Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space
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Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space. / Longden, James; Robin, Xavier; Engel, Mathias; Ferkinghoff-Borg, Jesper; Kjaer, Ida; Horak, Ivan D.; Pedersen, Mikkel W.; Linding, Rune.
I: Cell Reports, Bind 34, Nr. 3, 108657, 19.01.2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space
AU - Longden, James
AU - Robin, Xavier
AU - Engel, Mathias
AU - Ferkinghoff-Borg, Jesper
AU - Kjaer, Ida
AU - Horak, Ivan D.
AU - Pedersen, Mikkel W.
AU - Linding, Rune
PY - 2021/1/19
Y1 - 2021/1/19
N2 - It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode states of signaling networks and unravel cellular mechanisms hidden to conventional approaches. We perform high-content screening of 17 cancer cell lines, generating more than 500 billion data points from similar to 850 million cells. We analyze these data using a deep learning model, resulting in the identification of a continuous 27-dimension space describing all of the observed cell morphologies. From its morphology alone, we could thus predict whether a cell was resistant to ErbB-family drugs, with an accuracy of 74%, and predict the potential mechanism of resistance, subsequently validating the role of MET and insulin-like growth factor 1 receptor (IGF1R) as drivers of cetuximab resistance in in vitro models of lung and head/neck cancer.
AB - It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode states of signaling networks and unravel cellular mechanisms hidden to conventional approaches. We perform high-content screening of 17 cancer cell lines, generating more than 500 billion data points from similar to 850 million cells. We analyze these data using a deep learning model, resulting in the identification of a continuous 27-dimension space describing all of the observed cell morphologies. From its morphology alone, we could thus predict whether a cell was resistant to ErbB-family drugs, with an accuracy of 74%, and predict the potential mechanism of resistance, subsequently validating the role of MET and insulin-like growth factor 1 receptor (IGF1R) as drivers of cetuximab resistance in in vitro models of lung and head/neck cancer.
KW - CANCER
KW - KINASE
KW - EGFR
KW - HETEROGENEITY
KW - KINOME
KW - TOPHAT
KW - TARGET
KW - GROWTH
KW - GENE
U2 - 10.1016/j.celrep.2020.108657
DO - 10.1016/j.celrep.2020.108657
M3 - Journal article
C2 - 33472071
VL - 34
JO - Cell Reports
JF - Cell Reports
SN - 2211-1247
IS - 3
M1 - 108657
ER -
ID: 256885750