Jeffrey West

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 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.

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.