In the first study of its kind in cancer, researchers have applied artificial intelligence to measure the amount of muscle in patients with brain tumours to help improve prognosis and treatment.

Dr. Ella Mi, a clinical research fellow at Imperial College London (UK) will tell the NCRI Virtual Showcase, that using deep learning to evaluate MRI brain scans of a muscle in the head was as accurate and reliable as a trained person, and was considerably quicker. Furthermore, her research showed that the amount of muscle measured in this way could be used to predict how long a patient might survive their disease as it was an indicator of a patient’s overall condition.

Glioblastoma is an aggressive brain tumour that is very difficult to treat successfully. Average survival after diagnosis is 12-18 months and fewer than 5% of patients are still alive after five years. Some patients do better than others, and so the ability to assess objectively patients’ frailty and physical condition provides important information that can improve prognosis and help guide decisions on treatments, diet and exercise. If patients have sarcopenia—degenerative loss of skeletal muscle—they may be unable to tolerate surgery, chemotherapy or radiotherapy as well as patients without the condition. This can lead to adverse reactions to therapy, early discontinuation of treatment, accelerated progression of the disease and death.

Dr. Mi said: “Finding a better way to assess patients’ physical condition, general well-being and ability to carry out everyday activities is important in glioblastoma, and indeed in many cancers, because, at present, it’s often evaluated subjectively, resulting in inaccuracy and a high degree of variability depending on who is looking at it. So indicators that can be assessed objectively, such as measures of sarcopenia, are needed.”

Dr. Mi and her colleagues from the Computational Oncology Group at

An international team of researchers led by Cleveland Clinic’s Lerner Research Institute has performed for the first time a wide-scale characterization of missense variants from 1,330 disease-associated genes. Published in Proceedings of the National Academy of Sciences, the study identifies features associated with pathogenic and benign variants that reveal the effects of the mutations at a molecular level.

“Our study serves as a powerful resource for the translation of personal genomics to personal diagnostics and precision medicine, and can aid variant interpretation, inform experiments and help accelerate personalized drug discovery,” said Dennis Lal, Ph.D., assistant staff, Genomic Medicine, and the study’s lead author.Recent large-scale DNA sequencing efforts have detected millions of missense variants, where mistakes in the DNA code change the amino acid (molecular building block of a protein) makeup of proteins. Some of these variants are pathogenic, meaning they alter the structure and function of a protein in a way that leads to disease, while others are benign with no impact on health. The vast majority, however, are considered variants of uncertain significance because their effects remain unknown.

While methods to predict variant pathogenicity exist, they do not elucidate why some variants are more or less likely to cause disease than others or establish their functional impact. Additionally, pathogenic and benign variants can co-exist in almost every disease-associated gene. As such, gaining a better understanding of the mechanistic differences between benign and pathogenic variants will be a critical next step in the development of novel therapies for genetic disorders.

Considering that a protein’s function is closely linked to its three-dimensional structure, in this study the research team identified and compared the protein features of amino acids affected by pathogenic versus benign missense variants. Features that are more frequently mutated in pathogenic variants compared