Michael L. Miller

High-dose radiotherapy (RT) in cancer is immunogenic but also induces an immunosuppressive tumor microenvironment (TME) that limits the efficacy of immune checkpoint inhibitors (ICIs). Overcoming this immunosuppressive barrier is critical to unlocking the full potential of RT-ICI combinations. As Notch signaling regulates TME, we hypothesized that the γ-secretase inhibitor (GSI), AL101, would suppress radiation-induced immunosuppression and enhance antitumor efficacy when combined with RT and anti-PD-1 (aPD-1) therapy.Syngeneic neuroblastoma (9464D) and triple-negative breast cancer (EO771) tumors were established in C57BL/6, macrophage-depleted C57BL/6 mice, or athymic mice. Mice received 12 Gy RT, AL101, and aPD-1. Tumors were analyzed by spectral flow cytometry and single-cell RNA sequencing (scRNA-seq), and lung metastases were evaluated histologically.The triple combination of RT, aPD-1, and GSI produced durable tumor growth inhibition and significantly prolonged survival in both models compared to all other treatment groups. Triple therapy also markedly reduced lung metastases in EO771 mice. These effects were abrogated in athymic nude mice and macrophage-depleted immunocompetent mice. Based on scRNA-seq and spectral flow cytometry analysis, RT alone increased exhausted T cells and immunosuppressive macrophages, while triple therapy reversed these effects, including expansion of activated CD8+ T cells, reduction of Tregs and exhausted T cells, restoration of cross-presenting CD103+ dendritic cells, and reprogramming of myeloid cells toward a proinflammatory state.GSI remodels RT-induced immunosuppressive TME and potentiates the efficacy of RT + ICI. GSI combined with RT+aPD-1 reprograms the TME toward an immunostimulatory state and supports GSI as a promising immuno-radiotherapeutic strategy.

Comparison of Spatial Survival Prediction with and without PD-L1

Comparison of Survival Prediction of Base and Base + Topology Spatial Biomarkers applying ALL versus Tumor-Only Parameters

Comparison of Survival Prediction of Topology-based Spatial Biomarkers with traditional protein biomarkers and clinical features

Topology-based Biomarkers are more sensitive and race invariant predictors of breast cancer survival than estrogen receptor expression

Comparison of predictive values (C-index, survival) of different spatial features and input data

Respresentative samples demonstrating range of PD-L1 intensities

Published clinical trials data

Indexing flowchart and schematic for phenotyping of mQIF channels and point cloud generation

Topology-based Spatial Biomarkers predict patient response to neoadjuvant chemotherapy in breast cancer clinical trials