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Carrere, C.

Publications and source records attributed to Carrere, C..

2 recordsLinked to original sources

Circumventing glioblastoma resistance to temozolomide through optimal drug combinations designed by quantitative systems pharmacology and machine learning

Glioblastoma is currently associated to a dismal prognosis despite intensive treatment involving maximal-safe surgery, radiotherapy and temozolomide (TMZ)-based chemotherapy. Disease progression or relapse is often due to initial or acquired resistance to temozolomide, which may be mediated by the over-expression of the repair enzyme MGMT. To design TMZ-based drug combinations circumventing the initial resistance of MGMT-overexpressing cells, a quantitative systems pharmacology (QSP) model representing TMZ cellular pharmacokinetics-pharmacodynamics and their connection to the most altered pathways in GBM was developed. This digital network representation of TMZ cellular pharmacology successfully integrates, in a mechanistic fashion, multi-type time- and dose-resolved datasets, available in control or MGMT-overexpressing cells. In silico target inhibition screening identified an optimal antitumor strategy consisting in priming cancer cells with inhibitors of the base excision repair and of the homologous recombination pathway prior to TMZ exposure. This drug combination was validated in dedicated experiments, thus allowing to re-sensitize cells which were initially resistant to TMZ. Using machine learning, functional signatures of response to such optimal multiagent therapy were derived to assist decision making about administering it to other cancer cell lines or patients. The developed framework can be extended to account for additional patientspecific altered pathways and may be translated towards the clinics by representing the tumor micro-environment and drug whole-body pharmacokinetics. Overall, we successfully demonstrated the relevance of combined QSP and machine learning to design multi-agent pharmacotherapies circumventing initial tumor resistance. One Sentence SummaryAn integrated in vitro-in silico approach allowed to design optimal drug combinations re-sensitizing temozolomide-resistant glioblastoma cells.

cancer biology↗

Metronomic therapy prevents emergence of drug resistance by maintaining the dynamic of intratumor heterogeneity.

Despite recent advances in deciphering cancer drug resistance mechanisms, relapse is a widely observed phenomenon in advanced cancers, mainly due to intratumor clonal heterogeneity. How tumor clones progress and impact each other remains elusive. By better understanding clone dynamics, we could reveal valuable biological insights and unveil vulnerabilities that could be therapeutically exploited. In this study, we developed 2D and 3D non-small cell lung cancer co-culture systems and defined a phenomenological mathematical model. Our results demonstrated a dominant role of the drug-sensitive clones over the drug-resistant ones under untreated conditions. Model predictions and their experimental in vitro and in vivo validations indicated that metronomic schedule leads to a better regulation of tumor cell heterogeneity over time than maximum-tolerated dose schedule, while achieving control of global tumor progression. We finally showed that drug-sensitive clones exert a suppressive effect on the proliferation of the drug-resistant ones through a paracrine mechanism way, which is linked to metabolic cell clone activity. Altogether, these computational and experimental approaches allow assessment of drug schedules controlling drug-sensitive and -resistant clone balance and highlight the potential of targeting cell metabolism to manage intratumor heterogeneity. SignificanceCombined computational and experimental models reveal how drug-sensitive tumor cells exert their dominance over drug-resistant cells and how it impacts optimal chemotherapy scheduling.

cancer biology↗