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Deep Neural Networks Identify Signaling Mechanisms of ErbB-Family Drug Resistance From a Continuous Cell Morphology State Space

42 Pages Posted: 26 May 2018 Publication Status: Published

See all articles by James Longden

James Longden

University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

Xavier Robin

University of Copenhagen

Mathias Engel

University of Copenhagen - Biotech Research and Innovation Centre (BRIC); University of Copenhagen - Niels Bohr Institute

Jesper Ferkinghoff- Borg

University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

Ida Kjaer

Symphogen A/S

Ivan D. Horak

Symphogen A/S

Mikkel Pedersen

Symphogen A/S

Rune Linding

University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

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Abstract

It is well known that the development of drug resistance in cancer cells can lead to a change in cell morphology. We reasoned that machine-learning techniques could thus be used to elucidate far greater insight into the relationship between cell shape and signaling. To test this hypothesis we performed a large high content screen on drug sensitive and drug resistance cancer cells, and analysed the shape of these cells using a deep neural network. Our model identified a continuous 27-dimension space describing all of the observed cell morphologies from which we were able to predict drug resistance with an accuracy of 74%. In addition, analyzing changes in cell morphology identified signaling networks that, when perturbed, caused the death of drug resistant cells. These findings suggests that complex morphologies can decode states of signaling networks seemingly unrelated to cell shape, and that analysis of this information can unravel cellular mechanisms hidden to conventional measurements.

Suggested Citation

Longden, James and Robin, Xavier and Engel, Mathias and Ferkinghoff- Borg, Jesper and Kjaer, Ida and Horak, Ivan D. and Pedersen, Mikkel W. and Linding, Rune, Deep Neural Networks Identify Signaling Mechanisms of ErbB-Family Drug Resistance From a Continuous Cell Morphology State Space (2018). Available at SSRN: https://ssrn.com/abstract=3185112 or http://dx.doi.org/10.2139/ssrn.3185112
This version of the paper has not been formally peer reviewed.

James Longden

University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

Nørregade 10
Copenhagen, København DK-2200
Denmark

Xavier Robin

University of Copenhagen

Nørregade 10
Copenhagen, København DK-1165
Denmark

Mathias Engel

University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

Nørregade 10
Copenhagen, København DK-2200
Denmark

University of Copenhagen - Niels Bohr Institute

Copenhagen, DK-2200
Denmark

Jesper Ferkinghoff- Borg

University of Copenhagen - Biotech Research and Innovation Centre (BRIC)

Nørregade 10
Copenhagen, København DK-2200
Denmark

Ida Kjaer

Symphogen A/S

Pederstrupvej 93
Ballerup, 2750
Denmark

Ivan D. Horak

Symphogen A/S

Pederstrupvej 93
Ballerup, 2750
Denmark

Mikkel W. Pedersen

Symphogen A/S ( email )

Pederstrupvej 93
Ballerup, 2750
Denmark

Rune Linding (Contact Author)

University of Copenhagen - Biotech Research and Innovation Centre (BRIC) ( email )

Nørregade 10
Copenhagen, København DK-2200
Denmark

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