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Dirix, L. Y.

Publications and source records attributed to Dirix, L. Y..

2 recordsLinked to original sources

Multiscale Modeling Uncovers Macrophage Infiltration and TNF-α Signaling Networks for Targeting in Inflammatory Breast Cancer Tumor Emboli

Inflammatory breast cancer (IBC) tumors are characterized by diffuse clusters of cells found in dermal tissue and lymphatic vessels, known as tumor emboli. Thus, IBC needs a novel treatment because it is the most aggressive breast cancer subtype. We hypothesized that the interaction between tumor emboli and the tumor immune microenvironment (TiME) fosters survival signaling, leading to the aggressiveness of the IBC. In this study, ex vivo tumor emboli were generated from patient-derived cell lines cultured in a lymphatic-like environment, which was compared to 2D monolayer cultures, revealing upregulation of TNFR signaling networks, CXCL8, and immune cell chemotaxis genes. Spatial immunophenotyping of IBC patient tumors demonstrated high levels of CD163+ tumor-associated macrophages (TAMs). Furthermore, intravital imaging of CX3cr1GFP mice confirmed macrophage movement toward tumor cell clusters. Finally, targeting macrophage-associated TNF--signaling using Birinapant, a SMAC mimetic, inhibited the tumor emboli phenotype in vivo, identifying Birinapant as a potential therapeutic agent to disrupt this signaling axis.

cancer biology↗

Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. We therefore created SIMMS: an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We applied SIMMS to multiple data-types across four diseases, and in each it reproducibly identified subtypes, made superior predictions to the best bespoke approaches, and identified known and novel signaling nodes. To demonstrate its ability on a new dataset, we measured 33 genes/nodes of the PIK3CA pathway in 1,734 FFPE breast tumours and created a four-subnetwork prediction model. This model significantly out-performed existing clinically-used molecular tests in an independent 1,742-patient validation cohort. SIMMS is generic and can work with any molecular data or biological network, and is freely available at: https://cran.r-project.org/web/packages/SIMMS.

bioinformatics↗