Oncotarget

Research Papers:

Genetic and therapeutic landscapes in cohort of pancreatic adenocarcinomas: next-generation sequencing and machine learning for full tumor exome analysis

P.A. Shatalov, N.A. Falaleeva, E.A. Bykova, D.O. Korostin, V.A. Belova, A.A. Zabolotneva, A.P. Shinkarkina, A. Yu Gorbachev, M.B. Potievskiy, V.S. Surkova, Zh V. Khailova, N.A. Kulemin, Denis Baranovskii _, A.A. Kostin, A.D. Kaprin and P.V. Shegai

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Oncotarget. 2024; 15:91-103. https://doi.org/10.18632/oncotarget.28512

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Abstract

P.A. Shatalov1, N.A. Falaleeva1, E.A. Bykova1, D.O. Korostin2, V.A. Belova2, A.A. Zabolotneva2, A.P. Shinkarkina1, A. Yu Gorbachev3, M.B. Potievskiy1, V.S. Surkova1, Zh V. Khailova1, N.A. Kulemin1, Denis Baranovskii1,4, A.A. Kostin4, A.D. Kaprin1,4 and P.V. Shegai1

1 National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation, Obninsk 249036, Russia

2 Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia

3 FSBI “Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine” FMBA, Moscow 119435, Russia

4 Peoples Friendship University of Russia (RUDN University), Moscow 117198, Russia

Correspondence to:

Denis Baranovskii, email: [email protected]

Keywords: pancreatic cancer; tumor mutation burden; somatic mutations; artificial intelligence; machine learning

Abbreviations: ECOG: Eastern Cooperative Oncology Group; NGS: Next Generation Sequencing; PCa: pancreatic cancer

Received: June 04, 2023     Accepted: September 04, 2023     Published: February 05, 2024

Copyright: © 2024 Shatalov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ABSTRACT

About 7% of all cancer deaths are caused by pancreatic cancer (PCa). PCa is known for its lowest survival rates among all oncological diseases and heterogenic molecular profile. Enormous amount of genetic changes, including somatic mutations, exceeds the limits of routine clinical genetic laboratory tests and further stagnates the development of personalized treatments. We aimed to build a mutational landscape of PCa in the Russian population based on full exome next-generation sequencing (NGS) of the limited group of patients. Applying a machine learning model on full exome individual data we received personalized recommendations for targeted treatment options for each clinical case and summarized them in the unique therapeutic landscape.


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