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 - 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 -