New <i>in vitro</i> system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

Kischkel, Frank C.; Eich, Julia; Meyer, C; Weidemüller, Paula; Krapfl, Jens; Yassin-Kelepir, Rauaa; Job, Laura; Fraefel, Marius; Braicu, Elena Ioana; Kopp‐Schneider, Annette; Sehouli, Jalid; Wilde, Rudy Leon De

doi:10.7717/peerj.3030

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…Serendipitous discovery in the clinic has been a traditional source of effective drug combinations 5,6 . Yet systematic large-scale efforts to identify them have only recently been pursued, with a growing number of preclinical experimental efforts to identify synergistic combinations [6][7][8][9][10][11] being reported in literature. The sheer number of available and possible drug-like molecules 12 and an exponential number of their combinations, however, make the process of finding new therapeutic combinations by purely experimental means highly inefficient.…”

Section: Introductionmentioning

confidence: 99%

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

Sidorov

Naulaerts

Ariey-Bonnet

et al. 2018

Preprint

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Background. Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic.Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Methods. Each cell line is modeled using primarily two machine learning techniques, RandomForest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5000 pair-wise drug combinations, 60 cell lines, 4 types of models and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated.Results. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on 2 the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least two-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models.Conclusions. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

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Section: Introductionmentioning

confidence: 99%

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

Sidorov

Naulaerts

Ariey-Bonnet

et al. 2018

Preprint

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“…We added the two scores of the single measurements to obtain the score and the resistance class of the combination. We recently published a paper where we described an in vitro system to predict the chemotherapeutic efficacy of drug combinations [ 15 ]. With this system it is possible to use single measurements to predict the efficacy of drug combinations in certain cases and to measure combinations directly.…”

Section: Discussionmentioning

confidence: 99%

“…Soft agar matrix (0.4%) selective for tumor growth was placed in 24-well polystyrene culture dishes and 50 μl of one specific chemotherapeutic drug was then added to appropriate wells. The drug concentrations can be found in [ 15 ]. Tumor cells suspended in 0.5 ml tissue culture medium and 0.2% agarose were then added to each well.…”

Section: Methodsmentioning

confidence: 99%

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test)

et al. 2017

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BackgroundIn order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations.MethodsThe CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications.ResultsAccurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990.ConclusionsChemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.Electronic supplementary materialThe online version of this article (10.1186/s13048-017-0365-9) contains supplementary material, which is available to authorized users.

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“…Serendipitous discovery in the clinic has been a traditional source of effective drug combinations (Zoli et al, 2001; Kurtz et al, 2015). Yet systematic large-scale efforts to identify them have only recently been pursued, with a growing number of preclinical experimental efforts to identify synergistic combinations (Zoli et al, 2001; Budman et al, 2012; Lieu et al, 2013; Kashif et al, 2015; Yu et al, 2015; Kischkel et al, 2017) being reported in literature. The sheer number of available and possible drug-like molecules (Polishchuk et al, 2013) and an exponential number of their combinations, however, make the process of finding new therapeutic combinations by purely experimental means highly inefficient.…”

Section: Introductionmentioning

confidence: 99%

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data

et al. 2019

View full text Add to dashboard Cite

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

show abstract

New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

Cited by 5 publications

References 32 publications

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

Predicting synergism of cancer drug combinations using NCI-ALMANAC data

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test)

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data

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