Results about: cancer
Researchers at IRB Barcelona study how altered protein degradation contributes to the development of tumours
Published in the journal Nature Cancer, the study analyses how genetic alterations in tumour cells prevent the correct degradation of the proteins involved in tumour development and growth, thereby leading to abnormal cell behaviour.
A machine-learning model has allowed the scientists to obtain the most extensive annotation of the ubiquitin-mediated protein degradation system.
The analysis proposes a potential new clinical approach for cancer through the inhibition of oncoproteins with impaired degradation systems.
Scientists at IRB Barcelona determine the genetic alterations in the cells of cancer patients caused by the main cancer therapies.
This is an important step towards understanding the long-term side effects and optimizing treatments against cancer.
The results have been published in the journal Nature Genetics.
The foundation will be funding 43 cancer research projects.
Scientists build machine learning-based algorithm to predict which cancer patients benefit from immunotherapy
Using machine learning, researchers have built a tool that detects genetic mutations that trigger the immune system, helping identify which cancer patients are more likely to benefit from immunotherapy
The algorithm also reveals which people living with hereditary diseases may benefit from drugs that already exist
The new technology’s potential is described today in Nature Genetics by researchers at IRB Barcelona, the Centre for Genomic Regulation and Radboud University
Identification of genes responsible for sex-related differences in cancer aggressiveness in the vinegar fly
An understanding of the molecular basis of differences in the incidence and survival of cancer between men and women may allow the discovery of specific and more effective treatments.
The study, published in Science Advances, compares the brain tumours of male and female flies at the molecular level and identifies proteins responsible for the different degree of aggressiveness.