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

Prson Gautam, Alok Jaiswal, Tero Aittokallio, Hassan Al-Ali, Krister Wennerberg

Research output: Contribution to journalArticle

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)970-979.e4
JournalCell Chemical Biology
Volume26
Issue number7
DOIs
StatePublished - Jul 18 2019

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Keywords

  • AI
  • Akt
  • AURKA
  • cancer cell line
  • dependency
  • drug screening
  • FGFR
  • gene silencing
  • kinase
  • kinase inhibitors
  • machine learning
  • PKIS
  • target deconvolution
  • TNBC

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Molecular Biology
  • Pharmacology
  • Drug Discovery
  • Clinical Biochemistry

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