Mark Robertson‐Tessi

This study shows that durable CAR-T therapy in lymphoma requires cooperation with endogenous CD8 T cells, and tumors lacking MHC class I or CD58 resist treatment.

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.

This study uses a 3D computational model to show that tumor growth rates depend on tissue density and metabolic strategies like glycolysis or angiogenesis in lung cancer.

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 rapidly oscillating molecular clocks to quantify human stem cell dynamics and finds differences in stem cell replacement rates between small intestine and colon, as well as effects of germline mutations.

The paper models tumor-immune dynamics in colorectal cancer progression, showing immune evasion enables transition from immune-hot adenomas to immune-cold carcinomas.

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.