Researchers at The University of Texas MD Anderson Cancer Center have developed a new approach to quantify tumor-specific total mRNA levels from patient tumor samples, which contain both cancer and non-cancer cells. Using this technique on tumors from more than 6,500 patients across 15 cancer types, medicine box japanese dress the researchers demonstrated that higher mRNA levels in cancer cells were associated with reduced patient survival.
The study, published today in Nature Biotechnology, suggests this computational approach could permit large-scale analyses of tumor-specific total mRNA levels from tumor samples, which could serve as a prognostic biomarker for many types of cancers.
“Single-cell sequencing studies have shown us that total mRNA content in cancer cells is correlated with biological features of the tumor, but it’s not feasible to use single-cell approaches for analyzing large patient cohorts,” said corresponding author Wenyi Wang, Ph.D., professor of Bioinformatics & Computational Biology. “With this study, we propose a novel mathematical deconvolution technique to study this important biological feature of cancer at scale, using widely available bulk tumor sequencing data.”
Whereas single-cell sequencing approaches can profile thousands of individual cells from a sample, bulk sequencing generates an overall picture of the tumor across a larger number of cells. Because a tumor sample contains a diverse mixture of cancer and non-cancer cells, additional steps are required to isolate the cancer-specific information from bulk sequencing data.
Deconvolution is a computational technique designed to separate bulk sequencing data into its different components. This study is the first to report a deconvolution approach for quantifying total tumor-specific mRNA levels from bulk sequencing data, providing a scalable complement to single-cell analysis.
Together with Wang, the study was led by Shaolong Cao, Ph.D., former postdoctoral fellow, Jennifer R. Wang, M.D., assistant professor of Head & Neck Surgery, and Shuangxi Ji, Ph.D., postdoctoral fellow in Bioinformatics & Computational Biology.
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