Refine
Has Fulltext
- yes (11)
Is part of the Bibliography
- yes (11)
Document Type
- Journal article (11)
Language
- English (11)
Keywords
- multiple myeloma (3)
- angiogenesis (2)
- kidney cancer (2)
- tumor microenvironment (2)
- Activation (1)
- BRAF mutation (1)
- Capicua transcriptional repressor (1)
- Drug resistance (1)
- EGFR (1)
- Extramedullary disease (1)
- Fak regulation (1)
- HB-EGF (1)
- Induced apoptosis (1)
- Mechanisms (1)
- Migration (1)
- Multiple (1)
- Multiple myeloma (1)
- NF-Kappa-B (1)
- PCa (1)
- PSMA (1)
- Pathogenesis (1)
- Phase- (1)
- RCC (1)
- Stromal cells (1)
- Sunitinib (1)
- TKI (1)
- Tyrosine kinase inhibition (1)
- UMAP (1)
- adhesion molecule (1)
- angiogenesis inhibitors (1)
- biomarker (1)
- bone marrow angiogenesis (1)
- bone marrow homing (1)
- bone marrow immune-microenvironment (1)
- cancer (1)
- cancer stem cells (1)
- cell of origin (1)
- clinical trials (1)
- endothelial cells (1)
- high-risk Prostate Cancer (1)
- immune-checkpoint inhibitor (1)
- immunotherapy (1)
- mTOR (1)
- machine learning (1)
- metastasis (1)
- miR (1)
- microRNA-221 (1)
- mitochondrial DNA (1)
- molecular subtypes (1)
- mtDNA (1)
- pan-RCC (1)
- precision medicine (1)
- predictive factors (1)
- prognostic-biomarkers (1)
- prostate adenocarcinoma (1)
- renal cell carcinoma (1)
- risk stratification (1)
- t-SNE (1)
- transcriptomic analysis (1)
- venous infiltration (1)
- visual clustering (1)
Institute
- Urologische Klinik und Poliklinik (7)
- Medizinische Klinik und Poliklinik II (6)
- Comprehensive Cancer Center Mainfranken (5)
- Pathologisches Institut (3)
- Center for Computational and Theoretical Biology (2)
- Klinik und Poliklinik für Dermatologie, Venerologie und Allergologie (2)
- Theodor-Boveri-Institut für Biowissenschaften (2)
- Klinik und Poliklinik für Nuklearmedizin (1)
Sonstige beteiligte Institutionen
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.
Combined MEK‐BRAF inhibition is a well‐established treatment strategy in BRAF‐mutated cancer, most prominently in malignant melanoma with durable responses being achieved through this targeted therapy. However, a subset of patients face primary unresponsiveness despite presence of the activating mutation at position V600E, and others acquire resistance under treatment. Underlying resistance mechanisms are largely unknown, and diagnostic tests to predict tumor response to BRAF‐MEK inhibitor treatment are unavailable.
Multiple myeloma represents the second most common hematologic malignancy, and point mutations in BRAF are detectable in about 10% of patients. Targeted inhibition has been successfully applied, with mixed responses observed in a substantial subset of patients mirroring the widespread spatial heterogeneity in this genomically complex disease. Central nervous system (CNS) involvement is an extremely rare, extramedullary form of multiple myeloma that can be diagnosed in less than 1% of patients. It is considered an ultimate high‐risk feature, associated with unfavorable cytogenetics, and, even with intense treatment applied, survival is short, reaching less than 12 months in most cases. Here we not only describe the first patient with an extramedullary CNS relapse responding to targeted dabrafenib and trametinib treatment, we furthermore provide evidence that a point mutation within the capicua transcriptional repressor (CIC) gene mediated the acquired resistance in this patient.
Mantle cell lymphoma and other lymphoma subtypes often spread to the bone marrow, and stromal interactions mediated by focal adhesion kinase frequently enhance survival and drug resistance of the lymphoma cells. To study the role of focal adhesion kinase in mantle cell lymphoma, immunohistochemistry of primary cases and functional analysis of mantle cell lymphoma cell lines and primary mantle cell lymphoma cells co-cultured with bone marrow stromal cells (BMSC) using small molecule inhibitors and RNAi-based focal adhesion kinase silencing was performed. We showed that focal adhesion kinase is highly expressed in bone marrow infiltrates of mantle cell lymphoma and in mantle cell lymphoma cell lines. Stroma-mediated activation of focal adhesion kinase led to activation of multiple kinases (AKT, p42/44 and NF-kappa B), that are important for prosurvival and proliferation signaling. Interestingly, RNAi-based focal adhesion kinase silencing or inhibition with small molecule inhibitors (FAKi) resulted in blockage of targeted cell invasion and induced apoptosis by inactivation of multiple signaling cascades, including the classic and alternative NF-kappa B pathway. In addition, the combined treatment of ibrutinib and FAKi was highly synergistic, and ibrutinib resistance of mantle cell lymphoma could be overcome. These data demonstrate that focal adhesion kinase is important for stroma-mediated survival and drug resistance in mantle cell lymphoma, providing indications for a targeted therapeutic strategy.
(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.
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.
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.
Epidermal growth factor receptor (EGFR) and its ligand heparin-binding EGF-like growth factor (HB-EGF) sustain endothelial cell proliferation and angiogenesis in solid tumors, but little is known about the role of HB-EGF–EGFR signaling in bone marrow angiogenesis and multiple myeloma (MM) progression. We found that bone marrow endothelial cells from patients with MM express high levels of EGFR and HB-EGF, compared with cells from patients with monoclonal gammopathy of undetermined significance, and that overexpressed HB-EGF stimulates EGFR expression in an autocrine loop. We also found that levels of EGFR and HB-EGF parallel MM plasma cell number, and that HB-EGF is a potent inducer of angiogenesis in vitro and in vivo. Moreover, blockade of HB-EGF–EGFR signaling, by an anti-HB-EGF neutralizing antibody or the EGFR inhibitor erlotinib, limited the angiogenic potential of bone marrow endothelial cells and hampered tumor growth in an MM xenograft mouse model. These results identify HB-EGF–EGFR signaling as a potential target of anti-angiogenic therapy, and encourage the clinical investigation of EGFR inhibitors in combination with conventional cytotoxic drugs as a new therapeutic strategy for MM.
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.
Growing evidence points to multiple myeloma (MM) and its stromal microenvironment using several mechanisms to subvert effective immune and anti-tumor responses. Recent advances have uncovered the tumor-stromal cell influence in regulating the immune-microenvironment and have envisioned targeting these suppressive pathways to improve therapeutic outcomes. Nevertheless, some subgroups of patients include those with particularly unfavorable prognoses. Biological stratification can be used to categorize patient-, disease- or therapy-related factors, or alternatively, these biological determinants can be included in a dynamic model that customizes a given treatment to a specific patient. Genetic heterogeneity and current knowledge enforce a systematic and comprehensive bench-to-bedside approach. Given the increasing role of cancer stem cells (CSCs) in better characterizing the pathogenesis of solid and hematological malignancies, disease relapse, and drug resistance, identifying and describing CSCs is of paramount importance in the management of MM. Even though the function of CSCs is well-known in other cancer types, their role in MM remains elusive. With this review, we aim to provide an update on MM homing and resilience in the bone marrow micro milieu. These data are particularly interesting for clinicians facing unmet medical needs while designing novel treatment approaches for MM.
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.