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Some RNA Analyses May Not Capture RNA Repeats With Key Tumor Roles

NEW YORK (GenomeWeb) – If RNA repeats were better characterized within tumor samples, they could serve as biomarkers for immunotherapy, according to a new study.

Typically, researchers rely on a poly(A)-enrichment protocol to prepare RNA sequencing samples. As this approach focuses on enriching messenger RNA, other RNA classes can be left by the wayside.

Researchers from the Icahn School of Medicine at Mount Sinai and their colleagues uncovered slightly more than two dozen patient samples from The Cancer Genome Atlas for which both poly(A)-enriched RNA and total RNA sequencing data was available. As they reported today in Cell Reports, the researchers found the poly(A)-enriched RNA samples didn't fully capture the expression of RNA repeats and that these repeats, when obtained, could be used as biomarkers for immunotherapy.

"Our conclusions make the case that non­coding RNA in tumors, particularly repetitive elements, is under­quantified," senior author Benjamin Greenbaum said in a statement. "We feel that critical findings will arise from analysis of the full breadth of a tumor's non­coding RNA interplay with the immune system."

Though gene expression values generated through poly(A)-enrichment and total RNA approaches cannot be directly compared, the researchers found that when they normalized them by applying the trimmed mean of M-value approach and clustering them, the values grouped by sample source. This indicated to the researchers that the biological differences between the samples outweighed the technical ones.

However, Greenbaum and his colleagues also found that the expression values of repetitive elements differed between the two sample preparation approaches. Typically, the expression determined using the total RNA protocol was greater than that determined by the poly(A) protocol. Of 967 repeat elements, 3 percent had lower and 88 percent had higher expression levels using the total RNA protocol, the researchers reported, noting that that analysis revealed technical, not biological differences. This indicated to them that the poly(A) protocol failed to detect a number of RNA repeats.

Previous studies have noted a link between the expression of ERV within tumors, the expression of viral defense genes, and anti-tumor response, which the researchers said indicates that epigenetic dysregulation within tumors leads to ERV expression that then turns on innate immune pattern recognition receptor to trigger an anti-tumoral innate immune response.

As one study suggested that the presence of ERV was linked to better immunotherapy response, the researchers examine ERV expression levels within RNA-seq datasets they obtained from patients treated with the cancer immunotherapy PD-L1 blockade. They found that the samples clustered into high and low ERV expression groups and that there was a significant association between ERV association and patient response to treatment.

They further noted that in this dataset, ERV repeat expression was a better indicator of immunotherapy response than was the viral defense gene signature.

However, the researchers found that ERV expression doesn't always correlate with patient response to immunotherapy. In a dataset of metastatic melanoma cases that had received anti-PD-1 therapy, neither ERV expression nor viral defense gene expression were associated with response.

Greenbaum and his colleagues also looked at repeat expression among tumors not treated with immunotherapies. When they focused on LINE and ERV expression levels — the two are often co-expressed — within colon and rectal adenocarcinoma samples from TCGA, the researchers found that high ERV expression levels were associated with lower survival.

Additionally, samples with high LINE1 expression have down-regulated expression of immune-related genes like ones involved in leukocyte migration, complement activation, phagocytosis, and more. This suggested to the researchers that the expression of these repeats affects the immune response in these cancers.

Based on their findings, Greenbaum and his colleagues call for better ways to analyze non-coding RNA. "We therefore demonstrate the need for total RNA protocols and associated bioinformatics tools to uncover the currently hidden, yet likely critical, signaling RNAs in the cancer immune microenvironment," they wrote in their paper.