NEW YORK – A research team from Sweden, the UK, Singapore, Finland, and the US has incorporated genomic profiles with treatment responses across drug classes in glioblastoma (GBM) cell lines, uncovering tumor alterations that seem to coincide with better or worse responses to proteasome inhibitors and other compounds.
"By characterizing the cells at multiple levels, we discovered unexpected associations between important genes and pathways, and different drugs," senior author Sven Nelander, an immunology, genetics, and pathology researcher at Uppsala University, said in a statement.
"This in turn led us to find new ways to combine different drugs to maximize the effect," Nelander explained. "Our results thus provide a good starting point for further research aiming to increase precision and adapt the therapy for different glioblastoma patients. They can also be used to discover new purposes for already existing drugs."
For a pharmacogenomic analysis published in Cell Reports on Tuesday, the researchers tested more than 1,500 drugs on exome-sequenced, patient-derived GBM cell cultures collected through Uppsala University Hospital's Human Glioma Cell Culture (HGCC), using computational modeling of the drug responses detected to narrow in on tumor clusters with distinct responses to individual drugs or drug combinations. Among other treatment clues, for example, they found that response to proteasome inhibitor treatments tended to vary with the presence or absence of mutually exclusive alterations affecting the TP53 or CDKN1A/B genes.
"The functionally characterized HGCC cell collection provides a resource for drug development, which we expect will enhance and expedite the development of new interventions against GBM," the authors explained, noting that "patient-derived GBM cells are primarily grouped by their sensitivity to proteasome inhibitors, and secondarily grouped by their response to other classes of drugs."
Using 100 patient-derived GBM cell cultures from HGCC that were exome sequenced and systematically exposed to 1,544 compounds encompassing more than 100 mechanisms of action, the researchers searched for and computationally modeled recurrent genetic alterations that appeared to correspond with better or worse responses to a range of drug classes.
"The resulting map substantially extends the set of drug categories that can be predicted with accuracy in GBM, defines 51 associations between drug classes and hallmark pathways, and nominates biomarkers for drugs with both oncology and non-oncology indications," they reported.
Although drug responses did not seem to track closely with the proneural, classical, and mesenchymal subtypes of GBM, the team found that proteasome inhibitor response did appear to vary depending on the presence or absence of mutually exclusive tumor mutations affecting CDKN2A/B or TP53 genes.
The authors further noted that the apparent ties between proteasome inhibitor response and TP53 mutations may be particularly relevant to individuals with Li Fraumeni syndrome, who carry germline alterations in the gene that put them at enhanced risk of GBM and several other cancer types.
"Our proposed classification of glioma cells based on p53 and CDKN2A/B status has potential applications in the prospective design and post hoc interpretation of proteasome inhibitor clinical trials in GBM," the authors wrote, noting that "germline mutation of both p53 (Li-Fraumeni syndrome) and CDKN2A (melanoma-astrocytoma syndrome) are associated with substantial risk increase for brain tumors."