Literature DB >> 27552144

Development of the Theta Comparative Cell Scoring Method to Quantify Diverse Phenotypic Responses Between Distinct Cell Types.

Scott J Warchal1, John C Dawson1, Neil O Carragher1.   

Abstract

In this article, we have developed novel data visualization tools and a Theta comparative cell scoring (TCCS) method, which supports high-throughput in vitro pharmacogenomic studies across diverse cellular phenotypes measured by multiparametric high-content analysis. The TCCS method provides a univariate descriptor of divergent compound-induced phenotypic responses between distinct cell types, which can be used for correlation with genetic, epigenetic, and proteomic datasets to support the identification of biomarkers and further elucidate drug mechanism-of-action. Application of these methods to compound profiling across high-content assays incorporating well-characterized cells representing known molecular subtypes of disease supports the development of personalized healthcare strategies without prior knowledge of a drug target. We present proof-of-principle data quantifying distinct phenotypic response between eight breast cancer cells representing four disease subclasses. Application of the TCCS method together with new advances in next-generation sequencing, induced pluripotent stem cell technology, gene editing, and high-content phenotypic screening are well placed to advance the identification of predictive biomarkers and personalized medicine approaches across a broader range of disease types and therapeutic classes.

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Year:  2016        PMID: 27552144      PMCID: PMC5015429          DOI: 10.1089/adt.2016.730

Source DB:  PubMed          Journal:  Assay Drug Dev Technol        ISSN: 1540-658X            Impact factor:   1.738


  39 in total

1.  Chemosensitivity prediction by transcriptional profiling.

Authors:  J E Staunton; D K Slonim; H A Coller; P Tamayo; M J Angelo; J Park; U Scherf; J K Lee; W O Reinhold; J N Weinstein; J P Mesirov; E S Lander; T R Golub
Journal:  Proc Natl Acad Sci U S A       Date:  2001-09-11       Impact factor: 11.205

Review 2.  Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches.

Authors:  Jonathan A Lee; Ellen L Berg
Journal:  J Biomol Screen       Date:  2013-09-30

3.  The cellular thermal shift assay for evaluating drug target interactions in cells.

Authors:  Rozbeh Jafari; Helena Almqvist; Hanna Axelsson; Marina Ignatushchenko; Thomas Lundbäck; Pär Nordlund; Daniel Martinez Molina
Journal:  Nat Protoc       Date:  2014-08-07       Impact factor: 13.491

4.  Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

Authors:  Kevin Smith; Peter Horvath
Journal:  J Biomol Screen       Date:  2014-03-18

Review 5.  Building the foundation for genomics in precision medicine.

Authors:  Samuel J Aronson; Heidi L Rehm
Journal:  Nature       Date:  2015-10-15       Impact factor: 49.962

6.  Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR.

Authors:  Makoto Maemondo; Akira Inoue; Kunihiko Kobayashi; Shunichi Sugawara; Satoshi Oizumi; Hiroshi Isobe; Akihiko Gemma; Masao Harada; Hirohisa Yoshizawa; Ichiro Kinoshita; Yuka Fujita; Shoji Okinaga; Haruto Hirano; Kozo Yoshimori; Toshiyuki Harada; Takashi Ogura; Masahiro Ando; Hitoshi Miyazawa; Tomoaki Tanaka; Yasuo Saijo; Koichi Hagiwara; Satoshi Morita; Toshihiro Nukiwa
Journal:  N Engl J Med       Date:  2010-06-24       Impact factor: 91.245

7.  Realizing the promise of reverse phase protein arrays for clinical, translational, and basic research: a workshop report: the RPPA (Reverse Phase Protein Array) society.

Authors:  Rehan Akbani; Karl-Friedrich Becker; Neil Carragher; Ted Goldstein; Leanne de Koning; Ulrike Korf; Lance Liotta; Gordon B Mills; Satoshi S Nishizuka; Michael Pawlak; Emanuel F Petricoin; Harvey B Pollard; Bryan Serrels; Jingchun Zhu
Journal:  Mol Cell Proteomics       Date:  2014-04-28       Impact factor: 5.911

Review 8.  Target deconvolution techniques in modern phenotypic profiling.

Authors:  Jiyoun Lee; Matthew Bogyo
Journal:  Curr Opin Chem Biol       Date:  2013-01-18       Impact factor: 8.822

Review 9.  Increasing the Content of High-Content Screening: An Overview.

Authors:  Shantanu Singh; Anne E Carpenter; Auguste Genovesio
Journal:  J Biomol Screen       Date:  2014-04-07

10.  Correlating chemical sensitivity and basal gene expression reveals mechanism of action.

Authors:  Matthew G Rees; Brinton Seashore-Ludlow; Jaime H Cheah; Drew J Adams; Edmund V Price; Shubhroz Gill; Sarah Javaid; Matthew E Coletti; Victor L Jones; Nicole E Bodycombe; Christian K Soule; Benjamin Alexander; Ava Li; Philip Montgomery; Joanne D Kotz; C Suk-Yee Hon; Benito Munoz; Ted Liefeld; Vlado Dančík; Daniel A Haber; Clary B Clish; Joshua A Bittker; Michelle Palmer; Bridget K Wagner; Paul A Clemons; Alykhan F Shamji; Stuart L Schreiber
Journal:  Nat Chem Biol       Date:  2015-12-14       Impact factor: 15.040

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  5 in total

Review 1.  Machine learning and image-based profiling in drug discovery.

Authors:  Christian Scheeder; Florian Heigwer; Michael Boutros
Journal:  Curr Opin Syst Biol       Date:  2018-08

2.  Evaluation of Machine Learning Classifiers to Predict Compound Mechanism of Action When Transferred across Distinct Cell Lines.

Authors:  Scott J Warchal; John C Dawson; Neil O Carragher
Journal:  SLAS Discov       Date:  2019-01-29       Impact factor: 3.341

3.  High-Content Phenotypic Profiling in Esophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery.

Authors:  Rebecca E Hughes; Richard J R Elliott; Alison F Munro; Ashraff Makda; J Robert O'Neill; Ted Hupp; Neil O Carragher
Journal:  SLAS Discov       Date:  2020-05-22       Impact factor: 3.341

4.  High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways.

Authors:  Scott J Warchal; John C Dawson; Emelie Shepherd; Alison F Munro; Rebecca E Hughes; Ashraff Makda; Neil O Carragher
Journal:  Bioorg Med Chem       Date:  2019-11-09       Impact factor: 3.641

5.  A phenomics approach for antiviral drug discovery.

Authors:  Jonne Rietdijk; Marianna Tampere; Aleksandra Pettke; Polina Georgiev; Maris Lapins; Ulrika Warpman-Berglund; Ola Spjuth; Marjo-Riitta Puumalainen; Jordi Carreras-Puigvert
Journal:  BMC Biol       Date:  2021-08-02       Impact factor: 7.431

  5 in total

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