Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability - Sorokin, Maksim; Rabushko, Elizaveta; Efimov, Victor; Poddubskaya, Elena; Sekacheva, Marina; Simonov, Alexander; Nikitin, Daniil; Drobyshev, Aleksey; Suntsova, Maria; Buzdin, Anton (Frontiers in Molecular Biosciences, 2021)
Microsatellite instability (MSI) is important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian and gastric cancers, it can guide prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by size of marker PCR products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical (IHC) interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is emerging source of data for 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 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 CRC (n=619), endometrial carcinoma (n=533), gastric cancer (n=380), uterine carcinosarcoma (n=55), 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) range 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 significantly lower AUC of 0.69-0.88. Software for calculating these MSI signatures using RNAseq data is included.