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.

Figure 1: Calculating tumor specific total mRNA level.

Figure 1 shows that Using this technique on tumors from more than 6,500 patients across 15 cancer types, the researchers demonstrated that higher mRNA levels in cancer cells were associated with reduced patient survival.

"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 Wenyi Wang, Ph.D. [1]

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. To develop their deconvolution tool, the research team started by analyzing single-cell sequencing data generated from 48,913 cells across 10 patients with four different cancer types. [2]

the study found that the correlation may depend on the stage of cancer. In certain cohorts, looking at specific stages of cancer showed high total mRNA levels were instead associated with improved outcomes. Because there are different treatment regimens for early- and late-stage cancers, the authors suggest that total mRNA levels have the potential to be useful in predicting both prognosis and response to some treatments.

The findings must be confirmed with larger prospective trials, but the researchers suggest that tumour-specific total mRNA levels could be adapted into a prognostic biomarker to stratify high-risk patients and guide treatment selection.

“From currently available clinical tools, we know that analysing expression changes in a given pathway or set of genes can have value in guiding patient care,” Jennifer Wang said. “The findings from this study emphasize that looking at the transcriptome as a whole may be even more powerful.” [3]

References:

1. https://medicalxpress.com/news/2022-06-tumor-specific-total-mrna-cancer-outcomes.html
2. https://www.sciencedaily.com/releases/2022/06/220613124544.htm
3. https://vervetimes.com/study-reports-first-mathematical-approach-to-measure-total-tumor-specific-mrna-from-mixed-tumor-samples-sciencedaily/

Cite this article:

Sri Vasagi K (2022), Cancer Outcomes Predicts by Calculating Tumor Specific Total mRNA Level, AnaTechMaz, pp.46