논문 2022, International Journal of Molecular Sciences, Patient-Derived Tum…
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Abstract
An obstacle to effective uniform treatment of glioblastoma, especially at recurrence, is genetic and
cellular intertumoral heterogeneity. Hence, personalized strategies are necessary, as are means to stratify
potential targeted therapies in a clinically relevant timeframe. Functional profiling of drug candidates against
patient-derived glioblastoma organoids (PD-GBO) holds promise as an empirical method to preclinically
discover potentially effective treatments of individual tumors. Here, we describe our establishment of a PDGBO-based functional profiling platform and the results of its application to four patient tumors. We show
that our PD-GBO model system preserves key features of individual patient glioblastomas in vivo. As proof
of concept, we tested a panel of 41 FDA-approved drugs and were able to identify potential treatment options
for three out of four patients; the turnaround from tumor resection to discovery of treatment option was 13, 14,
and 15 days, respectively. These results demonstrate that this approach is a complement and, potentially, an
alternative to current molecular profiling efforts in the pursuit of effective personalized treatment discovery
in a clinically relevant time period. Furthermore, these results warrant the use of PD-GBO platforms for
preclinical identification of new drugs against defined morphological glioblastoma features.
An obstacle to effective uniform treatment of glioblastoma, especially at recurrence, is genetic and
cellular intertumoral heterogeneity. Hence, personalized strategies are necessary, as are means to stratify
potential targeted therapies in a clinically relevant timeframe. Functional profiling of drug candidates against
patient-derived glioblastoma organoids (PD-GBO) holds promise as an empirical method to preclinically
discover potentially effective treatments of individual tumors. Here, we describe our establishment of a PDGBO-based functional profiling platform and the results of its application to four patient tumors. We show
that our PD-GBO model system preserves key features of individual patient glioblastomas in vivo. As proof
of concept, we tested a panel of 41 FDA-approved drugs and were able to identify potential treatment options
for three out of four patients; the turnaround from tumor resection to discovery of treatment option was 13, 14,
and 15 days, respectively. These results demonstrate that this approach is a complement and, potentially, an
alternative to current molecular profiling efforts in the pursuit of effective personalized treatment discovery
in a clinically relevant time period. Furthermore, these results warrant the use of PD-GBO platforms for
preclinical identification of new drugs against defined morphological glioblastoma features.
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