A. R. A. Anderson

This study uses computational modeling to explore how spatial interactions between immune cells and collagen fibers influence tumor growth and immune infiltration.

This study tests adaptive therapy protocols using gemcitabine and capecitabine on a mouse model of endocrine-resistant breast cancer, finding improved survival with adaptive strategies over high-dose combination therapy.

This study examines how varying chemotherapy dose schedules affect tumor regression and cachexia, finding that high variance schedules can improve outcomes without increasing total dose.

This study develops an adaptive therapy protocol for PARP inhibitors by combining mathematical modeling and experiments to optimize treatment scheduling and mitigate resistance.

The paper analyzes multiplexed histological images from NSCLC patients to identify spatial immune ecologies that predict disease progression and serve as potential biomarkers for therapy response.

This paper presents a multiscale mathematical model of tumor-immune interactions that identifies metaphenotypes driving immune escape and metabolic transformation in cancer.

The paper maps MRI-localized glioma biopsies to MRI habitats, identifying transcriptional tissue states and their association with cellular subpopulations and MRI features.

VALIS automates registration of immunohistochemistry and immunofluorescence images, enabling spatial analysis of whole slide images with low error and minimal computational time.

Mistic provides an open-source tool to visualize multiplexed images using t-SNE or UMAP layouts for exploratory analysis of tumor tissue ecology.

This paper develops a mathematical model of stochastic phenotype switching in cancer cells to show how non-genetic inheritance contributes to treatment resistance and proposes a therapeutic strategy to enhance treatment efficacy.

This study develops mathematical models to predict adaptive therapy benefits for metastatic melanoma, showing potential delays in progression by 6-25 months compared to continuous therapy.

This paper introduces a mathematical framework to quantify cancer cell line fragility or antifragility to treatment variability and applies it to treatment resistance and collateral sensitivity.

This study develops a mathematical model of neoantigen evolution in tumors and validates it with colorectal cancer data, showing how negative selection shapes tumor immunogenotype and clonality.

The paper uses a phenotype-structured model to show how fluctuating oxygen levels affect cell population dynamics and competitive advantage through phenotypic variation rates.

EvoFreq is a tool for visualizing evolutionary frequencies of sequence and model data across time, offering flexible methods for analyzing clonal dynamics.

This study presents a mechanistic epidermal model showing homeostasis leads to log-linear subclone size distributions in skin cells, with larger subclones being older due to persistence of driver mutations.

This paper develops a hybrid agent-based model to study how intratumor heterogeneity affects glioblastoma growth and treatment response using multiscale data and mathematical frameworks.

The paper unifies the cancer stem cell and stochastic models using a continuum model of microenvironmental forces, supported by clonogenic assay results.

The paper develops a mathematical model to optimize neoadjuvant combination therapy for ER+ breast cancer by balancing tumor burden and metastatic potential using game theory.

This study models how spatial heterogeneity and evolutionary dynamics affect cancer recurrence under different treatment schedules.