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 - Argentiero, Antonella A1 - Solimando, Antonio Giovanni A1 - Krebs, Markus A1 - Leone, Patrizia A1 - Susca, Nicola A1 - Brunetti, Oronzo A1 - Racanelli, Vito A1 - Vacca, Angelo A1 - Silvestris, Nicola T1 - Anti-angiogenesis and immunotherapy: novel paradigms to envision tailored approaches in renal cell-carcinoma JF - Journal of Clinical Medicine N2 - Although decision making strategy based on clinico-histopathological criteria is well established, renal cell carcinoma (RCC) represents a spectrum of biological ecosystems characterized by distinct genetic and molecular alterations, diverse clinical courses and potential specific therapeutic vulnerabilities. Given the plethora of drugs available, the subtype-tailored treatment to RCC subtype holds the potential to improve patient outcome, shrinking treatment-related morbidity and cost. The emerging knowledge of the molecular taxonomy of RCC is evolving, whilst the antiangiogenic and immunotherapy landscape maintains and reinforces their potential. Although several prognostic factors of survival in patients with RCC have been described, no reliable predictive biomarkers of treatment individual sensitivity or resistance have been identified. In this review, we summarize the available evidence able to prompt more precise and individualized patient selection in well-designed clinical trials, covering the unmet need of medical choices in the era of next-generation anti-angiogenesis and immunotherapy. KW - renal cell carcinoma KW - angiogenesis KW - immune-checkpoint inhibitor KW - tumor microenvironment KW - molecular subtypes KW - prognostic-biomarkers KW - predictive factors Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205846 SN - 2077-0383 VL - 9 IS - 5 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 - Solimando, Antonio Giovanni A1 - Kalogirou, Charis A1 - Krebs, Markus T1 - Angiogenesis as therapeutic target in metastatic prostate cancer – narrowing the gap between bench and bedside JF - Frontiers in Immunology N2 - Angiogenesis in metastatic castration-resistant prostate cancer (mCRPC) has been extensively investigated as a promising druggable biological process. Nonetheless, targeting angiogenesis has failed to impact overall survival (OS) in patients with mCRPC despite promising preclinical and early clinical data. This discrepancy prompted a literature review highlighting the tumor heterogeneity and biological context of Prostate Cancer (PCa). Narrowing the gap between the bench and bedside appears critical for developing novel therapeutic strategies. Searching clinicaltrials.gov for studies examining angiogenesis inhibition in patients with PCa resulted in n=20 trials with specific angiogenesis inhibitors currently recruiting (as of September 2021). Moreover, several other compounds with known anti-angiogenic properties – such as Metformin or Curcumin – are currently investigated. In general, angiogenesis-targeting strategies in PCa include biomarker-guided treatment stratification – as well as combinatorial approaches. Beyond established angiogenesis inhibitors, PCa therapies aiming at PSMA (Prostate Specific Membrane Antigen) hold the promise to have a substantial anti-angiogenic effect – due to PSMA´s abundant expression in tumor vasculature. KW - prostate adenocarcinoma KW - PCa KW - angiogenesis inhibitors KW - TKI KW - immunotherapy KW - tumor microenvironment KW - clinical trials KW - PSMA Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-263061 SN - 1664-3224 VL - 13 ER - TY - JOUR A1 - Marquardt, André A1 - Kollmannsberger, Philip A1 - Krebs, Markus A1 - Argentiero, Antonella A1 - Knott, Markus A1 - Solimando, Antonio Giovanni A1 - Kerscher, Alexander Georg T1 - Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls JF - Genes N2 - 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. KW - visual clustering KW - t-SNE KW - UMAP KW - transcriptomic analysis KW - cancer KW - metastasis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281872 SN - 2073-4425 VL - 13 IS - 8 ER - TY - JOUR A1 - Solimando, Antonio Giovanni A1 - Krebs, Markus A1 - Bittrich, Max A1 - Einsele, Hermann T1 - The urgent need for precision medicine in cancer and its microenvironment: the paradigmatic case of multiple myeloma JF - Journal of Clinical Medicine N2 - No abstract available KW - precision medicine KW - multiple myeloma Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288164 SN - 2077-0383 VL - 11 IS - 18 ER - TY - JOUR A1 - Kotlyar, Mischa J. A1 - Krebs, Markus A1 - Solimando, Antonio Giovanni A1 - Marquardt, André A1 - Burger, Maximilian A1 - Kübler, Hubert A1 - Bargou, Ralf A1 - Kneitz, Susanne A1 - Otto, Wolfgang A1 - Breyer, Johannes A1 - Vergho, Daniel C. A1 - Kneitz, Burkhard A1 - Kalogirou, Charis T1 - Critical evaluation of a microRNA-based risk classifier predicting cancer-specific survival in renal cell carcinoma with tumor thrombus of the inferior vena cava JF - Cancers N2 - (1) Background: Clear cell renal cell carcinoma extending into the inferior vena cava (ccRCC\(^{IVC}\)) represents a clinical high-risk setting. However, there is substantial heterogeneity within this patient subgroup regarding survival outcomes. Previously, members of our group developed a microRNA(miR)-based risk classifier — containing miR-21-5p, miR-126-3p and miR-221-3p expression — which significantly predicted the cancer-specific survival (CSS) of ccRCC\(^{IVC}\) patients. (2) Methods: Examining a single-center cohort of tumor tissue from n = 56 patients with ccRCC\(^{IVC}\), we measured the expression levels of miR-21, miR-126, and miR-221 using qRT-PCR. The prognostic impact of clinicopathological parameters and miR expression were investigated via single-variable and multivariable Cox regression. Referring to the previously established risk classifier, we performed Kaplan–Meier analyses for single miR expression levels and the combined risk classifier. Cut-off values and weights within the risk classifier were taken from the previous study. (3) Results: miR-21 and miR-126 expression were significantly associated with lymphonodal status at the time of surgery, the development of metastasis during follow-up, and cancer-related death. In Kaplan–Meier analyses, miR-21 and miR-126 significantly impacted CSS in our cohort. Moreover, applying the miR-based risk classifier significantly stratified ccRCC\(^{IVC}\) according to CSS. (4) Conclusions: In our retrospective analysis, we successfully validated the miR-based risk classifier within an independent ccRCC\(^{IVC}\) cohort. KW - kidney cancer KW - RCC KW - venous infiltration KW - biomarker KW - miR KW - risk stratification Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311040 SN - 2072-6694 VL - 15 IS - 7 ER -