Team links gene expression, immune system with cancer survival rates
A database compiled by Ash Alizadeh and his team provides broad patterns that correlate with poor or good survival rates for a variety of cancers.
Physicians have long sought a way to accurately predict cancer patients’ survival outcomes by looking at biological details of the specific cancers they have. But despite concerted efforts, no such clinical crystal ball exists for the majority of cancers.
Now, researchers at the Stanford University School of Medicine have compiled a database that integrates gene expression patterns of 39 types of cancer from nearly 18,000 patients with data about how long those patients lived.
Combining the data from so many people and cancers allowed the researchers to overcome reproducibility issues inherent in smaller studies. As a result, the researchers were able to clearly see broad patterns that correlate with poor or good survival outcomes. This information could help them pinpoint potential therapeutic targets.
“We were able to identify key pathways that can dramatically stratify survival across diverse cancer types,” said Ash Alizadeh, MD, PhD, an assistant professor of medicine and a member of the Stanford Cancer Institute. “The patterns were very striking, especially because few such examples are currently available for the use of genes or immune cells for cancer prognosis.”
In particular, the researchers found that high expression of a gene called FOXM1, which is involved in cell growth, was associated with a poor prognosis across multiple cancers, while the expression of the KLRB1 gene, which modulates the body’s immune response to cancer, seemed to confer a protective effect.
The new database, which will be available to physicians and researchers, is called PRECOG, an abbreviation for “prediction of cancer outcomes from genomic profiles.” Stanford’s Department of Medicine