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: | doc.baranovsky@gmail.com |
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
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.