Menck, K. | Search bioRxiv

bioRxiv2020

WNT11 is a novel ligand for ROR2 in human breast cancer

Breast cancer has been associated with activation of the WNT signaling pathway, although the underlying molecular mechanisms are still unclear. Here, we found the WNT receptor ROR2 to be highly expressed in aggressive breast tumors and associated with worse metastasis-free survival. In order to understand the molecular basis of these observations, we overexpressed ROR2 in human breast cancer cell lines, inducing a BRCAness-like phenotype and rendering them resistant to PARP inhibition. High levels of ROR2 were associated with defects in cell morphology and cell-cell-contacts leading to increased tumor invasiveness. Using gene expression analysis we demonstrated an upregulation of several non-canonical WNT ligands in ROR2-overexpressing breast cancer cells, in particular WNT11. Co-immunoprecipitation confirmed that WNT11 is indeed a novel ligand for ROR2 that interacts with its cysteine-rich domain and triggers the invasion-promoting signaling via RHO/ROCK. Knockdown of WNT11 reversed the pro-invasive phenotype and the cellular changes in ROR2-overexpressing cells. Taken together, our studies revealed a novel auto-stimulatory loop in which ROR2 triggers the expression of its own ligand, WNT11, resulting in enhanced tumor invasion associated with breast cancer metastasis.

cancer biology↗

bioRxiv2020

Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

MotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g. distant metastasis in cancer, for each individual patient. ResultsWe extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset, and then applied the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. As a result this method could be potentially highly useful on interpreting classification results on the individual patient level, as for example in precision medicine approaches or a molecular tumor board. Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrp https://frankkramer-lab.github.io/MetaRelSubNetVis/ Contacttim.beissbarth@bioinf.med.uni-goettingen.de

bioinformatics↗