A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

Flobak, Åsmund; Niederdorfer, Barbara; Nakstad, Vu To; Thommesen, Liv; Klinkenberg, Geir; Lægreid, Astrid

doi:10.1038/s41597-019-0255-7

Cited by 36 publications

(44 citation statements)

References 31 publications

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“…The Bliss expectation (E AB, Bliss ) is calculated for drugs A and B from effect (E) as E AB, Bliss = E A + E B − E A E B , and synergy is called if the observed effect of the combination is larger than the expectation. Synergy scores were calculated per biological replicate, followed by calculation of mean synergy scores across biological replicates as presented in 50 . We report both average and standard deviation of Bliss excess values per dose and biological replicate, as well as across the matrix.…”

Section: Synergy Scoringmentioning

confidence: 99%

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Folkesson

Niederdorfer

Nakstad

et al. 2020

Sci Rep

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Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout-and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines. Colorectal cancer (CRC) is the third most common neoplastic malignancy worldwide 1 , and although improvements in standard treatments have increased the survival rates over the past 20 years 2 , far from all patients benefit from currently available therapies. Targeted therapy, using drugs aimed to target specific molecules involved in tumour growth, is being regarded as a promising tool to increase response rates to cancer therapy. However, the number of such therapies that have made it all the way to the clinic has been limited. This may be explained by lack of therapy response due to adaptive drug resistance, or transient response due to acquired resistance. Drug combinations are being discussed as a promising strategy to overcome the resistance frequently observed upon administration of targeted monotherapy 3,4. The augmented effect of targeted drug combination treatment is frequently ascribed to the drugs' ability to jointly interfere with the growth-promoting signalling network of cancer cells at multiple points. High-throughput cell line screening platforms have been successfully employed as tools to uncover novel synergistic drug combinations. In the study ALMANAC of the National Cancer Institute (NCI), where a large number of pairwise combinations of FDA-approved cancer drugs were screened in vitro, several novel pairs of synergistic drug combinations were identified, whereof roughly a third also were shown to be efficient and synergistic in vivo 5. Another example is the Merck Research Laboratories screen, in which 583 combinations of experimental and approved cancer drugs were screened in a panel of cancer cell lines, identifying well-known as well as novel synergistic drug combinations in vitro 6. Despite large combination screening efforts with successful hits in vitro, putative treatments are challenged by low bench-to-bed translational efficiency. The insufficient ability of cell lines grown on planar surfaces to correctly recapitulate drug response in vivo has been debated as a possible explanation ...

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Section: Synergy Scoringmentioning

confidence: 99%

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Folkesson

Niederdorfer

Nakstad

et al. 2020

Sci Rep

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“…The remaining potential synergistic drug pairs can subsequently be tested in cancer cell line cultures to validate the synergy predictions. 111 From this proven system, the next challenge is to implement it in a clinical setting, and to develop patient-specific logical models by using biomarker data from tumor biopsies obtained from a cancer patient, use these to select potential synergistic drug pairs, and test these on in vitro cultured spheroids or organoids derived from the same tumor material ( Fig. 4, see also refs.…”

Section: Cancer Pathways and Personalized Therapymentioning

confidence: 99%

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Comte

Baumbach

Benis

et al. 2020

Network and Systems Medicine

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Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to

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“…Drug combination data for model testing was obtained from our previously performed high-throughput screening (Flobak et al, 2019). The following cancer cell lines were the focus of this study: AGS (gastric adenocarcinoma), COLO 205 (colorectal cancer), DU-145 (prostate cancer), and SW-620 (colorectal cancer).…”

Section: Testing Datamentioning

confidence: 99%

“…Model-generated predictions were classified as TP, FP, TN and FN, by comparing the predicted synergies to the observed synergies in our drug screen (Flobak et al, 2019). To evaluate model performance, we have calculated a list of parameters.…”

Section: Evaluation Of Model Performancementioning

confidence: 99%

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Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction

et al. 2020

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Discrete dynamical modeling shows promise in prioritizing drug combinations for screening efforts by reducing the experimental workload inherent to the vast numbers of possible drug combinations. We have investigated approaches to predict combination responses across different cancer cell lines using logic models generated from one generic prior-knowledge network representing 144 nodes covering major cancer signaling pathways. Cell-line specific models were configured to agree with baseline activity data from each unperturbed cell line. Testing against experimental data demonstrated a high number of true positive and true negative predictions, including also cell-specific responses. We demonstrate the possible enhancement of predictive capability of models by curation of literature knowledge further detailing subtle biologically founded signaling mechanisms in the model topology. In silico model analysis pinpointed a subset of network nodes highly influencing model predictions. Our results indicate that the performance of logic models can be improved by focusing on high-influence node protein activity data for model configuration and that these nodes accommodate high information flow in the regulatory network.

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A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

Cited by 36 publications

References 31 publications

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction

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