Genetic and therapeutic landscapes in cohort of pancreatic adenocarcinomas: next-generation sequencing and machine learning for full tumor exome analysis.
P A ShatalovN A FalaleevaE A BykovaD O KorostinV A BelovaA A ZabolotnevaA P ShinkarkinaA Yu GorbachevM B PotievskiyV S SurkovaZh V KhailovaN A KuleminDenis BaranovskiiA A KostinA D KaprinP V ShegaiPublished in: Oncotarget (2024)
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.
Keyphrases
- copy number
- machine learning
- genome wide
- end stage renal disease
- big data
- dna methylation
- ejection fraction
- newly diagnosed
- clinical practice
- chronic kidney disease
- artificial intelligence
- squamous cell
- single cell
- prognostic factors
- peritoneal dialysis
- electronic health record
- cancer therapy
- deep learning
- gene expression
- data analysis
- free survival