Understanding the specific mutations that contribute to different forms of cancer is critical to improving diagnosis and treatment. But limitations in DNA sequencing technology make it difficult to detect some major mutations often linked to cancer, such as the loss or duplication of parts of chromosomes.
Now, methods developed by Princeton computer scientists will allow researchers to more accurately identify these mutations in cancerous tissue, yielding a clearer picture of the evolution and spread of tumors than was previously possible.
Losses or duplications in chromosomes are known to occur in most solid tumors, such as ovarian, pancreatic, breast and prostate tumors. As cells grow and divide, slip-ups in the processes of copying and separating DNA can also lead to the deletion or duplication of individual genes on chromosomes, or the duplication of a cell’s entire genome—all 23 pairs of human chromosomes. These changes can activate cancer-promoting genes or inactivate genes that suppress cancerous growth.
“They’re important driver events in cancer in their own right, and they interact with other types of mutations in cancer,” said Ben Raphael, a professor of computer science who co-authored the studies with Simone Zaccaria, a former postdoctoral research associate at Princeton.
Although medical science has recognized the mutations as critical parts of cancer development, identifying these losses or duplications in chromosomes is difficult with current technology. That is because DNA sequencing technologies cannot read whole chromosomes from end to end. Instead, the technologies allow researchers to sequence snippets of the chromosome, from which they assemble a picture of the entire strand. The weakness of this method is that