Genetic and Therapeutic Landscapes in Cohort of Pancreatic Adenocarcinomas Using NGS and Machine Learning

“Our study was designed to build specific mutational and therapeutic landscapes of pancreatic cancer among the Russian population.”

BUFFALO, NY- February 14, 2024 – A new research paper was published in Oncotarget's Volume 15 on February 5, 2024, entitled, “Genetic and therapeutic landscapes in cohort of pancreatic adenocarcinomas: next-generation sequencing and machine learning for full tumor exome analysis.”

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. 

In this new study, researchers 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 from the Ministry of Health of the Russian Federation, Pirogov Russian National Research Medical University, Federal Medical-Biological Agency, Moscow, and the Peoples Friendship University of Russia (RUDN University) 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.”

Read the full paper: DOI: https://doi.org/10.18632/oncotarget.28512 

Correspondence to: Denis Baranovskii

Email: [email protected] 

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

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