Prediction and identification of synergistic compound combinations against pancreatic cancer cells

Summary: Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compo...

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Main Authors:	Yasaman KalantarMotamedi, Ran Joo Choi, Siang-Boon Koh, Jo L. Bramhall, Tai-Ping Fan, Andreas Bender
Format:	Article
Language:	English
Published:	Elsevier 2021-09-01
Series:	iScience
Subjects:	Molecular biology Computational bioinformatics Cancer systems biology
Online Access:	http://www.sciencedirect.com/science/article/pii/S2589004221010488

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spelling	doaj-69cd70dbe9ff4fdb8050f751b95523022021-09-25T05:10:40ZengElsevieriScience2589-00422021-09-01249103080Prediction and identification of synergistic compound combinations against pancreatic cancer cellsYasaman KalantarMotamedi0Ran Joo Choi1Siang-Boon Koh2Jo L. Bramhall3Tai-Ping Fan4Andreas Bender5Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UKCentre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UKCancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UKCancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UKDepartment of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, UKCentre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Corresponding authorSummary: Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82–5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells.http://www.sciencedirect.com/science/article/pii/S2589004221010488Molecular biologyComputational bioinformaticsCancer systems biology
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yasaman KalantarMotamedi Ran Joo Choi Siang-Boon Koh Jo L. Bramhall Tai-Ping Fan Andreas Bender
spellingShingle	Yasaman KalantarMotamedi Ran Joo Choi Siang-Boon Koh Jo L. Bramhall Tai-Ping Fan Andreas Bender Prediction and identification of synergistic compound combinations against pancreatic cancer cells iScience Molecular biology Computational bioinformatics Cancer systems biology
author_facet	Yasaman KalantarMotamedi Ran Joo Choi Siang-Boon Koh Jo L. Bramhall Tai-Ping Fan Andreas Bender
author_sort	Yasaman KalantarMotamedi
title	Prediction and identification of synergistic compound combinations against pancreatic cancer cells
title_short	Prediction and identification of synergistic compound combinations against pancreatic cancer cells
title_full	Prediction and identification of synergistic compound combinations against pancreatic cancer cells
title_fullStr	Prediction and identification of synergistic compound combinations against pancreatic cancer cells
title_full_unstemmed	Prediction and identification of synergistic compound combinations against pancreatic cancer cells
title_sort	prediction and identification of synergistic compound combinations against pancreatic cancer cells
publisher	Elsevier
series	iScience
issn	2589-0042
publishDate	2021-09-01
description	Summary: Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82–5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells.
topic	Molecular biology Computational bioinformatics Cancer systems biology
url	http://www.sciencedirect.com/science/article/pii/S2589004221010488
work_keys_str_mv	AT yasamankalantarmotamedi predictionandidentificationofsynergisticcompoundcombinationsagainstpancreaticcancercells AT ranjoochoi predictionandidentificationofsynergisticcompoundcombinationsagainstpancreaticcancercells AT siangboonkoh predictionandidentificationofsynergisticcompoundcombinationsagainstpancreaticcancercells AT jolbramhall predictionandidentificationofsynergisticcompoundcombinationsagainstpancreaticcancercells AT taipingfan predictionandidentificationofsynergisticcompoundcombinationsagainstpancreaticcancercells AT andreasbender predictionandidentificationofsynergisticcompoundcombinationsagainstpancreaticcancercells
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Prediction and identification of synergistic compound combinations against pancreatic cancer cells

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