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Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

View ORCID ProfileCristian Axenie, Daria Kurz
doi: https://doi.org/10.1101/2020.06.13.140715
Cristian Axenie
1Audi Konfuzius-Institut Ingolstadt Lab, Ingolstadt, Germany,
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  • ORCID record for Cristian Axenie
  • For correspondence: cristian.axenie@audi-konfuzius-institut-ingolstadt.de cristian.axenie@audi-konfuzius-institut-ingolstadt.de
Daria Kurz
2Interdisciplinary Breast Center, Munich, Germany,
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  • For correspondence: daria.kurz@helios-gesundheit.de
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Abstract

Neoplastic processes are described by complex and heterogeneous dynamics. The interaction of neoplastic cells with their environment describes tumor growth and is critical for the initiation of cancer invasion. Despite the large spectrum of tumor growth models, there is no clear guidance on how to choose the most appropriate model for a particular cancer and how this will impact its subsequent use in therapy planning. Such models need parametrization that is dependent on tumor biology and hardly generalize to other tumor types and their variability. Moreover, the datasets are small in size due to the limited or expensive measurement methods. Alleviating the limitations that incomplete biological descriptions, the diversity of tumor types, and the small size of the data bring to mechanistic models, we introduce Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics (GLUECK) a novel, data-driven model based on a neural network capable of unsupervised learning of cancer growth curves. Employing mechanisms of competition, cooperation, and correlation in neural networks, GLUECK learns the temporal evolution of the input data along with the underlying distribution of the input space. We demonstrate the superior accuracy of GLUECK, against four typically used tumor growth models, in extracting growth curves from a four clinical tumor datasets. Our experiments show that, without any modification, GLUECK can learn the underlying growth curves being versatile between and within tumor types.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 15, 2020.
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Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics
Cristian Axenie, Daria Kurz
bioRxiv 2020.06.13.140715; doi: https://doi.org/10.1101/2020.06.13.140715
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Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics
Cristian Axenie, Daria Kurz
bioRxiv 2020.06.13.140715; doi: https://doi.org/10.1101/2020.06.13.140715

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