Genetic analyses and other tests performed by both researchers and physicians can be significantly skewed when normal cells, especially immune cells, are intermixed with cancerous cells in a tissue sample, according to study findings published in Nature Communications (doi:10.1038/ncomms9971).

Tumors contain a variety of healthy cells as well as cancerous cells, and this heterogeneity is believed to underlie resistance to various cancer therapies. But the problem has not been thoroughly investigated, said researchers from the University of California San Francisco (UCSF). Factoring precise measures of tumor purity into common analytical techniques may clarify some basic principles of cancer biology as well as open new therapeutic avenues. Pure tumors are entirely, or mostly, composed of cancer cells.

In a medically relevant example from the new work, the researchers found that measures used to predict the effectiveness of checkpoint-inhibitor drugs, the most widely used form of cancer immunotherapy, are accurate only when the extent of infiltration of immune cells into the tumor was explicitly quantified. When this aspect of tumor purity is not accounted for, estimates of the likely success of immunotherapy was too high or too low.

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“Tumor purity is a big problem when you’re dealing with fresh tissue from real patients rather than with cell lines, and there has been no systematic analysis of this issue,” said Dvir Aran, PhD, a postdoctoral associate in the laboratory of Atul Butte, MD, PhD, director of UCSF Institute for Computational Health Sciences (ICHS) and lead author of the study.

“In the case of immunotherapy, it’s an expensive treatment and it can have side effects,” Aran explained, “so it’s important to know which patients are most likely to benefit. If we pay more attention to the immune cells that are actually in tumors we may have more success.”

The research group made use of The Cancer Genome Atlas (TCGA), a joint initiative of the National Cancer Institute and the National Human Genome Research Institute, for this study. The TCGA dataset is derived from samples of tumors and normal tissue from 11,000 patients, and represents 33 types of cancer.

Using 4 different methods, the researchers measured tumor purity in more than 10 000 TCGA samples representing 21 cancer types to examined how purity might affect the reliability of 3 common genomic methods used in cancer research: correlation, clustering, and differential analysis.

“Cancer isn’t just one big blob,” said Butte. “Instead, tumors are a complex microenvironment containing a number of cell types, normal and cancerous, that all act upon one another. If we hope to advance our understanding of cancer and to devise new treatments we need to truly understand how tumors are made up, and to take that makeup into account when we do genomic studies.”