@article{SolimandoBittrichShahinietal.2023,
  author    = {Solimando, Antonio G. and Bittrich, Max and Shahini, Endrit and Albanese, Federica and Fritz, Georg and Krebs, Markus},
  title     = {Determinants of COVID-19 disease severity - lessons from primary and secondary immune disorders including cancer},
  series = {International Journal of Molecular Sciences},
  volume    = {24},
  journal   = {International Journal of Molecular Sciences},
  number    = {10},
  issn      = {1422-0067},
  doi       = {10.3390/ijms24108746},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319412},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{SolimandoPalumboPragnelletal.2022,
  author    = {Solimando, Antonio G. and Palumbo, Carmen and Pragnell, Mary Victoria and Bittrich, Max and Argentiero, Antonella and Krebs, Markus},
  title     = {Aplastic anemia as a roadmap for bone marrow failure: an overview and a clinical workflow},
  series = {International Journal of Molecular Sciences},
  volume    = {23},
  journal   = {International Journal of Molecular Sciences},
  number    = {19},
  issn      = {1422-0067},
  doi       = {10.3390/ijms231911765},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290440},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{SolimandoKrebsBittrichetal.2022,
  author    = {Solimando, Antonio Giovanni and Krebs, Markus and Bittrich, Max and Einsele, Hermann},
  title     = {The urgent need for precision medicine in cancer and its microenvironment: the paradigmatic case of multiple myeloma},
  series = {Journal of Clinical Medicine},
  volume    = {11},
  journal   = {Journal of Clinical Medicine},
  number    = {18},
  issn      = {2077-0383},
  doi       = {10.3390/jcm11185461},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-288164},
  year      = {2022},
  abstract  = {No abstract available},
  language  = {en}
}
@article{MarquardtSolimandoKerscheretal.2021,
  author    = {Marquardt, Andr{\´e} and Solimando, Antonio Giovanni and Kerscher, Alexander and Bittrich, Max and Kalogirou, Charis and K{\"u}bler, Hubert and Rosenwald, Andreas and Bargou, Ralf and Kollmannsberger, Philip and Schilling, Bastian and Meierjohann, Svenja and Krebs, Markus},
  title     = {Subgroup-Independent Mapping of Renal Cell Carcinoma — Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries},
  series = {Frontiers in Oncology},
  volume    = {11},
  journal   = {Frontiers in Oncology},
  issn      = {2234-943X},
  doi       = {10.3389/fonc.2021.621278},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-232107},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}