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Synergistic Highly Potent Targeted Drug Combinations in Different Pheochromocytoma Models Including Human Tumor Cultures.

ABSTRACT: There are no officially approved therapies for metastatic pheochromocytomas apart from ultratrace 131I-metaiodbenzylguanidine therapy, which is approved only in the United States. We have, therefore, investigated the antitumor potential of molecular-targeted approaches in murine pheochromocytoma cell lines [monocyte chemoattractant protein (MPC)/monocyte chemoattractant protein/3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT)], immortalized mouse chromaffin Sdhb-/- cells, three-dimensional pheochromocytoma tumor models (MPC/MTT spheroids), and human pheochromocytoma primary cultures. We identified the specific phosphatidylinositol-3-kinase α inhibitor BYL719 and the mammalian target of rapamycin inhibitor everolimus as the most effective combination in all models. Single treatment with clinically relevant doses of BYL719 and everolimus significantly decreased MPC/MTT and Sdhb-/- cell viability. A targeted combination of both inhibitors synergistically reduced MPC and Sdhb-/- cell viability and showed an additive effect on MTT cells. In MPC/MTT spheroids, treatment with clinically relevant doses of BYL719 alone or in combination with everolimus was highly effective, leading to a significant shrinkage or even a complete collapse of the spheroids. We confirmed the synergism of clinically relevant doses of BYL719 plus everolimus in human pheochromocytoma primary cultures of individual patient tumors with BYL719 attenuating everolimus-induced AKT activation. We have thus established a method to assess molecular-targeted therapies in human pheochromocytoma cultures and identified a highly effective combination therapy. Our data pave the way to customized combination therapy to target individual patient tumors.

SUBMITTER: Fankhauser M

PROVIDER: S-EPMC6795182 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Synergistic Highly Potent Targeted Drug Combinations in Different Pheochromocytoma Models Including Human Tumor Cultures.

Fankhauser Maria M Bechmann Nicole N Lauseker Michael M Goncalves Judith J Favier Judith J Klink Barbara B William Doreen D Gieldon Laura L Maurer Julian J Spöttl Gerald G Rank Petra P Knösel Thomas T Orth Michael M Ziegler Christian G CG Aristizabal Prada Elke Tatjana ET Rubinstein German G Fassnacht Martin M Spitzweg Christine C Grossman Ashley B AB Pacak Karel K Beuschlein Felix F Bornstein Stefan R SR Eisenhofer Graeme G Auernhammer Christoph J CJ Reincke Martin M Nölting Svenja S

Endocrinology 20191101 11

There are no officially approved therapies for metastatic pheochromocytomas apart from ultratrace 131I-metaiodbenzylguanidine therapy, which is approved only in the United States. We have, therefore, investigated the antitumor potential of molecular-targeted approaches in murine pheochromocytoma cell lines [monocyte chemoattractant protein (MPC)/monocyte chemoattractant protein/3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT)], immortalized mouse chromaffin Sdhb-/- cells, three ...[more]

PMID: 31322702

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Project description:While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. SCIENTIFIC CONTRIBUTION: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.

Dataset Information

Synergistic Highly Potent Targeted Drug Combinations in Different Pheochromocytoma Models Including Human Tumor Cultures.

Publications

Synergistic Highly Potent Targeted Drug Combinations in Different Pheochromocytoma Models Including Human Tumor Cultures.

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