Materials Map

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

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Publications (4/4 displayed)

  • 2023Discriminant Principal Component Analysis of ToF-SIMS Spectra for Deciphering Compositional Differences of MSC-Secreted Extracellular Matrices8citations
  • 2023Clinical Text Reports to Stratify Patients Affected with Myeloid Neoplasms Using Natural Language Processing4citations
  • 2023Risk Stratification of Patients with RUNX1-mutated Acute Myeloid Leukemiacitations
  • 2022Venetoclax synergizes with gilteritinib in FLT3 wild-type high-risk acute myeloid leukemia by suppressing MCL-169citations

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Co-Authors (by relevance)

  • Freudenberg, Uwe
  • Wobus, Manja
  • Werner, Carsten
  • Sockel, Katja
  • Nitschke, Mirko
  • Stölzel, Friedrich
  • Magno, Valentina
  • Zimmermann, Ralf
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document

Risk Stratification of Patients with RUNX1-mutated Acute Myeloid Leukemia

  • Schneider, Stephanie
  • Jurinovic, Vindi
  • Rothenberg-Thurley, Maja
  • Hornung, Roman
  • Schetelig, Johannes
  • Sauerland, Maria Cristina
  • Uy, Geoffrey L.
  • Stasik, Sebastian
  • Nicolet, Deedra
  • Krug, Utz
  • Müller-Tidow, Carsten
  • Serve, Hubert
  • Röllig, Christoph
  • Woermann, Bernhard J.
  • Mansmann, Ulrich
  • Thiede, Christian
  • Dufour, Annika Maria
  • Hiddemann, Wolfgang
  • Metzeler, Klaus H.
  • Rausch, Christian
  • Ksienzyk, Bianka
  • Bamopoulos, Stefanos A.
  • Batcha, Aarif M. N.
  • Platzbecker, Uwe
  • Stock, Wendy
  • Middeke, Moritz
  • Görlich, Dennis
  • Byrd, John C.
  • Egger-Heidrich, Katharina
  • Meggendorfer, Manja
  • Herold, Tobias
  • Haferlach, Torsten
  • Kern, Wolfgang
  • Eisfeld, Ann-Kathrin
  • Berdel, Wolfgang E.
  • Bornhäuser, Martin
  • Spiekermann, Karsten
  • Neusser, Michaela
  • Braess, Jan
  • Baldus, Claudia D.
Abstract

<jats:title/><jats:p>Mutations in RUNX1 ( RUNX1mut) occur in ~15% of intensively treated AML cases. RUNX1mut have no specific hotspot and various types of alteration are observed. The European LeukemiaNet (ELN) risk stratification assigns adverse prognosis to RUNX1mut if they do not co-occur with favorable-risk genotypes. Considering the biological complexity of RUNX1 it seems implausible that all alterations have similar consequences.</jats:p><jats:p>Using clinical and genetic variables, we developed a prognostic risk stratification model for ELN adverse-risk RUNX1mut AML patients.</jats:p><jats:p>We combined data from five groups, totaling 609 patients with intensively treated RUNX1mut AML, to develop the model. Our training set included 448 patients treated on trials of the AML Cooperative Group (AMLCG; Herold et al, Leukemia, 2020; (n=178)), AML Study Group (Gerstung et al, NEJM, 2016; (n=116)) and Study Alliance Leukemia (n=154). Patients from the Munich Leukemia Laboratory (MLL; (n=107)) and of the Alliance group (trials NCT00048958, NCT00899223, NCT00900224; Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org; (n=54)) served as independent validation cohorts. Additionally, 955 patients without RUNX1mut treated on AMLCG trials served as controls. Patients with t(15;17), prior treatment, or RUNX1mut with co-occurring favorable-risk genotypes according to ELN 2017 were excluded.</jats:p><jats:p>Differences between RUNX1mut patients and controls were investigated using univariate logistic regression. Testing was performed using likelihood ratio tests and adjusted for study group. Univariate analyses were adjusted for multiple testing using the Benjamini-Hochberg procedure. We obtained risk prediction models using multivariate Cox regression. Missing values were imputed using the missForest approach. Model selection was performed using forward selection based on the Bayesian information criterion. Cut-offs were based on the 25 th- 50 th- and 75 th-percentile score values obtained in the training data. Performance was evaluated using Kaplan-Meier curves. For internal validation Harrell's C index was estimated using cross-study validation. For external validation, the final risk prediction models were separately applied to the external datasets.</jats:p><jats:p>RUNX1mut were more common in older, male patients and sAML (table). White blood cells, lactate dehydrogenase and bone marrow blasts were lower in RUNX1mut patients. Mutations in several myelodysplasia-related genes were enriched in RUNX1mut patients ( ASXL1, BCOR, BCORL1, EZH2, KMT2A, PHF6, and STAG2, SF3B1, SRSF2, and U2AF1), whereas DNMT3A, NPM1 and FLT3 were more frequently altered in controls. A strong association with mutations of the splicing factor complex was identified (49% vs. 13%, p&amp;lt;0.0001). Contradicting previous reports, we found no association with IDH mutations.</jats:p><jats:p>The risk prediction score we obtained for OS is as follows: 0.03054 x age (y) + 0.74996 x adverse MRC + 0.43779 x FLT3-ITD + 0.00317 x WBC count (10^9/L) - 0.00158 x platelet count (10^9/L) + 0.37401 x NRAS-mutation, where the obtained cut-off values are: &amp;lt;1.592 (low risk); 1.592 to 2.303 (moderate risk) &amp;gt;2.303 (high risk). Binary variables are coded as 0 or 1. Harrell's C index estimated using internal validation was 0.6. Kaplan-Meier curves estimated using the training set suggest strong differences in survival between the risk categories. Median overall survival (OS) was 2.5, 1.0 and 0.6 years for low-, intermediate-, and high-risk. External validation using the Alliance cohort shows similar results (Figure). Results obtained for the MLL cohort show smaller differences. However, we still observe clear separation of risk groups with a significant difference between low- and high-risk groups (adjusted p-value: 0.0266). Scores for relapse-free survival (RFS) as well as for OS and RFS censored for allogeneic transplant show similar results.</jats:p><jats:p>We analyzed a large collection of intensively treated RUNX1mut AML patients and observed heterogenous outcomes that could be predicted by applying few variables (age, MRC-score, FLT3/NRAS mutation-status, and WBC/platelet count). The OS of RUNX1mut high-risk patients is discouraging, highlighting the unmet need of these patients. In addition, our work demonstrates that ELN risk groups can be further stratified and that integrated approaches using routinely available variables can further advance risk prediction.</jats:p>

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  • impedance spectroscopy