Scientists identify genes tied to increased risk of ovarian cancer
A team of researchers led by Dana-Farber Cancer Institute have identified 34 new genes that are associated with an increased risk for developing earliest-stage ovarian cancer. The findings, published in the journal Nature Genetics, will both help identify women who are at highest risk of developing ovarian cancer, and pave the way for identifying new therapies that can target these specific genes.
The study was co-led by Alexander Gusev, PhD, a cancer genetics researcher in the Division of Population Sciences at Dana-Farber, Simon Gayther, PhD, director of the Center for Bioinformatics and Functional Genomics at Cedars-Sinai, Bogdan Pasaniuc, PhD, associate professor of pathology and laboratory medicine at the David Geffen School of Medicine at UCLA.
Currently, there are no effective screening tests for ovarian cancer and the disease is notorious for presenting in later stages when survival rates are poor. However, if ovarian cancer is caught early, survival rates increase dramatically, underscoring the need to identify those who may be at risk for developing the disease.
The study by Gusev and colleagues builds on previous research of large-scale genetic data gathered over more than a decade by the Ovarian Cancer Association Consortium. Those researchers compared the genetic profiles of about 25,000 women with ovarian cancer and 45,000 women without the disease. The investigators found more than 30 regions in the genome that are associated with ovarian cancer.
"One novelty of this work is that we looked at risk genetic variants that operate through alternative splicing rather than just the total abundance of a gene, which led us to genes we would not have otherwise identified,” explained Gusev. “Beyond a better understanding, if these risk mechanisms really operate through splicing, that also opens up new drug-target opportunities,".
“Now that we’ve identified all these regions in the genome that increase the risk for ovarian cancer, we're at the stage where we are mapping the genes of these risk regions,” Pasaniuc said. “Ultimately, that will lead to better prediction, and that will lead to better stratification of women of different risk categories.”
To pinpoint the genes, the researchers analyzed patient data to try to find mutations that explain the patient status.
The idea of combing through large amounts of data to establish which specific genes drive ovarian cancer development may seem simple, but there are thousands of possible gene targets that can be affected by numerous mechanisms, so putting the pieces together is a huge computational and statistical effort.
The study discovered that in women at greatest risk of ovarian cancer because of their genetic blueprint, “there is an interplay between their genetics and specific genes that drive the very earliest stages of cancer development,” said Gayther, also co-director of the Applied Genomics, Computation and Translational Core at Cedars-Sinai.
“The main challenge has to do with the number of genes that are in one region of the genome,” Pasaniuc said. “Whenever you inherit a piece of DNA from your parents, you don't inherit just every base pair of the genome, you inherit big chunks. That means that if you inherit a gene mutation in a given region, you inherit the entire region, which can carry 10 to 20 genes at a time. This makes it very hard to pinpoint specific genes from specific regions.”
To help identify the genes in these particular regions, the team compared the large-scale genetic data from the Ovarian Cancer Association Consortium with a different data type that shows the mutations that disrupt the genes in ovarian and other tissues. By putting these two pieces of information together, the researchers were able to distinguish what genes in the genomes are actually the risk genes. Through this computational technique, the team identified 34 genes that are associated with an increased risk for developing ovarian cancer.
The study was funded by National Institutes of Health awards, including a R21 award under grant number CA22007801, a U19 award under grant number CA207456, RO1 awards under grant numbers CA211707, CA207456, CA204954, CA227237, and in part by the Ovarian cancer Research Fund Alliance Program, among additional NIH and other awards.