MicroRNAs Expression Patterns Predict Tumor Mutational Burden in Colorectal Cancer.

Abstract

BACKGROUND:

Tumor mutational burden (TMB) could be a measure of response to immune checkpoint inhibitors therapy for patients with colorectal cancer (CRC). MicroRNAs (miRNAs) participate in anticancer immune responses. In the present study, we determined miRNA expression patterns in patients with CRC and built a signature that predicts TMB.

METHODS:

Next generation sequencing (NGS) on formalin-fixed paraffin-embedded samples from CRC patients was performed to measure TMB levels. We used datasets from The Cancer Genome Atlas to compare miRNA expression patterns in samples with high and low TMB from patients with CRC. We created an miRNA-based signature index using the selection operator (LASSO) and least absolute shrinkage method from the training set. We used an independent test set as internal validation. We used real-time polymerase chain reaction (RT-PCR) to validate the miRNA-based signature classifier.

RESULTS:

Twenty-seven samples from CRC patients underwent NGS to determine the TMB level. We identified four miRNA candidates in the training set for predicting TMB (N = 311). We used the test set (N = 204) for internal validation. The four-miRNA-based signature classifier was an accurate predictor of TMB, with accuracy 0.963 in the training set. In the test set, it was 0.902; and it was 0.946 in the total set. The classifier was superior to microsatellite instability (MSI) for predicting TMB in TCGA dataset. In the validation cohort, MSI status more positively correlated with TMB levels than did the classifier. Validation from RT-qPCR showed good target discrimination of the classifier for TMB prediction.

CONCLUSION:

To our knowledge, this is the first miRNA-based signature classifier validated using high quality clinical data to accurately predict TMB level in patients with CRC.

• Authors: Jiahao Huang, Haizhou Liu, Yang Zhao, Tao Luo, Jungang Liu, Junjie Liu, Xiaoyan Pan, Weizhong Tang
• Journal: Frontiers in oncology (Front Oncol) Vol. 10 Pg. 550986 ( 2020) ISSN: 2234-943X [Print] Switzerland
• PMID: 33634010 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Huang, Liu, Zhao, Luo, Liu, Liu, Pan and Tang.