Massive DNA study of human cancers offers new clues about their causes
UK scientists analyzed the complete genetic makeup of 12,000 tumors from NHS patients.
A team of UK scientists has analyzed the complete genetic makeup of 12,000 tumors from NHS patients and discovered 58 new mutations that provide clues about their potential causes. The team, composed of scientists from Cambridge University Hospitals and the University of Cambridge, used data from the 100,000 Genomes Project. That's a British initiative to sequence the whole genomes of patients with cancers and rare diseases.
Team leader Professor Serena Nik-Zainal said this is the largest study of its kind and that the vast amount of data her team worked with allowed them to detect patterns in the genetic alterations or "mutational signatures" found in the tumors. By comparing their results with other studies, they were able to confirm that 58 of the mutational signatures they found were previously unknown. Some of them are pretty common, while some are rare.
"The reason it is important to identify mutational signatures is because they are like fingerprints at a crime scene — they help to pinpoint cancer culprits," Nik-Zainal explained. Some signatures could show that past exposure to environmental causes such as smoking or UV light had triggered the cancer, while others could have treatment implications. They could, for instance, pinpoint genetic abnormalities that could be targeted by specific drugs.
Professor Matt Brown, chief scientific officer of Genomics England said: "Mutational signatures are an example of using the full potential of [whole genome sequencing]. We hope to use the mutational clues seen in this study and apply them back into our patient population, with the ultimate aim of improving diagnosis and management of cancer patients."
In addition to conducting DNA analysis and publishing its results in Science, the team also developed an algorithm called FitMS that will give clinicians easy access to the new information they discovered. FitMS looks for both common and rare signatures in the results of a patient's whole genome sequencing test. Doctors can use the algorithm to find out if their patients exhibit any of the newly discovered mutations for a more accurate diagnosis and for personalized treatments.