Machine Learning-Directed Conversion of Glioblastoma Cells to Dendritic Cell-like Antigen-Presenting Cells as Cancer Immunotherapy.

ABSTRACT

Immunotherapy has limited efficacy in glioblastoma (GBM) due to the blood-brain barrier and the immunosuppressed or "cold" tumor microenvironment (TME) of GBM, which is dominated by immune-inhibitory cells and depleted of cytotoxic T lymphocytes (CTL) and dendritic cells (DC). Here, we report the development and application of a machine-learning precision method to identify cell fate determinants (CFD) that specifically reprogram GBM into induced antigen-presenting cells with DC-like functions (iDC-APC). In murine GBM models, iDC-APCs acquired DC-like morphology, regulatory gene expression profile, and functions comparable to natural DCs. Among these acquired functions were phagocytosis, direct presentation of endogenous antigens, and cross presentation of exogenous antigens. The latter endowed the iDC-APCs with the ability to prime naïve CD8+ CTLs, a hallmark DC function critical for antitumor immunity. Intratumor iDC-APCs reduced tumor growth and improved survival only in immunocompetent animals, which coincided with extensive infiltration of CD4+ T cells and activated CD8+ CTLs in the TME. The reactivated TME synergized with an intratumor soluble PD-1 decoy immunotherapy and a DC-based GBM vaccine, resulting in robust killing of highly resistant GBM cells by tumor-specific CD8+ CTLs and significantly extended survival. Lastly, we defined a unique CFD combination specifically for the human GBM to iDC-APC conversion of both glioma stem-like cells (GSC) and non-GSC GBM cells, confirming the clinical utility of a computationally directed, tumor-specific conversion immunotherapy for GBM and potentially other solid tumors.