492P - Linking cellular drug responses to corresponding metabolomic tissue signatures in gliomas

Background

Functional precision medicine is the next step in individualized patient centered medicine, seeking to guide treatment based on ex-vivo drug response of patient-derived tumor cells (PDCs). In this study we aimed to evaluate drug response (combination of potency and efficacy) to a panel of cytotoxic and targeted agents and to link these assessed drug responses to corresponding metabolomic tissue signatures in IDH-(wildtype) wt and IDH-(mutant) mut gliomas. To gain further insight into the aggressive behavior, we aimed to characterized the metabolic heterogeneity within IDH-wt gliomas to uncover potential therapeutic vulnerabilities.

Methods

Tumor tissues of patients (n=50) with glioma were used for PDC isolation prepared by mechanical and enzymatic tissue dissociation. After short-term cultivation (7-14 days) under standard culture conditions, spheroids were dissociated into single-cell suspensions and subjected to a high-throughput drug-screening platform. The drug panel consisted of 80 chemotherapeutic and targeted drugs. Dose response curve (DRC) fitting per drug was performed and the area under the curve (AUC) was evaluated. Corresponding tumor tissues were analyzed by HILIC-HRMS. We first correlated the metabolic profiles to drug responses and then performed computer analysis developed by Garofano et al. to analyze heterogeneity (Nat Cancer 20021; PMID:33681822).

Results

An initial statistical correlation analysis between AUC values and metabolite levels showed substantially more significant correlation (criteria: |r|> 0.5, p<0.05) between drug response and metabolites in the IDH-wt gliomas (1329 vs. 926). Furthermore, we found two metabolically distinct IDH-wt populations in a first cohort. One of these populations (n=10) had increased levels of metabolites involved in glucose and lipid metabolism whereas the other (n=14) exhibited a neurodevelopmental metabolic phenotype.

Conclusions

These initial analyses showed a correlation between drug response and metabolism using a holistic approach, as well as a differentiation into metabolically distinct subgroups. In the next step, we plan to investigate the metabolic heterogeneity within the enlarged group in more detail to find out whether there is an influence on the response to treatment.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Work done in “CBmed” was funded by the Austrian Federal Government within the COMET K1 Centre Program, Land Steiermark and Land Wien.

Disclosure

All authors have declared no conflicts of interest.