Targeting FLT3 with a new-generation antibody-drug conjugate in combination with kinase inhibitors for treatment of AML.

Fms-like tyrosine kinase 3 (FLT3) is often overexpressed or constitutively activated by internal tandem duplication (ITD) and tyrosine kinase domain (TKD) mutations in acute myeloid leukemia (AML). Despite the use of receptor tyrosine kinase inhibitors (TKI) in FLT3-ITD-positive AML, the prognosis of patients is still poor, and further improvement of therapy is required. Targeting FLT3 independent of mutations by antibody-drug conjugates (ADCs) is a promising strategy for AML therapy. Here, we report the development and preclinical characterization of a novel FLT3-targeting ADC, 20D9-ADC, which was generated by applying the innovative P5 conjugation technology. In vitro, 20D9-ADC mediated potent cytotoxicity to Ba/F3 cells expressing transgenic FLT3 or FLT3-ITD, to AML cell lines, and to FLT3-ITD-positive patient-derived xenograft AML cells. In vivo, 20D9-ADC treatment led to a significant tumor reduction and even durable complete remission in AML xenograft models. Furthermore, 20D9-ADC demonstrated no severe hematotoxicity in in vitro colony formation assays using concentrations that were cytotoxic in AML cell line treatment. The combination of 20D9-ADC with the TKI midostaurin showed strong synergy in vitro and in vivo, leading to reduction of aggressive AML cells below the detection limit. Our data indicate that targeting FLT3 with an advanced new-generation ADC is a promising and potent antileukemic strategy, especially when combined with FLT3-TKI in FLT3-ITD-positive AML.

Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.

BACKGROUND: Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. METHODS: In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell's C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. RESULTS: Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. CONCLUSIONS: Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.

Combined proteomics and CRISPR‒Cas9 screens in PDX identify ADAM10 as essential for leukemia in vivo.

BACKGROUND: Acute leukemias represent deadly malignancies that require better treatment. As a challenge, treatment is counteracted by a microenvironment protecting dormant leukemia stem cells. METHODS: To identify responsible surface proteins, we performed deep proteome profiling on minute numbers of dormant patient-derived xenograft (PDX) leukemia stem cells isolated from mice. Candidates were functionally screened by establishing a comprehensive CRISPR‒Cas9 pipeline in PDX models in vivo. RESULTS: A disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) was identified as an essential vulnerability required for the survival and growth of different types of acute leukemias in vivo, and reconstitution assays in PDX models confirmed the relevance of its sheddase activity. Of translational importance, molecular or pharmacological targeting of ADAM10 reduced PDX leukemia burden, cell homing to the murine bone marrow and stem cell frequency, and increased leukemia response to conventional chemotherapy in vivo. CONCLUSIONS: These findings identify ADAM10 as an attractive therapeutic target for the future treatment of acute leukemias.

Acute megakaryoblastic leukaemia shows high frequency of chromosome 1q aberrations and dismal outcome.

Acute megakaryoblastic leukaemia (AMKL) is associated with poor prognosis. Limited information is available on its cytogenetics, molecular genetics and clinical outcome. We performed genetic analyses, evaluated prognostic factors and the value of allogeneic haematopoietic stem cell transplantation (allo-HSCT) in a homogenous adult AMKL patient cohort. We retrospectively analysed 38 adult patients with AMKL (median age: 58 years, range: 21-80). Most received intensive treatment in AML Cooperative Group (AMLCG) trials between 2001 and 2016. Cytogenetic data showed an accumulation of adverse risk markers according to ELN 2017 and an unexpected high frequency of structural aberrations on chromosome arm 1q (33%). Most frequently, mutations occurred in TET2 (23%), TP53 (23%), JAK2 (19%), PTPN11 (19%) and RUNX1 (15%). Complete remission rate in 33 patients receiving intensive chemotherapy was 33% and median overall survival (OS) was 33 weeks (95% CI: 21-45). Patients undergoing allo-HSCT (n = 14) had a superior median OS (68 weeks; 95% CI: 11-126) and relapse-free survival (RFS) of 27 weeks (95% CI: 4-50), although cumulative incidence of relapse after allo-HSCT was high (62%). The prognosis of AMKL is determined by adverse genetic risk factors and therapy resistance. So far allo-HSCT is the only potentially curative treatment option in this dismal AML subgroup.

Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning.

Achievement of complete remission signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. We used nine machine learning (ML) models to predict complete remission and 2-year overall survival in a large multicenter cohort of 1,383 AML patients who received intensive induction therapy. Clinical, laboratory, cytogenetic and molecular genetic data were incorporated and our results were validated on an external multicenter cohort. Our ML models autonomously selected predictive features including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, and U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin concentration at initial diagnosis were statistically significant markers predictive of complete remission, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, and TP53 mutations, age, white blood cell count, peripheral blast count, serum lactate dehydrogenase level and hemoglobin concentration at initial diagnosis as well as extramedullary manifestations were predictive for 2-year overall survival. For prediction of complete remission and 2-year overall survival areas under the receiver operating characteristic curves ranged between 0.77-0.86 and between 0.63-0.74, respectively in our test set, and between 0.71-0.80 and 0.65-0.75 in the external validation cohort. We demonstrated the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms, using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology.

The evolving landscape of COVID-19 and post-COVID condition in patients with chronic lymphocytic leukemia: A study by ERIC, the European research initiative on CLL.

In this retrospective international multicenter study, we describe the clinical characteristics and outcomes of patients with chronic lymphocytic leukemia (CLL) and related disorders (small lymphocytic lymphoma and high-count monoclonal B lymphocytosis) infected by SARS-CoV-2, including the development of post-COVID condition. Data from 1540 patients with CLL infected by SARS-CoV-2 from January 2020 to May 2022 were included in the analysis and assigned to four phases based on cases disposition and SARS-CoV-2 variants emergence. Post-COVID condition was defined according to the WHO criteria. Patients infected during the most recent phases of the pandemic, though carrying a higher comorbidity burden, were less often hospitalized, rarely needed intensive care unit admission, or died compared to patients infected during the initial phases. The 4-month overall survival (OS) improved through the phases, from 68% to 83%, p = .0015. Age, comorbidity, CLL-directed treatment, but not vaccination status, emerged as risk factors for mortality. Among survivors, 6.65% patients had a reinfection, usually milder than the initial one, and 16.5% developed post-COVID condition. The latter was characterized by fatigue, dyspnea, lasting cough, and impaired concentration. Infection severity was the only risk factor for developing post-COVID. The median time to resolution of the post-COVID condition was 4.7 months. OS in patients with CLL improved during the different phases of the pandemic, likely due to the improvement of prophylactic and therapeutic measures against SARS-CoV-2 as well as the emergence of milder variants. However, mortality remained relevant and a significant number of patients developed post-COVID conditions, warranting further investigations.

Fusion gene detection by RNA-sequencing complements diagnostics of acute myeloid leukemia and identifies recurring NRIP1-MIR99AHG rearrangements.

Identification of fusion genes in clinical routine is mostly based on cytogenetics and targeted molecular genetics, such as metaphase karyotyping, fluorescence in situ hybridization and reverse-transcriptase polymerase chain reaction. However, sequencing technologies are becoming more important in clinical routine as processing time and costs per sample decrease. To evaluate the performance of fusion gene detection by RNAsequencing compared to standard diagnostic techniques, we analyzed 806 RNA-sequencing samples from patients with acute myeloid leukemia using two state-of-the-art software tools, namely Arriba and FusionCatcher. RNA-sequencing detected 90% of fusion events that were reported by routine with high evidence, while samples in which RNA-sequencing failed to detect fusion genes had overall lower and inhomogeneous sequence coverage. Based on properties of known and unknown fusion events, we developed a workflow with integrated filtering strategies for the identification of robust fusion gene candidates by RNA-sequencing. Thereby, we detected known recurrent fusion events in 26 cases that were not reported by routine and found discrepancies in evidence for known fusion events between routine and RNA-sequencing in three cases. Moreover, we identified 157 fusion genes as novel robust candidates and comparison to entries from ChimerDB or Mitelman Database showed novel recurrence of fusion genes in 14 cases. Finally, we detected the novel recurrent fusion gene NRIP1- MIR99AHG resulting from inv(21)(q11.2;q21.1) in nine patients (1.1%) and LTN1-MX1 resulting from inv(21)(q21.3;q22.3) in two patients (0.25%). We demonstrated that NRIP1-MIR99AHG results in overexpression of the 3' region of MIR99AHG and the disruption of the tricistronic miRNA cluster miR-99a/let-7c/miR-125b-2. Interestingly, upregulation of MIR99AHG and deregulation of the miRNA cluster, residing in the MIR99AHG locus, are known mechanisms of leukemogenesis in acute megakaryoblastic leukemia. Our findings demonstrate that RNA-sequencing has a strong potential to improve the systematic detection of fusion genes in clinical applications and provides a valuable tool for fusion discovery.

The retinoic acid receptor co-factor NRIP1 is uniquely upregulated and represents a therapeutic target in acute myeloid leukemia with chromosome 3q rearrangements.

Aberrant expression of Ecotropic Viral Integration Site 1 (EVI1) is a hallmark of acute myeloid leukemia (AML) with inv(3) or t(3;3), which is a disease subtype with especially poor outcome. In studying transcriptomes from AML patients with chromosome 3q rearrangements, we identified a significant upregulation of the Nuclear Receptor Interacting Protein 1 (NRIP1) as well as its adjacent non-coding RNA LOC101927745. Utilizing transcriptomic and epigenomic data from over 900 primary samples from patients as well as genetic and transcriptional engineering approaches, we have identified several mechanisms that can lead to upregulation of NRIP1 in AML. We hypothesize that the LOC101927745 transcription start site harbors a context-dependent enhancer that is bound by EVI1, causing upregulation of NRIP1 in AML with chromosome 3 abnormalities. Furthermore, we showed that NRIP1 knockdown negatively affects the proliferation and survival of 3qrearranged AML cells and increases their sensitivity to all-trans retinoic acid, suggesting that NRIP1 is relevant for the pathogenesis of inv(3)/t(3;3) AML and could serve as a novel therapeutic target in myeloid malignancies with 3q abnormalities.

Clinical and molecular relevance of genetic variants in the non-coding transcriptome of patients with cytogenetically normal acute myeloid leukemia.

Expression levels of long non-coding RNA (lncRNA) have been shown to associate with clinical outcome of patients with cytogenetically normal acute myeloid leukemia (CN-AML). However, the frequency and clinical significance of genetic variants in the nucleotide sequences of lncRNA in AML patients is unknown. Herein, we analyzed total RNA sequencing data of 377 younger adults (aged <60 years) with CN-AML, who were comprehensively characterized with regard to clinical outcome. We used available genomic databases and stringent filters to annotate genetic variants unequivocally located in the non-coding transcriptome of AML patients. We detected 981 variants, which are recurrently present in lncRNA that are expressed in leukemic blasts. Among these variants, we identified a cytosine-to-thymidine variant in the lncRNA RP5-1074L1.4 and a cytosine-to-thymidine variant in the lncRNA SNHG15, which independently associated with longer survival of CN-AML patients. The presence of the SNHG15 cytosine-to-thymidine variant was also found to associate with better outcome in an independent dataset of CN-AML patients, despite differences in treatment protocols and RNA sequencing techniques. In order to gain biological insights, we cloned and overexpressed both wild-type and variant versions of the SNHG15 lncRNA. In keeping with its negative prognostic impact, overexpression of the wild-type SNHG15 associated with higher proliferation rate of leukemic blasts when compared with the cytosine-to-thymidine variant. We conclude that recurrent genetic variants of lncRNA that are expressed in the leukemic blasts of CN-AML patients have prognostic and potential biological significance.

HERVs characterize normal and leukemia stem cells and represent a source of shared epitopes for cancer immunotherapy.

Human endogenous retroviruses (HERVs) represent 8% of the human genome. The expression of HERVs and their immune impact have not been extensively studied in Acute Myeloid Leukemia (AML). In this study, we used a reference of 14 968 HERV functional units to provide a thorough analysis of HERV expression in normal and AML bone marrow cells. We show that the HERV retrotranscriptome accurately characterizes normal and leukemic cell subpopulations, including leukemia stem cells, in line with different epigenetic profiles. We then show that HERV expression delineates AML subtypes with different prognoses. We finally propose a method to select and prioritize CD8+ T cell epitopes derived from AML-specific HERVs and we show that lymphocytes infiltrating patient bone marrow at diagnosis contain naturally occurring CD8+ T cells against these HERV epitopes. We also provide in vitro data supporting the functionality of HERV-specific CD8+ T-cells against AML cells. These results show that HERVs represent an important source of genetic information that can help enhancing disease stratification or biomarker identification and an important reservoir of alternative tumor-specific T cell epitopes relevant for cancer immunotherapy.

A 17-gene stemness score for rapid determination of risk in acute leukaemia.

Refractoriness to induction chemotherapy and relapse after achievement of remission are the main obstacles to cure in acute myeloid leukaemia (AML). After standard induction chemotherapy, patients are assigned to different post-remission strategies on the basis of cytogenetic and molecular abnormalities that broadly define adverse, intermediate and favourable risk categories. However, some patients do not respond to induction therapy and another subset will eventually relapse despite the lack of adverse risk factors. There is an urgent need for better biomarkers to identify these high-risk patients before starting induction chemotherapy, to enable testing of alternative induction strategies in clinical trials. The high rate of relapse in AML has been attributed to the persistence of leukaemia stem cells (LSCs), which possess a number of stem cell properties, including quiescence, that are linked to therapy resistance. Here, to develop predictive and/or prognostic biomarkers related to stemness, we generated a list of genes that are differentially expressed between 138 LSC+ and 89 LSC- cell fractions from 78 AML patients validated by xenotransplantation. To extract the core transcriptional components of stemness relevant to clinical outcomes, we performed sparse regression analysis of LSC gene expression against survival in a large training cohort, generating a 17-gene LSC score (LSC17). The LSC17 score was highly prognostic in five independent cohorts comprising patients of diverse AML subtypes (n = 908) and contributed greatly to accurate prediction of initial therapy resistance. Patients with high LSC17 scores had poor outcomes with current treatments including allogeneic stem cell transplantation. The LSC17 score provides clinicians with a rapid and powerful tool to identify AML patients who do not benefit from standard therapy and who should be enrolled in trials evaluating novel upfront or post-remission strategies.

Low-density lipoprotein receptor (LDLR) is an independent adverse prognostic factor in acute myeloid leukaemia.

The low-density lipoprotein receptor (LDLR) is a membrane receptor that mediates the endocytosis of low-density lipoprotein (LDL). Uptake of LDL has been proposed to contribute to chemotherapy resistance of acute myeloid leukaemia (AML) cell lines in vitro. In the present study, we analysed LDLR expression and survival using bone marrow biopsies from 187 intensively treated patients with AML. Here, increasing LDLR expression was associated with decreasing overall (58·4%, 44·2%, and 24·4%; P = 0·0018), as well as event-free survival (41·7%, 18·1%, and 14·3%; P = 0·0077), and an increasing cumulative incidence of relapse (33·9%, 55·1%, and 71·4%; P = 0·0011). Associations of LDLR expression with survival were confirmed in 557 intensively treated patients from two international validation cohorts. In the analytic and validation cohorts, LDLR expression remained associated with outcome in multivariable regression analyses including the European LeukemiaNet genetic risk classification. Thus, LDLR predicts outcome of patients with AML beyond existing risk factors. Furthermore, we found low expression levels of LDLR in most healthy tissues, suggesting it as a promising target for antibody-based pharmacodelivery approaches in AML.

Dynamics of SARS-CoV-2 shedding in the respiratory tract depends on the severity of disease in COVID-19 patients.

A fraction of COVID-19 patients progress to a severe disease manifestation with respiratory failure and the necessity of mechanical ventilation. Identifying patients at risk is critical for optimised care and early therapeutic interventions. We investigated the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding relative to disease severity.We analysed nasopharyngeal and tracheal shedding of SARS-CoV-2 in 92 patients with diagnosed COVID-19. Upon admission, standardised nasopharyngeal swab or sputum samples were collected. If patients were mechanically ventilated, endotracheal aspirate samples were additionally obtained. Viral shedding was quantified by real-time PCR detection of SARS-CoV-2 RNA.45% (41 out of 92) of COVID-19 patients had a severe disease course with the need for mechanical ventilation (severe group). At week 1, the initial viral shedding determined from nasopharyngeal swabs showed no significant difference between nonsevere and severe cases. At week 2, a difference could be observed as the viral shedding remained elevated in severely ill patients. A time-course of C-reactive protein, interleukin-6 and procalcitonin revealed an even more protracted inflammatory response following the delayed drop of virus shedding load in severely ill patients. A significant proportion (47.8%) of patients showed evidence of prolonged viral shedding (>17 days), which was associated with severe disease courses (73.2%).We report that viral shedding does not differ significantly between severe and nonsevere COVID-19 cases upon admission to the hospital. Elevated SARS-CoV-2 shedding in the second week of hospitalisation, a systemic inflammatory reaction peaking between the second and third week, and prolonged viral shedding are associated with a more severe disease course.

Elevated levels of IL-6 and CRP predict the need for mechanical ventilation in COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) can manifest as a viral-induced hyperinflammation with multiorgan involvement. Such patients often experience rapid deterioration and need for mechanical ventilation. Currently, no prospectively validated biomarker of impending respiratory failure is available. OBJECTIVE: We aimed to identify and prospectively validate biomarkers that allow the identification of patients in need of impending mechanical ventilation. METHODS: Patients with COVID-19 who were hospitalized from February 29 to April 9, 2020, were analyzed for baseline clinical and laboratory findings at admission and during the disease. Data from 89 evaluable patients were available for the purpose of analysis comprising an initial evaluation cohort (n = 40) followed by a temporally separated validation cohort (n = 49). RESULTS: We identified markers of inflammation, lactate dehydrogenase, and creatinine as the variables most predictive of respiratory failure in the evaluation cohort. Maximal IL-6 level before intubation showed the strongest association with the need for mechanical ventilation, followed by maximal CRP level. The respective AUC values for IL-6 and CRP levels in the evaluation cohort were 0.97 and 0.86, and they were similar in the validation cohort (0.90 and 0.83, respectively). The calculated optimal cutoff values during the course of disease from the evaluation cohort (IL-6 level > 80 pg/mL and CRP level > 97 mg/L) both correctly classified 80% of patients in the validation cohort regarding their risk of respiratory failure. CONCLUSION: The maximal level of IL-6, followed by CRP level, was highly predictive of the need for mechanical ventilation. This suggests the possibility of using IL-6 or CRP level to guide escalation of treatment in patients with COVID-19-related hyperinflammatory syndrome.

Clinical and preclinical characterization of CD99 isoforms in acute myeloid leukemia.

In an effort to identify target genes in acute myeloid leukemia (AML), we compared gene expression profiles between normal and AML cells from various publicly available datasets. We identified CD99, a gene that is up-regulated in AML patients. In 186 patients from The Cancer Genome Atlas AML dataset, CD99 was over-expressed in patients with FLT3-ITD and was down-regulated in patients with TP53 mutations. CD99 is a trans-membrane protein expressed on leukocytes and plays a role in cell adhesion, trans-endothelial migration, and T-cell differentiation. The CD99 gene encodes two isoforms with distinct expression and functional profiles in both normal and malignant tissues. Here we report that, although the CD99 long isoform initially induces an increase in cell proliferation, it also induces higher levels of reactive oxygen species, DNA damage, apoptosis and a subsequent decrease in cell viability. In several leukemia murine models, the CD99 long isoform delayed disease progression and resulted in lower leukemia engraftment in the bone marrow. Furthermore, the CD99 monoclonal antibody reduced cell viability, colony formation, and cell migration, and induced cell differentiation and apoptosis in leukemia cell lines and primary blasts. Mechanistically, CD99 long isoform resulted in transient induction followed by a dramatic decrease in both ERK and SRC phosphorylation. Altogether, our study provides new insights into the role of CD99 isoforms in AML that could potentially be relevant for the preclinical development of CD99 targeted therapy.

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.

BACKGROUND: The inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g., clinical, transcriptomic, and methylation data. To date, there exist a few computationally intensive approaches that make use of block structures of this kind. RESULTS: In this paper we present priority-Lasso, an intuitive and practical analysis strategy for building prediction models based on Lasso that takes such block structures into account. It requires the definition of a priority order of blocks of data. Lasso models are calculated successively for every block and the fitted values of every step are included as an offset in the fit of the next step. We apply priority-Lasso in different settings on an acute myeloid leukemia (AML) dataset consisting of clinical variables, cytogenetics, gene mutations and expression variables, and compare its performance on an independent validation dataset to the performance of standard Lasso models. CONCLUSION: The results show that priority-Lasso is able to keep pace with Lasso in terms of prediction accuracy. Variables of blocks with higher priorities are favored over variables of blocks with lower priority, which results in easily usable and transportable models for clinical practice.

GPR56 identifies primary human acute myeloid leukemia cells with high repopulating potential in vivo.

Acute myeloid leukemia (AML) is a genetically heterogeneous hematologic malignancy, which is initiated and driven by a rare fraction of leukemia stem cells (LSCs). Despite the difficulties of identifying a common LSC phenotype, there is increasing evidence that high expression of stem cell gene signatures is associated with poor clinical outcome. Identification of functionally distinct subpopulations in this disease is therefore crucial to dissecting the molecular machinery underlying LSC self-renewal. Here, we combined next-generation sequencing technology with in vivo assessment of LSC frequencies and identified the adhesion G protein-coupled receptor 56 (GPR56) as a novel and stable marker for human LSCs for the majority of AML samples. High GPR56 expression was significantly associated with high-risk genetic subgroups and poor outcome. Analysis of GPR56 in combination with CD34 expression revealed engraftment potential of GPR56(+)cells in both the CD34(-)and CD34(+)fractions, thus defining a novel LSC compartment independent of the CD34(+)CD38(-)LSC phenotype.

Spectrum and prognostic relevance of driver gene mutations in acute myeloid leukemia.

The clinical and prognostic relevance of many recently identified driver gene mutations in adult acute myeloid leukemia (AML) is poorly defined. We sequenced the coding regions or hotspot areas of 68 recurrently mutated genes in a cohort of 664 patients aged 18 to 86 years treated on 2 phase 3 trials of the German AML Cooperative Group (AMLCG). The median number of 4 mutations per patient varied according to cytogenetic subgroup, age, and history of previous hematologic disorder or antineoplastic therapy. We found patterns of significantly comutated driver genes suggesting functional synergism. Conversely, we identified 8 virtually nonoverlapping patient subgroups, jointly comprising 78% of AML patients, that are defined by mutually exclusive genetic alterations. These subgroups, likely representing distinct underlying pathways of leukemogenesis, show widely divergent outcomes. Furthermore, we provide detailed information on associations between gene mutations, clinical patient characteristics, and therapeutic outcomes in this large cohort of uniformly treated AML patients. In multivariate analyses including a comprehensive set of molecular and clinical variables, we identified DNMT3A and RUNX1 mutations as important predictors of shorter overall survival (OS) in AML patients <60 years, and particularly in those with intermediate-risk cytogenetics. NPM1 mutations in the absence of FLT3-ITD, mutated TP53, and biallelic CEBPA mutations were identified as important molecular prognosticators of OS irrespective of patient age. In summary, our study provides a comprehensive overview of the spectrum, clinical associations, and prognostic relevance of recurrent driver gene mutations in a large cohort representing a broad spectrum and age range of intensively treated AML patients.

Genetics of acute myeloid leukemia in the elderly: mutation spectrum and clinical impact in intensively treated patients aged 75 years or older.

A cute myeloid leukemia is a disease of the elderly (median age at diagnosis, 65-70 years). The prognosis of older acute myeloid leukemia patients is generally poor. While genetic markers have become important tools for risk stratification and treatment selection in young and middle-aged patients, their applicability in very old patients is less clear. We sought to validate existing genetic risk classification systems and identify additional factors associated with outcomes in intensively treated patients aged ≥75 years. In 151 patients who received induction chemotherapy in the AMLCG-1999 trial, we investigated recurrently mutated genes using a targeted sequencing assay covering 64 genes. The median number of mutated genes per patient was four. The most commonly mutated genes were TET2 (42%), DNMT3A (35%), NPM1 (32%), SRSF2 (25%) and ASXL1 (21%). The complete remission rate was 44% and the 3-year survival was 21% for the entire cohort. While adverse-risk cytogenetics (MRC classification) were associated with shorter overall survival (P=0.001), NPM1 and FLT3-ITD mutations (present in 18%) did not have a significant impact on overall survival. Notably, none of the 13 IDH1-mutated patients (9%) reached complete remission. Consequently, the overall survival of this subgroup was significantly shorter than that of IDH1-wildtype patients (P<0.001). In summary, even among very old, intensively treated, acute myeloid leukemia patients, adverse-risk cytogenetics predict inferior survival. The spectrum and relevance of driver gene mutations in elderly patients differs from that in younger patients. Our data implicate IDH1 mutations as a novel marker for chemorefractory disease and inferior prognosis. (AMLCG-1999 trial: clinicaltrials.gov identifier, NCT00266136).

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia.

Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856) to develop a predictor of therapy resistance, which was validated in an independent cohort analyzed by RNA sequencing (n=250). In addition to gene expression markers, standard clinical and laboratory variables as well as the mutation status of 68 genes were considered during construction of the model. The final predictor (PS29MRC) consisted of 29 gene expression markers and a cytogenetic risk classification. A continuous predictor is calculated as a weighted linear sum of the individual variables. In addition, a cut off was defined to divide patients into a high-risk and a low-risk group for resistant disease. PS29MRC was highly significant in the validation set, both as a continuous score (OR=2.39, P=8.63·10-9, AUC=0.76) and as a dichotomous classifier (OR=8.03, P=4.29·10-9); accuracy was 77%. In multivariable models, only TP53 mutation, age and PS29MRC (continuous: OR=1.75, P=0.0011; dichotomous: OR=4.44, P=0.00021) were left as significant variables. PS29MRC dominated all models when compared with currently used predictors, and also predicted overall survival independently of established markers. When integrated into the European LeukemiaNet (ELN) 2017 genetic risk stratification, four groups (median survival of 8, 18, 41 months, and not reached) could be defined (P=4.01·10-10). PS29MRC will make it possible to design trials which stratify induction treatment according to the probability of response, and refines the ELN 2017 classification.