Researchers have compiled a database that integrates gene expression patterns of 39 types of cancer from nearly 18,000 patients with data on how long those patients lived. This study, which may help to accurately predict patients’ survival outcomes, was published in Nature Medicine (2015; doi:10.1038/nm.3909).

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 at Stanford University in California. “The patterns were very striking, especially because few such examples are currently available for the use of genes or immune cells for cancer prognosis.”

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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.”

In addition to identifying potentially useful gene expression patterns in cancers, the researchers also used Cibersort, a technique to determine the composition of white blood cells that flock to a tumor. Cibersort assesses the relative levels of specific immune cells from a mishmash of cancer and normal cells and deduces the cell types from genes expressed in the bulk tumor, somewhat like analyzing a smoothie to identify its component fruits and berries.

“We were able to infer which immune cells are present or absent in individual solid tumors, to estimate their prevalence and to correlate that information with patient survival,” said co-author Aaron Newman, PhD. “We found you can even broadly distinguish cancer types just based on what kind of immune cells have infiltrated the tumor.”

Alizadeh explained that, by connecting gene expression data with patient outcome for thousands of people at once, the researchers could then ask what they could learn more broadly.

By looking at the forest, rather than the trees, the researchers made some surprising findings. They observed that prognostic genes were often shared among distinct cancer types, suggesting that similar biological programs impact survival across cancers. They were able to identify the top 10 genes that seemed to confer adverse outcomes, and the top 10 associated with more positive outcomes. Many of these genes are involved in aspects of cell division or are associated with distinct types of white blood cells that flood a tumor.

They were also able to identify combinations of white blood cells that appear favorable. In particular, the presence of elevated numbers of plasma cells, which secrete large amounts of antibodies, and certain types of T cells correlated with better patient survival rates across many different types of solid cancers, including lung and breast cancers. Conversely, a high proportion of neutrophils, also known as granulocytes, were associated with adverse outcomes.