Identification of combinations of somatic mutations that predict cancer survival and immunotherapy benefit
| Autor: | Noam Auslander, Eugene V. Koonin, Ayal B. Gussow |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
AcademicSubjects/SCI01140 AcademicSubjects/SCI01060 Somatic cell medicine.medical_treatment AcademicSubjects/SCI00030 Cancer Patient survival Cancer survival General Medicine Computational biology Immunotherapy Biology medicine.disease AcademicSubjects/SCI01180 Immune checkpoint Can Combio 03 medical and health sciences 030104 developmental biology 0302 clinical medicine 030220 oncology & carcinogenesis medicine Identification (biology) AcademicSubjects/SCI00980 Survival rate |
| Zdroj: | Nar Cancer |
| ISSN: | 2632-8674 |
| Popis: | Cancer evolves through the accumulation of somatic mutations over time. Although several methods have been developed to characterize mutational processes in cancers, these have not been specifically designed to identify mutational patterns that predict patient prognosis. Here we present CLICnet, a method that utilizes mutational data to cluster patients by survival rate. CLICnet employs Restricted Boltzmann Machines, a type of generative neural network, which allows for the capture of complex mutational patterns associated with patient survival in different cancer types. For some cancer types, clustering produced by CLICnet also predicts benefit from anti-PD1 immune checkpoint blockade therapy, whereas for other cancer types, the mutational processes associated with survival are different from those associated with the improved anti-PD1 survival benefit. Thus, CLICnet has the ability to systematically identify and catalogue combinations of mutations that predict cancer survival, unveiling intricate associations between mutations, survival, and immunotherapy benefit. |
| Databáze: | OpenAIRE |
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