Katarzyna Anna Rejniak

This paper develops a virtual cohort framework to stratify individuals into treatment subgroups and applies it to a murine bladder cancer dataset to generate synthetic mice replicating tumor dynamics and experimental outcomes.

PETIL predicts TIL expansion in bladder cancer using demographic, clinical, and biological features, achieving AUC values of 0.740 and 0.857 in testing and validation cohorts.

The paper optimizes vaccine and anti-PD1 combination therapy for melanoma by using a mathematical model and optimization algorithm to determine the best treatment protocol.

This study shows hypoxia alters T cell transcriptomes in melanoma, correlating with poor clinical outcomes in non-responders to immunotherapy.

The paper introduces LinG3D software to visualize spatio-temporal clonal evolution in tumors, demonstrating its use in simulations and experimental assays.

The paper uses a cell-based model to study how tumor cells remodel collagen fibrils in the extracellular matrix and identify patterns associated with tumor behavior.

This paper links radiologic image data to cell-scale simulations to explain oxygen fluctuations in tumors, showing vascular changes, not cellular absorption, drive these fluctuations.

The paper compares IC50 values in monolayer and spheroid cultures to identify conditions affecting drug efficacy and toxicity.

This study uses a hybrid agent-based model to analyze how drug resistance develops in tumor cells through spatio-temporal lineage tracking and identifies adaptation mechanisms in microenvironmental niches.

The paper uses mathematical models and computational optimization to design effective cancer treatment protocols that leverage hypoxia in pancreatic and melanoma cancers.

The paper develops microPKPD models to assess patient-specific drug efficacy by simulating drug delivery challenges in tumor microenvironments.