Tumor Sample Purity May Significantly Bias Genomic Analyses

The proportion of normal cells, especially immune cells, intermixed with cancerous cells in a given tissue sample may significantly skew the results of genetic analyses and other tests performed by physicians selecting precision therapies.

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 was not accounted for, estimates of the likely success of immunotherapy were either too high or too low.


Scientists at the University of California, San Francisco (CA, USA) made use of a massive dataset known as The Cancer Genome Atlas (TCGA), a joint initiative of the National Cancer Institute and the National Human Genome Research Institute. The TCGA dataset is derived from samples of tumors and normal tissue from 11,000 patients, and represents 33 types of cancer. The team used four different methods to measure tumor purity in more than 10,000 TCGA samples representing 21 cancer types, and examined how purity might affect the reliability of three of the most common genomic methods used in cancer research: correlation, clustering, and differential analysis.

In a type of bladder cancer known as bladder carcinoma, for example, two genes called Janus Kinase 3 (JAK3) and Colony Stimulating Factor 1 Receptor (CSF1R) tend to be jointly expressed at high levels, which suggests that they somehow act together to drive the cancer. The team found that the tandem expression of JAK3 and CSF1R in bladder carcinoma varied widely if tumor purity was taken into account as in the purest samples there was little correlation between the expression of the two genes, calling their potential joint role in a cancer-driving pathway into question.

In their analyses of samples of lung cancer, kidney cancer, and thyroid cancer, the group found that, if tumor purity were not taken into account, differential analysis could yield misleading results on the relative expression of proteins called cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and Cluster of Differentiation 86 (CD86), both important targets in cancer immunotherapy.

Marina Sirota, PhD, a coauthor of the study said, “Mutational burden is a useful measure, because it identifies genes and pathways that may lead tumors to respond to conventional targeted drugs. But if it is the greater infiltration of immune cells in a tumor that makes it more sensitive to immunotherapy, we should try to measure that directly as well.” The study was published on December 4, 2015, in the journal Nature Communications.

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