“What’s my prognosis?” is a question that’s likely on the mind, and lips, of nearly every person newly diagnosed with any form of cancer. But, with a few exceptions, there’s still not a good way for clinicians to answer. Every tumor is highly individual, and it’s difficult to identify anything more than general trends with regard to the type and stage of the tumor.
Now, hematologist and oncologist Ash Alizadeh, MD, PhD; radiologist Sylvia Plevritis, PhD; postdoctoral scholar Aaron Newman, PhD; and senior research scientist Andrew Gentles, PhD, have created a database that links the gene-expression patterns of individual cancers of 39 types with the survival data of the more than 18,000 patients from whom they were isolated. The researchers hope that the resource, which they’ve termed PRECOG, for “prediction of cancer outcomes from genomic profiles” will provide a better understanding of why some cancer patients do well, and some do poorly. Their research was published today in Nature Medicine.
As I describe in our release:
Researchers have tried for years to identify specific patterns of gene expression in cancerous tumors that differ from those in normal tissue. By doing so, it may be possible to learn what has gone wrong in the cancer cells, and give ideas as to how best to block the cells’ destructive growth. But the extreme variability among individual patients and tumors has made the process difficult, even when focused on particular cancer types.
Instead, the researchers pulled back and sought patterns that might become clear only when many types of cancers, and thousands of patients were lumped together for study:
Gentles and Alizadeh first collected publicly available data on gene expression patterns of many types of cancers. They then painstakingly matched the gene expression profiles with clinical information about the patients, including their age, disease status and how long they survived after diagnosis. Together with Newman, they combined the studies into a final database.
“We wanted to be able to connect gene expression data with patient outcome for thousands of people at once,” said Alizadeh. “Then we could ask what we could learn more broadly.”
The researchers found that they were able to identify key molecular pathways that could stratify survival across many cancer types:
In particular, [they] 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.
Alizadeh and Plevritis are both members of the Stanford Cancer Institute.
Previously: What is big data?, Identifying relapse in lymphoma patients with circulating tumor DNA, Smoking gun or hit-and-run? How oncogenes make good cells go bad and Big data = big finds: Clinical trial for deadly lung cancer launched by Stanford study
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