Biology subjects

Poskus, M. D.

Publications and source records attributed to Poskus, M. D..

2 recordsLinked to original sources

bioRxiv2024

Rational design of HER2-targeted combination therapies to reverse drug resistance in fibroblast-protected HER2+ breast cancer cells.

IntroductionFibroblasts, an abundant cell type in the breast tumor microenvironment, interact with cancer cells and orchestrate tumor progression and drug resistance. However, the mechanisms by which fibroblast-derived factors impact drug sensitivity remain poorly understood. Here, we develop rational combination therapies that are informed by proteomic profiling to overcome fibroblast-mediated therapeutic resistance in HER2+ breast cancer cells. MethodsDrug sensitivity to the HER2 kinase inhibitor lapatinib was characterized under conditions of monoculture and exposure to breast fibroblast-conditioned medium. Protein expression was measured using reverse phase protein arrays. Candidate targets for combination therapy were identified using differential expression and multivariate regression modeling. Follow-up experiments were performed to evaluate the effects of HER2 kinase combination therapies in fibroblast-protected cancer cell lines and fibroblasts. ResultsCompared to monoculture, fibroblast-conditioned medium increased the expression of plasminogen activator inhibitor-1 (PAI1) and cell cycle regulator polo like kinase 1 (PLK1) in lapatinib-treated breast cancer cells. Combination therapy of lapatinib with inhibitors targeting either PAI1 or PLK1, eliminated fibroblast-protected cancer cells, under both conditions of direct coculture with fibroblasts and protection by fibroblast-conditioned medium. Analysis of publicly available, clinical transcriptomic datasets revealed that HER2-targeted therapy fails to suppress PLK1 expression in stroma-rich HER2+ breast tumors and that high PAI1 gene expression associates with high stroma density. Furthermore, we showed that an epigenetics-directed approach using a bromodomain and extraterminal inhibitor to globally target fibroblast-induced proteomic adaptions in cancer cells, also restored lapatinib sensitivity. ConclusionsOur data-driven framework of proteomic profiling in breast cancer cells identified the proteolytic degradation regulator PAI1 and the cell cycle regulator PLK1 as predictors of fibroblast-mediated treatment resistance. Combination therapies targeting HER2 kinase and these fibroblast-induced signaling adaptations eliminates fibroblast-protected HER2+ breast cancer cells.

bioengineering↗

bioRxiv2020

Numerical Model to Predict Hemolysis and Transport in a Membrane-Based Microfluidic Device

Microfluidic devices promise to overcome the limitations of conventional hemodialysis and oxygenation technologies by incorporating novel membranes with ultrahigh permeability into portable devices with low blood volume. However, the characteristically small dimensions of these devices contribute to both non-physiologic shear that could damage blood components and laminar flow that inhibits transport. While many studies have applied empirical and computational models of hemolysis to medical devices, such as valves and blood pumps, little is known about blood damage in the microfluidic flow regime. In this study, four design variants of a microfluidic membrane-based oxygenator and two controls (positive and negative) are introduced and modeled using a Computational Fluid Dynamics (CFD) model to predict hemolysis. The simulations were performed in ANSYS Fluent for nine shear stress-based Power Law hemolysis model variants. Empirical testing of the devices in a recirculating loop revealed levels of hemolysis significantly lower (3 ppm hemolysis for pump, tube, and device combined) than the hemolysis ranges (>10 ppm) observed in conventional oxygenators. We found that most of the nine tested hemolysis models overpredict (5x to 10x) hemolysis compared to empirical experiments. However, two models demonstrated higher predictive accuracy for hemolysis values in devices characterized by low shear conditions, while another set of three models exhibited better performance for devices operating under higher shear conditions. Our study highlights the limitations of combining hemolysis models with computational fluid dynamics models for a priori in silico device-induced hemolysis. Nevertheless, with a judicious selection of hemolysis models based on the shear ranges of the test device, we propose that computational modeling can complement empirical testing in the development of novel micro-dialyzers or oxygenators, allowing for a more efficient iterative design process. Furthermore, the low device-induced hemolysis (< 2 ppm) measured in our study at physiologically relevant flow rates is promising for the future development of microfluidic dialyzers and oxygenators.

bioengineering↗