Breeze: an integrated quality control and data analysis application for high-throughput drug screening
Open accessAbstract
Summary: High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose–response curve fitting and quantification of the drug responses along with interactive visualization of the results. Availability and implementation: The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. Contact: swapnil.potdar@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online.
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In a cross-sectional study of 151 cancer patients, over a quarter did not have daily access to the Internet.
“A cross-sectional study of 151 cancer patients explored barriers and enablers of patients' uptake of technology for healthcare services and found over a quarter did not have daily access to the Internet, and nearly a third did not own a smartphone capable of displaying mobile applications [53].”
From Patient-facing cancer mobile apps that enable patient reported outcome data to be collected: A systematic review of content, functionality, quality, and ability to integrate with electronic health records (2023) · cites this paper as [53]
The Summary describes Breeze as an application for quality control and analysis of high-throughput drug-screening data, not a survey of patients’ technology access. Searches for 151 cancer patients, daily Internet access, and smartphone ownership found no passage in our copy addressing the claimed study or its findings. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIn that cross-sectional study of 151 cancer patients, nearly a third did not own a smartphone capable of displaying mobile applications.
“A cross-sectional study of 151 cancer patients explored barriers and enablers of patients' uptake of technology for healthcare services and found over a quarter did not have daily access to the Internet, and nearly a third did not own a smartphone capable of displaying mobile applications [53].”
From Patient-facing cancer mobile apps that enable patient reported outcome data to be collected: A systematic review of content, functionality, quality, and ability to integrate with electronic health records (2023) · cites this paper as [53]
The Abstract (Summary) and Introduction describe Breeze, an application for high-throughput drug-screening analysis, not a survey of patients’ access to technology. Searches for the 151-patient study, smartphone ownership, and daily Internet access found no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Cites
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works
Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer
Lee, S. H., Hu, W., Matulay, J. T., Silva, M. V., Owczarek, T. B., Kim, K., Chua, C. W., Barlow, L. J., Kandoth, C., Williams, A. B., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.017 · Cited by 873 · see citing works
Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation
Pemovska, T., Johnson, E., Kontro, M., Repasky, G. A., Chen, J., Wells, P., Cronin, C. N., McTigue, M., Kallioniemi, O., Porkka, K., et al. (2015) · Nature · doi:10.1038/nature14119 · Cited by 234 · see citing works
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Research works printed but not linked: 17
- Boutros M. et al (2006) Analysis of cell-based RNAi screens. Genome Biol., 7, R66. 16869968 PMC1779553
- Findlay J.W.A., Dillard R.F. (2007) Appropriate calibration curve fitting in ligand binding assays. AAPS J., 9, E260– E267. 17907767 PMC2751416
- Chen L. et al (2016) mQC: a heuristic quality-control metric for high-throughput drug combination screening. Sci. Rep., 6, 37741. 27883049 PMC5121902
- Fourches D. et al (2014) HTS navigator: freely accessible cheminformatics software for analyzing high-throughput screening data. Bioinformatics, 30, 588– 589. 24376084 PMC3928525
- Haverty P.M. et al (2016) Reproducible pharmacogenomic profiling of cancer cell line panels. Nature, 533, 333– 337. 27193678
- Kodack D.P. et al (2017) Primary patient-derived cancer cells and their potential for personalized cancer patient care. Cell Rep., 21, 3298– 3309. 29241554 PMC5745232
- Mpindi J.P. et al (2015) Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data. Bioinformatics, 31, 3815– 3821. 26254433 PMC4653387
- Mpindi J.P. et al (2016) Consistency in drug response profiling. Nature, 540, E5– E6. 27905421
- Pemovska T. et al (2013) Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov., 3, 1416– 1429. 24056683
- Saeed K. et al (2017) Comprehensive drug testing of patient-derived conditionally reprogrammed cells from castration-resistant prostate cancer. Eur. Urol., 71, 319– 327. 27160946
- Seashore-Ludlow B. et al (2015) Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov., 5, 1210– 1223. 26482930 PMC4631646
- Smirnov P. et al (2016) PharmacoGx: an R package for analysis of large pharmacogenomic datasets. Bioinformatics, 32, 1244– 1246. 26656004
- Vølund A. (1978) Application of the four-parameter logistic model to bioassay: comparison with slope ratio and parallel line models. Biometrics, 34, 357– 365. 719119
- Yadav B. et al (2015) Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci. Rep., 4, 5193. PMC4046135 24898935
- Yang W. et al (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res., 41 (Database issue), D955–D 961. 23180760 PMC3531057
- Zhang J.H. (1999) A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen., 4, 67– 73. 10838414
- Zhang X.D. (2007) A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays. Genomics, 89, 552– 561. 17276655
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Malani, D., Kumar, A., Brück, O., Kontro, M., Yadav, B., Hellesøy, M., Kuusanmäki, H., Dufva, O., Kankainen, M., Eldfors, S., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0410 · Cited by 193 · see citing works
Self-citationVercell, A., Gasteiger, N., Yorke, J., & Dowding, D. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104931 · Cited by 36 · see citing works
Adnan Awad, S., Dufva, O., Klievink, J., Karjalainen, E., Ianevski, A., Pietarinen, P., Kim, D., Potdar, S., Wolf, M., Lotfi, K., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101521 · Cited by 14 · see citing works
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- Authors
- Potdar, S., Ianevski, A., Mpindi, J.-P., Bychkov, D., Fiere, C., Ianevski, P., Yadav, B., Wennerberg, K., Aittokallio, T., Kallioniemi, O., Saarela, J., & Östling, P.
- Affiliations
- University of Helsinki (FI)
- University of Copenhagen (DK)
- Corresponding author
- Potdar, Swapnil
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-03-02
- Volume
- 36
- Issue
- 11
- Pages
- 3602-3604
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7267830
- OpenAlex
- W3010256878
- Cited by
- 102
- References
- 3
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 21 · 2023: 15 · 2022: 21 · 2021: 14 · 2020: 7
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
- Työväentutkimus Vuosikirja (submittedVersion, cc-by-nc)
- UTUPub (University of Turku) (submittedVersion, cc-by-nc)
- Aaltodoc (Aalto University) (submittedVersion, other-oa)
- Funders
- University of Helsinki and Biocenter Finland
- Sigrid Juselius Foundation
- Cancer Society of Finland, Academy of Finland (292611, 326238, 310507)
- Magnus Ehrnrooth Foundation, Biocentrum Helsinki
- St. Baldrick’s Foundation
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- high-throughput screening, quality control, breeze, dose-response curve fitting, drug sensitivity, drug resistance, precision medicine, open source software, R package, data visualization
- MeSH terms
- Data Analysis, Drug Evaluation, Preclinical, Humans, Quality Control, Software
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