Identifying long-term survivors and those at higher or lower risk of relapse among patients with cytogenetically normal acute myeloid leukemia using a high-dimensional mixture cure model.

Patients with cytogenetically normal acute myeloid leukemia (CN-AML) may harbor prognostically relevant gene mutations and thus be categorized into one of the three 2022 European LeukemiaNet (ELN) genetic-risk groups. Nevertheless, there remains heterogeneity with respect to relapse-free survival (RFS) within these genetic-risk groups. Our training set included 306 adults on Alliance for Clinical Trials in Oncology studies with de novo CN-AML aged < 60 years who achieved a complete remission and for whom centrally reviewed cytogenetics, RNA-sequencing, and gene mutation data from diagnostic samples were available (Alliance trial A152010). To overcome deficiencies of the Cox proportional hazards model when long-term survivors are present, we developed a penalized semi-parametric mixture cure model (MCM) to predict RFS where RNA-sequencing data comprised the predictor space. To validate model performance, we employed an independent test set from the German Acute Myeloid Leukemia Cooperative Group (AMLCG) consisting of 40 de novo CN-AML patients aged < 60 years who achieved a complete remission and had RNA-sequencing of their pre-treatment sample. For the training set, there was a significant non-zero cure fraction (p = 0.019) with 28.5% of patients estimated to be cured. Our MCM included 112 genes associated with cure, or long-term RFS, and 87 genes associated with latency, or shorter-term time-to-relapse. The area under the curve and C-statistic were respectively, 0.947 and 0.783 for our training set and 0.837 and 0.718 for our test set. We identified a novel, prognostically relevant molecular signature in CN-AML, which allows identification of patient subgroups independent of 2022 ELN genetic-risk groups.Trial registration Data from companion studies CALGB 8461, 9665 and 20202 (trials registered at www.clinicaltrials.gov as, respectively, NCT00048958, NCT00899223, and NCT00900224) were obtained from Alliance for Clinical Trials in Oncology under data sharing study A152010. Data from the AMLCG 2008 trial was registered at www.clinicaltrials.gov as NCT01382147.

Prognostic impact of CEBPA mutational subgroups in adult AML.

Despite recent refinements in the diagnostic and prognostic assessment of CEBPA mutations in AML, several questions remain open, i.e. implications of different types of basic region leucin zipper (bZIP) mutations, the role of co-mutations and the allelic state. Using pooled primary data analysis on 1010 CEBPA-mutant adult AML patients, a comparison was performed taking into account the type of mutation (bZIP: either typical in-frame insertion/deletion (InDel) mutations (bZIPInDel), frameshift InDel or nonsense mutations inducing translational stop (bZIPSTOP) or single base-pair missense alterations (bZIPms), and transcription activation domain (TAD) mutations) and the allelic state (single (smCEBPA) vs. double mutant (dmCEBPA)). Only bZIPInDel patients had significantly higher rates of complete remission and longer relapse free and overall survival (OS) compared with all other CEBPA-mutant subgroups. Moreover, co-mutations in bZIPInDel patients (e.g. GATA2, FLT3, WT1 as well as ELN2022 adverse risk aberrations) had no independent impact on OS, whereas in non-bZIPInDel patients, grouping according to ELN2022 recommendations added significant prognostic information. In conclusion, these results demonstrate bZIPInDel mutations to be the major independent determinant of outcome in CEBPA-mutant AML, thereby refining current classifications according to WHO (including all dmCEBPA and smCEBPA bZIP) as well as ELN2022 and ICC recommendations (including CEBPA bZIPms).

Sex-associated differences in frequencies and prognostic impact of recurrent genetic alterations in adult acute myeloid leukemia (Alliance, AMLCG).

Clinical outcome of patients with acute myeloid leukemia (AML) is associated with demographic and genetic features. Although the associations of acquired genetic alterations with patients' sex have been recently analyzed, their impact on outcome of female and male patients has not yet been comprehensively assessed. We performed mutational profiling, cytogenetic and outcome analyses in 1726 adults with AML (749 female and 977 male) treated on frontline Alliance for Clinical Trials in Oncology protocols. A validation cohort comprised 465 women and 489 men treated on frontline protocols of the German AML Cooperative Group. Compared with men, women more often had normal karyotype, FLT3-ITD, DNMT3A, NPM1 and WT1 mutations and less often complex karyotype, ASXL1, SRSF2, U2AF1, RUNX1, or KIT mutations. More women were in the 2022 European LeukemiaNet intermediate-risk group and more men in adverse-risk group. We found sex differences in co-occurring mutation patterns and prognostic impact of select genetic alterations. The mutation-associated splicing events and gene-expression profiles also differed between sexes. In patients aged <60 years, SF3B1 mutations were male-specific adverse outcome prognosticators. We conclude that sex differences in AML-associated genetic alterations and mutation-specific differential splicing events highlight the importance of patients' sex in analyses of AML biology and prognostication.

[Artificial Intelligence for computer-aided leukemia diagnostics]. / Künstliche Intelligenz für die computerunterstützte Leukämiediagnostik.

The manual examination of blood and bone marrow specimens for leukemia patients is time-consuming and limited by intra- and inter-observer variance. The development of AI algorithms for leukemia diagnostics requires high-quality sample digitization and reliable annotation of large datasets. Deep learning-based algorithms using these datasets attain human-level performance for some well-defined, clinically relevant questions such as the blast character of cells. Methods such as multiple - instance - learning allow predicting diagnoses from a collection of leukocytes, but are more data-intensive. Using "explainable AI" methods can make the prediction process more transparent and allow users to verify the algorithm's predictions. Stability and robustness analyses are necessary for routine application of these algorithms, and regulatory institutions are developing standards for this purpose. Integrated diagnostics, which link different diagnostic modalities, offer the promise of even greater accuracy but require more extensive and diverse datasets.

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.

Impact of extramedullary disease in AML patients undergoing sequential RIC for HLA-matched transplantation: occurrence, risk factors, relapse patterns, and outcome.

We sought to evaluate the role of extramedullary disease (EMD) in sequential RIC retrospectively analyzing data of 144 high-risk AML patients undergoing HLA-matched transplantation. Median long-term follow-up was 11.6 years. Eighteen percent of patients (n = 26/144) presented with extramedullary AML (EM AML) or a history of EMD at time of transplantation. Overall relapse rate was 25% (n = 36/144) with 15% (n = 21/144) of all patients developing isolated BM relapse and 10% (n = 15/144) developing EM AML relapse with or without concomitant BM relapse (EM ± BM). Manifestation of EM relapse after transplantation occurred frequently at multiple sites and presented mostly as solid tumor mass. Only 3/15 patients with EM ± BM relapse showed a prior EMD manifestation. EMD prior to allogeneic transplantation had no impact on post-transplant OS when compared to non-EMD (median post-transplant OS 3.8 years versus 4.8 years; ns). Risk factors (p = < 0.1) for EM ± BM relapse included younger age and a higher number of prior intensive chemotherapies, whereas the presence of chronic GVHD was a protective factor. Median post-transplant OS (15.5 months vs. 15.5 months), RFS (9.6 months vs 7.3 months), and post-relapse OS (6.7 months vs. 6.3 months) were not significantly different between patients with isolated BM vs. EM ± BM relapse. Taken together, occurrence of EMD prior to as well as of EM ± BM AML relapse after transplantation was moderate, presenting mostly as solid tumor mass after transplantation. However, diagnosis of those does not seem to influence outcomes after sequential RIC. A higher number of chemotherapy cycles prior to transplantation was identified as recent risk factor for EM ± BM relapse.

Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles.

BACKGROUND: Increasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven unsupervised learning can detect inherent patterns in such data sets. METHODS: While unsupervised analysis in the medical literature commonly only utilizes a single clustering algorithm for a given data set, we developed a large-scale model with 605 different combinations of target dimensionalities as well as transformation and clustering algorithms and subsequent meta-clustering of individual results. With this model, we investigated a large cohort of 1383 patients from 59 centers in Germany with newly diagnosed acute myeloid leukemia for whom 212 clinical, laboratory, cytogenetic and molecular genetic parameters were available. RESULTS: Unsupervised learning identifies four distinct patient clusters, and statistical analysis shows significant differences in rate of complete remissions, event-free, relapse-free and overall survival between the four clusters. In comparison to the standard-of-care hypothesis-driven European Leukemia Net (ELN2017) risk stratification model, we find all three ELN2017 risk categories being represented in all four clusters in varying proportions indicating unappreciated complexity of AML biology in current established risk stratification models. Further, by using assigned clusters as labels we subsequently train a supervised model to validate cluster assignments on a large external multicenter cohort of 664 intensively treated AML patients. CONCLUSIONS: Dynamic data-driven models are likely more suitable for risk stratification in the context of increasingly complex medical data than rigid hypothesis-driven models to allow for a more personalized treatment allocation and gain novel insights into disease biology.

There are various ways in which clinicians can predict the risk of disease progression in patients with leukemia, helping them to treat the patients accordingly. However, these approaches are usually designed by human experts and might not fully capture the complexity of a patient's disease. Here, with a large cohort of patients with acute myeloid leukemia, we design an unsupervised machine learning model ­ a type of computer model that learns from patterns in data without human input­to separate these patients into subgroups according to risk. We identify four distinct groups which differ with regards to patient genetics, laboratory values, and clinical characteristics. These groups have differences in response to treatment and patient survival, and we validate our findings in another dataset. Our approach might help clinicians to better predict outcomes in patients with leukemia and make decisions on treatment.

Validation and refinement of the 2022 European LeukemiaNet genetic risk stratification of acute myeloid leukemia.

The revised 2022 European LeukemiaNet (ELN) AML risk stratification system requires validation in large, homogeneously treated cohorts. We studied 1118 newly diagnosed AML patients (median age, 58 years; range, 18-86 years) who received cytarabine-based induction chemotherapy between 1999 and 2012 and compared ELN-2022 to the previous ELN-2017 risk classification. Key findings were validated in a cohort of 1160 mostly younger patients. ELN-2022 reclassified 15% of patients, 3% into more favorable, and 12% into more adverse risk groups. This was mainly driven by patients reclassified from intermediate- to adverse-risk based on additional myelodysplasia-related mutations being included as adverse-risk markers. These patients (n = 79) had significantly better outcomes than patients with other adverse-risk genotypes (5-year OS, 26% vs. 12%) and resembled the remaining intermediate-risk group. Overall, time-dependent ROC curves and Harrel's C-index controlling for age, sex, and AML type (de novo vs. sAML/tAML) show slightly worse prognostic discrimination of ELN-2022 compared to ELN-2017 for OS. Further refinement of ELN-2022 without including additional genetic markers is possible, in particular by recognizing TP53-mutated patients with complex karyotypes as "very adverse". In summary, the ELN-2022 risk classification identifies a larger group of adverse-risk patients at the cost of slightly reduced prognostic accuracy compared to ELN-2017.

CD33 BiTE® molecule-mediated immune synapse formation and subsequent T-cell activation is determined by the expression profile of activating and inhibitory checkpoint molecules on AML cells.

Bispecific T-cell engager (BiTE®) molecules recruit T cells to cancer cells through CD3ε binding, independently of T-cell receptor (TCR) specificity. Whereas physiological T-cell activation is dependent on signal 1 (TCR engagement) and signal 2 (co-stimulation), BiTE molecule-mediated T-cell activation occurs without additional co-stimulation. As co-stimulatory and inhibitory molecules modulate the strength and nature of T-cell responses, we studied the impact of the expression profile of those molecules on target cells for BiTE molecule-mediated T-cell activation in the context of acute myeloid leukemia (AML). Accordingly, we created a novel in vitro model system using murine Ba/F3 cells transduced with human CD33 ± CD86 ± PD-L1. T-cell fitness was assessed by T-cell function assays in co-cultures and immune synapse formation by applying a CD33 BiTE molecule (AMG 330). Using our cell-based model platform, we found that the expression of positive co-stimulatory molecules on target cells markedly enhanced BiTE molecule-mediated T-cell activation. The initiation and stability of the immune synapse between T cells and target cells were significantly increased through the expression of CD86 on target cells. By contrast, the co-inhibitory molecule PD-L1 impaired the stability of BiTE molecule-induced immune synapses and subsequent T-cell responses. We validated our findings in primary T-cell-AML co-cultures, demonstrating a PD-L1-mediated reduction in redirected T-cell activation. The addition of the immunomodulatory drug (IMiD) lenalidomide to co-cultures led to stabilization of immune synapses and improved subsequent T-cell responses. We conclude that target cells modulate CD33 BiTE molecule-dependent T-cell activation and hence, combinatorial strategies might contribute to enhanced efficacy.

Mechanosensing via a GpIIb/Src/14-3-3ζ axis critically regulates platelet migration in vascular inflammation.

Platelets are not only the first responders in thrombosis and hemostasis but also central players in inflammation. Compared with platelets recruited to thrombi, immune-responsive platelets use distinct effector functions including actin-related protein complex 2/3-dependent migration along adhesive substrate gradients (haptotaxis), which prevents inflammatory bleeding and contributes to host defense. How platelet migration in this context is regulated on a cellular level is incompletely understood. Here, we use time-resolved morphodynamic profiling of individual platelets to show that migration, in contrast to clot retraction, requires anisotropic myosin IIa-activity at the platelet rear which is preceded by polarized actin polymerization at the front to initiate and maintain migration. Integrin GPIIb-dependent outside-in signaling via Gα13 coordinates polarization of migrating platelets to trigger tyrosine kinase c-Src/14-3-3ζ-dependent lamellipodium formation and functions independent of soluble agonists or chemotactic signals. Inhibitors of this signaling cascade, including the clinically used ABL/c-Src inhibitor dasatinib, interfere predominantly with the migratory capacity of platelets, without major impairment of classical platelet functions. In murine inflammation models, this translates to reduced migration of platelets visualized by 4D intravital microscopy, resulting in increased inflammation-associated hemorrhage in acute lung injury. Finally, platelets isolated from patients with leukemia treated with dasatinib who are prone to clinically relevant hemorrhage exhibit prominent migration defects, whereas other platelet functions are only partially affected. In summary, we define a distinct signaling pathway essential for migration and provide novel mechanistic insights explaining dasatinib-related platelet dysfunction and bleeding.

[Infections in patients with acute myeloid leukemia]. / Infektionen bei Patient*innen mit Akuter Myeloischer Leukämie.

Infections represent one of the most frequent complications during therapy of acute myeloid leukemia (AML). In addition to associated prolonged phases of neutropenia, damage to the mucosal barrier by cytotoxic agents favors infections caused by endogenous pathogens. The source often remains unknown with bacteremia being the most common evidence of infection. Infections with gram-positive bacteria predominate, however, infections with gram-negative bacteria more often lead to sepsis and death. Due to prolonged neutropenia, patients with AML are furthermore at risk for invasive fungal infections. Viruses, on the other hand, are rarely the cause of neutropenic fever. Because of the limited inflammatory response in neutropenic patients, fever is often the only sign of infection and therefore always represents a hematologic emergency. Prompt diagnosis and initiation of an adequate anti-infective therapy are critical to avoid progression to sepsis and possibly death.

Explainable AI identifies diagnostic cells of genetic AML subtypes.

Explainable AI is deemed essential for clinical applications as it allows rationalizing model predictions, helping to build trust between clinicians and automated decision support tools. We developed an inherently explainable AI model for the classification of acute myeloid leukemia subtypes from blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient's blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms.

Allogeneic Hematopoietic Cell Transplantation vs Standard Consolidation Chemotherapy in Patients With Intermediate-Risk Acute Myeloid Leukemia: A Randomized Clinical Trial.

Importance: The ideal postremission strategy in intermediate-risk acute myeloid leukemia (AML) in first complete remission (CR) has been a matter of debate. Objective: To explore the optimal therapy for patients with intermediate-risk AML after first complete remission. Design, Settings, and Participants: This investigator-initiated, open-label, 2-armed, phase 3 randomized clinical trial assessed patients at 16 hospitals in Germany from February 2, 2011, until July 1, 2018. Key eligibility criteria included cytogenetically defined intermediate-risk AML according to Medical Research Council classification, first CR or CR with incomplete blood cell count recovery after conventional induction therapy, age of 18 to 60 years, and availability of a human leukocyte antigen (HLA)-matched sibling or unrelated donor. A detailed statistical analysis plan was written and finalized on July 7, 2020. Data were exported for analysis on April 13, 2021. Interventions: Patients were randomized 1:1 to receive allogeneic hematopoietic cell transplantation (HCT) or high-dose cytarabine for consolidation and salvage HCT only in case of relapse. Strata for randomization included age (18-40 vs 41-60 years), NPM1 and CEBPA variation status, and donor type (unrelated vs related). Main Outcomes and Measures: End points included overall-survival as the primary outcome and disease-free survival, cumulative incidence of relapse, treatment-related mortality, and quality of life measured according to the Medical Outcomes Study 36-Item Short-Form Health Survey as secondary outcomes. Results: A total of 143 patients (mean [SD] age, 48.2 [9.8] years; 81 [57%] male) with AML who fulfilled the eligibility criteria were randomized. In the intention-to-treat analysis, the probability of survival at 2 years was 74% (95% CI, 62%-83%) after primary allogeneic HCT and 84% (95% CI, 73%-92%) after consolidation chemotherapy (P = .22). Disease-free survival after HCT at 2 years was 69% (95% CI, 57%-80%) compared with 40% (95% CI, 28%-53%) after consolidation chemotherapy (P = .001). Allogeneic HCT during the first CR was associated with a cumulative incidence of relapse at 2 years of 20% (95% CI, 13%-31%) compared with 58% (95% CI, 47%-71%; P < .001). Nonrelapse mortality at 2 years after primary allogeneic HCT was 9% (95% CI, 5%-19%) and 2% (95% CI, 0%-11%) after consolidation chemotherapy (P = .005). Similar outcomes were observed when analyses were confined to the 96 patients at intermediate risk according to the European Leukemia Network classification. Most importantly, all 41 patients relapsing after consolidation chemotherapy (36 hematologic, 4 molecular, and 1 extramedullary) proceeded to allogeneic HCT. No significant differences in health-related quality of life measures were observed between groups. Conclusions and Relevance: Primary allogeneic HCT during first CR was not associated with superior overall survival compared with consolidation chemotherapy in patients 60 years or younger with intermediate-risk AML during the first CR and an available donor. Trial Registration: ClinicalTrials.gov Identifier: NCT01246752.

AGIHO guideline on evidence-based management of COVID-19 in cancer patients: 2022 update on vaccination, pharmacological prophylaxis and therapy in light of the omicron variants.

The novel coronavirus SARS-CoV-2 and the associated infectious disease COVID-19 pose a significant challenge to healthcare systems worldwide. Patients with cancer have been identified as a high-risk population for severe infections, rendering prophylaxis and treatment strategies for these patients particularly important. Rapidly evolving clinical research, resulting in the recent advent of various vaccines and therapeutic agents against COVID-19, offers new options to improve care and protection of cancer patients. However, ongoing epidemiological changes and rise of new virus variants require repeated revisions and adaptations of prophylaxis and treatment strategies to meet these new challenges. Therefore, this guideline provides an update on evidence-based recommendations with regard to vaccination, pharmacological prophylaxis and treatment of COVID-19 in cancer patients in light of the currently dominant omicron variants. It was developed by an expert panel of the Infectious Diseases Working Party (AGIHO) of the German Society for Hematology and Medical Oncology (DGHO) based on a critical review of the most recent available data.

A randomized phase 2 trial of nintedanib and low-dose cytarabine in elderly patients with acute myeloid leukemia ineligible for intensive chemotherapy.

We investigated the safety and efficacy of nintedanib added to low-dose cytarabine (LDAC) in a phase 1/2 study in patients 60 years or older with newly diagnosed or relapsed/refractory (r/r) AML ineligible for intensive chemotherapy. The results of the dose-finding phase 1 part have been previously published. Patients were randomized 1:1 to LDAC plus nintedanib or LDAC plus placebo stratified by AML status (newly diagnosed vs r/r). LDAC was applied subcutaneously at 20 mg twice daily on days 1 to 10. Nintedanib/placebo was orally administered twice daily on days 1 to 28 in 28-day cycles. The primary endpoint was overall survival (OS). Between 05/2017 and 09/2019, 31 patients were randomized and 30 were treated, before the study was terminated prematurely due to slow recruitment. Median (range) age of patients was 76 (60-84) years. Twenty-two patients (73%) had r/r AML. Median OS in patients treated with LDAC and nintedanib was 3.4 months, compared with 3.6 months in those treated in the placebo arm, with a HR adjusted for AML status of 1.19 (corresponding confirmatory adjusted 95% CI, 0.55-2.56; univariate log-rank P = 0.96). In the 22 patients with r/r AML, median OS was 3.0 months in the nintedanib and 3.6 months in the placebo arm (P = 0.36). One patient in the nintedanib and two patients in the placebo arm achieved a CR and entered maintenance treatment. Nintedanib showed no superior therapeutic activity over placebo when added to LDAC in elderly AML patients considered unfit for intensive chemotherapy. The trial was registered at clinicaltrials.gov NCT01488344.