Ortmann, J. | Search bioRxiv

bioRxiv2026

Microporous annealed particle scaffolds avoid foreign body response by down regulating complement-fibroblast-macrophage signaling loop

Biomaterial implantation can trigger a foreign body response (FBR) that impedes tissue-implant integration. To investigate how implant porosity influences this response, we compared the immune response to subcutaneous implants of microporous annealed particle (MAP) scaffolds and nanoporous hydrogels using mass cytometry, single-cell RNA sequencing, and multiplex cytokine assays. MAP scaffolds promoted vascularization and tissue integration, marked by increased endothelial and regulatory T cells, and reduced proinflammatory immune cells and cytokines. In contrast, nanoporous hydrogels demonstrated enrichment of basophils, natural killer cells, and macrophage populations associated with fibrosis. Transcriptomic and proteomic analyses revealed that MAP scaffolds suppressed activation of the complement-fibroblast-macrophage signaling loop, particularly the C5a signaling crosstalk pathway. This was confirmed using C5-deficient mice, where complement-driven cytokine production was significantly reduced only in nanoporous implants. These findings demonstrate that scaffold porosity modulates immune and complement responses, identifying a key mechanism by which MAP scaffolds reduce FBR and improve biomaterial integration.

bioengineering↗

bioRxiv2020

KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, and yet remains a challenging task. A preferred measure to quantify this response should take into account as much of the experimental data as possible, i.e. both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. KuLGaP is more selective than currently existing measures, reduces the risk of false positive calls and improves translation of the lab results to clinical practice.

bioinformatics↗

bioRxiv2018

Integrative Pharmacogenomics Analysis of Patient Derived Xenografts

One of the key challenges in cancer precision medicine is finding robust biomarkers of drug response. Patient-derived tumor xenografts (PDXs) have emerged as reliable preclinical models since they better recapitulate tumor response to chemo- and targeted therapies. However, the lack of standard tools poses a challenge in the analysis of PDXs with molecular and pharmacological profiles. Efficient storage, access and analysis is key to the realization of the full potential of PDX pharmacogenomic data. We have developed Xeva (XEnograft Visualization & Analysis), an open-source software package for processing, visualization and integrative analysis of a compendium of in vivo pharmacogenomic datasets. The Xeva package follows the PDX minimum information (PDX-MI) standards and can handle both replicate-based and 1x1x1 experimental designs. We used Xeva to characterize the variability of gene expression and pathway activity across passages. We found that only a few genes and pathways have passage specific alterations (median intraclass correlation of 0.53 for genes and positive enrichment score for 92.5% pathways). For example, activity of the mRNA 3-end processing and elongation arrest and recovery pathways were strongly affected by model passaging (gene set enrichment analysis false discovery rate [FDR] <5%). We then leveraged our platform to link the drug response and the pathways whose activity is consistent across passages by mining the Novartis PDX Encyclopedia (PDXE) data containing 1,075 PDXs spanning 5 tissue types and 62 anticancer drugs. We identified 87 pathways significantly associated with response to 51 drugs (FDR < 5%), including associations such as erlotinib response and signaling by EGFR in cancer pathways and MAP kinase activation in TLR cascade and binimetinib response. Among the significant pathway-drug associations, we found novel biomarkers based on gene expressions, Copy Number Aberrations (CNAs) and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, a major step toward precision oncology.

bioinformatics↗