Detection of Microsatellite Instability in Colorectal Cancer Patients With a Plasma-Based Real-Time PCR Analysis

Abstract

A microsatellite instability (MSI) test is crucial for screening for HNPCC (Hereditary nonpolyposis colorectal cancer; Lynch syndrome) and optimization of colorectal cancer (CRC) treatment. Mismatch repair (MMR) deficiency is a predictor for good response of immune checkpoint inhibitors in various malignancies. In this study, we evaluated the results of a newly developed plasma-based real-time PCR kit for the detection of MSI in CRC patients. We assessed a peptide nucleotide acid (PNA) probe-mediated real-time PCR test (U-TOP MSI Detection Kit Plus) that determines MSI status by using amplicon melting analysis of five markers (NR21, NR24, NR27, BAT25, and BAT26) from plasma. Eighty-four CRC patients (46 dMMR and 38 pMMR) with colorectal cancer were analyzed. The concordance rate of MSI status assessment between the plasma kit and IHC was 63.0% in dMMR patients (29/46), but in the pMMR evaluation, a 100% (38/38) concordance rate was observed. In the evaluation of the performance of a custom tissue U-TOP MSI Detection Kit and plasma kit in 28 patients, sensitivity, specificity, PPV (positive predictive value) and NPV (negative predictive value) of plasma kit were 68.4, 100, 100, and 44.4%, respectively, with the tissue U-TOP MSI Detection Kit. Our results demonstrate the feasibility of a non-invasive and rapid plasma-based real-time PCR kit (U-TOP MSI Detection Kit Plus) for the detection of MSI in colorectal cancer.

Microsatellite instability evaluation by a novel PCR‐based 8‐loci test kit in colorectal cancer

“…Multiple fluorescence polymerase chain reaction capillary electrophoresis (PCR-CE) detection, PCR melting curve method, and next-generation sequencing (NGS) are employed to detect mutated microsatellite markers and categorize MSI status. 20,21 The most commonly used panel is the Bethesda panel, which includes two mononucleotides (BAT-25 and BAT-26) and three dinucleotide (D2S123, D5S346, and D17S250) repeats. 22 MSI status is categorized based on the number of mutated markers: MSI-H classification is assigned when two or more of the five markers exhibit instability, MSI-L when only one marker shows instability, and MSS when no markers display instability.…”

Enhancing Local Context of Histology Features in Vision Transformers

“…Recent research has demonstrated the application of deep learning to predict a patient's response to therapy and to predict biologically relevant information from morphological features extracted from standard histology slides. For example, microsatellite instability (MSI) is one of the key markers informing the treatment decision in colorectal cancer (CRC) [17,1]. Research has shown it is possible to predict MSI status using deep learning from standard hematoxylin and eosin (H&E) stained slide images [3,7,11].…”