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 - Keppler, Sarah A1 - Weißbach, Susann A1 - Langer, Christian A1 - Knop, Stefan A1 - Pischimarov, Jordan A1 - Kull, Miriam A1 - Stühmer, Thorsten A1 - Steinbrunn, Torsten A1 - Bargou, Ralf A1 - Einsele, Hermann A1 - Rosenwald, Andreas A1 - Leich, Ellen T1 - Rare SNPs in receptor tyrosine kinases are negative outcome predictors in multiple myeloma JF - Oncotarget N2 - Multiple myeloma (MM) is a plasma cell disorder that is characterized by a great genetic heterogeneity. Recent next generation sequencing studies revealed an accumulation of tumor-associated mutations in receptor tyrosine kinases (RTKs) which may also contribute to the activation of survival pathways in MM. To investigate the clinical role of RTK-mutations in MM, we deep-sequenced the coding DNA-sequence of EGFR, EPHA2, ERBB3, IGF1R, NTRK1 and NTRK2 which were previously found to be mutated in MM, in 75 uniformly treated MM patients of the “Deutsche Studiengruppe Multiples Myelom”. Subsequently, we correlated the detected mutations with common cytogenetic alterations and clinical parameters. We identified 11 novel non-synonymous SNVs or rare patient-specific SNPs, not listed in the SNP databases 1000 genomes and dbSNP, in 10 primary MM cases. The mutations predominantly affected the tyrosine-kinase and ligand-binding domains and no correlation with cytogenetic parameters was found. Interestingly, however, patients with RTK-mutations, specifically those with rare patient-specific SNPs, showed a significantly lower overall, event-free and progression-free survival. This indicates that RTK SNVs and rare patient-specific RTK SNPs are of prognostic relevance and suggests that MM patients with RTK-mutations could potentially profit from treatment with RTK-inhibitors. KW - multiple myeloma KW - rare SNP KW - amplicon sequencing KW - receptor tyrosine kinases Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-177840 VL - 7 IS - 25 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 - TY - JOUR A1 - Weißbach, Susann A1 - Heredia-Guerrero, Sofia Catalina A1 - Barnsteiner, Stefanie A1 - Großhans, Lukas A1 - Bodem, Jochen A1 - Starz, Hanna A1 - Langer, Christian A1 - Appenzeller, Silke A1 - Knop, Stefan A1 - Steinbrunn, Torsten A1 - Rost, Simone A1 - Einsele, Hermann A1 - Bargou, Ralf Christian A1 - Rosenwald, Andreas A1 - Stühmer, Thorsten A1 - Leich, Ellen T1 - Exon-4 Mutations in KRAS Affect MEK/ERK and PI3K/AKT Signaling in Human Multiple Myeloma Cell Lines JF - Cancers N2 - Approximately 20% of multiple myeloma (MM) cases harbor a point mutation in KRAS. However, there is still no final consent on whether KRAS-mutations are associated with disease outcome. Specifically, no data exist on whether KRAS-mutations have an impact on survival of MM patients at diagnosis in the era of novel agents. Direct blockade of KRAS for therapeutic purposes is mostly impossible, but recently a mutation-specific covalent inhibitor targeting KRAS\(^{p.G12C}\) entered into clinical trials. However, other KRAS hotspot-mutations exist in MM patients, including the less common exon-4 mutations. For the current study, the coding regions of KRAS were deep-sequenced in 80 newly diagnosed MM patients, uniformely treated with three cycles of bortezomib plus dexamethasone and cyclophosphamide (VCD)-induction, followed by high-dose chemotherapy and autologous stem cell transplantation. Moreover, the functional impact of KRAS\(^{p.G12A}\) and the exon-4 mutations p.A146T and p.A146V on different survival pathways was investigated. Specifically, KRAS\(^{WT}\), KRAS\(^{p.G12A}\), KRAS\(^{p.A146T}\), and KRAS\(^{p.A146V}\) were overexpressed in HEK293 cells and the KRAS\(^{WT}\) MM cell lines JJN3 and OPM2 using lentiviral transduction and the Sleeping Beauty vector system. Even though KRAS-mutations were not correlated with survival, all KRAS-mutants were found capable of potentially activating MEK/ERK- and sustaining PI3K/AKT-signaling in MM cells. KW - multiple myeloma KW - KRAS KW - MEK/ERK-signaling KW - AKT-signaling KW - amplicon sequencing Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200617 SN - 2072-6694 VL - 12 IS - 2 ER -