High MACC1 expression in combination with mutated KRAS G13 indicates poor survival of colorectal cancer patients

BACKGROUND: The metastasis-associated in colon cancer 1 (MACC1) gene has been identified as prognostic biomarker for colorectal cancer (CRC). Here, we aimed at the refinement of risk assessment by separate and combined survival analyses of MACC1 expression with any of the markers KRAS mutated in codon 12 (KRAS G12) or codon 13 (KRAS G13), BRAF V600 mutation and MSI status in a retrospective study of 99 CRC patients with tumors UICC staged I, II and III. FINDINGS: We showed that only high MACC1 expression (HR: 6.09, 95% CI: 2.50-14.85, P < 0.001) and KRAS G13 mutation (HR: 5.19, 95% CI: 1.06-25.45, P = 0.042) were independent prognostic markers for shorter metastasis-free survival (MFS). Accordingly, Cox regression analysis revealed that patients with high MACC1 expression and KRAS G13 mutation exhibited the worst prognosis (HR: 14.48, 95% CI: 3.37-62.18, P < 0.001). Patients were classified based on their molecular characteristics into four clusters with significant differences in MFS (P = 0.003) by using the SPSS 2-step cluster function and Kaplan-Meier survival analysis. CONCLUSION: According to our results, patients with high MACC1 expression and mutated KRAS G13 exhibited the highest risk for metachronous metastases formation. Moreover, we demonstrated that the “Traditional pathway” with an intermediate risk for metastasis formation can be further subdivided by assessing MACC1 expression into a low and high risk group with regard to MFS prognosis. This is the first report showing that identification of CRC patients at high risk for metastasis is possible by assessing MACC1 expression in combination with KRAS G13 mutation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12943-015-0316-2) contains supplementary material, which is available to authorized users

Data-driven Disease Assessment from time-resolved Fluorescence Optical Imaging

Fluorescence Optical Imaging (FOI) is a new method to assess Rheumatoid Arthritis, Psoriasis, and other inflammatory diseases. It can reveal inflammatory tissues and microcirculatory disorders with high spatial and temporal resolution. However, the analysis of the image data is currently performed manually with no or weak consideration of the time component. To date, there is no automatic image analysis pipeline for inflammatory diseases based on FOI data. Furthermore, the distinct phenotypes of Rheumatoid Arthritis, Psoriatic Arthritis, and comparable diseases are not fully described in FOI, yet. This thesis proposes a new unsupervised, data-driven approach, that enables disease assessment of inflammatory diseases of the hands under the unfavorable conditions (e.g., low data-availability) of medical imaging. Data-driven methods such as deep neural networks often require extensive and well-annotated data sets, which are rare and expensive in clinical research. The here presented approach uses a Variational Autoencoder and reduces the complexity of the problem by learning a low-dimensional latent space. This latent space enables further analyses such as data exploration, subgroup classification, and analysis of the underlying dynamics under low data-availability and low quality of clinical labels. For data exploration, subgroups can be summarized in latent space and then be decoded back into an image. This feature-wise average results in superior images compared to the pixel-wise average. Furthermore, the latent space allows for cluster identification by employing two-dimensional projections such as UMAP. The latent space representations enable classification tasks under low data-availability with a two-step approach. Therefore, extensions of the proposed model using Neural Networks and Random Forests are evaluated and compared. The approach can distinguish between Psoriasis Vulgaris and Psoriatic Arthritis with accuracies over 70 %. On synthetical data, accuracies of up to 97 % are achieved. In combination with the Koopman Operator Theory the underlying dynamics can be approximated linearly. This approach decomposes the temporal effects within the data, and it enables subgroup comparisons and outlier detection. This thesis investigates the application of the proposed pipeline with respect to the quality of the underlying data and discusses the necessary conditions to learn a generalizing model. The dependency of high-quality labels for supervised approaches is demonstrated with synthetical and clinical datasets.Optische Fluoreszenz Bildgebung (Fluorescence Optical Imaging, FOI) ist ein neues Verfahren, mit dessen Hilfe entzündliche Gelenkkrankheiten wie rheumatische Arthritis und Psoriasis bewertet werden können. Entzündetes Gewebe und Störungen der Mikrozirkulation können mit hoher räumlicher und zeitlicher Auflösung dargestellt werden. Die Analyse dieser Bilddaten erfolgt zurzeit jedoch hauptsächlich manuell ohne Berücksichtigung der Zeitkomponente. Derzeit existiert kein automatischer Bildanalyseprozess für entzündliche Gelenkerkrankungen auf Basis von FOI-Daten. Darüber hinaus sind die charakteristischen Phänotypen der einzelnen Krankheiten wie rheumatischer Arthritis oder Psoriasis noch nicht vollständig für FOI beschrieben. Diese Arbeit präsentiert einen neuen datengetriebenen Ansatz mit Hilfe des unüberwachten Lernens, der eine Krankheitsbewertung bei entzündlichen Gelenkerkrankungen der Hände auch unter den ungünstigen Bedingungen (z.B. geringe Datenverfügbarkeit) der medizinischen Bildgebung ermöglicht. Datengetriebene Methoden wie die Tiefen Neuronalen Netzwerke erfordern häufig eine große Menge an gut annotierten Daten, die in der klinischen Forschung selten und teuer sind. Der hier vorgestellte Ansatz benutzt einen Variational Autoencoder, um einen niedrigdimensionalen latenten Raum zu lernen, der die Komplexität des ursprünglichen Problems drastisch reduziert. Dieser latente Raum ermöglichte somit weitere Auswertungen, darunter die Datenexploration, Klassifikation von Teilgruppen sowie die Analyse der zugrundeliegenden Dynamiken, auch wenn die Datenverfügbarkeit und die Qualität der Zielvariablen gering sind. Zur Datenexploration können Teilgruppen im latenten Raum zusammengefasst und wieder in ein Bild übersetzt werden. Diese Bilder auf Basis eines Durchschnitts der Merkmale sind Bildern eines pixelbasierten Durchschnitts überlegen. Darüber hinaus können Cluster von ähnlichen Patienten in einer zwei-dimensionalen Projektion mittels UMAP identifiziert werden. In einem Zwei-Schritt-Verfahren ermöglicht die Darstellungen im latenten Raum die Klassifikation von Teilgruppen, auch wenn die Verfügbarkeit von Zielvariablen eingeschränkt ist. Dafür wird das vorgeschlagene Modell um ein weiteres Neuronales Netzwerk oder einen Random Forest erweitert und evaluiert. Dieser Ansatz kann zwischen Psoriasis Vulgaris und Psoriatischer Arthritis mit einer Genauigkeit von über 70 % unterscheiden. Auf synthetischen FOI-Daten werden bis zu 97 % erreicht. In Anlehnung an die Theorie der Koopman Operatoren können die zugrundeliegenden Dynamiken linear approximiert werden. Dieser Ansatz zerlegt die unterschiedlichen zeitlichen Effekte innerhalb der Daten und ermöglicht so Vergleiche von Teilgruppen, sowie die Detektion von Ausreißern. Diese Arbeit untersucht die Anwendung des vorgeschlagenen Ansatzes in Bezug auf die Qualität der zugrundeliegenden Daten und diskutiert die notwendigen Bedingungen, um ein verallgemeinerndes Modell zu lernen. Die Abhängigkeit von hochwertigen Zielvariablen für überwachte Ansätze wird an synthetischen und klinischen Datensätzen veranschaulicht

Role of bone morphogenetic proteins in sprouting angiogenesis: differential BMP receptor-dependent signaling pathways balance stalk vs. tip cell competence

Before the onset of sprouting angiogenesis, the endothelium is prepatterned for the positioning of tip and stalk cells. Both cell identities are not static, as endothelial cells (ECs) constantly compete for the tip cell position in a dynamic fashion. Here, we show that both bone morphogenetic protein (BMP) 2 and BMP6 are proangiogenic in vitro and ex vivo and that the BMP type I receptors, activin receptor-like kinase (ALK)3 and ALK2, play crucial and distinct roles in this process. BMP2 activates the expression of tip cell-associated genes, such as DLL4 (delta-like ligand 4) and KDR (kinase insert domain receptor), and p38-heat shock protein 27 (HSP27)-dependent cell migration, thereby generating tip cell competence. Whereas BMP6 also triggers collective cell migration via the p38-HSP27 signaling axis, BMP6 induces in addition SMAD1/5 signaling, thereby promoting the expression of stalk cell-associated genes, such as HES1 (hairy and enhancer of split 1) and FLT1 (fms-like tyrosine kinase 1). Specifically, ALK3 is required for sprouting from HUVEC spheroids, whereas ALK2 represses sprout formation. We demonstrate that expression levels and respective complex formation of BMP type I receptors in ECs determine stalk vs. tip cell identity, thus contributing to endothelial plasticity during sprouting angiogenesis. As antiangiogenic monotherapies that target the VEGF or ALK1 pathways have not fulfilled efficacy objectives in clinical trials, the selective targeting of the ALK2/3 pathways may be an attractive new approach.-Benn, A., Hiepen, C., Osterland, M., Schütte, C., Zwijsen, A., Knaus, P. Role of bone morphogenetic proteins in sprouting angiogenesis: differential BMP receptor-dependent signaling pathways balance stalk vs. tip cell competence.status: publishe

RAGE mediates S100A4-induced cell motility via MAPK/ERK and hypoxia signaling and is a prognostic biomarker for human colorectal cancer metastasis

Survival of colorectal cancer patients is strongly dependent on development of distant metastases. S100A4 is a prognostic biomarker and inducer for colorectal cancer metastasis. Besides exerting intracellular functions, S100A4 is secreted extracellularly. The receptor for advanced glycation end products (RAGE) is one of its interaction partners. The impact of the S100A4-RAGE interaction for cell motility and metastasis formation in colorectal cancer has not been elucidated so far. Here we demonstrate the RAGE-dependent increase in migratory and invasive capabilities of colorectal cancer cells via binding to extracellular S100A4. We show the direct interaction of S100A4 and RAGE, leading to hyperactivated MAPK/ERK and hypoxia signaling. The S100A4-RAGE axis increased cell migration (P<0.005) and invasion (P<0.005), which was counteracted with recombinant soluble RAGE and RAGE-specific antibodies. In colorectal cancer patients, not distantly metastasized at surgery, high RAGE expression in primary tumors correlated with metachronous metastasis, reduced overall (P=0.022) and metastasis-free survival (P=0.021). In summary, interaction of S100A4-RAGE mediates S100A4-induced colorectal cancer cell motility. RAGE by itself represents a biomarker for prognosis of colorectal cancer. Thus, therapeutic approaches targeting RAGE or intervening in S100A4-RAGE-dependent signaling early in tumor progression might represent alternative strategies restricting S100A4-induced colorectal cancer metastasis