Dataset Information

Improved Detection of Microsatellite Instability in Early Colorectal Lesions.

ABSTRACT: Microsatellite instability (MSI) occurs in over 90% of Lynch syndrome cancers and is considered a hallmark of the disease. MSI is an early event in colon tumor development, but screening polyps for MSI remains controversial because of reduced sensitivity compared to more advanced neoplasms. To increase sensitivity, we investigated the use of a novel type of marker consisting of long mononucleotide repeat (LMR) tracts. Adenomas from 160 patients, ranging in age from 29-55 years old, were screened for MSI using the new markers and compared with current marker panels and immunohistochemistry standards. Overall, 15 tumors were scored as MSI-High using the LMRs compared to 9 for the NCI panel and 8 for the MSI Analysis System (Promega). This difference represents at least a 1.7-fold increase in detection of MSI-High lesions over currently available markers. Moreover, the number of MSI-positive markers per sample and the size of allelic changes were significantly greater with the LMRs (p = 0.001), which increased confidence in MSI classification. The overall sensitivity and specificity of the LMR panel for detection of mismatch repair deficient lesions were 100% and 96%, respectively. In comparison, the sensitivity and specificity of the MSI Analysis System were 67% and 100%; and for the NCI panel, 75% and 97%. The difference in sensitivity between the LMR panel and the other panels was statistically significant (p<0.001). The increased sensitivity for detection of MSI-High phenotype in early colorectal lesions with the new LMR markers indicates that MSI screening for the early detection of Lynch syndrome might be feasible.

SUBMITTER: Bacher JW

PROVIDER: S-EPMC4529134 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Improved Detection of Microsatellite Instability in Early Colorectal Lesions.

Bacher Jeffery W JW Sievers Chelsie K CK Albrecht Dawn M DM Grimes Ian C IC Weiss Jennifer M JM Matkowskyj Kristina A KA Agni Rashmi M RM Vyazunova Irina I Clipson Linda L Storts Douglas R DR Thliveris Andrew T AT Halberg Richard B RB

PloS one 20150807 8

Microsatellite instability (MSI) occurs in over 90% of Lynch syndrome cancers and is considered a hallmark of the disease. MSI is an early event in colon tumor development, but screening polyps for MSI remains controversial because of reduced sensitivity compared to more advanced neoplasms. To increase sensitivity, we investigated the use of a novel type of marker consisting of long mononucleotide repeat (LMR) tracts. Adenomas from 160 patients, ranging in age from 29-55 years old, were screened ...[more]

PMID: 26252492

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Project description:Background & aimsMicrosatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed.MethodsWe collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N = 6406 specimens) and validated in an external cohort (n = 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC).ResultsThe deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound, 0.91; upper bound, 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC of 0.95 (range, 0.92-0.96) without image preprocessing and an AUROC of 0.96 (range, 0.93-0.98) after color normalization.ConclusionsWe developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using H&E-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens.

Project description:BackgroundMicrosatellite instability (MSI) is a key biomarker for cancer immunotherapy and prognosis. Integration of MSI testing into a next-generation-sequencing (NGS) panel could save tissue sample, reduce turn-around time and cost, and provide MSI status and comprehensive genomic profiling in single test. We aimed to develop an MSI calling model to detect MSI status along with the NGS panel-based profiling test using tumor-only samples.MethodsFrom January 2019 to December 2020, a total of 174 colorectal cancer (CRC) patients were enrolled, including 31 MSI-high (MSI-H) and 143 microsatellite stability (MSS) cases. Among them, 56 paired tumor and normal samples (10 MSI-H and 46 MSS) were used for modeling, and another 118 tumor-only samples were used for validation. MSI polymerase chain reaction (MSI-PCR) was performed as the gold standard. A baseline was built for the selected microsatellite loci using the NGS data of 56 normal blood samples. An MSI detection model was constructed by analyzing the NGS data of tissue samples. The performance of the model was compared with the results of MSI-PCR.ResultsWe first intersected the target genomic regions of the NGS panels used in this study to select common microsatellite loci. A total of 42 loci including 23 mononucleotide repeat sites and 19 longer repeat sites were candidates for modeling. As mononucleotide repeat sites are more sensitive and specific for detecting MSI status than sites with longer length motif and the mononucleotide repeat sites performed even better than the total sites, a model containing 23 mononucleotide repeat sites was constructed and named Colorectal Cancer Microsatellite Instability test (CRC-MSI). The model achieved 100% sensitivity and 100% specificity when compared with MSI-PCR in both training and validation sets. Furthermore, the CRC-MSI model was robust with the tumor content as low as 6%. In addition, 8 out of 10 MSI-H samples showed alternations in the four mismatch repair genes ( MLH1 , MSH2 , MSH6 , and PMS2 ).ConclusionMSI status can be accurately determined along the targeted NGS panels using only tumor samples. The performance of mononucleotide repeat sites surpasses loci with longer repeat motif in MSI calling.

Dataset Information

Improved Detection of Microsatellite Instability in Early Colorectal Lesions.

Publications

Improved Detection of Microsatellite Instability in Early Colorectal Lesions.

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