AI Analysis of Cancer Mutations may Improve Therapy
Cancer has many faces - no wonder, then, that the range of cancer-causing mutations is huge as well. The totality of such genomic alterations in an individual is what experts call a "mutational landscape." These landscapes differ from one another depending on the type of cancer. Somatic structural variants (SVs) have been shown to account for more than half of all cancer-driving mutations. These are those mutations in cells that emerge over the course of life. Although somatic SVs play a crucial role in cancer development, relatively little is known about them. That's changing thanks to new research findings, which Dr. Ashley Sanders's recently published in the journal Nature Biotechnology along with the European Molecular Biology Laboratory (EMBL). "We developed a computational analysis method to detect and identify the functional effects of somatic SVs," she reports. This enabled the team to understand the molecular consequences of individual somatic mutations in different leukemia patients, giving them new insights into the mutation-specific alterations. Sanders says it may also be possible to use these findings to develop therapies that target the mutated cells, adding that “they open up exciting new avenues for personalized medicine."
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