@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}
}
@article{MarquardtKollmannsbergerKrebsetal.2022,
  author    = {Marquardt, Andr{\´e} and Kollmannsberger, Philip and Krebs, Markus and Argentiero, Antonella and Knott, Markus and Solimando, Antonio Giovanni and Kerscher, Alexander Georg},
  title     = {Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls},
  series = {Genes},
  volume    = {13},
  journal   = {Genes},
  number    = {8},
  issn      = {2073-4425},
  doi       = {10.3390/genes13081335},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281872},
  year      = {2022},
  abstract  = {Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.},
  language  = {en}
}