TY - JOUR A1 - Marquardt, André A1 - Hartrampf, Philipp A1 - Kollmannsberger, Philip A1 - Solimando, Antonio G. A1 - Meierjohann, Svenja A1 - Kübler, Hubert A1 - Bargou, Ralf A1 - Schilling, Bastian A1 - Serfling, Sebastian E. A1 - Buck, Andreas A1 - Werner, Rudolf A. A1 - Lapa, Constantin A1 - Krebs, Markus T1 - Predicting microenvironment in CXCR4- and FAP-positive solid tumors — a pan-cancer machine learning workflow for theranostic target structures JF - Cancers N2 - (1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database — representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets. KW - machine learning KW - tumor microenvironment KW - immune infiltration KW - angiogenesis KW - mRNA KW - miRNA KW - transcriptome Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-305036 SN - 2072-6694 VL - 15 IS - 2 ER - 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 - 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 - Krebs, Markus A1 - Solimando, Antonio Giovanni A1 - Kalogirou, Charis A1 - Marquardt, André A1 - Frank, Torsten A1 - Sokolakis, Ioannis A1 - Hatzichristodoulou, Georgios A1 - Kneitz, Susanne A1 - Bargou, Ralf A1 - Kübler, Hubert A1 - Schilling, Bastian A1 - Spahn, Martin A1 - Kneitz, Burkhard T1 - miR-221-3p Regulates VEGFR2 Expression in High-Risk Prostate Cancer and Represents an Escape Mechanism from Sunitinib In Vitro JF - Journal of Clinical Medicine N2 - Downregulation of miR-221-3p expression in prostate cancer (PCa) predicted overall and cancer-specific survival of high-risk PCa patients. Apart from PCa, miR-221-3p expression levels predicted a response to tyrosine kinase inhibitors (TKI) in clear cell renal cell carcinoma (ccRCC) patients. Since this role of miR-221-3p was explained with a specific targeting of VEGFR2, we examined whether miR-221-3p regulated VEGFR2 in PCa. First, we confirmed VEGFR2/KDR as a target gene of miR-221-3p in PCa cells by applying Luciferase reporter assays and Western blotting experiments. Although VEGFR2 was mainly downregulated in the PCa cohort of the TCGA (The Cancer Genome Atlas) database, VEGFR2 was upregulated in our high-risk PCa cohort (n = 142) and predicted clinical progression. In vitro miR-221-3p acted as an escape mechanism from TKI in PC3 cells, as displayed by proliferation and apoptosis assays. Moreover, we confirmed that Sunitinib induced an interferon-related gene signature in PC3 cells by analyzing external microarray data and by demonstrating a significant upregulation of miR-221-3p/miR-222-3p after Sunitinib exposure. Our findings bear a clinical perspective for high-risk PCa patients with low miR-221-3p levels since this could predict a favorable TKI response. Apart from this therapeutic niche, we identified a partially oncogenic function of miR-221-3p as an escape mechanism from VEGFR2 inhibition. KW - microRNA-221 KW - high-risk Prostate Cancer KW - angiogenesis KW - Sunitinib KW - Tyrosine kinase inhibition Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203168 SN - 2077-0383 VL - 9 IS - 3 ER - TY - JOUR A1 - Effenberger, Madlen A1 - Bommert, Kathryn S. A1 - Kunz, Viktoria A1 - Kruk, Jessica A1 - Leich, Ellen A1 - Rudelius, Martina A1 - Bargou, Ralf A1 - Bommert, Kurt T1 - Glutaminase inhibition in multiple myeloma induces apoptosis via MYC degradation JF - Oncotarget N2 - Multiple Myeloma (MM) is an incurable hematological malignancy affecting millions of people worldwide. As in all tumor cells both glucose and more recently glutamine have been identified as important for MM cellular metabolism, however there is some dispute as to the role of glutamine in MM cell survival. Here we show that the small molecule inhibitor compound 968 effectively inhibits glutaminase and that this inhibition induces apoptosis in both human multiple myeloma cell lines (HMCLs) and primary patient material. The HMCL U266 which does not express MYC was insensitive to both glutamine removal and compound 968, but ectopic expression of MYC imparted sensitivity. Finally, we show that glutamine depletion is reflected by rapid loss of MYC protein which is independent of MYC transcription and post translational modifications. However, MYC loss is dependent on proteasomal activity, and this loss was paralleled by an equally rapid induction of apoptosis. These findings are in contrast to those of glucose depletion which largely affected rates of proliferation in HMCLs, but had no effects on either MYC expression or viability. Therefore, inhibition of glutaminolysis is effective at inducing apoptosis and thus serves as a possible therapeutic target in MM. KW - Multiple Myeloma KW - glutaminase inhibition KW - apoptosis KW - MYC Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-170168 VL - 8 IS - 49 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 -