Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space

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Standard

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

Harvard

Longden, J, Robin, X, Engel, M, Ferkinghoff-Borg, J, Kjaer, I, Horak, ID, Pedersen, MW & Linding, R 2021, 'Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space', Cell Reports, bind 34, nr. 3, 108657. https://doi.org/10.1016/j.celrep.2020.108657

APA

Longden, J., Robin, X., Engel, M., Ferkinghoff-Borg, J., Kjaer, I., Horak, I. D., Pedersen, M. W., & Linding, R. (2021). Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space. Cell Reports, 34(3), [108657]. https://doi.org/10.1016/j.celrep.2020.108657

Vancouver

Longden J, Robin X, Engel M, Ferkinghoff-Borg J, Kjaer I, Horak ID o.a. Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space. Cell Reports. 2021 jan. 19;34(3). 108657. https://doi.org/10.1016/j.celrep.2020.108657

Author

Longden, James ; Robin, Xavier ; Engel, Mathias ; Ferkinghoff-Borg, Jesper ; Kjaer, Ida ; Horak, Ivan D. ; Pedersen, Mikkel W. ; Linding, Rune. / Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space. I: Cell Reports. 2021 ; Bind 34, Nr. 3.

Bibtex

@article{e947965a302c470d93c4a8adda5c30f7,
title = "Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space",
abstract = "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.",
keywords = "CANCER, KINASE, EGFR, HETEROGENEITY, KINOME, TOPHAT, TARGET, GROWTH, GENE",
author = "James Longden and Xavier Robin and Mathias Engel and Jesper Ferkinghoff-Borg and Ida Kjaer and Horak, {Ivan D.} and Pedersen, {Mikkel W.} and Rune Linding",
year = "2021",
month = jan,
day = "19",
doi = "10.1016/j.celrep.2020.108657",
language = "English",
volume = "34",
journal = "Cell Reports",
issn = "2211-1247",
publisher = "Cell Press",
number = "3",

}

RIS

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