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 - Stangl, Stephanie A1 - Haas, Kirsten A1 - Eichner, Felizitas A. A1 - Grau, Anna A1 - Selig, Udo A1 - Ludwig, Timo A1 - Fehm, Tanja A1 - Stübner, Tanja A1 - Rashid, Asarnusch A1 - Kerscher, Alexander A1 - Bargou, Ralf A1 - Hermann, Silke A1 - Arndt, Volker A1 - Meyer, Martin A1 - Wildner, Manfred A1 - Faller, Hermann A1 - Schrauder, Michael G. A1 - Weigel, Michael A1 - Schlembach, Ulrich A1 - Heuschmann, Peter U. A1 - Wöckel, Achim T1 - Development and proof-of-concept of a multicenter, patient-centered cancer registry for breast cancer patients with metastatic disease — the “Breast cancer care for patients with metastatic disease” (BRE-4-MED) registry JF - Pilot and Feasibility Studies N2 - Background: Patients with metastatic breast cancer (MBC) are treated with a palliative approach with focus oncontrolling for disease symptoms and maintaining high quality of life. Information on individual needs of patients andtheir relatives as well as on treatment patterns in clinical routine care for this specific patient group are lacking or arenot routinely documented in established Cancer Registries. Thus, we developed a registry concept specifically adaptedfor these incurable patients comprising primary and secondary data as well as mobile-health (m-health) data. Methods: The concept for patient-centered “Breast cancer care for patients with metastatic disease”(BRE-4-MED)registry was developed and piloted exemplarily in the region of Main-Franconia, a mainly rural region in Germanycomprising about 1.3 M inhabitants. The registry concept includes data on diagnosis, therapy, progression, patient-reported outcome measures (PROMs), and needs of family members from several sources of information includingroutine data from established Cancer Registries in different federal states, treating physicians in hospital as well as inoutpatient settings, patients with metastatic breast cancer and their family members. Linkage with routine cancerregistry data was performed to collect secondary data on diagnosis, therapy, and progression. Paper and online-basedquestionnaires were used to assess PROMs. A dedicated mobile application software (APP) was developed to monitorneeds, progression, and therapy change of individual patients. Patient’s acceptance and feasibility of data collection inclinical routine was assessed within a proof-of-concept study. Results: The concept for the BRE-4-MED registry was developed and piloted between September 2017 and May 2018.In total n= 31 patients were included in the pilot study, n= 22 patients were followed up after 1 month. Recordlinkage with the Cancer Registries of Bavaria and Baden-Württemberg demonstrated to be feasible. The voluntary APP/online questionnaire was used by n= 7 participants. The feasibility of the registry concept in clinical routine waspositively evaluated by the participating hospitals. Conclusion: The concept of the BRE-4-MED registry provides evidence that combinatorial evaluation of PROMs, needsof family members, and raising clinical parameters from primary and secondary data sources as well as m-healthapplications are feasible and accepted in an incurable cancer collective. KW - Metastatic breast cancer KW - Patient-centered registry KW - Patient’s needs KW - m-Health KW - Health care service research Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-229149 VL - 6 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 -