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 language | English (US) |
|---|---|
| Pages (from-to) | 970-979.e4 |
| Journal | Cell Chemical Biology |
| Volume | 26 |
| Issue number | 7 |
| DOIs | |
| State | Published - 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
Cite this
Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets. / Gautam, Prson; Jaiswal, Alok; Aittokallio, Tero; Al-Ali, Hassan; Wennerberg, Krister.
In: Cell Chemical Biology, Vol. 26, No. 7, 18.07.2019, p. 970-979.e4.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets
AU - Gautam, Prson
AU - Jaiswal, Alok
AU - Aittokallio, Tero
AU - Al-Ali, Hassan
AU - Wennerberg, Krister
PY - 2019/7/18
Y1 - 2019/7/18
N2 - 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.
AB - 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.
KW - AI
KW - Akt
KW - AURKA
KW - cancer cell line
KW - dependency
KW - drug screening
KW - FGFR
KW - gene silencing
KW - kinase
KW - kinase inhibitors
KW - machine learning
KW - PKIS
KW - target deconvolution
KW - TNBC
UR - http://www.scopus.com/inward/record.url?scp=85068833898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068833898&partnerID=8YFLogxK
U2 - 10.1016/j.chembiol.2019.03.011
DO - 10.1016/j.chembiol.2019.03.011
M3 - Article
C2 - 31056464
AN - SCOPUS:85068833898
VL - 26
SP - 970-979.e4
JO - Cell Chemical Biology
JF - Cell Chemical Biology
SN - 2451-9448
IS - 7
ER -