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

21 Pages Posted: 18 Jan 2019 Publication Status: Review Complete

See all articles by Prson Gautam

Prson Gautam

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Alok Jaiswal

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Tero Aittokallio

University of Helsinki - Institute for Molecular Medicine Finland (FIMM); University of Turku - Department of Mathematics and Statistics

Hassan Al-Ali

University of Miami - Department of Neurosurgery

Krister Wennerberg

University of Helsinki - Institute for Molecular Medicine Finland (FIMM); University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

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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 cancerselective 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.

Suggested Citation

Gautam, Prson and Jaiswal, Alok and Aittokallio, Tero and Al-Ali, Hassan and Wennerberg, Krister, Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets (October 9, 2018). Available at SSRN: https://ssrn.com/abstract=3263637 or http://dx.doi.org/10.2139/ssrn.3263637
This version of the paper has not been formally peer reviewed.

Prson Gautam (Contact Author)

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Helsinki, FIN-00014
Finland

Alok Jaiswal

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Helsinki, FIN-00014
Finland

Tero Aittokallio

University of Helsinki - Institute for Molecular Medicine Finland (FIMM) ( email )

Helsinki, FIN-00014
Finland

University of Turku - Department of Mathematics and Statistics ( email )

FIN-20014
United States

Hassan Al-Ali

University of Miami - Department of Neurosurgery

1475 N.W. 12th Ave.
Miami, FL 33136
United States

Krister Wennerberg

University of Helsinki - Institute for Molecular Medicine Finland (FIMM)

Helsinki, FIN-00014
Finland

University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

Nørregade 10
Copenhagen, København DK-2200
Denmark

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