Scientists from the University of Cambridge and their collaborators have developed an algorithm that identifies unique patterns caused by insertions and deletions in tumor DNA. Details of the algorithm, which is called PRRDetect, are published in a new Nature Genetics paper titled “A refined InDel taxonomy provides insights into mutational signatures.”
As explained in the paper, the scientists developed the algorithm by analyzing DNA sequences from over 4,000 tumors from seven types of cancer from Genomic England’s 100,000 Genomes project. They were most interested in indels with faulty DNA repair mechanisms known as post-replicative repair dysfunction or PRRd.
This research builds on prior work from Serena Nik-Zainal, MD, lead author of the Nature Genetics study, and others who analyzed tens of thousands of genomes from the Genomics England project and identified previously unseen patterns of cancer-linked mutations. For the current study, the researchers focused on cancers with a higher proportion of tumors with PRRd mutations including bowel, brain, endometrial, skin, lung, bladder, and stomach cancers.
They identified 37 different patterns of indel mutations across the seven cancer types included in this study. Ten of these patterns were already linked to known causes of cancer, such as smoking and exposure to UV light. Eight patterns were linked to PRRd. The remaining 19 patterns were new and could be linked to causes of cancer that are not fully understood yet or mechanisms within cells that can go wrong when a cell becomes cancerous.
They also used the data to design the PRRDetect algorithm to more efficiently identify tumors with the PRRd signature from a full DNA sequence.
The scientists believe that their algorithm could one day help doctors figure out which patients are more likely to respond to specific treatments and increase their chances of survival. PRRd tumors are more sensitive to immunotherapies and the scientists posit that the algorithm could work like a “metal detector” to identify patients who are more likely to be successfully treated with immunotherapies.
“To use genomics most effectively in the clinic, we need tools that give us meaningful information about how a person’s tumor might respond to treatment. This is especially important in cancers where survival is poorer, like lung cancer and brain tumors,” said Nik-Zainal, who is also a professor of genomic medicine and bioinformatics at the University of Cambridge. “Cancers with faulty DNA repair are more likely to be treated successfully. PRRDetect helps us better identify those cancers and, as we sequence more and more cancers routinely in the clinic, it could ultimately help doctors better tailor treatments to individual patients.”
The post Novel Predictive Algorithm Identifies InDel Signatures Associated with Cancer appeared first on GEN - Genetic Engineering and Biotechnology News.
As explained in the paper, the scientists developed the algorithm by analyzing DNA sequences from over 4,000 tumors from seven types of cancer from Genomic England’s 100,000 Genomes project. They were most interested in indels with faulty DNA repair mechanisms known as post-replicative repair dysfunction or PRRd.
This research builds on prior work from Serena Nik-Zainal, MD, lead author of the Nature Genetics study, and others who analyzed tens of thousands of genomes from the Genomics England project and identified previously unseen patterns of cancer-linked mutations. For the current study, the researchers focused on cancers with a higher proportion of tumors with PRRd mutations including bowel, brain, endometrial, skin, lung, bladder, and stomach cancers.
They identified 37 different patterns of indel mutations across the seven cancer types included in this study. Ten of these patterns were already linked to known causes of cancer, such as smoking and exposure to UV light. Eight patterns were linked to PRRd. The remaining 19 patterns were new and could be linked to causes of cancer that are not fully understood yet or mechanisms within cells that can go wrong when a cell becomes cancerous.
They also used the data to design the PRRDetect algorithm to more efficiently identify tumors with the PRRd signature from a full DNA sequence.
The scientists believe that their algorithm could one day help doctors figure out which patients are more likely to respond to specific treatments and increase their chances of survival. PRRd tumors are more sensitive to immunotherapies and the scientists posit that the algorithm could work like a “metal detector” to identify patients who are more likely to be successfully treated with immunotherapies.
“To use genomics most effectively in the clinic, we need tools that give us meaningful information about how a person’s tumor might respond to treatment. This is especially important in cancers where survival is poorer, like lung cancer and brain tumors,” said Nik-Zainal, who is also a professor of genomic medicine and bioinformatics at the University of Cambridge. “Cancers with faulty DNA repair are more likely to be treated successfully. PRRDetect helps us better identify those cancers and, as we sequence more and more cancers routinely in the clinic, it could ultimately help doctors better tailor treatments to individual patients.”
The post Novel Predictive Algorithm Identifies InDel Signatures Associated with Cancer appeared first on GEN - Genetic Engineering and Biotechnology News.