@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}
}
@article{LuekeHallerUtpateletal.2022,
  author    = {L{\"u}ke, Florian and Haller, Florian and Utpatel, Kirsten and Krebs, Markus and Meidenbauer, Norbert and Scheiter, Alexander and Spoerl, Silvia and Heudobler, Daniel and Sparrer, Daniela and Kaiser, Ulrich and Keil, Felix and Schubart, Christoph and T{\"o}gel, Lars and Einhell, Sabine and Dietmaier, Wolfgang and Huss, Ralf and Dintner, Sebastian and Sommer, Sebastian and Jordan, Frank and Goebeler, Maria-Elisabeth and Metz, Michaela and Haake, Diana and Scheytt, Mithun and Gerhard-Hartmann, Elena and Maurus, Katja and Br{\"a}ndlein, Stephanie and Rosenwald, Andreas and Hartmann, Arndt and M{\"a}rkl, Bruno and Einsele, Hermann and Mackensen, Andreas and Herr, Wolfgang and Kunzmann, Volker and Bargou, Ralf and Beckmann, Matthias W. and Pukrop, Tobias and Trepel, Martin and Evert, Matthias and Claus, Rainer and Kerscher, Alexander},
  title     = {Identification of disparities in personalized cancer care — a joint approach of the German WERA consortium},
  series = {Cancers},
  volume    = {14},
  journal   = {Cancers},
  number    = {20},
  issn      = {2072-6694},
  doi       = {10.3390/cancers14205040},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290311},
  year      = {2022},
  abstract  = {(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 W{\"u}rzburg, 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.},
  language  = {en}
}