Robert A. Gatenby

This paper develops an evolutionary model to show how cancer evolvability affects therapy outcomes, finding that targeted therapy is better for low-evolvability cancers and chemotherapy for high-evolvability ones, with a combined approach effective against facultative evolvability.

This paper develops an evolutionary model to evaluate extinction therapy strategies for cancer, showing it outperforms combination therapy when cross-resistance exists.

This study identifies three phases of UV-induced skin carcinogenesis using a mouse model, showing the emergence of large clades, clonal selection, and lesion formation.

This paper models VEGF and GLUT1 as coadapted foraging strategies in cancer, showing that resource-sharing dynamics influence their expression and therapeutic effectiveness.

This study proposes integrating migrastatics into adaptive therapy to delay metastasis and improve cancer treatment outcomes.

This paper demonstrates that magnetoelectric nanoparticles (MENPs) can target and kill cancer cells via irreversible electroporation when activated by MRI, showing significant tumor volume reduction with no toxicities.

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.

The paper shows polyaneuploid prostate cancer cells under hypoxia exhibit enhanced motility and aerotaxis driven by HIF-1α and RhoA, enabling invasion and intravasation.

This study combines miRNA markers and MRI radiomics to improve detection of aggressive prostate cancer, achieving higher AUC values than either method alone.

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 ENDURES consortium to study extreme survivors of glioblastoma by integrating clinical data and creating patient-specific tumor models.