David Basanta

This paper introduces a spatial-statistical framework to quantify stroma-tumor cell interactions from histology, showing geometric limits and applying it to identify proliferation bias in cancer.

This study shows that stromal niches protect lung cancers from ALK inhibitors through multiple mechanisms, and targeting these mechanisms together is more effective than targeting them individually.

This paper identifies CYP3A4 hyperactivation as a tumor-extrinsic resistance mechanism in NSCLC, showing it reduces drug exposure and leads to resistance, and proposes dosing optimization and CYP3A4 inhibition as solutions.

The paper shows that targeted therapy resistance in cancer selects for cells with higher phenotypic plasticity, which enhances their ability to adapt to other stressors and promotes metastasis.

This paper uses an agent-based model to simulate how different treatment-free intervals during T-cell engager exposure affect T cell exhaustion and tumor control in B-ALL.

This study shows that mutation impacts on populations depend on their adaptive state, with increased mutation rates beneficial near extinction but harmful in well-adapted populations.

This study uses a spatial agent-based model to show that persister cells reduce treatment efficacy in adaptive cancer therapy by providing a reservoir for genetic resistance.

This study develops a computational model of the AML bone marrow microenvironment and shows that combining TKIs with miR-126 inhibition can overcome treatment resistance by disrupting the protective vascular niche.

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 challenges the classic U-shape model of resistance by proposing alternative population-level models for ruxolitinib resistance in CMML and explores the role of oxygen and cytokine signaling in resistance dynamics.

This study develops a hybrid model showing stromal reactivity influences prostate cancer progression and prognosis by shaping tumor cell evolution and aggressiveness.

This paper uses evolutionary game theory to model tumour heterogeneity in glycolysis, angiogenesis, and oxygen availability, showing dynamic regimes and the importance of treatment timing.

This paper applies evolutionary game theory to model cell-cell interactions and their impact on cancer evolution.

This paper shows spatial structure in tumor models can promote invasive cell strategies at tumor edges, using a game-theoretic approach to analyze metastatic potential.