Algorithm Creates Cancer Genome Blueprint

An international research team has used artificial intelligence (AI) to categorize the size and scale of DNA changes that occur when cancer forms and grows. The study authors hope the algorithm could soon be used to guide doctors managing cancer treatment.

“Cancer is a complex disease, but we’ve demonstrated that there are remarkable similarities in the changes to chromosomes that happen when it starts and how it grows,” says Dr. Ludmil Alexandrov, Associate Professor at University of California San Diego and co-lead author of the study. “Just as Netflix can predict which shows you’ll choose to binge watch next, we believe that we will be able to predict how your cancer is likely to behave, based on the changes its genome has previously experienced.”

When cancer starts, mutations in the DNA can cause large-scale “faults" to occur across their whole genome. These faults can result in too many or too few chromosomes compared to healthy cells—a condition known as aneuploidy. Tumors can also develop faults in the mechanisms designed to repair their DNA, which in turn leads to further faults in the structure of DNA within chromosomes, as well as errors when the DNA tries to make copies of itself.

The algorithm sifts through thousands of lines of genomic data and picks out common patterns in how the chromosomes organize and arrange themselves. It can then categorize the patterns that emerge and help scientists establish the types of faults that can occur in cancer. 

Using the algorithm, the team looked for patterns in the fully sequenced genomes from 9,873 patients with 33 different types of cancer. The algorithm identified 21 common faults to the structure and number of chromosomes in tumors and categorized them into different “genres” called copy number signatures.  The 21 copy number signatures will now be used to create a blueprint that researchers can use to assess how aggressive the cancer will be, find its weak spots and design new treatments for it.

The scientists further investigated the copy number signatures that most strongly affected outcomes for cancer patients. Of the 21 signatures identified by the algorithm, the scientists found that tumors where the chromosomes have shattered and reformed—known as chromothripsis—were associated with the worst survival outcomes. For example, the study found that patients with glioblastoma, an aggressive type of brain tumor, had worse survival outcomes if their tumor had undergone chromothripsis. On average, glioblastoma patients without chromothripsis survived 6 months longer than glioblastoma patients whose tumors had chromothripsis.

The scientists hope that they will be able to refine the algorithm to enable doctors to find out how your cancer is likely to behave, based on the genetic traits it acquired when it started and the genetic changes it picks up as it grows.

“To stay one step ahead of cancer, we need to anticipate how it adapts and changes,” says Dr. Nischalan Pillay, Associate Professor in Sarcoma and Genomics at UCL. “Mutations are the key drivers of cancer, but a lot of our understanding is focused on changes to individual genes in cancer. We’ve been missing the bigger picture of how vast swathes of genes can be copied, moved around or deleted without catastrophic consequences for the tumor.”

Dr. Alexandrov says the goal is to reach a point where a doctor can look at a patient’s fully sequenced tumor and match the key features of the tumor against the blueprint for genomic faults. “Armed with that information, we believe that doctors will be able to offer better and more personalized cancer treatment in the future,” he adds.

The work was published recently in the journal Nature.