@article{07f889e47be649f0a67e9ada2ef462da,
title = "Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets",
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.",
keywords = "AI, AURKA, Akt, FGFR, PKIS, TNBC, cancer cell line, dependency, drug screening, gene silencing, kinase, kinase inhibitors, machine learning, target deconvolution",
author = "Prson Gautam and Alok Jaiswal and Tero Aittokallio and Hassan Al-Ali and Krister Wennerberg",
note = "Funding Information: We sincerely thank Laura Turunen, Janni Saarela, Karoliina Laamanen, and other members of High Throughput Biomedicine unit of the FIMM Technology Centre (supported by University of Helsinki / Biocenter Finland Research infrastructure funds) for technical assistance. This project was supported by grants from Academy of Finland ( 277293 , to K.W.), ( 292611 , 295504 , 310507 , and 313267 to T.A.), Sigrid Jus{\'e}lius Foundation (to K.W. and T.A.), Cancer Society of Finland (to K.W., T.A., and P.G.), Novo Nordisk Foundation (Novo Nordisk Foundation Center for Stem Cell Biology, DanStem; grant NNF17CC0027852 , to K.W.), H.A. was supported by grants from the NIH ( 1R41TR002293 ), the Wallace H Coulter Center (University of Miami), and the Miami Project to Cure Paralysis. Funding Information: Hassan Al-Ali is co-founder and Chief Scientific/Operating Officer of Truvitech, LLC, a startup company based on intellectual property used in this study. He is also an inventor on a patent application filed by the University of Miami to cover the target deconvolution method (US Patent application no. US 15/360,428). Krister Wennerberg and Tero Aittokallio have received research funding from Novartis Pharma AG for projects unrelated to this study. Prson Gautam receives part-time salary funding from Bayer for an unrelated task. ",
year = "2019",
month = jul,
day = "18",
doi = "10.1016/j.chembiol.2019.03.011",
language = "English (US)",
volume = "26",
pages = "970--979.e4",
journal = "Cell Chemical Biology",
issn = "2451-9448",
publisher = "Elsevier Inc.",
number = "7",
}