<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>Generation of chimeric antigen receptor macrophages from human pluripotent stem cells to target glioblastoma</dc:title><dc:creator>Jin, G.; Chang, Y.; Bao, X.</dc:creator><dc:corporate_author/><dc:editor/><dc:description>Background: Glioblastoma (GBM) is an aggressive brain tumor giving a poor prognosis with the current treatment
options. The advent of chimeric antigen receptor (CAR) T-cell therapy revolutionized the field of immunotherapy and
has provided a new set of therapeutic options for refractory blood cancers. In an effort to apply this therapeutic
approach to solid tumors, various immune cell types and CAR constructs are being studied. Notably, macrophages
have recently emerged as potential candidates for targeting solid tumors, attributed to their inherent tumorinfiltrating capacity and abundant presence in the tumor microenvironment.
Materials and methods: In this study, we developed a chemically defined differentiation protocol to generate
macrophages from human pluripotent stem cells (hPSCs). A GBM-specific CAR was genetically incorporated into
hPSCs to generate CAR hPSC-derived macrophages.
Results: The CAR hPSC-derived macrophages exhibited potent anticancer activity against GBM cells in vitro.
Conclusion: Our findings demonstrate the feasibility of generating functional CAR-macrophages from hPSCs for
adoptive immunotherapy, thereby opening new avenues for the treatment of solid tumors, particularly GBM.</dc:description><dc:publisher>Elsevier</dc:publisher><dc:date>2023-12-01</dc:date><dc:nsf_par_id>10490398</dc:nsf_par_id><dc:journal_name>Immuno-Oncology and Technology</dc:journal_name><dc:journal_volume>20</dc:journal_volume><dc:journal_issue>C</dc:journal_issue><dc:page_range_or_elocation>100409</dc:page_range_or_elocation><dc:issn>2590-0188</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1016/j.iotech.2023.100409</dc:doi><dcq:identifierAwardId>2143064</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>