Circular RNAs of the nucleophosmin (NPM1) gene in acute myeloid leukemia

In acute myeloid leukemia, there is growing evidence for splicing pattern deregulation, including differential expression of linear splice isoforms of the commonly mutated gene nucleophosmin (NPM1). In this study, we detect circular RNAs of NPM1 and quantify circRNA hsa_circ_0075001 in a cohort of NPM1 wild-type and mutated acute myeloid leukemia (n=46). Hsa_circ_0075001 expression correlates positively with total NPM1 expression, but is independent of the NPM1 mutational status. High versus low hsa_circ_0075001 expression defines patient subgroups characterized by distinct gene expression patterns, such as lower expression of components of the Toll-like receptor signaling pathway in high hsa_circ_0075001 expression cases. Global evaluation of circRNA expression in sorted healthy hematopoietic controls (n=10) and acute myeloid leukemia (n=10) reveals circRNA transcripts for 47.9% of all highly expressed genes. While circRNA expression correlates globally with parental gene expression, we identify hematopoietic differentiation-associated as well as acute myeloid leukemia subgroup-specific circRNA signatures

Functional characterization of BRCC3 mutations in acute myeloid leukemia with t(8;21)(q22;q22.1)

BRCA1/BRCA2-containing complex 3 (BRCC3) is a Lysine 63-specific deubiquitinating enzyme (DUB) involved in inflammasome activity, interferon signaling, and DNA damage repair. Recurrent mutations in BRCC3 have been reported in myelodysplastic syndromes (MDS) but not in de novo AML. In one of our recent studies, we found BRCC3 mutations selectively in 9/191 (4.7%) cases with t(8;21)(q22;q22.1) AML but not in 160 cases of inv(16)(p13.1q22) AML. Clinically, AML patients with BRCC3 mutations had an excellent outcome with an event-free survival of 100%. Inactivation of BRCC3 by CRISPR/Cas9 resulted in improved proliferation in t(8;21)(q22;q22.1) positive AML cell lines and together with expression of AML1-ETO induced unlimited self-renewal in mouse hematopoietic progenitor cells in vitro. Mutations in BRCC3 abrogated its deubiquitinating activity on IFNAR1 resulting in an impaired interferon response and led to diminished inflammasome activity. In addition, BRCC3 inactivation increased release of several cytokines including G-CSF which enhanced proliferation of AML cell lines with t(8;21)(q22;q22.1). Cell lines and primary mouse cells with inactivation of BRCC3 had a higher sensitivity to doxorubicin due to an impaired DNA damage response providing a possible explanation for the favorable outcome of BRCC3 mutated AML patients

Genomic classification and prognosis in acute myeloid leukemia

BACKGROUND: Recent studies have provided a detailed census of genes that are mutated in acute myeloid leukemia (AML). Our next challenge is to understand how this genetic diversity defines the pathophysiology of AML and informs clinical practice. METHODS: We enrolled a total of 1540 patients in three prospective trials of intensive therapy. Combining driver mutations in 111 cancer genes with cytogenetic and clinical data, we defined AML genomic subgroups and their relevance to clinical outcomes. RESULTS: We identified 5234 driver mutations across 76 genes or genomic regions, with 2 or more drivers identified in 86% of the patients. Patterns of co-mutation compartmentalized the cohort into 11 classes, each with distinct diagnostic features and clinical outcomes. In addition to currently defined AML subgroups, three heterogeneous genomic categories emerged: AML with mutations in genes encoding chromatin, RNAsplicing regulators, or both (in 18% of patients); AML with TP53 mutations, chromosomal aneuploidies, or both (in 13%); and, provisionally, AML with IDH2R172 mutations (in 1%). Patients with chromatin-spliceosome and TP53-aneuploidy AML had poor outcomes, with the various class-defining mutations contributing independently and additively to the outcome. In addition to class-defining lesions, other co-occurring driver mutations also had a substantial effect on overall survival. The prognostic effects of individual mutations were often significantly altered by the presence or absence of other driver mutations. Such gene-gene interactions were especially pronounced for NPM1-mutated AML, in which patterns of co-mutation identified groups with a favorable or adverse prognosis. These predictions require validation in prospective clinical trials. CONCLUSIONS: The driver landscape in AML reveals distinct molecular subgroups that reflect discrete paths in the evolution of AML, informing disease classification and prognostic stratification

Epidemiological, genetic, and clinical characterization by age of newly diagnosed acute myeloid leukemia based on an academic population-based registry study (AMLSG BiO)

We describe genetic and clinical characteristics of acute myeloid leukemia (AML) patients according to age from an academic population-based registry. Adult patients with newly diagnosed AML at 63 centers in Germany and Austria were followed within the AMLSG BiO registry (NCT01252485). Between January 1, 2012, and December 31, 2014, data of 3525 patients with AML (45% women) were collected. The median age was 65 years (range 18–94). The comparison of age-specific AML incidence rates with epidemiological cancer registries revealed excellent coverage in patients 0 were associated with non-intensive treatment or best supportive care. The AMLSG BiO registry provides reliable population-based distributions of genetic, clinical, and treatment characteristics according to age

Cancer-specific changes in DNA methylation reveal aberrant silencing and activation of enhancers in leukemia

Acute myeloid leukemia (AML) is characterized by an impaired differentiation process leading to an accumulation of immature blasts in the blood. One feature of cytogenetically normal AML is alterations to the DNA methylome. We analyzed 57 AML patients with normal karyotype by using Illumina's 450k array and showed that aberrant DNA methylation is significantly altered at enhancer regions and that the methylation levels at specific enhancers predict overall survival of AML patients. The majority of sites that become differentially methylated in AML occur in regulatory elements of the human genome. Hypermethylation associates with enhancer silencing. In addition, chromatin immunoprecipitation sequencing analyses showed that a subset of hypomethylated sites correlate with enhancer activation, indicated by increased H3K27 acetylation. DNA hypomethylation is therefore not sufficient for enhancer activation. Some sites of hypomethylation occur at weak/poised enhancers marked with H3K4 monomethylation in hematopoietic progenitor cells. Other hypomethylated regions occur at sites inactive in progenitors and reflect the de novo acquisition of AML-specific enhancers. Altered enhancer dynamics are reflected in the gene expression of enhancer target genes, including genes involved in oncogenesis and blood cell development. This study demonstrates that histone variants and different histone modifications interact with aberrant DNA methylation and cause perturbed enhancer activity in cytogenetically normal AML that contributes to a leukemic transcriptome

Evaluating the impact of genetic and epigenetic aberrations on survival and response in acute myeloid leukemia patients receiving epigenetic therapy

Treatment with hypomethylating agents such as decitabine, which results in overall response rates of up to 50%, has become standard of care in older patients with acute myeloid leukemia (AML) who are not candidates for intensive chemotherapy. However, there still exists a lack of prognostic and predictive molecular biomarkers that enable selection of patients who are likely to benefit from epigenetic therapy. Here, we investigated distinct genetic (FLT3-ITD, NPM1, DNMT3A) and epigenetic (estrogen receptor alpha (ERα), C/EBPα, and OLIG2) aberrations in 87 AML patients from the recently published phase II decitabine trial (AML00331) to identify potential biomarkers for patients receiving hypomethylating therapy. While FLT3-ITD and NPM1 mutational status were not associated with survival or response to therapy, patients harboring DNMT3A R882 mutations showed a non-significant association towards shorter overall survival (hazard ratio (HR) 2.15, 95% confidence interval (CI) 0.91-5.12, p = 0.08). Promoter DNA methylation analyses using pyrosequencing also revealed a non-significant association towards shorter overall survival of patients with higher levels of methylation of ERα (HR 1.50, CI 0.97-2.32, p = 0.07) and OLIG2 CpG4 (HR 1.52, CI 0.96-2.41, p = 0.08), while DNA methylation of C/EBPα showed no association with outcome. Importantly, in multivariate analyses adjusted for clinical baseline parameters, the impact of ERα and OLIG2 CpG4 methylation was conserved (HR 1.76, CI 1.01-3.06, p = 0.05 and HR 1.67, CI 0.91-3.08, p = 0.10, respectively). In contrast, none of the investigated genetic and epigenetic markers was associated with response to treatment. Additional to the previously reported adverse prognostic clinical parameters such as patients' age, reduced performance status, and elevated lactate dehydrogenase levels, DNMT3A R882 mutation status, as well as ERα and OLIG2 CpG4 DNA methylation status, may prove to be molecular markers in older AML patients prior to hypomethylating therapy

Dissecting Genetic and Phenotypic Heterogeneity to Map Molecular Phylogenies and Deliver Personalized Outcome and Treatment Predictions in AML

For many years, clinical management of Acute Myeloid Leukemia (AML) has relied on patient classification into molecular groups, mostly defined by fusion genes. Recent insights of AML genomes have uncovered extended heterogeneity implicating >100 recurrently mutated genes, many of which are infrequently mutated. In each patient, multiple mutations are present defining unique genetic and clonal constellations. This genetic diversity significantly complicates the translation of molecular findings into routine clinical practice. We present our full analysis on the genomic characterization of 1540 AML patients enrolled in clinical trials of the German-Austrian AML Study Group. Together with cytogenetic profiling we map 5234 pathogenic lesions across 77 genomic loci. Amongst these, we characterise a cluster of hotspot mutations in the MYC oncogene. Overall we find 651 driver mutation in 96% patients, and 652 in 85%. The earliest mutations in AML evolution implicate genes mutated in age-related clonal hematopoiesis (DNMT3A, ASXL1, TET2) or fusion genes, followed by ordered acquisition of mutations in transcription, chromatin or splicing regulators. RTK/RAS mutations frequently represent late events with evidence of parallel evolution in 14% of AML. We formally model genomic structure and find that AML is subdivided in at least 11 molecular and clinically distinct classes defined by t(15;17), t(8;21), inv(16)/t(16;16), t(6;9), inv(3)/t(3;3), AML defined by MLL- rearrangements, CEBPAbi-allelic, NPM1, TP53/complex karyotype, AML with chromatin/splicing factor mutations, and provisionally AML with <3 aneuploidies. ~87% of patients with acquired mutations are molecularly classified. Each class is defined by a distinct subset of genetic lesions, with evidence of preferred order in mutation acquisition, thus guiding future development of minimal residual disease and combination therapy protocols. 19% (n=291) of patients were classified in the chromatin/spliceosome class. In this group, mutations in splicing factor genes and/or RUNX1 cluster with mutations in chromatin modifiers (ASXL1, EZH2, STAG2, MLLPTD). Patients in this group mostly represented Intermediate risk AML (ELN recommendations), were older, with lower WBC/blasts, inferior response rates to induction chemotherapy, poor long-term clinical outlook, higher rates of secondary AML and MDS-related morphology. Compared to classes defined by fusion genes, classes defined by genes are considerably more complex. We explore whether variability of clinical response (complete remission, relapse, relapse related mortality and overall survival) is at least in part accounted for by the extended genomic landscape. We find that the recurrent secondary and tertiary genotypes (often implicating rare genes/mutation-hotspots) markedly redefine clinical response and long-term curability beyond those predicted by single classifier lesions. To this effect, we apply global statistical models to calculate the contributions of genomic variables to overall risk whilst taking into account demographic, diagnostic and treatment factors. We find that gene-by-gene interactions are associated with additive as well as epistatic effects to patients risk, and contribute ~10% of relapse related mortality risk. We build prognostication models tailored to individual patients molecular, demographic and clinical variables at time of diagnosis and deliver more accurate risk predictions. For example, on the basis of the composite genomic and clinical profiles subsets of patients categorized as Favorable/Intermediate risk AML show risk estimates associated with adverse prognosis. Such patients are evaluated for therapeutic protocol selection tailored to higher risk groups (transplant at first CR instead of relapse), and ascertained for overall survival benefit. We apply same approaches for high-risk patients associated with favorable profiles and collectively deliver a paradigm of personally tailored risk assessment coupled with appropriate selection of therapeutic intervention. Taken together comprehensive genome profiling shows that genetic heterogeneity in AML is not random. Characterization of the extended genetic framework beyond single classifier lesions, informs future strategies for personalized prognostication, minimal residual disease monitoring and combination therapy protocols

Azacitidine combined with the selective FLT3 kinase inhibitor crenolanib disrupts stromal protection and inhibits expansion of residual leukemia-initiating cells in FLT3-ITD AML with concurrent epigenetic mutations.

Effectively targeting leukemia-initiating cells (LIC) in FLT3-ITD-mutated acute myeloid leukemia (AML) is crucial for cure. Tyrosine kinase inhibitors (TKI) have limited impact as single agents, failing to eradicate LIC in the bone marrow. Using primary AML samples and a patient-derived xenograft model, we investigated whether combining the FLT3-selective TKI crenolanib with the hypomethylating agent azacitidine (AZA) eliminates FLT3-ITD LIC and whether efficacy of this combination depends on co-existing mutations. Using multiparameter flow cytometry, we show FLT3-ITD occurs within the most primitive Lin(-)/CD33((+))/CD45(dim)/CD34(+) CD38(-) LIC compartment. Crenolanib alone could not target FLT3-ITD LIC in contact with niche cells while addition of AZA overcame stromal protection resulting in dramatically reduced clonogenic capacity of LIC in vitro and severely impaired engraftment in NSG mice. Strikingly, FLT3-mutated samples harboring TET2 mutations were completely resistant to crenolanib whereas neither NPM1 nor DNMT3A mutations influenced response. Conversely, primary AML LIC harboring either TET2, DNMT3A or NPM1 mutations did not show increased sensitivity to AZA. In summary, resistance of FLT3-ITD LIC to TKI depends on co-existing epigenetic mutations. However, AZA + crenolanib effectively abrogates stromal protection and inhibits survival of FLT3-ITD LIC irrespective of mutations, providing evidence for this combination as a means to suppress residual LIC

Personally Tailored Risk Prediction of AML Based on Comprehensive Genomic and Clinical Data

Over the past years it has emerged that acute myeloid leukemia (AML) is a disease often driven by multiple co-occurring genomic lesions. It is a great challenge to understand the logic of these mutational patterns and how the particular constellation of genomic risk factors affects a patient's outcome in conjunction with common clinical variables such as blood counts. Here we present a novel prognostic framework based genomic sequencing data of 111 cancer genes matched with detailed diagnostic, treatment and survival data from 1,540 patients with AML enrolled in three different trials run by the German-Austrian AML Study Group (AML-HD 98A, AML-HD 98B, and AMLSG 07-04). A systematic evaluation of risk modeling strategies reveals that much of the risk determining overall survival is captured in our comprehensive panel of genomic and prognostic clinical variables. Cox proportional hazards models with random effects achieved the highest cross-validated prognostic accuracy (Harrel's concordance C=0.72), better than models with variable selection (C=0.70 for AIC and BIC), and clearly superior to the ELN risk classification (C=0.63). It emerges that patient risk is the aggregate of many small and few large factors, such as previously established mutations in NPM1, CEBPA-/-, FLT3ITD and TP53; fusion genes generated by t(15;17), inv(16), and inv(3) rearrangements; and complex karyotype, del(5q) and trisomy 21. Multiple risk factors act mostly additively, with the exception of gene-gene interaction terms, including NPM1:FLT3ITD:DNMT3A (n=93; HR=1.50; P<0.03; Wald test, Benjamini-Yekutieli adjusted) that indicate the presence of epistatic effects on outcome. We found substantial heterogeneity in the presence of risk factors with almost unique constellations for each patient. We observed that approximately 2/3 of the predicted inter-patient risk variation was related to genomic factors (balanced rearrangements, copy number changes and point mutations), the remainder being mostly attributed to diagnostic blood counts, demographic data and treatment. Hence a large share, but not all, prognostic information seems to be determined by genomic factors. Using multistage models with random effects we have assessed differential effects of prognostic variables at different stages of therapy. These models yield detailed predictions about the probability of being alive in induction, first complete remission and after relapse, as well as the mortality during each of the three stages. Importantly, our model computes how these probabilities change depending on a patient's constellation of risk factors. The resulting personalized predictions provide a quantitative risk assessment and allow evaluating the effect of treatment decisions such as allogeneic stem cell transplant versus standard chemotherapy in first complete remission. Our analysis shows that detailed and accurate predictions can be made based on knowledge banks of genomic and clinical data. As a proof of principle we have implemented our prediction framework into a web portal to explore risk predictions. Our method is able to impute missing variables and quantify the uncertainty due to missingness and finite training data. Power calculations show that cohorts of 10,000 patients will be needed for precise clinical decision support