Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets

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http://hdl.handle.net/10138/312912

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Gautam , P , Jaiswal , A , Aittokallio , T , Al-Ali , H & Wennerberg , K 2019 , ' Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets ' , Cell chemical biology , vol. 26 , no. 7 , pp. 970-+ . https://doi.org/10.1016/j.chembiol.2019.03.011

Title: Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets
Author: Gautam, Prson; Jaiswal, Alok; Aittokallio, Tero; Al-Ali, Hassan; Wennerberg, Krister
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Tero Aittokallio / Principal Investigator
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2019-07-18
Language: eng
Number of pages: 14
Belongs to series: Cell chemical biology
ISSN: 2451-9448
URI: http://hdl.handle.net/10138/312912
Abstract: The lack of functional understanding of most mutations in cancer, combined with the non-druggability of most proteins, challenge genomics-based identification of oncology drug targets. We implemented a machine-learning-based approach (idTRAX), which relates cell-based screening of small-molecule compounds to their kinase inhibition data, to directly identify effective and readily druggable targets. We applied idTRAX to triple-negative breast cancer cell lines and efficiently identified cancer-selective targets. For example, we found that inhibiting AKT selectively kills MFM-223 and CAL148 cells, while inhibiting FGFR2 only kills MFM-223. Since the effects of catalytically inhibiting a protein can diverge from those of reducing its levels, targets identified by idTRAX frequently differ from those identified through gene knockout/knockdown methods. This is critical if the purpose is to identify targets specifically for small-molecule drug development, whereby idTRAX may produce fewer false-positives. The rapid nature of the approach suggests that it may be applicable in personalizing therapy.
Subject: 1ST-LINE THERAPY
DRUG-TARGET
KINASE
LETROZOLE
1182 Biochemistry, cell and molecular biology
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