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Article

Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision

by
Michel-Edwar Mickael
1,*,
Norwin Kubick
2,
Atanas G. Atanasov
1,3,
Petr Martinek
4,
Jarosław Olav Horbańczuk
1,
Nikko Floretes
5,
Michael Michal
4,
Tomas Vanecek
4,
Justyna Paszkiewicz
6,
Mariusz Sacharczuk
1,7 and
Piotr Religa
8,*
1
Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, Postepu 36A, 05-552 Jastrzebiec, Poland
2
Department of Biology, Institute of Plant Science and Microbiology, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany
3
Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
4
Department of Pathology, Biopticka Laboratory s.r.o., Mikulasske nam. 4, 326 00 Plzen, Czech Republic
5
College of Engineering, Samar State University, University Access Rd, Catbalogan City 6700, Philippines
6
Department of Health, John Paul II University of Applied Sciences, Sidorska 95/97, 21-500 Biala Podlaska, Poland
7
Department of Pharmacodynamics, Faculty of Pharmacy, Medical University of Warsaw, Banacha 1B, 02-091 Warsaw, Poland
8
Department of Medicine, Karolinska Institute, Visionsgatan 18, 171 76 Solna, Sweden
*
Authors to whom correspondence should be addressed.
Curr. Issues Mol. Biol. 2024, 46(8), 8301-8319; https://doi.org/10.3390/cimb46080490
Submission received: 20 May 2024 / Revised: 18 July 2024 / Accepted: 22 July 2024 / Published: 1 August 2024
(This article belongs to the Collection Bioinformatics Approaches to Biomedicine)
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Abstract

The accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks—one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.
Keywords: copy number variant; Ai; CNV copy number variant; Ai; CNV

Share and Cite

MDPI and ACS Style

Mickael, M.-E.; Kubick, N.; Atanasov, A.G.; Martinek, P.; Horbańczuk, J.O.; Floretes, N.; Michal, M.; Vanecek, T.; Paszkiewicz, J.; Sacharczuk, M.; et al. Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision. Curr. Issues Mol. Biol. 2024, 46, 8301-8319. https://doi.org/10.3390/cimb46080490

AMA Style

Mickael M-E, Kubick N, Atanasov AG, Martinek P, Horbańczuk JO, Floretes N, Michal M, Vanecek T, Paszkiewicz J, Sacharczuk M, et al. Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision. Current Issues in Molecular Biology. 2024; 46(8):8301-8319. https://doi.org/10.3390/cimb46080490

Chicago/Turabian Style

Mickael, Michel-Edwar, Norwin Kubick, Atanas G. Atanasov, Petr Martinek, Jarosław Olav Horbańczuk, Nikko Floretes, Michael Michal, Tomas Vanecek, Justyna Paszkiewicz, Mariusz Sacharczuk, and et al. 2024. "Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision" Current Issues in Molecular Biology 46, no. 8: 8301-8319. https://doi.org/10.3390/cimb46080490

APA Style

Mickael, M.-E., Kubick, N., Atanasov, A. G., Martinek, P., Horbańczuk, J. O., Floretes, N., Michal, M., Vanecek, T., Paszkiewicz, J., Sacharczuk, M., & Religa, P. (2024). Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision. Current Issues in Molecular Biology, 46(8), 8301-8319. https://doi.org/10.3390/cimb46080490

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