TY - JOUR A1 - Solimando, Antonio G. A1 - Bittrich, Max A1 - Shahini, Endrit A1 - Albanese, Federica A1 - Fritz, Georg A1 - Krebs, Markus T1 - Determinants of COVID-19 disease severity – lessons from primary and secondary immune disorders including cancer JF - International Journal of Molecular Sciences N2 - At the beginning of the COVID-19 pandemic, patients with primary and secondary immune disorders — including patients suffering from cancer — were generally regarded as a high-risk population in terms of COVID-19 disease severity and mortality. By now, scientific evidence indicates that there is substantial heterogeneity regarding the vulnerability towards COVID-19 in patients with immune disorders. In this review, we aimed to summarize the current knowledge about the effect of coexistent immune disorders on COVID-19 disease severity and vaccination response. In this context, we also regarded cancer as a secondary immune disorder. While patients with hematological malignancies displayed lower seroconversion rates after vaccination in some studies, a majority of cancer patients’ risk factors for severe COVID-19 disease were either inherent (such as metastatic or progressive disease) or comparable to the general population (age, male gender and comorbidities such as kidney or liver disease). A deeper understanding is needed to better define patient subgroups at a higher risk for severe COVID-19 disease courses. At the same time, immune disorders as functional disease models offer further insights into the role of specific immune cells and cytokines when orchestrating the immune response towards SARS-CoV-2 infection. Longitudinal serological studies are urgently needed to determine the extent and the duration of SARS-CoV-2 immunity in the general population, as well as immune-compromised and oncological patients. KW - COVID-19 KW - SARS-CoV-2 KW - disorder of immunity KW - cancer Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319412 SN - 1422-0067 VL - 24 IS - 10 ER - TY - JOUR A1 - Lüke, Florian A1 - Haller, Florian A1 - Utpatel, Kirsten A1 - Krebs, Markus A1 - Meidenbauer, Norbert A1 - Scheiter, Alexander A1 - Spoerl, Silvia A1 - Heudobler, Daniel A1 - Sparrer, Daniela A1 - Kaiser, Ulrich A1 - Keil, Felix A1 - Schubart, Christoph A1 - Tögel, Lars A1 - Einhell, Sabine A1 - Dietmaier, Wolfgang A1 - Huss, Ralf A1 - Dintner, Sebastian A1 - Sommer, Sebastian A1 - Jordan, Frank A1 - Goebeler, Maria-Elisabeth A1 - Metz, Michaela A1 - Haake, Diana A1 - Scheytt, Mithun A1 - Gerhard-Hartmann, Elena A1 - Maurus, Katja A1 - Brändlein, Stephanie A1 - Rosenwald, Andreas A1 - Hartmann, Arndt A1 - Märkl, Bruno A1 - Einsele, Hermann A1 - Mackensen, Andreas A1 - Herr, Wolfgang A1 - Kunzmann, Volker A1 - Bargou, Ralf A1 - Beckmann, Matthias W. A1 - Pukrop, Tobias A1 - Trepel, Martin A1 - Evert, Matthias A1 - Claus, Rainer A1 - Kerscher, Alexander T1 - Identification of disparities in personalized cancer care — a joint approach of the German WERA consortium JF - Cancers N2 - (1) Background: molecular tumor boards (MTBs) are crucial instruments for discussing and allocating targeted therapies to suitable cancer patients based on genetic findings. Currently, limited evidence is available regarding the regional impact and the outreach component of MTBs; (2) Methods: we analyzed MTB patient data from four neighboring Bavarian tertiary care oncology centers in Würzburg, Erlangen, Regensburg, and Augsburg, together constituting the WERA Alliance. Absolute patient numbers and regional distribution across the WERA-wide catchment area were weighted with local population densities; (3) Results: the highest MTB patient numbers were found close to the four cancer centers. However, peaks in absolute patient numbers were also detected in more distant and rural areas. Moreover, weighting absolute numbers with local population density allowed for identifying so-called white spots—regions within our catchment that were relatively underrepresented in WERA MTBs; (4) Conclusions: investigating patient data from four neighboring cancer centers, we comprehensively assessed the regional impact of our MTBs. The results confirmed the success of existing collaborative structures with our regional partners. Additionally, our results help identifying potential white spots in providing precision oncology and help establishing a joint WERA-wide outreach strategy. KW - precision oncology KW - MTB KW - patient access KW - cancer care KW - outreach KW - real world data KW - outcomes research Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-290311 SN - 2072-6694 VL - 14 IS - 20 ER - TY - JOUR A1 - Solimando, Antonio G. A1 - Palumbo, Carmen A1 - Pragnell, Mary Victoria A1 - Bittrich, Max A1 - Argentiero, Antonella A1 - Krebs, Markus T1 - Aplastic anemia as a roadmap for bone marrow failure: an overview and a clinical workflow JF - International Journal of Molecular Sciences N2 - In recent years, it has become increasingly apparent that bone marrow (BM) failures and myeloid malignancy predisposition syndromes are characterized by a wide phenotypic spectrum and that these diseases must be considered in the differential diagnosis of children and adults with unexplained hematopoiesis defects. Clinically, hypocellular BM failure still represents a challenge in pathobiology-guided treatment. There are three fundamental topics that emerged from our review of the existing data. An exogenous stressor, an immune defect, and a constitutional genetic defect fuel a vicious cycle of hematopoietic stem cells, immune niches, and stroma compartments. A wide phenotypic spectrum exists for inherited and acquired BM failures and predispositions to myeloid malignancies. In order to effectively manage patients, it is crucial to establish the right diagnosis. New theragnostic windows can be revealed by exploring BM failure pathomechanisms. KW - hematopoietic stem cells KW - bone marrow immune-microenvironment KW - bone marrow failure KW - cytopenia KW - aplastic anemia Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-290440 SN - 1422-0067 VL - 23 IS - 19 ER - TY - JOUR A1 - Solimando, Antonio Giovanni A1 - Krebs, Markus A1 - Bittrich, Max A1 - Einsele, Hermann T1 - The urgent need for precision medicine in cancer and its microenvironment: the paradigmatic case of multiple myeloma JF - Journal of Clinical Medicine N2 - No abstract available KW - precision medicine KW - multiple myeloma Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288164 SN - 2077-0383 VL - 11 IS - 18 ER - TY - JOUR A1 - Marquardt, André A1 - Solimando, Antonio Giovanni A1 - Kerscher, Alexander A1 - Bittrich, Max A1 - Kalogirou, Charis A1 - Kübler, Hubert A1 - Rosenwald, Andreas A1 - Bargou, Ralf A1 - Kollmannsberger, Philip A1 - Schilling, Bastian A1 - Meierjohann, Svenja A1 - Krebs, Markus T1 - Subgroup-Independent Mapping of Renal Cell Carcinoma — Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries JF - Frontiers in Oncology N2 - Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups. Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256). Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients. Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment. KW - kidney cancer KW - pan-RCC KW - machine learning KW - mitochondrial DNA KW - mtDNA KW - mTOR Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-232107 SN - 2234-943X VL - 11 ER -