Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability

Abstract

Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94–1 for the experimental CRCs and 0.94–1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91–0.97, whereas the third signature showed a significantly lower AUC of 0.69–0.88. Software for calculating these MSI signatures using RNAseq data is included.

Pancancer Analyses Reveal Genomics and Clinical Characteristics of the SETDB1 in Human Tumors

“…The SETDB1 expression and HLA-related genes in ACC, CESC, and KIRC were positively correlated but were negatively correlated in other cancers in TCGA ( Figure 12(c) ). TMB and MSI are emerging predictors associated with survival and response to immunotherapy [ 30 , 31 ]. This study showed that SETDB1 was positively correlated with MSI in BLCA, CESC, LUAD, LUSC, READ, and SARC, while negatively correlated with MSI in DLBC, but did not show correlation with MSI in other cancers ( Figure 12(e) ).…”

Cancer-specific functional profiling in microsatellite-unstable (MSI) colon and endometrial cancers using combined differentially expressed genes and biclustering analysis

“…To the best of our knowledge, this is the first study in which the differential functional profiles of MSI colon and endometrial cancers have been assessed using gene expression data. To date, most transcriptomic studies on MSI colon and endometrial cancers have tended to focus on the differentiation of MSI from MSS cancers or on molecular subtyping, [36][37][38][39][40] and we suspect that the lack of research addressing these issues can probably be attributed to the extreme methodological dependence of DEG analysis.…”

Clinically relevant fusion oncogenes: detection and practical implications

“…Thus, NGS is a more accurate and sensitive method that allows one to analyze various genetic variants in one experimental procedure, eliminating the need to conduct multiple tests in sequence to identify significant biomarkers, thereby significantly reducing the time for selecting therapy. 5 , 197 – 199 It should be noted that in order to obtain informative results, NGS technologies require a minimum amount of tumor material; they are also able to capture DNA variants present in a minority of cells. 200 To date, many approaches have been proposed for the detection of molecular markers of tumors based on NGS.…”

“… 3 At the nucleotide level, it generates mostly single nucleotide substitutions, short insertions and deletions, insertion of transposable elements, 4 and variability of the microsatellite loci. 5 , 6 In turn, chromosomal instability implies shuffling of the bigger fragments of the genome and is associated with aneuploidy, 7 deletions or amplifications of genes, 8 chromosomal rearrangements, 7 translocations, and gene fusions. 9 Chromosomal instability is currently considered one of the most common features of cancer cells.…”

“…256 Zhang. 257 RT 178 Cruz-Rico et al, 180 Lyu et al, 192 Sorber et al, 194 Abbou et al, 195 5 Sorokin et al, 197 Winters et al, 225 Peng et al 240 Single-cell RNA-seq Measures clonality of tumors.…”