Retained functional normal and preleukemic HSCs at diagnosis are associated with good prognosis in DNMT3AmutNPM1mut AMLs.

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by high rate of relapse and mortality. Current chemotherapies whilst successful in eradicating blasts, are less effective in eliminating relapse-causing leukemic stem cells (LSCs). Although LSCs are usually identified as CD34+CD38- cells, there is significant heterogeneity in surface marker expression, and CD34- LSCs exist particularly in NPM1mut AMLs. By analyzing diagnostic primary DNMT3AmutNPM1mut AML samples, we suggest a novel flow cytometry sorting strategy particularly useful for CD34neg AML subtypes. To enrich for LSCs independently of CD34 status, positive selection for GPR56 and negative selection for NKG2D ligands are used. We show that the functional reconstitution capacity of CD34- and CD34+ LSCs as well as their transcriptomes are very similar which support phenotypic plasticity. Furthermore, we show that although CD34+ subpopulations can contain next to LSCs also normal and/or preleukemic hematopoietic stem cells (HSCs), this is not the case in CD34-GPR56+NKG2DL- enriched LSCs which thus can be isolated with high purity. Finally, we show that patients with AML, who retain at the time of diagnosis a reserve of normal and/or preleukemic HSCs in their bone marrow able to reconstitute immunocompromised mice, have significantly longer relapse-free and overall survival than patients with AML in whom functional HSCs are no longer detectable.

Comparison of extraction methods for intracellular metabolomics of human tissues.

Analyses of metabolic compounds inside cells or tissues provide high information content since they represent the endpoint of biological information flow and are a snapshot of the integration of many regulatory processes. However, quantification of the abundance of metabolites requires their careful extraction. We present a comprehensive study comparing ten extraction protocols in four human sample types (liver tissue, bone marrow, HL60, and HEK cells) aiming to detect and quantify up to 630 metabolites of different chemical classes. We show that the extraction efficiency and repeatability are highly variable across protocols, tissues, and chemical classes of metabolites. We used different quality metrics including the limit of detection and variability between replicates as well as the sum of concentrations as a global estimate of analytical repeatability of the extraction. The coverage of extracted metabolites depends on the used solvents, which has implications for the design of measurements of different sample types and metabolic compounds of interest. The benchmark dataset can be explored in an easy-to-use, interactive, and flexible online resource (R/shiny app MetaboExtract: http://www.metaboextract.shiny.dkfz.de) for context-specific selection of the optimal extraction method. Furthermore, data processing and conversion functionality underlying the shiny app are accessible as an R package: https://cran.r-project.org/package=MetAlyzer.

Gene expression-based prediction of pazopanib efficacy in sarcoma.

BACKGROUND: The multi-receptor tyrosine kinase inhibitor pazopanib is approved for the treatment of advanced soft-tissue sarcoma and has also shown activity in other sarcoma subtypes. However, its clinical efficacy is highly variable, and no reliable predictors exist to select patients who are likely to benefit from this drug. PATIENTS AND METHODS: We analysed the molecular profiles and clinical outcomes of patients with pazopanib-treated sarcoma enrolled in a prospective observational study by the German Cancer Consortium, DKTK MASTER, that employs whole-genome/exome sequencing and transcriptome sequencing to inform the care of young adults with advanced cancer across histology and patients with rare cancers. RESULTS: Among 109 patients with available whole-genome/exome sequencing data, there was no correlation between clinical parameters, specific genetic alterations or mutational signatures and clinical outcome. In contrast, the analysis of a subcohort of 62 patients who underwent molecular analysis before pazopanib treatment and had transcriptome sequencing data available showed that mRNA levels of NTRK3 (hazard ratio [HR] = 0.53, p = 0.021), IGF1R (HR = 1.82, p = 0.027) and KDR (HR = 0.50, p = 0.011) were independently associated with progression-free survival (PFS). Based on the expression of these receptor tyrosine kinase genes, i.e. the features NTRK3-high, IGF1R-low and KDR-high, we developed a pazopanib efficacy predictor that stratified patients into three groups with significantly different PFS (p < 0.0001). Application of the pazopanib efficacy predictor to an independent cohort of patients with pazopanib-treated sarcoma from DKTK MASTER (n = 43) confirmed its potential to separate patient groups with significantly different PFS (p = 0.02), whereas no such association was observed in patients with sarcoma from DKTK MASTER (n = 97) or The Cancer Genome Atlas sarcoma cohort (n = 256) who were not treated with pazopanib. CONCLUSION: A score based on the combined expression of NTRK3, IGF1R and KDR allows the identification of patients with sarcoma and with good, intermediate and poor outcome following pazopanib therapy and warrants prospective investigation as a predictive tool to optimise the use of this drug in the clinic.

Antigen presentation safeguards the integrity of the hematopoietic stem cell pool.

Hematopoietic stem and progenitor cells (HSPCs) are responsible for the production of blood and immune cells. Throughout life, HSPCs acquire oncogenic aberrations that can cause hematological cancers. Although molecular programs maintaining stem cell integrity have been identified, safety mechanisms eliminating malignant HSPCs from the stem cell pool remain poorly characterized. Here, we show that HSPCs constitutively present antigens via major histocompatibility complex class II. The presentation of immunogenic antigens, as occurring during malignant transformation, triggers bidirectional interactions between HSPCs and antigen-specific CD4+ T cells, causing stem cell proliferation, differentiation, and specific exhaustion of aberrant HSPCs. This immunosurveillance mechanism effectively eliminates transformed HSPCs from the hematopoietic system, thereby preventing leukemia onset. Together, our data reveal a bidirectional interaction between HSPCs and CD4+ T cells, demonstrating that HSPCs are not only passive receivers of immunological signals but also actively engage in adaptive immune responses to safeguard the integrity of the stem cell pool.

Deep Metabolic Profiling Assessment of Tissue Extraction Protocols for Three Model Organisms.

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimer's, Parkinson's disease or COVID-19. To better understand such diseases and their intricate metabolic pathways in human studies, model animals are regularly used. There, standardized rearing conditions and uniform sampling strategies are prerequisites towards a successful metabolomic study that can be achieved through model organisms. Although metabolomic approaches have been employed on model organisms before, no systematic assessment of different conditions to optimize metabolite extraction across several organisms and sample types has been conducted. We address this issue using a highly standardized metabolic profiling assay analyzing 630 metabolites across three commonly used model organisms (Drosophila, mouse, and zebrafish) to find an optimal extraction protocol for various matrices. Focusing on parameters such as metabolite coverage, concentration and variance between replicates we compared seven extraction protocols. We found that the application of a combination of 75% ethanol and methyl tertiary-butyl ether (MTBE), while not producing the broadest coverage and highest concentrations, was the most reproducible extraction protocol. We were able to determine up to 530 metabolites in mouse kidney samples, 509 in mouse liver, 422 in zebrafish and 388 in Drosophila and discovered a core overlap of 261 metabolites in these four matrices. To enable other scientists to search for the most suitable extraction protocol in their experimental context and interact with this comprehensive data, we have integrated our data set in the open-source shiny app "MetaboExtract". Hereby, scientists can search for metabolites or compound classes of interest, compare them across the different tested extraction protocols and sample types as well as find reference concentration values.

The proteogenomic subtypes of acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive blood cancer with a poor prognosis. We report a comprehensive proteogenomic analysis of bone marrow biopsies from 252 uniformly treated AML patients to elucidate the molecular pathophysiology of AML in order to inform future diagnostic and therapeutic approaches. In addition to in-depth quantitative proteomics, our analysis includes cytogenetic profiling and DNA/RNA sequencing. We identify five proteomic AML subtypes, each reflecting specific biological features spanning genomic boundaries. Two of these proteomic subtypes correlate with patient outcome, but none is exclusively associated with specific genomic aberrations. Remarkably, one subtype (Mito-AML), which is captured only in the proteome, is characterized by high expression of mitochondrial proteins and confers poor outcome, with reduced remission rate and shorter overall survival on treatment with intensive induction chemotherapy. Functional analyses reveal that Mito-AML is metabolically wired toward stronger complex I-dependent respiration and is more responsive to treatment with the BCL2 inhibitor venetoclax.

Treatment resistance analysis reveals GLUT-1-mediated glucose uptake as a major target of synthetic rocaglates in cancer cells.

Rocaglates are natural compounds that have been extensively studied for their ability to inhibit translation initiation. Rocaglates represent promising drug candidates for tumor treatment due to their growth-inhibitory effects on neoplastic cells. In contrast to natural rocaglates, synthetic analogues of rocaglates have been less comprehensively characterized, but were also shown to have similar effects on the process of protein translation. Here, we demonstrate an enhanced growth-inhibitory effect of synthetic rocaglates when combined with glucose anti-metabolite 2-deoxy-D-glucose (2DG) in different cancer cell lines. Moreover, we unravel a new aspect in the mechanism of action of synthetic rocaglates involving reduction of glucose uptake mediated by downregulation or abrogation of glucose transporter GLUT-1 expression. Importantly, cells with genetically induced resistance to synthetic rocaglates showed substantially less pronounced treatment effect on glucose metabolism and did not demonstrate GLUT-1 downregulation, pointing at the crucial role of this mechanism for the anti-tumor activity of the synthetic rocaglates. Transcriptome profiling revealed glycolysis as one of the major pathways differentially regulated in sensitive and resistant cells. Analysis of synthetic rocaglate efficacy in a 3D tissue context with a co-culture of tumor and normal cells demonstrated a selective effect on tumor cells and substantiated the mechanistic observations obtained in cancer cell lines. Increased glucose uptake and metabolism is a universal feature across different tumor types. Therefore, targeting this feature by synthetic rocaglates could represent a promising direction for exploitation of rocaglates in novel anti-tumor therapies.

RSPO2 inhibits BMP signaling to promote self-renewal in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a rapidly progressing cancer, for which chemotherapy remains standard treatment and additional therapeutic targets are requisite. Here, we show that AML cells secrete the stem cell growth factor R-spondin 2 (RSPO2) to promote their self-renewal and prevent cell differentiation. Although RSPO2 is a well-known WNT agonist, we reveal that it maintains AML self-renewal WNT independently, by inhibiting BMP receptor signaling. Autocrine RSPO2 signaling is also required to prevent differentiation and to promote self-renewal in normal hematopoietic stem cells as well as primary AML cells. Comprehensive datamining reveals that RSPO2 expression is elevated in patients with AML of poor prognosis. Consistently, inhibiting RSPO2 prolongs survival in AML mouse xenograft models. Our study indicates that in AML, RSPO2 acts as an autocrine BMP antagonist to promote cancer cell renewal and may serve as a marker for poor prognosis.

Cardiomyocyte depolarization triggers NOS-dependent NO transient after calcium release, reducing the subsequent calcium transient.

Cardiac excitation-contraction coupling and metabolic and signaling activities are centrally modulated by nitric oxide (NO), which is produced by one of three NO synthases (NOSs). Despite the significant role of NO in cardiac Ca2+ homeostasis regulation under different pathophysiological conditions, such as Duchenne muscular dystrophy (DMD), no precise method describes the production, source or effect of NO through two NO signaling pathways: soluble guanylate cyclase-protein kinase G (NO-sGC-PKG) and S-nitrosylation (SNO). Using a novel strategy involving isolated murine cardiomyocytes loaded with a copper-based dye highly specific for NO, we observed a single transient NO production signal after each electrical stimulation event. The NO transient signal started 67.5 ms after the beginning of Rhod-2 Ca2+ transient signal and lasted for approximately 430 ms. Specific NOS isoform blockers or NO scavengers significantly inhibited the NO transient, suggesting that wild-type (WT) cardiomyocytes produce nNOS-dependent NO transients. Conversely, NO transient in mdx cardiomyocyte, a mouse model of DMD, was dependent on inducible NOS (iNOS) and endothelial (eNOS). In a consecutive stimulation protocol, the nNOS-dependent NO transient in WT cardiomyocytes significantly reduced the next Ca2+ transient via NO-sGC-PKG. In mdx cardiomyocytes, this inhibitory effect was iNOS- and eNOS-dependent and occurred through the SNO pathway. Basal NO production was nNOS- and iNOS-dependent in WT cardiomyocytes and eNOS- and iNOS-dependent in mdx cardiomyocytes. These results showed cardiomyocyte produces NO isoform-dependent transients upon membrane depolarization at the millisecond time scale activating a specific signaling pathway to negatively modulate the subsequent Ca2+ transient.

Analysis of mutational signatures with yet another package for signature analysis.

Different mutational processes leave characteristic patterns of somatic mutations in the genome that can be identified as mutational signatures. Determining the contributions of mutational signatures to cancer genomes allows not only to reconstruct the etiology of somatic mutations, but can also be used for improved tumor classification and support therapeutic decisions. We here present the R package yet another package for signature analysis (YAPSA) to deconvolute the contributions of mutational signatures to tumor genomes. YAPSA provides in-built collections from the COSMIC and PCAWG SNV signature sets as well as the PCAWG Indel signatures and employs signature-specific cutoffs to increase sensitivity and specificity. Furthermore, YAPSA allows to determine 95% confidence intervals for signature exposures, to perform constrained stratified signature analyses to obtain enrichment and depletion patterns of the identified signatures and, when applied to whole exome sequencing data, to correct for the triplet content of individual target capture kits. With this functionality, YAPSA has proved to be a valuable tool for analysis of mutational signatures in molecular tumor boards in a precision oncology context. YAPSA is available at R/Bioconductor (http://bioconductor.org/packages/3.12/bioc/html/YAPSA.html).

ShinyButchR: Interactive NMF-based decomposition workflow of genome-scale datasets.

Non-negative matrix factorization (NMF) has been widely used for the analysis of genomic data to perform feature extraction and signature identification due to the interpretability of the decomposed signatures. However, running a basic NMF analysis requires the installation of multiple tools and dependencies, along with a steep learning curve and computing time. To mitigate such obstacles, we developed ShinyButchR, a novel R/Shiny application that provides a complete NMF-based analysis workflow, allowing the user to perform matrix decomposition using NMF, feature extraction, interactive visualization, relevant signature identification, and association to biological and clinical variables. ShinyButchR builds upon the also novel R package ButchR, which provides new TensorFlow solvers for algorithms of the NMF family, functions for downstream analysis, a rational method to determine the optimal factorization rank and a novel feature selection strategy.

Combination therapy targeting both innate and adaptive immunity improves survival in a pre-clinical model of ovarian cancer.

BACKGROUND: Despite major advancements in immunotherapy among a number of solid tumors, response rates among ovarian cancer patients remain modest. Standard treatment for ovarian cancer is still surgery followed by taxane- and platinum-based chemotherapy. Thus, there is an urgent need to develop novel treatment options for clinical translation. METHODS: Our approach was to analyze the effects of standard chemotherapy in the tumor microenvironment of mice harboring orthotopic, syngeneic ID8-Vegf-Defb29 ovarian tumors in order to mechanistically determine a complementary immunotherapy combination. Specifically, we interrogated the molecular and cellular consequences of chemotherapy by analyzing gene expression and flow cytometry data. RESULTS: These data show that there is an immunosuppressive shift in the myeloid compartment, with increased expression of IL-10 and ARG1, but no activation of CD3+ T cells shortly after chemotherapy treatment. We therefore selected immunotherapies that target both the innate and adaptive arms of the immune system. Survival studies revealed that standard chemotherapy was complemented most effectively by a combination of anti-IL-10, 2'3'-cGAMP, and anti-PD-L1. Immunotherapy dramatically decreased the immunosuppressive myeloid population while chemotherapy effectively activated dendritic cells. Together, combination treatment increased the number of activated T and dendritic cells as well as expression of cytotoxic factors. It was also determined that the immunotherapy had to be administered concurrently with the chemotherapy to reverse the acute immunosuppression caused by chemotherapy. Mechanistic studies revealed that antitumor immunity in this context was driven by CD4+ T cells, which acquired a highly activated phenotype. Our data suggest that these CD4+ T cells can kill cancer cells directly via granzyme B-mediated cytotoxicity. Finally, we showed that this combination therapy is also effective at delaying tumor growth substantially in an aggressive model of lung cancer, which is also treated clinically with taxane- and platinum-based chemotherapy. CONCLUSIONS: This work highlights the importance of CD4+ T cells in tumor immunology. Furthermore, the data support the initiation of clinical trials in ovarian cancer that target both innate and adaptive immunity, with a focus on optimizing dosing schedules.