Jeffrey West

The paper shows curvature-based schedule ordering remains unchanged in tumors despite treatment-induced selection toward resistance.

This paper develops a generalized resource-allocation growth law to model prostate cancer responses to intermittent androgen deprivation therapy, showing it predicts treatment outcomes and adapts to different schedules.

This paper curates a large corpus of mathematical oncology literature using AI, revealing its evolution from cancer biology to therapeutic modeling over 140 years.

This paper develops a mechanistic learning framework to predict BCR::ABL1 fusion gene status and minimal residual disease using clinical data, finding that stem-like cell state is a strong predictor.

The paper develops a combination therapy for ER+ breast cancer that uses an evolutionary double-bind strategy to suppress drug resistance.

The paper identifies collagen remodeling as a mechanism that activates a GPCR-mediated immune checkpoint in ADGRG1+ CD8+ T cells, influencing response to immunotherapy.

This paper develops a mathematical model of cell state transitions in B-ALL to correlate with BCR::ABL1 status, MRD, and relapse risk using flow cytometry data.

This paper develops and validates a treatment strategy called evolutionary antifragile therapy, which uses treatment fluctuations to limit tumor resistance evolution.

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

This study combines radiotherapy and NK cell immunotherapy to exploit an evolutionary double-bind strategy in prostate cancer, showing that RT-resistant cells become more vulnerable to NK cell killing.

This paper uses agent-based models to analyze how metastatic tumor dynamics inform adaptive therapy schedules and identifies how tumor heterogeneity affects treatment outcomes.

This study uses immunohistochemistry data to model how macrophages influence tumor evolution through spatial dynamics and immune escape strategies in ductal carcinomas.

The paper introduces the Hybrid Automata Library (HAL), a Java platform for building hybrid models in mathematical oncology, with real-time visualization and modular components.

The paper compares chemo-scheduling strategies using tumor growth models and shows low-dose metronomic therapies can be more effective for faster-growing tumors.

This paper develops a hybrid neural network/agent-based model to predict Pramlintide resistance in cutaneous squamous cell carcinoma and enable personalized treatment decisions.