Researcher in the lab

Photo by Darren Baker

A newly developed database may help physicians predict survival outcomes in patients with hematologic and solid tumor malignancies, according to a paper published in Nature Medicine.

The database, known as PRECOG, integrates gene expression patterns of 39 types of cancer from nearly 18,000 patients with data about how long those patients lived.

By combining these data, researchers were able to see broad patterns that correlate with survival. They also believe this information could help them pinpoint potential therapeutic targets for a range of cancers.

“We were able to identify key pathways that can dramatically stratify survival across diverse cancer types,” said Ash Alizadeh, MD, PhD, of 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.”

In addition to identifying potentially useful gene expression patterns, the researchers used an analytical tool called CIBERSORT to determine the composition of leukocytes that flock to a tumor.

“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 Aaron Newman, PhD, of Stanford University.

“We found you can even broadly distinguish cancer types just based on what kind of immune cells have infiltrated the tumor.”

Compiling the data

Researchers have tried for years to identify specific patterns of gene expression in cancerous tumors that differ from those in normal tissue. But the extreme variability among individual patients and tumors has made the process difficult, even when focused on particular cancer types.

“There are many more genes in a cell than there are patients with any one type of cancer, and this makes discovering the important genes for cancer outcomes a tough problem,” said Andrew Gentles, PhD, of Stanford University.

“Because it’s easy to find spurious associations that don’t hold up in follow-up studies, we combined information from a vast array of cancer types to better see meaningful correlations.”

The researchers first collected publicly available data on gene expression patterns of many types of cancers.

They then matched the gene expression profiles with clinical information about the patients, including their age, disease status, and how long they survived after diagnosis. Finally, the team combined the studies in a database.

“We wanted to be able to connect gene expression data with patient outcome for thousands of people at once,” Dr Alizadeh said. “Then, we could ask what we could learn more broadly.”

Surprising findings

The researchers were surprised to find 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—FOXM1, BIRC5, TOP2A, TPX2, NME1, CCNB1, CEP55, TYMS, CENPF, and CDKN3—and the top 10 genes associated with more positive outcomes—KLRB1, ITM2B, CBX7, CD2, CREBL2, SATB1, NR3C1, TMEM66, KLRK1, and FUCA1.

Many of these genes are involved in aspects of cell division or are associated with distinct leukocytes that flood a tumor.

The researchers were also able to identify combinations of leukocytes that appear to be correlated with outcomes.

In particular, elevated numbers of plasma cells and certain types of T cells correlated with better patient survival rates across many different solid tumors. But a high proportion of granulocytes was associated with adverse outcomes.

The researchers hope that PRECOG and CIBERSORT will increase our understanding of cancer biology and aid the development of new therapies for cancer patients. The team is applying these tools to better predict which patients will respond to new and emerging anticancer therapies.

Dr Alizadeh said this is especially important given recent advances in the development of drugs that engage immune responses but work well only for a subset of cancer patients.

Database may help predict cancer patients’ survival