletion, and missense GPV. However, whereas MDS was the predominant diagnosis within GPV (67.6% 23/34, AML 26.4% 9/34), AML was the most frequent diagnosis within GPV+Som (AML 51%.2 20/39, MDS 38.4% 15/39). Overall, there was a significantly higher likelihood of AML in the presence of a somatic variant (p<0.05), suggesting that there might be a dosage effect on the phenotype. This pattern was observed across all the GPV, with the exception of missense, suggesting that many might be benign polymorphysms. In addition, although somatic variants are typically subclonal (overall mean variant allele fraction VAF=9.87%, 3-27.8%), we observed a higher somatic VAF in AML over MDS cases (12.4% vs 7.7% respectively p<0.05). A lower bone marrow cellularity was also characteristic of GPV+Som vs GPV alone (mean observed/expected cellularity for age 0.9 vs 1.32 respectively p<0.05). In two cases, serial bone marrow morphology and NGS data were available, and we observed marked reduction in bone marrow cellularity upon acquisition of the R525H clone. The mechanisms of AML evolution appear different in DDX41 HMMS compared to sporadic cases. Interestingly, over half of our patients (21/34 GPV and 23/39 GPV+Som) did not exhibit additional somatic drivers. Our work suggests that acquisition of a somatic variant is a critical event in disease progression. Furthermore, the near universally subclonal nature of somatic variants suggest that they may affect disease phenotype through non-cell autonomous mechanisms. Citation Format: Ludovica Marando, Talha Badar, Yael Kusne, Audrey Morris, Terra Lasho, Abhishek Mangaonkar, Christy Finke, Carles Crawley, James M. Foran, Aref Al-Kali, Andrew King, Hassan Alkhateeb, Naseema Gangat, Chi Wong, Rong He, David Viswanatha, Faisal Basheer, Mark Litzow, Alejandro Ferrer, George Vassiliou, Mrinal Patnaik. Investigating the role of the somatic DDX41 variant in the context of DDX41 hereditary myeloid malignancy syndromes (HMMS) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A037.

Integrated Methylation and Transcriptional Landscape and Evolution of Pediatric AML

Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy associated with poor outcomes despite intensive therapies. The genomic landscape of pediatric AML has been well characterized and has led to translational benefits, such as improved relapse predication and effective targeted therapies for a subset of individuals (e.g., FLT3-ITD AML). Despite these advances, the majority of patients continue to receive therapeutic interventions that have not changed in three decades. This underscores the need to advance our understanding of AML biology beyond genomic alterations (e.g., somatic mutations). Here, we present the largest (N = 858 patients) integrative analysis of pediatric AML paired transcriptomes and methylomes to date, performed at diagnosis for patients enrolled on Children's Oncology Group clinical trials AAML0531 (NCT00372593) or AAML1031 (NCT01371981). We also analyzed N = 400 and 253 corresponding transcriptomes and methylomes at relapse, respectively. Additionally, we performed comprehensive mutation and fusion calling from NGS data and integrated clinical covariates and outcomes. This robust dataset allowed us to uncover how pediatric AML epigenetics are perturbed from normal hematopoiesis, characterize the evolution of transcriptional and methylation changes from leukemia diagnosis to relapse, and identify more accurate relapse prediction models for pediatric AML. We initially performed unbiased hierarchical clustering on CpG sites that are differentially methylated between AML and normal bone marrow to identify distinct AML-restricted methylation (ARM) signatures. This analysis demonstrated that that these ARM clusters were enriched in specific cytomolecular subtypes of pediatric AML. Specifically, we identified methylation signatures that recapitulated their adult counterparts (e.g., IDH1/2, DNMT3A, WT1) and pediatric specific methylation signatures that clustered with RUNX1::RUNX1T1, CBFB::MYH11, KMT2A-rearrangement, NUP98::NSD1, NUP98::KDM5A, and CEBPA mutant AML. Next, we investigated the transcriptional and methylation signatures for the relapsed cases and compared them to diagnosis. We demonstrated that relapsed transcriptional and methylation signatures clustered within their cytomolecular subtype counterparts at diagnosis (i.e., AMLs at relapse more closely resemble AMLs at diagnosis with the same underlying gene fusion than other relapsed AMLs) ( Fig. 1A). However, analyzing our data in the context of these cytomolecular subtypes, we found that leukemias uniformly acquired a more stem-like state at relapse. We focused our biomarker discovery efforts on KMT2A-rearranged AMLs since (as a cohort) these patients have an intermediate risk of relapse and existing molecular biomarkers (e.g., KMT2A fusion partner and measurable residual disease detection) result in only modest improvements in relapse prediction. First, we found that CpG islands associated with CD34 and HOXA gene expression were frequently (but variably) hypermethylated, which was also associated with downregulation of CD34 and HOXA gene expression and favorable survival outcomes. Next, we performed non-negative matrix factorization on diagnosis and relapsed KMT2A-rearranged AMLs and identified a novel gene set associated with relapse and poor outcomes. These three distinct methylation-transcriptional findings (i.e., CD34, HOXA, and relapsed signatures) were subsequently transformed into a novel methylation-transcription biomarker score (MTBS) using machine learning algorithms (i.e., LASSO Cox regression). We used KMT2A-rearranged AML patients from AAML1031 as our training cohort (N = 129) to generate a MTBS that was able to distinguish KMT2A-rearranged cases with disparate outcomes. This algorithm was validated in an independent cohort from AAML0531 (N = 28) ( Fig. 1B). Additional methylation-transcriptional biomarker discovery efforts for pediatric AML are underway. Since pediatric AML is associated with a low mutational burden, understanding leukemia-specific transcriptional and epigenetic alterations is of utmost importance. Our study demonstrates the translational promise offered by deep functional genomic characterization of pediatric AML and we believe that further interrogation may provide additional novel insights into leukemia initiation, relapse prediction, and novel treatment approaches.

Application of State Transition Theory and Treatment Modeling for Predicting Response to Chemotherapy in a Mouse Model of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a cancer of the myeloid line of blood cells, and the cancer originates in the bone marrow (BM) and circulates the body through the peripheral blood (PB), which can rapidly lead to BM failure and death. AML initiation and progression are composed of multiple biological properties, and we have previously shown that the state transition theory, state-space, and a model of the stochastic differential equation (SDE) could predict AML disease evolution using the mRNA of a conditional Cbfb-MYH11 (CM) knock-in mouse model. The model of the SDE represents AML evolution from health to disease as a state transition of the transcriptome state described as a particle undergoing Brownian motion in a double-well quasi-potential, where critical points define states of healthy state, c1, transition to AML, c2, and overt AML, c3. Along with the disease evolution of AML, prediction, and understanding of AML disease responses from chemotherapy aiming to provide optimized therapeutic strategies are required. Analyzing disease responses to chemotherapy from the state transition theory perspective will give us an understanding of post-chemotherapy disease dynamics and useful predictions which have the possibility to optimize treatment processes. To test the applicability of the AML state transition model, a time-series experiment using the conditional CM knock-in mouse model and state-transition modeling were performed. The mouse model was designed to recapitulate the human inv(16) AML, the leukemogenic fusion gene Cbfb-MYH11 (CM) in conditional CM knock-in mice (Cbfb56M/+/Mx1-Cre) cause the development of AML with a median survival of approximately 4 months after induction of CM. To induce CM expression, 6-8 weeks old CM knock-in mice were injected intraperitoneally with polyinosinic-polycytidylic acid [poly (I:C)] (InvivoGen, tlrl-picw-250) at 14 mg/kg/dose every other day for a total of 7 doses. CM leukemic mice were treated with a combination of cytarabine (50mg/kg/day; 5 days) and daunorubicin (1.5mg/kg/day; 3 days) after detection of overt leukemia, which is monitored by circulating leukemia blast (cKit+ > 20%) to model the 7+3 standard of care treatment for newly diagnosed AML. We used three independent experiments of mice as training and test cohorts (Group 1, Group 2, and Group 3). We collected 42 PBMC and 1 BM from CM-induced mice from Group 1 (4 AML and 1 control), 68 PBMC and 2 BM from Group 2 (4 AML), and 64 PBMC from Group 3 (3 AML and 2 control). The samples were collected weekly before, during, and following chemotherapy and subjected to RNA sequencing. The effects of chemotherapy were observed in the AML state-space with chronological time points as the transcriptome particle moving from leukemia state, c3, towards a state of healthy state, c1, crossing the unstable state, c2. The relapse was defined as the time of the first observation after the particle crosses back over unstable state, c2, towards the leukemic state, c3. The 10 AML mice out of 11 AML mice achieved a partial response with a mean time to relapse of 5 weeks. All 11 AML mice showed the anti-correlation between AML state in the state space was anti-correlated with the blast percentages (ckit%). We modified the state transition treatment model by introducing a parameter, γ, and the half-life of cytarabine (31.1 hours) and daunorubicin (21.9 hours). The gamma parameter is a force that works against the double-well quasi potential (γ >1) and represents the anti leukemogenic effects of cytarabine and daunorubicin. Gamma was determined by fitting the model to 6 AML mice data from the training cohort. The value was determined to be γ=2.7, providing a novel quantification of the treatment effect. We performed a mean arrival time analysis using simulations of the state transition treatment model, which accurately predicted the time to relapse with a prediction error of 6.6 days for the 3 AML mice from the test cohort. We could apply the state transition theory and propose the state transition treatment model to simulate the treatment process and the response from chemotherapy. Our model accurately predicted the time to respond to chemotherapy, from leukemia to health, and relapse, from health to leukemia, in the CM mouse model after chemotherapy. The state transition treatment model has implications for improving therapeutic strategies by targeting transcriptome state transition critical points in human AML.

Combining Gene Mutation with Transcriptomic Data Improves Outcome Prediction in Myelodysplastic Syndromes

Background and Aim. Myelodysplastic syndromes (MDS) are myeloid neoplasms characterized by peripheral blood cytopenias and risk of progression to acute myeloid leukemia (AML). Disease management is challenged by heterogeneity in clinical courses and survival probability. Recently, the genomic screening integration (by Molecular International Prognostic Scoring System, IPSS-M) into patient's assessment has resulted into a significant improvement in predicting clinical outcomes compared to the conventional prognostic score (Revised IPSS, IPSS-R). Many of the consequences of genetic and cytogenetic alterations will affect gene expression by means of transcriptional and epigenetic instability and altered microenviromental signaling. The aim of this project conducted by GenoMed4All and Synthema EU consortia is to link genomic information with transcriptomic data for possibly improving the prediction of clinical outcomes in MDS patients. Patients and Methods.Clinical, cytogenetic, genomic (somatic mutations screening of 31 target genes) and transcriptomic (bulk RNA-seq of CD34 + bone marrow cells) data were collected at diagnosis in 389 MDS patients. Transcriptomic and genomic profiles were processed and the former were normalized before Principal Component Analysis (PCA) dimensionality reduction to mine the interdependency of expression-wide perturbation and recurrent genomic alterations. The prognostic impacts of genetic, cytogenetic, transcriptomic, clinical and demographic features were assessed with a penalized Cox's proportional hazards model [Gerstung M et al, Nat Commun. 2015. 6, 5901] considering the Overall Survival (OS) as primary end point. A 5-fold cross-validating (CV) scheme was exploited to control bias in risk estimation. Model accuracy was assessed using Harrell's concordance index (C-index). An independent validation of the results on 202 patients was planned. Results.We first processed each data layer assessing data robustness, removed not informative variables and scaled quantitative ones. We considered recurrent genomic and cytogenetic lesions (present in ≥5 patients), platelets, hemoglobin and bone marrow blasts (%), age and sex as covariates. To explore the main patterns of expression changes, PCA was performed to reduce multidimensional correlated expression features (20 PCs was selected, explaining 42% of the total transcriptomic variability). To evaluate the prognostic power of each data layer we grouped all available features into five groups: gene mutations (n=15), cytogenetic alterations (n=7), expression data (n=20), blood counts (n=3) and demographic variables (n=2). Within a 5-fold CV we combined these variables in our integrative model to calculate MDS patients risk. The obtained predictive accuracy (C-index) for OS was 0.83, underlying that transcriptomic data significantly improved the current standard prognostic scoring systems. Accordingly, in our patient population, the C-index of the conventional IPSS-R score and the new IPSS-M were 0.68 and 0.76, respectively. A similar improvement by adding transcriptomic data was observed in prediction of the risk of AML evolution. Moreover, by analyzing the contribution of each feature category to the OS probability ( Figure 1), in term of explained variance, the relative impact of transcriptomic is 40%, with the remaining prognostic information distributed among genomic features (somatic gene mutations and cytogenetics lesions, 24%), demographics (20%) and clinical features (15%). An independent validation of these results on 202 patients is currently ongoing. Figure 2 shows an example of personalized survival prediction using patients from the study population. In two subjects with same clinical phenotype and mutations leading to a similar IPSS-M prognosis, the integrative model captures additional prognostic information and efficiently predicts clinical outcome. Given the complexity of our model, specific technological support is needed to combine data at individual patient level and to translate it into a personalized outcome prediction. To this aim, we created a prototype web portal based on our dataset for user-defined genomic/transcriptomic and clinical features. Conclusion. In predicting survival of MDS patients, genomic, transcriptomic and diagnostic clinical variables all have utility, with a significant contribution from the transcriptome.

Free Energy Changes at State-Transition Critical Points As a Patient-Specific Biomarker in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy with a poor overall survival rate. AML is a highly heterogeneous disease driven by combinations of genomic mutations, epigenetic alterations, and biochemical signaling. The heterogeneity underlying AML needs novel approaches for individualized clinical assessment and treatment selection. However, approaches that rely on differential expression or correlation analysis lack an underlying theoretical model of the disease to inform the interpretation of ongoing processes in each individual which is crucial for personalized diagnostics and treatment selection. We have previously shown that the state-transition (ST) theory, state-space could predict AML disease evolution using the mRNA of a conditional Cbfb-MYH11 (CM) knock-in mouse model. The ST model represents AML evolution from health to disease as a state transition of the transcriptome state described as a particle undergoing Brownian motion in a double-well quasi-potential, where critical points define states of healthy, c1, transition to AML, c2, and overt AML, c3. We integrated two approaches, the ST model and surprisal analysis (SA) from thermodynamics, to create a patient-specific characterization that can be used to identify the individualized state of the disease. The publicly available datasets of human blood including normal samples, AML samples of bone marrow (BM), and peripheral blood (PB) were utilized for the methods. A total of 858 samples from three publicly available RNA-seq datasets were used. The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset is a pediatric study, from which we analyzed 84 normal samples and 126 AML inv(16) samples, consisting of 105 BM and 21 PB samples with ages from 0.3 to 28 years. Two additional datasets from the BEATAML study and the Cancer Genome Atlas (TCGA), were downloaded from the Genomic Data Commons (GDC) portal using GDCRNATools. The BEATAML dataset included 21 normal and 476 AML samples consisting of 226 PB and 243 BM samples with ages from 2 to 87, and TCGA dataset contained 151 AML PB samples, with ages 21 to 88 years. We used a pseudotime approach to infer disease dynamics for ST using KIT and CD33. We found via t-test analysis that KIT expression between normal and AML samples was significantly different for both TARGET (p<0.01) and BEATAML (p<0.01). CD33 expression between normal and AML samples was significant in BEATAML (p=0.012), but not TARGET (p=0.30). We combined the ST with SA to analyze free energy changes (FEC) that occurred at ST critical points, which predicted AML progression as normal hematopoiesis, c1, transition to AML, c2, and overt AML, c3. Significant differences in FEC at the critical points c1, c2, and c3 were observed by t-test comparisons as c1 vs. c2 (p <0.001) and c1 vs. c3 (p <0.001) in TARGET dataset, and c2 vs. c3 (p < 0.01) in the BEATAML dataset. Our results showed that the transition disease state, c2, had higher FEC as compared to normal states, c1, suggesting that samples were less stable from a thermodynamic perspective. Certain samples at the more advanced state, c3, exhibited a trend toward decreasing FEC levels, suggesting a process of state stabilization at more advanced stages of the disease. SA and Gene Ontology analysis suggested that interleukin pathways such as IL1, IL2, IL8 and IL10, MAPK, NFkB, and migration-related pathways, which are known to be involved in AML progression, were found in multiple unbalanced processes in SA characterizing AML across all three datasets. The IL1 pathway was found in 3 unbalanced processes characterizing TARGET dataset and in four processes characterizing TCGA and BEATAML. IL2 related categories were found among induced transcripts in 4 unbalanced processes characterizing TARGET dataset, in 6 characterizing TCGA, and in 4 processes characterizing BEATAML. We observed that AML samples with comparable FEC levels or clinical characteristics could be defined by different sets of unbalanced processes, implying that this information might be used to determine tailored therapies in subgroups of individuals with similar clinical or thermodynamic characteristics. We showed that mapping FEC into an AML state-space given by ST critical points could provide a high resolution, patient-specific disease characterization, and these methods could be used in personalized diagnostics and individualized treatment procedures.

TP53 Gene allelic State in Myelodysplastic Syndromes (MDS) with Isolated 5q Deletion

Background & aim: The mutational status of TP53 gene is a significant prognostic factor in MDS, with two-thirds of patients harboring TP53 multihit alterations which are associated with poor outcomes. Approximately 20% of patients with MDS with isolated 5q deletion (MDS-del5q) exhibit TP53 aberrations, though their characteristics and prognostic impact have not been fully elucidated. In this study, we aimed to analyze the characteristics and impact of TP53 allelic state in patients with MDS-del5q. Methods: We conducted an international multicenter study in de novo MDS-del5q patients according to WHO 2017 classification. Clinical and biological characteristics, including the genetic profile were collected at diagnosis. Genetic profiling included conventional G-banding, TP53 deletion by FISH and/or SNP-arrays (for TP53 deletion and copy number neutral loss of heterozygosity). Also, the presence of mutations in myeloid-related genes was assessed by NGS or Sanger. Variant filtering and categorization were performed according to the Spanish Guidelines. Patients were classified as TP53 wild-type (TP53-wt), TP53-monoallelic, and TP53-multihit as proposed by Bernard et al. 2020. Clinical and molecular variables were evaluated for associations with acute myeloid leukemia (AML) evolution and overall survival (OS). In addition, a prognostic model to predict risk of AML evolution was developed using variables selected by the LASSO-cox method with minimum lambda. To calculate the points for each variable, a multivariate competitive risk model (Fine-Gray model) was fitted. Evolution to AML was used as the event and death as the competitive event. Statistical analysis was performed by R (4.2.2). Results: We included 682 patients with MDS-del5q (Table 1). Median follow-up was 66.8 months (CI95% 61.6-75.2). TP53 mutations were evaluated by NGS in 92.2% (n=629) and by Sanger in 7.8% (n=53) of patients. Overall, 18.7% (n=128) of MDS-del5q presented TP53 mutations. After integrating mutational data (FISH and SNP-arrays), 72.7% (n=93) of TP53-mutated patients were classified as TP53-monoallelic, whereas 27.3% (n=31) as TP53-multihit (4.5% of the whole cohort). Other recurrent mutations were SF3B1 (21%), DNMT3A (18%), TP53 (18%), TET2 (14%), ASXL1 (10%), CSNK1A1 (6%) and JAK2 (6%). Only bone marrow blasts differ between the TP53-wt and TP53-mutated groups ( p=0.031). Furthermore, only gender and VAF were different between TP53-monoallelic and TP53-multihit, being the number and type of co-occurring mutations similar among all subgroups (Table 1). Median OS of the whole cohort was 73.8 months (CI95% 63.7-83.4), and 77.3 months (CI95% 66.5-85.3) for MDS-del5q TP53-wt and 104 months (CI95% 56.9-89.4) for MDS-del5q TP53-mutated ( p=0.3). Similarly, there was no significant differences in the risk to AML evolution between TP53-wt and TP53-mutated patients (AML evolution at 60 months of 19.9% and 29.7%, respectively; p=0.307). Of note, however, the TP53-multihit group presented worse prognosis compared to TP53-monoallelic and TP53-wt patients (median OS of 55.2, 73.2 and 77.3 months, and AML evolution at 60 months of 40.4%, 25.5% and 19.9% in TP53-multihit, TP53-monoallelic and TP53-wt MDS-del5q) [Figure 1]. Interestingly, TP53-monoallelic MDS with a TP53 VAF >20% showed similar prognosis to TP53-multihit patients (median OS of 43.7 and 55.2 months, and AML evolution at 60 months of 36.7% and 40.4%, respectively). Finally, a risk score (MDS-del5q score) for AML evolution was developed with five variables, each assigned a different weight based on the regression coefficients: additional chromosomal abnormality, 2 points; TP53-multihit, 2 points; BM blast >2%, 2 points; platelets ≤ 100x10 9/L, 3 points, and SF3B1-mutation, 1 point. Three risk-groups were defined: Low, intermediate, and high-risk for ≤1, 2, or ≥3 points, respectively, with an AML evolution at 60 months of 11.5%, 23.3% and 43.7%, respectively; p <0.05) Conclusions: In contrast to previous findings in MDS without del5q, our series of MDS-del5q, the longest described to date, reveals that multihit TP53 status is uncommon in MDS-del5q. However, multihit alterations have prognostic significance in this subgroup. Finally, the MDS-del5q score allows to stratify patients into three distinct risk groups for AML evolution. These findings could hold considerable implications for guiding treatment decisions.

Deciphering the Role of RAS Pathway Mutations in the Biology of Human Acute Myeloid Leukemia Using In Vivo Models

Acute myeloid leukemia (AML) is a genetically heterogeneous hematologic malignancy for which the mutational profile of the leukemic cells is the most important prognostic factor for disease-free survival and overall survival. The failure of chemotherapy to completely eradicate the leukemic clones is the most common cause of death in AML. The genetic events that can contribute to chemoresistance are therefore important factors for the clinical outcome, but the relative importance of these events on chemoresistance and persistence of leukemia stem cells (LSC) remains poorly defined. MLL rearranged AMLs account for 5-10% of all AMLs and the RAS pathway is the most frequently mutated pathway in this sub-group of AMLs. The goal of this project is to study the role and biology of RAS pathway mutations in clonal evolution in human MLL-rearranged AML. Methods We have previously shown that human cord blood CD34+ cells transduced with a retrovirus encoding the MLL-AF9 fusion gene with an EGFP maker generate human AML when injected into immunodeficient NSG-SGM3 mice. Using a similar experimental design, we added a second hit to the CD34 transduced cells before their injection into NSG-SGM3 mice. MLL-AF9 cells were electroporated with Cas9 enzyme for the control and with Cas9 enzyme plus a single guide RNA (sgRNA) designed to induce the KRAS G13D or the NRAS G12D mutation. Mice were sacrificed when they showed sign of disease and the bone marrow and spleen were collected and analyzed. Kaplan-Meier curves, flow cytometry analyses, RNA and exome sequencing and secondary transplantation were performed on the samples. Results Mice injected with MLL-AF9 + KRAS G13D got sick around day 80 after injection and around day 60 for MLL-AF9 NRAS G12D, whereas it took ~150 days for control mice (MLL-AF9 KRAS WT) to get sick (fig 1). In multiple experiment, the latency was roughly cut by half by the KRAS G13D and NRAS G12D mutations. The cell surface phenotype and morphology were not significantly different between MLL-AF9 WT and MLL-AF9 KRAS/NRAS mutated. The variant allele frequency (VAF) for the KRAS mutation invariably increased from the day of injection to the day of sacrifice, where the percentages varied between 20-40%, which is typically observed in AML patients. In contrast, the VAF for NRAS mutated cells was lower (3-9%) but seems to be very aggressive into mice. We performed limiting dilution analyses in secondary mice to quantify the LSC frequencies in MLL-AF9 WT and KRAS G13D / NRAS G12D leukemia. The average frequencies of control MLL-AF9 AMLs were between 1/510 000 to 1/1 450 000, while MLL-AF9+KRAS G13D AMLs frequencies were >1/45 000 and 1/7 800 for MLL-AF9+NRAS G12D AMLs, demonstrating that these 2 mutations increase the LSC frequency by >20-100 fold in our modelled MLL-AF9 AML. Using transcriptome analyses, we looked at the gene expression changes induced by the KRAS G13D mutation in comparison to MLL-AF9 leukemias. These experiments are in progress for the NRAS G13D mutation. Many more genes were overexpressed in MLL-AF9 KRAS G13D vs MLL-AF9 KRAS WT than down regulated. Genes from the hedgehog (Hh) pathway such as Patched1 (PTCH1) and Smoothened (SMO) were amongst the genes the most differentially overexpressed by the KRAS mutation. It is particularly interesting because they have been shown important in stem cell biology and could explain the increase in LSC. Some SMO inhibitors have been tested in clinical trial and Glasdegib is FDA approved in combination with low dose cytarabine in elderly patients. Its effect on MLL leukemias or on KRAS mutated AMLs has not been tested/reported in a clinical study. Experiments are currently in progress to test the effect of Glasgebib on engraftment of MLL-AF9 KRAS mutated cells in our experimental setting. Single cell RNA sequencing data on the three conditions will be presented at the meeting. Given the success of generating these two point mutations, we are currently generating more RAS pathway mutations, including other KRAS mutations, PNTP11 and NF1 mutations. Conclusion These experiments showed: 1) It is possible to induce 2 oncogenic hits in human primary cells and get leukemia in vivo; 2) the KRAS G13D and NRAS G12D mutations shorten the latency of the disease and 3) increase the LSC frequency in secondary mice; 4) a possible involvement of the Hh pathway on stemness/LSC in RAS mutated cells; 5) our experimental approach is robust and very promising to decipher the RAS pathway in human MLL leukemias.

Engineering Sequential Mutations into Human HSPCs Yields an Aggressive Myeloid Malignancy Allowing for Interrogation of Preleukemic Transformation

Introduction: Acute Myeloid Leukemia (AML) is an aggressive myeloid malignancy that develops as the result of stepwise accumulation of mutations in hematopoietic stem and progenitor cells (HSPCs). Multiple studies of the clonal evolution of AML have determined that in general, the order of mutation acquisition follows a stereotypical fashion: mutations that occur early in development of AML involve epigenetic modifiers, whereas mutations that occur later more commonly affect proteins involved in signaling and proliferation. While multiple studies have demonstrated the ability of various combinations of mutations to promote leukemic transformation in mouse models, the majority of this work has studied concurrent alteration of driver genes, which may not accurately reflect human disease biology. To this end, we aimed to generate a model of AML by sequentially introducing mutations into human HSPCs to study driver gene dependent and independent pathways in preleukemic transformation. Methods: Our model of sequential mutation acquisition in human AML employs CRISPR/Cas9-mediated gene disruption and AAV6-mediated homology directed repair (HDR) in human cord blood CD34+ HSPCs, allowing for high-efficiency knockout of epigenetic modifiers ( DNMT3A or TET2) and expression of an activating signaling protein ( NRAS G12D) along with the BFP fluorescent marker. Mutations are introduced sequentially through a serial xenotransplantation approach in immunocompromised mice, allowing for stepwise characterization of the preleukemic and transformed states of disease. Results: To establish a model of preleukemic transformation using human HSPCs, we first developed a double synthetic guide RNA (sgRNA) knockout strategy that produces a ~55-65 bp deletion in either DNMT3A or TET2, leading to highly efficient INDEL (insertion or deletion) generation in cord blood CD34+ HSPCs and effective protein knockdown. Fourteen weeks after transplantation into immunocompromised mice, DNMT3A-KO, TET2-KO, and control cells demonstrate high levels of bi-lineage bone marrow engraftment (60-90%) and greater than 95% INDEL frequency at targeted loci. DNMT3A-KO, TET2-KO, and control CD34+ cells harvested from primary transplantation are subsequently edited to express NRAS G12D-BFP at efficiencies similar to freshly isolated cells (10-20%), and sorted BFP+ cells successfully engraft into secondary recipient mice. Consistent with observations from genetically engineered mouse models, we found that the combination of either DNMT3A-KO or TET2-KO with NRAS G12D in human cord blood CD34+ cells results in a rapidly fatal high-grade myeloid neoplasm in mice. Moribund mice develop peripheral cytopenias, decreased body weight, enlarged spleens, and have infiltration of CD45+ human cells in bone marrow, spleen, and liver. Human cells isolated from bone marrow retain near 100% BFP positivity and show persistence of >95% DNMT3A-KOor TET2-KO INDELs. BFP+ cells display a monocytic immunophenotype (CD34-, CD33+, CD14 variable, CD16 variable) and have a monocytic appearance on H&E staining. Experiments introducing TET2-KO/ NRAS G12D mutations using either a sequential or concurrent approach demonstrate mutational synergy between TET2-KO and NRAS G12Dwhen compared with NRAS G12D alone. Comparisons of disease kinetics and phenotype when TET2-KO and NRAS G12D mutations are introduced sequentially vs concurrently are currently underway to investigate how temporal dynamics in epigenetic changes after TET2-KO affect subsequent transformation with NRAS G12D. In addition, genome-wide DNA methylation and gene expression characterization from cells in preleukemic ( DNMT3A-KO and TET2-KO alone) and transformed states ( DNMT3A-KO/ NRAS G12D and TET2-KO/ NRAS G12D) are ongoing to explore mechanisms underlying these observations. Conclusion: Our work demonstrates that human HSPCs can be efficiently engineered to model the combination of early epigenetic modifier loss with expression of an activating signaling mutation, as is commonly seen in AML, resulting in a fully penetrant, high-grade myeloid neoplasm. To our knowledge, this represents the first sequential model of preleukemic transformation in genetically engineered human cells. Importantly, the sequential nature of our approach allows for manipulation of the preleukemic phase of disease and subsequent interrogation of myeloid transformation.

Abstract A49: Characterizing the order of mutation acquisition in acute myeloid leukemia using large-scale single-cell sequencing

Abstract Introduction: Acute myeloid leukemia (AML) has a poor prognosis, despite aggressive therapies and a recent expansion in the array of treatments. Treatment is often determined by mutations, which risk-stratify a patient’s leukemia or can identify mutations that serve as therapeutic targets. Although common mutations in AML have been extensively studied, less research has formally characterized the order by which mutations tend to occur and how this order relates to properties of the disease. Methods: We leveraged published, single-cell DNA sequencing data from three institutions to model the clonal evolution of AML. Sequencing had been performed using targeted sequencing panels covering 19 to 37 genes, and driver mutations were identified using conservative filters. Mutation trees were created using Single Cell Inference of Tumor Evolution (SCITE). Clonal evolution patterns, including across clinical timepoints, were identified and compared to patient characteristics, disease phenotype, and outcomes. The BeatAML dataset was leveraged to explore associations between mutation order and gene expression. Results: Using 835 driver mutations from 276 samples and 209 patients, we identified 223 total unique mutation patterns. Branched evolution primarily involves FLT3 and RAS pathway mutations, whereas certain mutation pairs, such as NPM1 and FLT3, always occurred linearly in the same clone. Although some mutation pairs, such as those related to DNA methylation versus the RAS pathway, tended to occur in a specific order, several cases exhibited atypical orderings. Early signaling gene mutations were associated with younger patient age and increased signaling mutation homozygosity while NRAS-first cases were associated with increased monocyte counts. NRAS-first cases were also associated with distinct gene expression patterns in the BeatAML dataset. Paired diagnosis and relapse samples revealed novel associations between mutations gained at relapse and their clonal context, and these analyses supported a relative fitness advantage for signaling mutations in clones containing mutations in NPM1 or genes affecting DNA methylation. Conclusions: Clonal evolution in AML can be reconstructed at scale using single-cell DNA sequencing data, and different mutation acquisition patterns are associated with distinct leukemia and patient characteristics, despite cases having similar co-mutation patterns. Citation Format: Matthew Schwede, Katharina Jahn, Linde A Miles, Robert L Bowman, Jack Kuipers, Troy M Robinson, Asiri Ediriwickrema, Andrew J Gentles, Ross Levine, Niko Beerenwinkel, Koichi Takahashi, Ravindra Majeti. Characterizing the order of mutation acquisition in acute myeloid leukemia using large-scale single-cell sequencing [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A49.

An Updated Overview of the Role of CYP450 during Xenobiotic Metabolization in Regulating the Acute Myeloid Leukemia Microenvironment.

Oxidative stress is associated with several acute and chronic disorders, including hematological malignancies such as acute myeloid leukemia, the most prevalent acute leukemia in adults. Xenobiotics are usually harmless compounds that may be detrimental, such as pharmaceuticals, environmental pollutants, cosmetics, and even food additives. The storage of xenobiotics can serve as a defense mechanism or a means of bioaccumulation, leading to adverse effects. During the absorption, metabolism, and cellular excretion of xenobiotics, three steps may be distinguished: (i) inflow by transporter enzymes, (ii) phases I and II, and (iii) phase III. Phase I enzymes, such as those in the cytochrome P450 superfamily, catalyze the conversion of xenobiotics into more polar compounds, contributing to an elevated acute myeloid leukemia risk. Furthermore, genetic polymorphism influences the variability and susceptibility of related myeloid neoplasms, infant leukemias associated with mixed-lineage leukemia (MLL) gene rearrangements, and a subset of de novo acute myeloid leukemia. Recent research has shown a sustained interest in determining the regulators of cytochrome P450, family 2, subfamily E, member 1 (CYP2E1) expression and activity as an emerging field that requires further investigation in acute myeloid leukemia evolution. Therefore, this review suggests that CYP2E1 and its mutations can be a therapeutic or diagnostic target in acute myeloid leukemia.

Measurable Residual Disease and Clonal Evolution in Acute Myeloid Leukemia from Diagnosis to Post-Transplant Follow-Up: The Role of Next-Generation Sequencing

It has now been ascertained that acute myeloid leukemias-as in most type of cancers-are mixtures of various subclones, evolving by acquiring additional somatic mutations over the course of the disease. The complexity of leukemia clone architecture and the phenotypic and/or genotypic drifts that can occur during treatment explain why more than 50% of patients-in hematological remission-could relapse. Moreover, the complexity and heterogeneity of clone architecture represent a hindrance for monitoring measurable residual disease, as not all minimal residual disease monitoring methods are able to detect genetic mutations arising during treatment. Unlike with chemotherapy, which imparts a relatively short duration of selective pressure on acute myeloid leukemia clonal architecture, the immunological effect related to allogeneic hematopoietic stem cell transplant is prolonged over time and must be overcome for relapse to occur. This means that not all molecular abnormalities detected after transplant always imply inevitable relapse. Therefore, transplant represents a critical setting where a measurable residual disease-based strategy, performed during post-transplant follow-up by highly sensitive methods such as next-generation sequencing, could optimize and improve treatment outcome. The purpose of our review is to provide an overview of the role of next-generation sequencing in monitoring both measurable residual disease and clonal evolution in acute myeloid leukemia patients during the entire course of the disease, with special focus on the transplant phase.

Role of MBD4 Mutation in Neoplastic Evolution of Acute Myeloid Leukemia

Unlike pediatric cases, adult patients with myeloid neoplasia (MN) are less often suspected to harbor germline (GL) driver or predisposition alterations. However, recent discoveries of various genes affected by GL alterations linked to long disease anticipation and incomplete penetrance indicate a growing role of familial predisposition in adult MN including, the impact of heterozygous alleles within recessive traits, biallelic (GL/somatic) or compound heterozygous (GL/GL and GL/somatic) configurations of various genes. Recently, genomic profiling of 3 early-onset AMLs identified MBD4 GL loss as an initiator of 5-methylcytosine (5mC)-dependent hypermutation with a 33-fold higher mutational burden (>95% being C>T in the context of a CG)1. In cases from our institution, a biallelic MBD4 c.1291C>T, p.Arg431* & c.1688T>A p.Leu563* (in trans) tracked with AML in brother and sister2. MBD4 is a known DNA glycohydrolase involved in DNA mismatch repair (MMR) essential for reducing genotoxic stress in normal cells. Loss of function mutation in MBD4 may result in compromised MMR leading to genomic instability. MBD4 binds to 5mC and 5-hydroxymethylcytosine (5hmC), oxidation products of TET2 dioxygenases, and initiates a multi-step MMR around methylated CpG DNA. Therefore, we hypothesized that loss of MBD4 may result in reduced MMR leading to increased genomic instability and acquisition of additional lesions which may facilitate clonal evolution. We first analyzed data from 1,692 MN patients (732 MDS and 960 AML) and found 134 (8%) patients with 22 MBD4 variants. Overall, 20 were non-synonymous missense (in 132 pts), while nonsense and frameshift deletion were also observed in 1 case each. After variant filtering and annotation, we retained 17 recurrent missense in 80 patients and 1 nonsense variant in 1 patient at an odds ratio of >1.5 vs. healthy control, for a total of 81 patients harboring MBD4 VUS (Fig.1A). Among them, 4 variants (p.Arg83His, p.Asp107Tyr, p.Ser135Ala, p.Arg149Ser) mapped within the methyl-CpG-binding domain, whereas 5 variants (p.Arg425Gln, p.Asn461Ser, p.Arg486Lys, p.Asp504His, p.Asp562His) were located in the glycosylase domain (Fig.1B). Of note is that all cases were heterozygous. Comparing MBD4MTvs.MBD4WT cases, there was no significant difference in age (median was 69 vs. 71 yrs), diagnosis, clinical parameters (WBC, ANC, Hb, Plt counts, peripheral and bone marrow blast percentage) and past history of malignancy. When we compared the somatic genotype of MBD4 VUS carriers vs. carriers of MBD4WT, somatic TET2 hits were found with a significantly lower frequency (15 vs. 25%, P=.046), in particular in AML (7 vs. 22%, P=.009) and in cases with TET2 truncations (9 vs. 19%, P=.018). To further investigate the biochemical basis of MBD4 dysfunction, and its connection to TET2 DNA dioxygenases, we knocked out MBD4 in TET2 mutant and in isogenic control cell lines. To test if the DNA hydrolase activity impacts on genomic accumulation of 5hmC in TET2-dependent manner, we assessed the impact of MBD4 loss on DNA methylation using 5mC and 5hmC ratio as read out in MBD4KO and WT cells in TET2MT and isogenic controls. Our results show that the loss of MBD4 resulted in increased 5hmC compared with the isogenic WT cells when accompanied with TET2WT but this effect was absent in TET2KO cells (mean fold change, 1.9±0.4 vs. 0.9±0.1; P=.03), suggesting a cross talk between MBD4 and TET2. Consequently, in agreement with our hypothesis, TET2WT cells with loss of MBD4 were significantly more sensitive to olaparib-mediated PARP1 inhibition. We also performed the RNA-seq analysis of MBD4WT and KO cells in the presence and absence of functional TET2. These results demonstrate that MBD4 variant carrier status may be a predisposition factor for the development of MN. As loss of MBD4 mimicked the loss of TET-dioxygenase function, MBD4 could function downstream of TET2 converting 5mC to 5hmC and to cytosine as a DNA damage repair associated protein (Fig. 1C). Our findings pave the way for consideration of MBD4 as a potential actionable target to regulate DNA methylation in association with TET2 during leukemogenesis. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Tracking Clonal Evolution in Pediatric AML

Acute myeloid leukemia (AML) makes up nearly 20% of acute pediatric leukemias and has a long-term survival rate of approximately 70%. A key driver of the low overall survival for children with AML is relapsed disease and less than 40% of the children with relapsed AML will survive past five years. We recently characterized relapsed pediatric AML (pAML) to better understand the spectrum of genomic alterations at disease recurrence (Umeda et al., 2022). However, the genome-wide patterns of genetic evolution from diagnosis to relapse have yet to be characterized for pAML. Thus, there is a gap in knowledge of how alterations at diagnosis evolve during treatment to drive relapse and poor outcome. We performed whole genome sequencing (WGS) and whole transcriptome sequencing (RNA-Seq) on a cohort of 50 pediatric AML patients with paired diagnosis and relapse samples. The median age at diagnosis was 10.7 yrs (range 0.7-20yrs), and the median time between diagnosis and relapse was 334 days (range 93-1,773 days). The main driver group contained a KMT2A fusion event (n=16, 32%). NUP98 fusion made up the second largest group at 14% (n=7). Also within the cohort are newly defined categories, including a BCL11B structural variant (n=1) and patients with UBTF tandem duplication AML (n=4). These features are overall similar to the molecular profiles of pediatric AML (Farrar et al., 2016; Umeda et al., 2022). Approximately 22,500 alterations (including point mutations, indels, and structural variants) were detected by WGS (median coverage 36X). The median number of alterations at diagnosis and relapse per patient was, respectively, 224 (range 118-867) and 411 (range 73-1,909). Most of the cohort noticed more alterations at relapse than diagnosis, but some (n=8) had fewer at relapse. Additionally, there was no trend with the time to relapse and the number of acquired alterations. The most commonly mutated genes regardless of timepoint included FLT3 (n=16, 6 were internal tandem duplications), WT1 (n=18), NRAS (n=16), and SETD2 (n=9). We identified over 11,000 relapse enriched variants (median 169, range 3-1,378), including alterations that had a significantly higher allele frequency (q-value < 0.05) at relapse compared to diagnosis. This included 1,028 relapse enriched variants that alter protein coding genes. FLT3 contained 7, 2, and 7 relapse enriched, relapse depleted, and time point stable alterations, respectively. WT1 contained 6, 2, and 10 relapse enriched, relapse depleted, and time point stable alterations, respectively. Furthermore, the relapse-enriched list included three SETD2 protein-truncating mutations and four AUTS2 structural variants often co-occurring with KMT2A fusions, indicating that these mutations may cooperate with KMT2A fusion and contribute to the disease progression. To increase our understanding of the clonal structure, 36 cases with additional DNA material were re-sequenced with a custom panel covering 6,300 variants, including all coding variants for each case. This led to a validation rate of 97% of all variants and the increased sequencing depth of the panel (median 265X for diagnosis and 274X for relapse) gave greater insight into the changes in clonal structure between diagnosis and relapse time points. Most of the cohort (70%) featured a relapsed enriched clone that was minor at diagnosis and became clonal in relapse. Additional cases (n=10) featured a clone that was undetectable by WGS at diagnosis and after treatment became the dominant clone in relapse. Only one case maintained the same major clone in both diagnosis and relapse. In summary, our data show a comprehensive characterization of changes in the clonal structure of pAML between diagnosis and relapse, revealing relapse-enriched gene alterations which may be driving relapse disease. Farrar, J. E., Schuback, H. L., Ries, R. E., Wai, D., Hampton, O. A., Trevino, L. R., . . . Meshinchi, S. (2016). Genomic Profiling of Pediatric Acute Myeloid Leukemia Reveals a Changing Mutational Landscape from Disease Diagnosis to Relapse. Cancer Res, 76(8), 2197-2205. doi:10.1158/0008-5472.CAN-15-1015 Umeda, M., Ma, J., Huang, B. J., Hagiwara, K., Westover, T., Abdelhamed, S., . . . Klco, J. M. (2022). Integrated genomic analysis identifies UBTF tandem duplications as a recurrent lesion in pediatric acute myeloid leukemia. Blood Cancer Discov. doi:10.1158/2643-3230.BCD-21-0160 Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Molecular and Clinical PHF6 Mutant Myeloid Neoplasia Provides Clues As to Their Pathogenesis and Therapeutic Targeting

Congenital PHF6 mutations (PHF6MT) cause the X-linked neurodevelopmental disorder, Börjeson-Forssman-Lehmann syndrome (BFLS). In contrast, acquired PHF6MT are frequently detected in hematologic neoplasms including acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and T-cell acute lymphoblastic leukemia (T-ALL). Indeed, PHF6 is highly expressed in brain/developing CNS as well as in hematopoietic subpopulations suggesting its role in neurogenesis and hematopoiesis consistent with the neurologic BFSL presentation of the germline and leukemias with somatic mutations of this gene. This study focuses on involvement of PHF6 lesions in AML. Here, we hypothesize that there are functional links between lymphoid and myeloid neoplasia (MN) with PHF6-deficiency that can be instructive in terms of the impact of this gene as a tumor suppressor gene (TSG). In total, we analyzed sequencing results of 6886 AML cases, including 616 cases from open sources such as The Beat AML Master dataset (Tyner JW , Tognon CE, Bottomly D, et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018;562(7728):526-531). We first investigated the clinical phenotype associated with PHF6MT. We detected a total of 111 (1.6% of cases) PHF6MT. While frameshifts were found across all the coding regions, missense and stop-gains were concentrated in the two PHD-type zinc finger domains and were overlapping between other MN and the previously reported T-ALL cases. PHF6MT were significantly more common in male cases (2.3 vs. 0.8%; p<.001) similar to other X-genes such as UBA1, ZRSR2, and STAG2 but different from ATRX, PIGA, and KDM6A (UTX). When comparing primary AML (pAML) with secondary AML (sAML), sAML had more PHF6MT than pAML (2.9 vs. 1.4%; p<.001). While PHF6MT were not associated with chromosomal abnormalities in male cases, female cases with X chromosome deletion (delX) showed higher frequency of PHF6MT compared to females with diploid X chromosome (6.5 vs. 0.8%; p=.0019). Consequently, when delX were included, a total of 2.3% of female patients had PHF6 hits with 10% of them in a homo/hemizygous configuration. While RUNX1, U2AF1, and ASXL1 mutations coincided with PHF6 hits in males, in female cases, delX and ASXL1 mutations were the most common associated defects (Fig.1). Interestingly, RUNX1 mutations (RUNX1MT) in females were enriched in cases with delX. As a result, PHF6MT were enriched into two distinct subclasses: while PHF6MT were enriched in male cases with sAML and RUNX1MT (8.8%), all female cases with sAML, RUNX1MT, and delX also acquired PHF6MT (Fig.2). A higher frequency of PHF6MT in advanced MN than early diseases (e.g., low-risk MDS), suggestive of a secondary rather than a founder role of PHF6MT prompted us to study the clonal architecture of PHF6MT cases using variant allelic frequencies. RUNX1MT were dominant/co-dominant to PHF6 hits in the clonal evolution of AML in contrast to T-ALL, in which PHF6MT appears to be an earlier event. Similar relationship was found with regard to PHF6 hits and other myeloid gene mutations (e.g., ASXL1, DNMT3A, or SRSF2) suggesting a lineage-restricted TSG function of PHF6 in AML following myeloid commitment (consistent with the physiologic expression of PHF6 in pre-B, pre-T, and erythroid progenitors during hematopoietic ontogeny). PHF6 mutations also affected AML patients’ outcome. Overall, PHF6MT showed worse outcome in male cases, but not in female cases. However, if PHF6MT cases also had RUNX1 mutations, the outcome was extremely low in both sexes. Unsupervised RNAseq analysis of PHF6 mutational status vs. expression pattern revealed that PHF6MT cases showed higher mRNA levels of several lymphoid genes (GCSAM, LY9, or BLNK). This finding was also confirmed by RNAseq of in-house PHF6 knock-out K562 and U-937 clones. Immunohistochemistry unveiled that LY9 was positive in illustrative PHF6MT cases and flow cytometry revealed the presence of lymphoid markers in 2/3 PHF6MT cases. Our study confirms that PHF6MT are more common in male AML. Also we demonstrate the intricate relationships between PHF6MT and RUNX1MT in male sAML patients which extend also to female cases with delX. In AML, PHF6MT are a late event compared to T-ALL, in which they occur early in leukemogenesis. This is also in line with higher frequency of PHF6MT in sAML. Some PHF6MT cases have higher expression of lymphoid genes, which may be candidate for molecular targeted therapy. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Integrated flow cytometry and sequencing to reconstruct evolutionary patterns from dysplasia to acute myeloid leukemia

AbstractClonal evolution in acute myeloid leukemia (AML) originates long before diagnosis and is a dynamic process that may affect survival. However, it remains uninvestigated during routine diagnostic workups. We hypothesized that the mutational status of bone marrow dysplastic cells and leukemic blasts, analyzed at the onset of AML using integrated multidimensional flow cytometry (MFC) immunophenotyping and fluorescence-activated cell sorting (FACS) with next-generation sequencing (NGS), could reconstruct leukemogenesis. Dysplastic cells were detected by MFC in 285 of 348 (82%) newly diagnosed patients with AML. Presence of dysplasia according to MFC and World Health Organization criteria had no prognostic value in older adults. NGS of dysplastic cells and blasts isolated at diagnosis identified 3 evolutionary patterns: stable (n = 12 of 21), branching (n = 4 of 21), and clonal evolution (n = 5 of 21). In patients achieving complete response (CR), integrated MFC and FACS with NGS showed persistent measurable residual disease (MRD) in phenotypically normal cell types, as well as the acquisition of genetic traits associated with treatment resistance. Furthermore, whole-exome sequencing of dysplastic and leukemic cells at diagnosis and of MRD uncovered different clonal involvement in dysplastic myelo-erythropoiesis, leukemic transformation, and chemoresistance. Altogether, we showed that it is possible to reconstruct leukemogenesis in ∼80% of patients with newly diagnosed AML, using techniques other than single-cell multiomics.

NOVEL CHROMOSOMAL ALTERATION DER(2)T(2;15)(Q37;Q21) IN A PEDIATRIC PATIENT WITH MYELODYSPLASTIC SYNDROME: MOLECULAR CYTOGENETIC STUDIES

Pediatric Myelodysplastic Syndrome (p-MDS) comprises a heterogeneous group of clonal hematopoietic stem cell diseases with an increased risk of evolution to Acute Myeloid Leukemia (AML). Cytogenetic is one of the most important prognostic factors for MDS and it is present into score prognostic systems as the Revised International Prognostic Scoring System (IPSS-R). Chromosomal translocations are rare cytogenetic abnormalities in de novo MDS. Therefore, their reporting is essential for the identification of new cytogenetic prognostic risk groups and possible alterations associated with the MDS pathogenesis.The aim of this study was describe a yet unreported der(2)t(2;15)(q37;q21) in a de novo p-MDS associated with evolution to AML. Cytogenetic analysis was performed in Bone Marrow (BM) cells by G-banding. Fluorescence In Situ Hybridization (FISH) was performed to investigate the possible rearrangement involving chromosomes 2 and 15, using a Whole Chromosome Painting (WCP) probe for chromosome 2 and LSI PML/RARA for chromosome 15 (PML gene). A 12-year-old girl was admitted to the Pediatrics Institute of Federal University of Rio de Janeiro in January 2022. Peripheral blood count showed Hemoglobin (Hb) level of 6.5 g/dL, 794.000 platelets/mm 3 , and 2.600 white blood cell (WBC)/mm 3 . The myelogram showed dysplasias and 16% of myeloid blasts being classified with MDS-EB-t. The conventional cytogenetic analysis showed the karyotype: 46,XX,add(2)(q37?)[25]. Using molecular cytogenetic methods, we characterized the chromosomal alteration as an unbalanced translocation involving chromosomes 2 and 15. This translocation was observed in 71.6% (190/265) of the interphase nuclei and metaphases. The final karyotype was: 46,XX,der(2)t(2;15)(q37;q21)[25]. Five months later, the patient showed in the myelogram 50.7% of blast cells compatible with AML. The patient was indicated for allogeneic Hematopoietic Stem Cell Transplantation (HSCT) and initiated chemotherapy. In p-MDS, chromosomal translocations as sole chromosomal abnormality are rare and their real prognostic impact is unknown. The patients with this type of cytogenetic alteration are classified as intermediate-risk group according to the IPSS-R. Our report describes the clinical outcome in a novo p-MDS showing a der(2)t(2;15)(q37;q21). A broad scientific review showed that the der(2)t(2;15)(q37;q21) was not yet reported in p-MDS. It is interesting to note that this chromosomal translocation was associated with a poor clinical outcome, with progression from MDS to AML. The cytogenetic and clinical features were important tools for the indication to allogeneic HSCT. This study and literature review highlight the importance of cytogenetics and clinical follow-up of de novo p-MDS patients for the identification for HSCT. This is the first study that describe the der(2)t(2;15)(q37;q21) in a p-MDS associated with AML evolution.

The landscape of extrachromosomal circular DNA (eccDNA) in the normal hematopoiesis and leukemia evolution

Elevated extrachromosomal circular DNA (eccDNA) has been reported to accelerate tumor pathogenesis. Although the eccDNA profiles of other tumors have been established, the landscape of the eccDNA of acute myeloid leukemia (AML) has not been revealed. Our study first depicted the eccDNA profile of normal hematopoiesis and AML evolution by exploiting the ATAC-seq and RNA-seq data from nine healthy donors and 12 AML patients, which contained a total of 137 cell samples and 96 RNA-seq samples (including 16 blood cell types of the normal hematopoietic and AML hierarchies). We found the number of eccDNAs generally increased with the evolution of normal hematopoiesis and AML. The ecDNAs and ring chromosomes were found to reappear both in normal hematopoiesis and AML cells. Furthermore, we compared the eccDNAs of AML with normal cells. There were almost 300 AML-specific genes, including the known oncogenes NRAS, MCL1, EVI1, GATA2, WT1, and PAK1. And the ecDNA (chr11: 58668376-58826008) occurred in five out of 17 AML evolution-related cells, which was associated with the high expression of the GLYATL1 gene and the high expressed GLYATL1 was a poor prognostic factor. In conclusion, the eccDNA profiles of normal hematopoiesis and AML evolution were depicted and the recurrent eccDNAs we revealed might be utilized in the treatment of AML as biomarkers.

Genome-Based Medicine for Acute Myeloid Leukemia: Study and Targeting of Molecular Alterations and Use of Minimal Residual Disease as a Biomarker

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy characterized by the clonal proliferation of hematopoietic stem and progenitor cells (HSPCs) and blockade of differentiation and proliferation of immature myeloid cells that accumulate in bone marrow at the expense of normal hematopoiesis. AMLs originate from the expansion of HSPCs progressively acquiring somatic mutations. The development of high-throughput sequencing techniques has helped to discover the genetic heterogeneity and complexity of AMLs, revise diagnostic and prognostic criteria, and to identify new therapeutic targets. These studies have allowed the identification of several recurrent driver mutations and the definition of a rational molecular classification of these tumors. In parallel, the development of techniques for the determination of single-cell mutational profiling has considerably contributed to understanding the clonal heterogeneity and evolution of AMLs. The acquisition of these genetic data coupled with the identification of molecular therapeutic targets has determined a considerable expansion of the therapeutic armamentarium, with the development of several new drugs highly active against specific AML subtypes. These developments have increased the interest and the need for sensitive techniques for the identification of minimal residual disease, the population of leukemia cells that survives despite morphological remission and causes disease relapse.