Overlapping Stromal Alterations in Myeloid and Lymphoid Neoplasms.

Myeloid and lymphoid neoplasms share the characteristics of potential bone marrow infiltration as a primary or secondary effect, which readily leads to hematopoietic insufficiency. The mechanisms by which clonal malignant cells inhibit normal hematopoietic stem and progenitor cells (HSPCs) in the bone marrow (BM) have not been unraveled so far. Given the pivotal role of mesenchymal stromal cells (MSCs) in the regulation of hematopoiesis in the BM niche it is assumed that MSCs also play a relevant role in the pathogenesis of hematological neoplasms. We aimed to identify overlapping mechanisms in MSCs derived from myeloid and lymphoid neoplasms contributing to disease progression and suppression of HSPCs to develop interventions that target these mechanisms. MSCs derived from healthy donors (n = 44) and patients diagnosed with myeloproliferative neoplasia (n = 11), myelodysplastic syndromes (n = 16), or acute myeloid leukemia (n = 25) and B-Non-Hodgkin lymphoma (n = 9) with BM infiltration and acute lymphoblastic leukemia (n = 9) were analyzed for their functionality and by RNA sequencing. A reduced growth and differentiation capacity of MSCs was found in all entities. RNA sequencing distinguished both groups but clearly showed overlapping differentially expressed genes, including major players in the BMP/TGF and WNT-signaling pathway which are crucial for growth, osteogenesis, and hematopoiesis. Functional alterations in healthy MSCs were inducible by exposure to supernatants from malignant cells, implicating the involvement of these factors in disease progression. Overall, we were able to identify overlapping factors that pose potential future therapeutic targets.

Characterization of CD34+ Cells from Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS) Using a t-Distributed Stochastic Neighbor Embedding (t-SNE) Protocol.

Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic cells from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 as representatives for a hierarchical hematopoietic stem and progenitor cell (HSPC) classification as well as programmed death ligand 1 (PD-L1). Rather than restricting the evaluation on a 2- or 3-dimensional analysis, we applied a t-distributed stochastic neighbor embedding (t-SNE) approach to obtain deeper insight and segregation between leukemic cells and normal HPSCs. For that purpose, we created a t-SNE map, which resulted in the visualization of 27 cell clusters based on their similarity concerning the composition and intensity of antigen expression. Two of these clusters were "leukemia-related" containing a great proportion of CD34+/CD38- hematopoietic stem cells (HSCs) or CD34+ cells with a strong co-expression of CD45RA/CD123, respectively. CD34+ cells within the latter cluster were also highly positive for PD-L1 reflecting their immunosuppressive capacity. Beyond this proof of principle study, the inclusion of additional markers will be helpful to refine the differentiation between normal HSPCs and leukemic cells, particularly in the context of minimal disease detection and antigen-targeted therapeutic interventions. Furthermore, we suggest a protocol for the assignment of new cell ensembles in quantitative terms, via a numerical value, the Pearson coefficient, based on a similarity comparison of the t-SNE pattern with a reference.

Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography.

Background: Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD. Methods: Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated. Results: Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements. Conclusion: Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.

Smart Conditioning with Venetoclax-Enhanced Sequential FLAMSA + RIC in Patients with High-Risk Myeloid Malignancies.

Up to 50% of patients with high-risk myeloid malignancies die of relapse after allogeneic stem cell transplantation. Current sequential conditioning regimens like the FLAMSA protocol combine intensive induction therapy with TBI or alkylators. Venetoclax has synergistic effects to chemotherapy. In a retrospective survey among German transplant centers, we identified 61 patients with myeloid malignancies that had received FLAMSA-based sequential conditioning with venetoclax between 2018 and 2022 as an individualized treatment approach. Sixty patients (98%) had active disease at transplant and 74% had genetic high-risk features. Patients received allografts from matched unrelated, matched related, or mismatched donors. Tumor lysis syndrome occurred in two patients but no significant non-hematologic toxicity related to venetoclax was observed. On day +30, 55 patients (90%) were in complete remission. Acute GvHD II°-IV° occurred in 17 (28%) and moderate/severe chronic GvHD in 7 patients (12%). Event-free survival and overall survival were 64% and 80% at 1 year as well as 57% and 75% at 2 years, respectively. The off-label combination of sequential FLAMSA-RIC with venetoclax appears to be safe and highly effective. To further validate these insights and enhance the idea of smart conditioning, a controlled prospective clinical trial was initiated in July 2023.

Insights into gut microbiomes in stem cell transplantation by comprehensive shotgun long-read sequencing.

The gut microbiome is a diverse ecosystem, dominated by bacteria; however, fungi, phages/viruses, archaea, and protozoa are also important members of the gut microbiota. Exploration of taxonomic compositions beyond bacteria as well as an understanding of the interaction between the bacteriome with the other members is limited using 16S rDNA sequencing. Here, we developed a pipeline enabling the simultaneous interrogation of the gut microbiome (bacteriome, mycobiome, archaeome, eukaryome, DNA virome) and of antibiotic resistance genes based on optimized long-read shotgun metagenomics protocols and custom bioinformatics. Using our pipeline we investigated the longitudinal composition of the gut microbiome in an exploratory clinical study in patients undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT; n = 31). Pre-transplantation microbiomes exhibited a 3-cluster structure, characterized by Bacteroides spp. /Phocaeicola spp., mixed composition and Enterococcus abundances. We revealed substantial inter-individual and temporal variabilities of microbial domain compositions, human DNA, and antibiotic resistance genes during the course of alloHSCT. Interestingly, viruses and fungi accounted for substantial proportions of microbiome content in individual samples. In the course of HSCT, bacterial strains were stable or newly acquired. Our results demonstrate the disruptive potential of alloHSCTon the gut microbiome and pave the way for future comprehensive microbiome studies based on long-read metagenomics.

Stromal alterations in patients with monoclonal gammopathy of undetermined significance, smoldering myeloma, and multiple myeloma.

ABSTRACT: The hallmark of multiple myeloma (MM) is a clonal plasma cell infiltration in the bone marrow accompanied by myelosuppression and osteolysis. Premalignant stages such as monoclonal gammopathy of undetermined significance (MGUS) and asymptomatic stages such as smoldering myeloma (SMM) can progress to MM. Mesenchymal stromal cells (MSCs) are an integral component of the bone marrow microenvironment and play an important role in osteoblast differentiation and hematopoietic support. Although stromal alterations have been reported in MM contributing to hematopoietic insufficiency and osteolysis, it is not clear whether alterations in MSC already occur in MGUS or SMM. In this study, we analyzed MSCs from MGUS, SMM, and MM regarding their properties and functionality and performed messenger RNA sequencing to find underlying molecular signatures in different disease stages. A high number of senescent cells and a reduced osteogenic differentiation capacity and hematopoietic support were already present in MGUS MSC. As shown by RNA sequencing, there was a broad spectrum of differentially expressed genes including genes of the BMP/TGF-signaling pathway, detected already in MGUS and that clearly increases in patients with SMM and MM. Our data may help to block these signaling pathways in the future to hinder progression to MM.

Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI.

Large-area processing of perovskite semiconductor thin-films is complex and evokes unexplained variance in quality, posing a major hurdle for the commercialization of perovskite photovoltaics. Advances in scalable fabrication processes are currently limited to gradual and arbitrary trial-and-error procedures. While the in situ acquisition of photoluminescence (PL) videos has the potential to reveal important variations in the thin-film formation process, the high dimensionality of the data quickly surpasses the limits of human analysis. In response, this study leverages deep learning (DL) and explainable artificial intelligence (XAI) to discover relationships between sensor information acquired during the perovskite thin-film formation process and the resulting solar cell performance indicators, while rendering these relationships humanly understandable. The study further shows how gained insights can be distilled into actionable recommendations for perovskite thin-film processing, advancing toward industrial-scale solar cell manufacturing. This study demonstrates that XAI methods will play a critical role in accelerating energy materials science.

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.

OBJECTIVES: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity. MATERIALS AND METHODS: Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM). RESULTS: The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001). CONCLUSION: Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration. CLINICAL RELEVANCE STATEMENT: Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping. KEY POINTS: • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).

Allogeneic hematopoietic stem cell transplantation and pre-transplant strategies in patients with NPM1-mutated acute myeloid leukemia: a single center experience.

Patients with acute myeloid leukemia (AML) and nucleophosmin 1 gene mutations (NPM1mut) show a favorable prognosis with chemotherapy (CT) in the absence of negative prognostic genetic abnormalities. Between 2008 and 2021 64 patients with NPM1mutAML received alloHSCT because of additional adverse prognostic factors (1st line), inadequate response to or relapse during or after CT (2nd line). To expand the evidence in alloTX in NPM1mut AML, clinical and molecular data were retrospectively analyzed with respect to pre-transplant strategies and outcome. Patients with minimal residual disease negative (MRD-) CR at transplant had better 2-y-PFS and 2-y-OS (77% and 88%) than patients with minimal residual disease positive (MRD+) CR (41% and 71%) or patients with active disease (AD) at transplant (20% and 52%). The 2nd line patients with relapse after completing CT responded well to high dose cytarabine based salvage chemotherapy (salvage CT) in contrast to patients relapsing while still on CT (90% vs 20%, P = 0.0170). 2-y-PFS and 2-y-OS was 86% in patients who achieved a 2nd MRD- CR pre alloHSCT. Outcome in NPM1mutAML depends on disease burden at alloHSCT. Time and type of relapse in relation to CT are predictive for response to salvage CT.

Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies.

Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at -24 h and 0.88 at -48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management.

Azacitidine, lenalidomide and donor lymphocyte infusions for relapse of myelodysplastic syndrome, acute myeloid leukemia and chronic myelomonocytic leukemia after allogeneic transplant: the Azalena-Trial.

Azacitidine (Aza) combined with donor lymphocyte infusions (DLI) is an established treatment for relapse of myeloid malignancies after allogeneic transplantation. Based on its immunomodulatory and anti-leukemic properties we considered Lenalidomide (Lena) to act synergistically with Aza/DLI to improve outcome. We, therefore, prospectively investigated tolerability and efficacy of this combination as first salvage therapy for adults with post-transplant relapse of acute myeloid leukemia, myelodysplastic syndromes and chronic myelomonocytic leukemia. Patients were scheduled for eight cycles Aza (75 mg/m2 day 1-7), Lena (2.5 or 5 mg, days 1-21) and up to three DLI with increasing T-cell dosages (0.5×106-1.5×107 cells/kg). Primary endpoint was safety, while secondary endpoints included response, graft-versus-host disease (GvHD) and overall survival (OS). Fifty patients with molecular (52%) or hematological (48%) relapse of myelodysplastic syndromes (n=24), acute myeloid leukemia (n=23) or chronic myelomonocytic leukemia (n=3) received a median of seven (range, 1-8) cycles including 14 patients with 2.5 mg and 36 with 5 mg Lena daily dosage. Concomitantly, 34 patients (68%) received at least one DLI. Overall response rate was 56% and 25 patients (50%) achieved complete remission being durable in 80%. Median OS was 21 months and 1-year OS rate 65% with no impact of type of or time to relapse and Lena dosages. Treatment was well tolerated indicated by febrile neutropenia being the only grade ≥3 non-hematologic adverse event in >10% of patients and modest acute (grade 2-4 24%) and chronic (moderate/severe 28%) GvHD incidences. In summary, Lena can be safely added to Aza/DLI without excess of GvHD and toxicity. Its significant anti-leukemic activity suggests that this combination is a novel salvage option for post-transplant relapse (clinicaltrials gov. Identifier: NCT02472691).

The Cell Tracking Challenge: 10 years of objective benchmarking.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

The mycotoxin viriditoxin induces leukemia- and lymphoma-specific apoptosis by targeting mitochondrial metabolism.

Inhibition of the mitochondrial metabolism offers a promising therapeutic approach for the treatment of cancer. Here, we identify the mycotoxin viriditoxin (VDT), derived from the endophytic fungus Cladosporium cladosporioides, as an interesting candidate for leukemia and lymphoma treatment. VDT displayed a high cytotoxic potential and rapid kinetics of caspase activation in Jurkat leukemia and Ramos lymphoma cells in contrast to solid tumor cells that were affected to a much lesser extent. Most remarkably, human hematopoietic stem and progenitor cells and peripheral blood mononuclear cells derived from healthy donors were profoundly resilient to VDT-induced cytotoxicity. Likewise, the colony-forming capacity was affected only at very high concentrations, which provides a therapeutic window for cancer treatment. Intriguingly, VDT could directly activate the mitochondrial apoptosis pathway in leukemia cells in the presence of antiapoptotic Bcl-2 proteins. The mitochondrial toxicity of VDT was further confirmed by inhibition of mitochondrial respiration, breakdown of the mitochondrial membrane potential (ΔΨm), the release of mitochondrial cytochrome c, generation of reactive oxygen species (ROS), processing of the dynamin-like GTPase OPA1 and subsequent fission of mitochondria. Thus, VDT-mediated targeting of mitochondrial oxidative phosphorylation (OXPHOS) might represent a promising therapeutic approach for the treatment of leukemia and lymphoma without affecting hematopoietic stem and progenitor cells.

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine.

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.

MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images.

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure. Moreover, flagging OoD cases may support human readers in identifying incidental findings. Due to the increased interest in OoD algorithms, benchmarks for different domains have recently been established. In the medical imaging domain, for which reliable predictions are often essential, an open benchmark has been missing. We introduce the Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain. The analysis of the submitted algorithms shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice. We also see a strong correlation between challenge ranking and performance on a simple toy test set, indicating that this might be a valuable addition as a proxy dataset during anomaly detection algorithm development.

Feasibility of Wearable-Based Remote Monitoring in Patients During Intensive Treatment for Aggressive Hematologic Malignancies.

PURPOSE: Intensive treatment protocols for aggressive hematologic malignancies harbor a high risk of serious clinical complications, such as infections. Current techniques of monitoring vital signs to detect such complications are cumbersome and often fail to diagnose them early. Continuous monitoring of vital signs and physical activity by means of an upper arm medical wearable allowing 24/7 streaming of such parameters may be a promising alternative. METHODS: This single-arm, single-center observational trial evaluated symptom-related patient-reported outcomes and feasibility of a wearable-based remote patient monitoring. All wearable data were reviewed retrospectively and were not available to the patient or clinical staff. A total of 79 patients (54 inpatients and 25 outpatients) participated and received standard-of-care treatment for a hematologic malignancy. In addition, the wearable was continuously worn and self-managed by the patient to record multiple parameters such as heart rate, oxygen saturation, and physical activity. RESULTS: Fifty-one patients (94.4%) in the inpatient cohort and 16 (64.0%) in the outpatient cohort reported gastrointestinal symptoms (diarrhea, nausea, and emesis), pain, dyspnea, or shivering in at least one visit. With the wearable, vital signs and physical activity were recorded for a total of 1,304.8 days. Recordings accounted for 78.0% (63.0-88.5; median [interquartile range]) of the potential recording time for the inpatient cohort and 84.6% (76.3-90.2) for the outpatient cohort. Adherence to the wearable was comparable in both cohorts, but decreased moderately over time during the trial. CONCLUSION: A high adherence to the wearable was observed in patients on intensive treatment protocols for a hematologic malignancy who experience high symptom burden. Remote patient monitoring of vital signs and physical activity was demonstrated to be feasible and of primarily sufficient quality.

Acute myeloid leukemia-induced functional inhibition of healthy CD34+ hematopoietic stem and progenitor cells.

Acute myeloid leukemia (AML) is characterized by an expansion of leukemic cells and a simultaneous reduction of normal hematopoietic precursors in the bone marrow (BM) resulting in hematopoietic insufficiency, but the underlying mechanisms are poorly understood in humans. Assuming that leukemic cells functionally inhibit healthy CD34+ hematopoietic stem and progenitor cells (HSPC) via humoral factors, we exposed healthy BM-derived CD34+ HSPC to cell-free supernatants derived from AML cell lines as well as from 24 newly diagnosed AML patients. Exposure to AML-derived supernatants significantly inhibited proliferation, cell cycling, colony formation, and differentiation of healthy CD34+ HSPC. RNA sequencing of healthy CD34+ HSPC after exposure to leukemic conditions revealed a specific signature of genes related to proliferation, cell-cycle regulation, and differentiation, thereby reflecting their functional inhibition on a molecular level. Experiments with paired patient samples showed that these inhibitory effects are markedly related to the immunomagnetically enriched CD34+ leukemic cell population. Using PCR, ELISA, and RNA sequencing, we detected overexpression of TGFß1 in leukemic cells on the transcriptional and protein level and, correspondingly, a molecular signature related to TGFß1 signaling in healthy CD34+ HSPC. This inhibitory effect of TGFß1 on healthy hematopoiesis was functionally corrobated and could be pharmacologically reverted by SD208, an inhibitor of TGFß receptor 1 signaling. Overall, these data indicate that leukemic cells induce functional inhibition of healthy CD34+ HSPC, at least in part, through TGFß1, suggesting that blockage of this pathway may improve hematopoiesis in AML.

Myelodysplastic syndrome patients display alterations in their immune status reflected by increased PD-L1-expressing stem cells and highly dynamic exhausted T-cell frequencies.

Little data are available for the expression of immune checkpoint (IC) molecules within myelodysplastic syndrome (MDS). Here, we report increased PD-L1+ CD34+ CD38- and PD-L1+ CD34+ CD38+ stem cell frequencies within MDS patients compared to stem cell recipients in remission. Additionally, we observed exceedingly similar PD1+ and Tim-3+ T-cell frequencies between acute myeloid leukaemia (AML) and MDS samples that were elevated compared to patients in remission. Furthermore, we found highly dynamic Tim-3+ and PD1+ T-cell frequencies within serial samples of relapsing MDS with excess blasts (MDS-EB II) patients, correlating with further disease markers. These findings support the idea of a potential successful implementation of IC inhibitor treatment in suitable MDS patients.

Prognostic impact of pretransplant measurable residual disease assessed by peripheral blood WT1-mRNA expression in patients with AML and MDS.

OBJECTIVE: As peripheral blood (PB) Wilm's Tumor 1 (WT1)-mRNA expression is established as MRD-marker during conventional AML chemotherapy, impact of pretransplant WT1 expression remains unclear. Therefore, we aimed to assess prognostic impact of pretransplant WT1 expression on post-transplant outcome in patients with AML/MDS. METHODS: In 64 AML/MDS patients, pretransplant WT1 expression was retrospectively analyzed using a standardized assay offering high sensitivity, specificity, and a validated cut-off. Patients were divided into three groups determined by pretransplant remission and WT1 expression. Post-transplant outcome of these groups was compared regarding cumulative incidence of relapse (CIR), relapse-free (RFS), and overall survival (OS). RESULTS: Pretransplant forty-six patients (72%) showed hematologic remission, including 21 (46%) MRD-negative and 25 (54%) MRD-positive patients indicated by WT1 expression, while 18 refractory patients (28%) showed active disease. Two-year estimates of post-transplant CIR, RFS, and OS were similar in MRD-positive (61%, 37%, 54%) and refractory patients (70%, 26%, 56%), but significantly inferior compared with MRD-negative patients (10%, 89%, 90%). After multivariable adjustment, pretransplant MRD negativity measured by WT1 expression retained its prognostic impact on CIR (P = .008), RFS (P = .005), and OS (P = .049). CONCLUSIONS: PB WT1 expression represents a useful method to estimate pretransplant MRD, which is highly predictable for post-transplant outcome and may help improving peri-transplant management in AML/MDS patients.