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Bradshaw, T.

Publications and source records attributed to Bradshaw, T..

2 recordsLinked to original sources

Intratumoral dose heterogeneity promotes adaptive anti-tumor immunity and predicts clinical response to radiopharmaceutical therapy

Radiopharmaceutical therapies (RPT) deliver non-uniform radiation dose in tumors and the impact of this on response is poorly understood. Dose heterogeneity could engender treatment resistance in low dose regions, yet we hypothesize that a broader array of dose-dependent immuno-radiobiological mechanisms in tumor microenvironments (TME) and preservation of immune function in low-dose regions could promote adaptive anti-tumor immunity and response. In murine models, non-uniform lutetium-177 delivering <2.5 Gy to >20 Gy in a TME induced broader immunomodulatory effects and T cell-dependent survival improvement compared to more uniform distributions. Preserving low-dose regions promoted dendritic cell activation and TME infiltration of clonally expanded CD8+ T cells. In three independent cohorts of patients with prostate cancer, heterogeneous tumor dose distribution strongly correlated with improved clinical outcomes. These findings defy expected radiobiological dose-response and define a novel mechanism of action for RPT, supporting clinical investigation of dose distribution for optimizing patient selection and personalized dosing.

cancer biology↗

Unbiased data-driven analysis of five amyloid-beta peptides for biomarker investigations in familial Alzheimer's disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSChanges to the relative abundance of amyloid-beta (A{beta}) peptides are hallmarks of Alzheimers disease (AD). iPSC-derived neurons offer a physiological model of A{beta} production. We employed unbiased, data-driven analyses to investigate combinations of A{beta} peptides as AD biomarkers and the relative contribution of peptides to AD pathogenesis. METHODSWe measured A{beta}37, A{beta}38, A{beta}40, A{beta}42 and A{beta}43 in ten iPSC-neuronal cultures from PSEN1 mutation carriers. We combined these data with published cell model data and used linear weighted combinations to 1) distinguish AD from controls, and 2) predict age-at-onset for PSEN1 mutations. RESULTSData-driven approaches distinguished A{beta}42 and A{beta}43 from shorter peptides, providing unbiased evidence for their contribution to disease. Weighted linear combinations of A{beta} peptides outperform A{beta}42/40 and provide insights into relative peptide contribution as biomarkers; the optimal ratio for all data is represented as (21 {middle dot} A{beta}37 + 10 {middle dot} A{beta}38 + 69 {middle dot} A{beta}40)/(94 {middle dot} A{beta}42 + 6 {middle dot} A{beta}43). DISCUSSIONThe algorithm discovered herein can be further refined to improve biomarkers for AD.

neuroscience↗