Microsatellite Instability Classification Research Articles

Abstract 7560: Microsatellite instability (MSI) detection using flow-based sequencing

Abstract Background: Microsatellite instability (MSI) is an important genomic biomarker associated with various cancers and hereditary conditions. Accurate and efficient classification of MSI status is essential for diagnosis, prognosis, and treatment decisions. Different approaches for testing MSI have been presented in the literature, including NGS-based methods which either rely on comparison with a matched control sample, a set of reference control samples, or the reference genome (no control). In this study, we explore the feasibility of accurately identifying MSI status using the UG100 platform, which utilizes flow-based sequencing chemistry. Since MSI detection is typically based on long homopolymers, we examined here whether the flow-based sequencing data can be effectively used for MSI classification. Methods: We performed whole genome sequencing (WGS) on a cohort of 24 cell lines with known MSI status, as well as on 15 tumor tissue samples, encompassing various cancer types, including colon, lung, bladder, and endometrial cancer. To determine the MSI status, polyA/T homopolymer loci were considered. We measured the MSI status in these loci using two methods: 1) Counting short indel variants detected using somatic Deep Variant, in comparison with matched blood normal, and 2) a novel MSI score based on the difference of the polyA/T homopolymer length, in comparison to the reference genome. We assessed the ability to evaluate the score for the full sequenced coverage (~100X) as well as when limiting to exome boundaries, and when sampling down to 1X coverage. Results and Conclusions: We established the performance of the MSI score on the cell lines with known MSI status. The score was verified on cell line mixtures (simulating lower tumor purity) as well as on clinical samples from various tissue sources, including colon samples annotated as mismatch-repair deficient. We demonstrate the ability to use the cost-effective UG100 sequencing combined with the two methodologies to identify MSI-high samples in these scenarios. Citation Format: Gila Lithwick-Yanai, Hila Benjamin, Jorge Cano-Nistal, Shir Geisler, Ariel Jaimovich, Nika Iremadze, Dror Kessler, Ilya Soifer, Danit Lebanony, Keren Ben Simhon, Shlomit Gilad, Eti Meiri, Doron Lipson, Yosef E. Maruvka. Microsatellite instability (MSI) detection using flow-based sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7560.

Immune Checkpoint Inhibition in Pediatric Oncology Patients: A Single-Institution Experience

Immunotherapy has emerged as a promising treatment approach in oncology, as it is specifically designed to boost the strength and accuracy of the immune system, allowing it to target tumor cells but spare non-tumor tissue. This treatment not only demonstrates potential for improved clinical outcomes but may also be associated with fewer adverse effects compared to traditional therapies. Despite its early success, the application of immunotherapy has largely been limited to adult cancer patients, with slow adoption noted in the treatment of pediatric cancer patients. Our objective is to demonstrate a single institution’s experience with immunotherapy in pediatric cancer patients and to discuss the use of these treatment modalities in this unique patient population. We performed a retrospective chart review and identified patients who received immune checkpoint inhibitors (ICIs) and/or underwent immunohistochemistry (IHC) testing for programmed death ligand 1 (PD-L1), quantification of tumor mutational burden (TMB), and classification of microsatellite instability (MSI) status. In total, we identified seven pediatric cancer patients who received therapy with ICIs. Four of these patients demonstrated positive PD-L1 expression, high TMB, and/or MSI-high status. These patients were treated with nivolumab alone or in combination with ipilimumab or brentuximab. The diagnoses included: multifocal epithelioid and spindle cell hemangioma (n = 1); metastatic melanoma (n = 2); histiocytic sarcoma (n = 1); rectal adenocarcinoma in the setting of constitutional mismatch repair deficiency syndrome (CMMRD) (n = 1); and Hodgkin lymphoma (n = 2). The patients received between four and nineteen cycles of immunotherapy. Immunotherapy-related adverse events included: mild allergic reaction; prodromal symptoms; anemia; neutropenia; transaminitis; endocrinopathies; and self-limiting neuritis. Of the seven patients, three are still being treated with immunotherapy (the patients with rectal adenocarcinoma, metastatic melanoma, and multifocal epithelioid and spindle cell hemangioma) with positive treatment responses observed on imaging, one is being treated with other modalities (the patient with Hodgkin lymphoma), two have achieved remission (the patients with metastatic melanoma and Hodgkin lymphoma), and one has relapsed (the patient with histiocytic sarcoma). The three patients who completed their immunotherapy regimens have been followed for 1 month, 4 months, and 10 months, respectively. This report of a single-institution experience with immunotherapy in pediatric cancer patients highlights the positive impact immunotherapy can have, especially when utilized to treat relapsed/refractory malignancies, as tumor regression or stabilization of disease burden was achieved in six of the patients described (CR = 2; PR = 4). Further research is needed to accurately identify pediatric oncology patients who could benefit from immunotherapy.

Evaluation of an eight marker-panel including long mononucleotide repeat markers to detect microsatellite instability in colorectal, gastric, and endometrial cancers

BackgroundAccurate determination of microsatellite instability (MSI) status is critical for optimal treatment in cancer patients. Conventional MSI markers can sometimes display subtle shifts that are difficult to interpret, especially in non-colorectal cases. We evaluated an experimental eight marker-panel including long mononucleotide repeat (LMR) markers for detection of MSI.MethodsThe eight marker-panel was comprised of five conventional markers (BAT-25, BAT-26, NR-21, NR-24, and NR-27) and three LMR markers (BAT-52, BAT-59 and BAT-62). MSI testing was performed against 300 specimens of colorectal, gastric, and endometrial cancers through PCR followed by capillary electrophoresis length analysis.ResultsThe MSI testing with eight marker-panel showed 99.3% (295/297) concordance with IHC analysis excluding 3 MMR-focal deficient cases. The sensitivity of BAT-59 and BAT-62 was higher than or comparable to that of conventional markers in gastric and endometrial cancer. The mean shift size was larger in LMR markers compared to conventional markers for gastric and endometrial cancers.ConclusionsThe MSI testing with eight maker-panel showed comparable performance with IHC analysis. The LMR markers, especially BAT-59 and BAT-62, showed high sensitivity and large shifts which can contribute to increased confidence in MSI classification, especially in gastric and endometrial cancers. Further study is needed with large number of samples for the validation of these LMR markers.

Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images.

Microsatellite instability (MSI) is one of the essential tumor biomarkers for cancer treatment and prognosis. The presence of more significant PD-L1 expression on the surface of tumor cells in endometrial cancer with MSI suggests that MSI may be a promising biomarker for anti-PD-1/PD-L1 immunotherapy. However, the conventional testing methods are labor-intensive and expensive for patients. Inspired by classifiers for MSI based on fast and low-cost deep-learning methods in previous investigations, a new architecture for MSI classification based on an attention module is proposed to extract features from pathological images. Especially, slide-level microsatellite status will be obtained by the bag of words method to aggregate probabilities predicted by the proposed model. The H&E-stained whole slide images (WSIs) from The Cancer Genome Atlas endometrial cohort are collected as the dataset. The performances of the proposed model were primarily evaluated by the area under the receiver-operating characteristic curve, accuracy, sensitivity, and F1-Score. On the randomly divided test dataset, the proposed model achieved an accuracy of 0.80, a sensitivity of 0.857, a F1-Score of 0.826, and an AUROC of 0.799. We then visualize the results of the microsatellite status classification to capture more specific morphological features, helping pathologists better understand how deep learning performs the classification. This study implements the prediction of microsatellite status in endometrial cancer cases using deep-learning methods directly from H&E-stained WSIs. The proposed architecture can help the model capture more valuable features for classification. In contrast to current laboratory testing methods, the proposed model creates a more convenient screening tool for rapid automated testing for patients. This method can potentially be a clinical method for detecting the microsatellite status of endometrial cancer.

DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer.

We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or tile-level annotations using Self-supervised pre-training and heterogeneity-aware deep Multiple Instance LEarning (DeepSMILE). We apply DeepSMILE to the task of Homologous recombination deficiency (HRD) and microsatellite instability (MSI) prediction. We utilize contrastive self-supervised learning to pre-train a feature extractor on histopathology tiles of cancer tissue. Additionally, we use variability-aware deep multiple instance learning to learn the tile feature aggregation function while modeling tumor heterogeneity. For MSI prediction in a tumor-annotated and color normalized subset of TCGA-CRC (n=360 patients), contrastive self-supervised learning improves the tile supervision baseline from 0.77 to 0.87 AUROC, on par with our proposed DeepSMILE method. On TCGA-BC (n=1041 patients) without any manual annotations, DeepSMILE improves HRD classification performance from 0.77 to 0.81 AUROC compared to tile supervision with either a self-supervised or ImageNet pre-trained feature extractor. Our proposed methods reach the baseline performance using only 40% of the labeled data on both datasets. These improvements suggest we can use standard self-supervised learning techniques combined with multiple instance learning in the histopathology domain to improve genomic label classification performance with fewer labeled data.

Transfer learning based classification of MSI and MSS gastrointestinal cancer

Gastrointestinal and Colorectal cancers are treated with chemotherapy and its other forms which are not able to provide higher survival rates [1]. Immunotherapy is increasingly becoming popular due to its promising response especially to mutated tumors such as MicroSatellite Instability (MSI) cancers with deficient DNA Mismatch-Repair system (dMMR). Generally, 85% of all the cases related to gastrointestinal and colorectal cancers have proficient DNA Mismatch-Repair system (pMMR) which are also called MicroSatellite Stability (MSS). Only about 15% of the gastrointestinal and colorectal cancer patients have deficient DNA Mismatch-Repair system (dMMR) causing MicroSatellite Instability (MSI) in their tumors. While Immunotherapy responds well to patients with MSI tumors, it is resistant to MSS tumors [2]. Hence, it’s important to classify MSI vs. MSS tumors so that appropriate treatment can be given to the patients. Clinically MSI cancers are difficult to be detected after stage III due to their sensitivity to pembrolizumab inhibitors [3] [4]. In this work, deep learning based transfer learning approach is detailed that can accurately classify MSI vs. MSS cancers using histological images which are derived from formalin-fixed paraffin-embedded (FFPE).

VGG16 CLASSIFICATION OF MICROSATELLITE INSTABILITY IN COLORECTAL CANCER BY DEEP LEARNING

Recently, with the development of technology, deep learning method, which is a branch of artificial intelligence, has become increasingly widespread in the field of health, with its use in the diagnosis, classification and determination of appropriate treatment. This study, which was carried out with this method, is important in determining the most appropriate treatment option for the patient, such as early estimation of microsatellite instability (MSI) in colorectal cancer patients, determination of appropriate treatment, reduction of undesirable side effects, and prevention of time and cost for the disease. Method: In this study, the VGG16 model was developed using deep learning techniques, which are convolutional neural network and transfer learning methods, and the classification of microsatellite instability was provided. Using the colorectal cancer hematoxylin and eosin (H&E) stained 150000 histopathological image dataset, which is open access via the Kaggle website, 80% was reserved for training and 20% was reserved for testing. With these separated data sets, the training of the deep learning-based VGG16 model was carried out using high graphics processing units (GPU) over Google Colab, a free cloud environment. Results: For the training of the model, the epoch, learning rate, momentum coefficient and group size hyperparameters were determined as 10, 0.00001, 0.9 and 64, respectively, to classify microsatellite instability in colorectal cancer. With the proposed model VGG16, classification performance accuracy 89.4%, precision 92.9%, sensitivity 85.3% and AUC 89.4% were obtained. Conclusion: In line with these results, it is believed that it will help pathologists to make computer-aided decisions in the clinical setting.

MSIFinder: A python package for detecting MSI status using random forest classifier.

2601 Background: Microsatellite instability (MSI) is a common genomic alteration in several tumors, such as colorectal cancer, endometrial carcinoma, and stomach, which is characterized as microsatellite instability-high (MSI-H) and microsatellite stable (MSS) based on a high degree of polymorphism in microsatellite lengths. MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer (CRC) patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; However, they are considerably affected by the sequencing depth and panel size. Methods: We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, RFC model were built by 54 feature markers from the training sets. Second. The software was validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. Results: With this test set, MSIFinder achieved a sensitivity (recall) of 0.997, a specificity of 1, an accuracy of 0.998, a positive predictive value (PPV) of 0.954, an F1 score of 0.977, and an area under curve (AUC) of 0.999. We discovered that MSIFinder is less affected by low sequencing depth and can achieve a concordance of 0.993, while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 m (million base). Conclusions: These results indicated that MSIFinder is a robust MSI classification tool and not affected by the panel size and sequencing depth. Furthermore, MSIFinder can provide reliable MSI detection for scientific and clinical purposes.[Table: see text]

Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer.

Simple SummaryDefects in a DNA repair pathway called mismatch repair (MMR) can lead to cancer, including colorectal cancer (CRC). The detection of mismatch repair deficiency (dMMR) is based on molecular tests, one of which is microsatellite instability (MSI) testing. Detecting tumors with dMMR/MSI is important for the identification of patients with Lynch Syndrome and determining if patients may benefit from immunotherapy. Recently, artificial intelligence has been evaluated as a method to predict MSI/dMMR directly from tissue slides that are available for most cancer patients. We review the data regarding the utility of machine learning for dMMR/MSI classification, including its accuracy and limitations, focusing on CRC. We also provide an overview of previous efforts to predict MSI from tissue slides and background regarding the use of artificial intelligence for image analyses. We summarize recent efforts to use artificial intelligence for the prediction of MSI and discuss the implications for predicting response to immunotherapy.Microsatellite instability (MSI) is a molecular marker of deficient DNA mismatch repair (dMMR) that is found in approximately 15% of colorectal cancer (CRC) patients. Testing all CRC patients for MSI/dMMR is recommended as screening for Lynch Syndrome and, more recently, to determine eligibility for immune checkpoint inhibitors in advanced disease. However, universal testing for MSI/dMMR has not been uniformly implemented because of cost and resource limitations. Artificial intelligence has been used to predict MSI/dMMR directly from hematoxylin and eosin (H&E) stained tissue slides. We review the emerging data regarding the utility of machine learning for MSI classification, focusing on CRC. We also provide the clinician with an introduction to image analysis with machine learning and convolutional neural networks. Machine learning can predict MSI/dMMR with high accuracy in high quality, curated datasets. Accuracy can be significantly decreased when applied to cohorts with different ethnic and/or clinical characteristics, or different tissue preparation protocols. Research is ongoing to determine the optimal machine learning methods for predicting MSI, which will need to be compared to current clinical practices, including next-generation sequencing. Predicting response to immunotherapy remains an unmet need.

Sensitive detection of microsatellite instability in tissues and liquid biopsies: Recent developments and updates.

Microsatellite instability (MSI), a phenotype displayed as deletions/insertions of repetitive genomic sequences, has drawn great attention due to its application in cancer including diagnosis, prognosis and immunotherapy response prediction. Several methods have been developed for the detection of MSI, facilitating the MSI classification of cancer patients. In view of recent interest in minimally-invasive detection of MSI via liquid biopsy samples, which requires methods with high sensitivity to identify small fractions of altered DNA in the presence of large amount of wild type copies, sensitive MSI detection approaches are emerging. Here we review the available MSI detection methods and their detection limits and focus on recently developed next-generation-sequencing based approaches and bioinformatics algorithms available for MSI analysis in various cancer types.

DNA Mismatch Repair Deficiency Detection in Colorectal Cancer by a New Microsatellite Instability Analysis System

BackgroundAlthough microsatellite instability (MSI) is most commonly detected in colorectal cancer (CRC), improvement in MSI analysis method can always help us better assessing MSI phenotypes and gaining useful information in challenging cases. The purpose of current study is to explore whether the ProDx® MSI analysis System (ProDx® MSI) can improve MSI classification in CRC.MethodsWe compared the MSI profiles of 97 FFPE samples from CRC patients by ProDx® MSI with Promega MSI analysis System 1.2 and NCI panel. The result is then confirmed by IHC test, which evaluate MMR protein expression. Furthermore, next generation sequencing was performed to double confirm the specimens with discordant results.ResultsAmong the total 97 CRC cases, 35 were scored as MSI-High by ProDx® MSI, Promega MSI analysis System 1.2, and NCI panel simultaneously. Three extra MSI-High cases were identified by ProDx® MSI. These three cases were classified as MSI-Low by NCI panel, while two of these as MSI-Low, and 1 as MSS by Promega MSI analysis System 1.2. ProDx® MSI had higher concordance with IHC detection compared with Promega MSI Analysis System 1.2 and NCI panel at 99.0%, 96.9%, and 95.9%, respectively. The ProDx® MSI distinguished MSI status with 100% sensitivity and 98.4% specificity. Our data showed that MSI-High phenotype occurred most frequently in tumor development stage I and stage II.ConclusionsThe colorectal cancer can be classified according to MSI status accurately by ProDx® MSI. More cases with MSI-High feature may be revealed by ProDx® MSI than by previous test systems in colorectal cancer.

A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine

BackgroundRecent studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in the induction of cancer through epigenetic regulation, transcriptional regulation, post-transcriptional regulation and other aspects, thus participating in various biological processes such as cell proliferation, differentiation and apoptosis. As a new nova of anti-tumor therapy, immunotherapy has been shown to be effective in many tumors of which PD-1/PD-L1 monoclonal antibodies has been proofed to increase overall survival rate in advanced gastric cancer (GC). Microsatellite instability (MSI) was known as a biomarker of response to PD-1/PD-L1 monoclonal antibodies therapy. The aim of this study was to identify lncRNAs signatures able to classify MSI status and create a predictive model associated with MSI for GC patients.MethodsUsing the data of Stomach adenocarcinoma from The Cancer Genome Atlas (TCGA), we developed and validated a lncRNAs model for automatic MSI classification using a machine learning technology – support vector machine (SVM). The C-index was adopted to evaluate its accuracy. The prognostic values of overall survival (OS) and disease-free survival (DFS) were also assessed in this model.ResultsUsing the SVM, a lncRNAs model was established consisting of 16 lncRNA features. In the training cohort with 94 GC patients, accuracy was confirmed with AUC 0.976 (95% CI, 0.952 to 0.999). Veracity was also confirmed in the validation cohort (40 GC patients) with AUC 0.950 (0.889 to 0.999). High predicted score was correlated with better DFS in the patients with stage I-III and lower OS with stage I-IV.ConclusionThis study identify 16 LncRNAs signatures able to classify MSI status. The correlation between lncRNAs and MSI status indicates the potential roles of lncRNAs interacting in immunotherapy for GC patients. The pathway of these lncRNAs which might be a target in PD-1/PD-L1 immunotherapy are needed to be further study.

MSIpred: a python package for tumor microsatellite instability classification from tumor mutation annotation data using a support vector machine

Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite lengths due to deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important indicator for patient prognosis. Conventional clinical diagnosis of MSI examines PCR products of a panel of microsatellite markers using electrophoresis (MSI-PCR), which is laborious, costly, and time consuming. We developed MSIpred, a python package for automatic MSI classification using a machine learning technology – support vector machine (SVM). MSIpred computes 22 features characterizing tumor somatic mutational load from mutation data in mutation annotation format (MAF) generated from paired tumor-normal exome sequencing data, subsequently using these features to predict tumor MSI status with a SVM classifier trained by MAF data of 1074 tumors belonging to four types. Evaluation of MSIpred on an independent testing set, MAF data of another 358 tumors, achieved overall accuracy of ≥98% and area under receiver operating characteristic (ROC) curve of 0.967. Further analysis on discrepant cases revealed that discrepancies were partially due to misclassification of MSI-PCR. Additional testing of MSIpred on non-TCGA data also validated its good classification performance. These results indicated that MSIpred is a robust pan-tumor MSI classification tool and can serve as a complementary diagnostic to MSI-PCR in MSI diagnosis.

Abstract 2267: A novel method for classification of microsatellite instability (MSI) using the Oncomine Tumor Mutation Load assay

Abstract Cancer-associated changes in short tandem repeats (STRs) throughout the genome have been shown to be predictive of response to immunotherapy treatment. Traditional assays efficiently rely on a small number of genomic loci to assign an MSI status, but are only effective in a small number of cancer types. Genome-wide profiling of aberrations in STRs has the potential to reveal a comprehensive signature of MSI and could allow for the association of response with samples that are labeled as microsatellite stable (MSS) by traditional assays.To facilitate the discovery of informative repeat sites across the genome, we have extended the Oncomine Tumor Mutation Load assay* to allow for the detection of variants in STRs. Our method addresses challenges of semiconductor sequencing of long STR elements including indel and partial incorporation signal errors. We developed an algorithm to avoid and correct these errors in flowspace, rather than base space, to ensure accurate signal assessment. Our pipeline is able to determine the optimal anchor locations used to identify the exact position of a repeat sequence within a read and then quantify the size, sequence, and number of reads associated with each allele. An additional complication of accurately quantifying the length of an STR is the increased error rates of the polymerase during DNA amplification. By evaluating the similarity of every loci between tumor and normal pairs of an individual using a metric derived from information theory, we are able to account for both the sequencing and PCR associated noise to robustly identify the genome-wide changes. Furthermore by evaluating a set of 24 MSI-High and 24 MSS samples along with their paired normal samples, we created a novel method to identify a highly informative subset of STR loci. Creating a classifier using only these highly informative loci, we are able to recapitulate the traditional MSI assay with 95% accuracy on these initial samples. This general methodology can be further extended to a model where MSI status is predicted using only the tumor sample, without the need for a control from the same individual. Due to the flexibility of this method, we are able to examine an increased number of potentially informative loci and use the total mutation load along with mutation calls in the MMR genes to provide comprehensive, multi-dimensional genomic perspective of samples. While this novel method has been developed and tested on the Oncomine Tumor Mutation Load assay, we have created a novel general approach that can be used on the output of any semiconductor sequencing assay. The extended information derived from individual mutations and the total mutation load, along with STR aberration information allows for a comprehensive evaluation of every sample and promotes the discovery of novel genomic components important for understanding the mechanisms of immune-oncology. *For research use only Citation Format: Aleksandr Pankov, Sameh El-Difrawy, Warren Tom, Jeffrey Conroy, Sean Glenn, Sarabjot Pabla, Carl Morrison, Fiona Hyland, Simon Cawley. A novel method for classification of microsatellite instability (MSI) using the Oncomine Tumor Mutation Load assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2267.

Modal variety of microsatellite instability in human endometrial carcinomas.

PurposeMicrosatellite instability (MSI) in human endometrial cancer (EC) was analysed using a unique fluorescent technique. MSI is associated with various human neoplasms. However, the reported frequency of MSI differs widely in each malignancy. Methodological difficulties have in fact been pointed out in its assay techniques.MethodsWe previously established a sensitive fluorescent technique in which the major methodological problems are overcome. Application of this technique has revealed two distinct modes of microsatellite alterations, i.e. Type A and Type B. In the present study, we have applied this technique to 94 ECs.ResultsSignificant microsatellite alterations were observed in 38 (40.4 %) tumours of the panel. The two modes, Type A and Type B, were indeed observed in this malignancy. More importantly, we found that the modes more closely correlated with the molecular and clinicopathological backgrounds of the tumours than the established and widely used MSI grades, MSI-H and MSI-L. Type B MSI widely correlated with family history of hereditary non-polyposis colorectal cancer-associated cancers, whereas MSI-H only did with that of colorectal cancer. Furthermore, mutation in the KRAS oncogene, which has been regarded as generally infrequent in microsatellite-unstable tumours, was clearly associated with Type A MSI.ConclusionsOur observations may suggest a biological relevance and a potential utility of the modal classification of MSI and, furthermore, added complexities to genomic instability underlying tumourigenesis in human endometrium.Electronic supplementary materialThe online version of this article (doi:10.1007/s00432-015-2030-2) contains supplementary material, which is available to authorized users.

Improved Detection of Microsatellite Instability in Early Colorectal Lesions.

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.

Detection of coding microsatellite frameshift mutations in DNA mismatch repair-deficient mouse intestinal tumors.

Different DNA mismatch repair (MMR)-deficient mouse strains have been developed as models for the inherited cancer predisposing Lynch syndrome. It is completely unresolved, whether coding mononucleotide repeat (cMNR) gene mutations in these mice can contribute to intestinal tumorigenesis and whether MMR-deficient mice are a suitable molecular model of human microsatellite instability (MSI)-associated intestinal tumorigenesis. A proof-of-principle study was performed to identify mouse cMNR-harboring genes affected by insertion/deletion mutations in MSI murine intestinal tumors. Bioinformatic algorithms were developed to establish a database of mouse cMNR-harboring genes. A panel of five mouse noncoding mononucleotide markers was used for MSI classification of intestinal matched normal/tumor tissues from MMR-deficient (Mlh1(-/-) , Msh2(-/-) , Msh2(LoxP/LoxP) ) mice. cMNR frameshift mutations of candidate genes were determined by DNA fragment analysis. Murine MSI intestinal tumors but not normal tissues from MMR-deficient mice showed cMNR frameshift mutations in six candidate genes (Elavl3, Tmem107, Glis2, Sdccag1, Senp6, Rfc3). cMNRs of mouse Rfc3 and Elavl3 are conserved in type and length in their human orthologs that are known to be mutated in human MSI colorectal, endometrial and gastric cancer. We provide evidence for the utility of a mononucleotide marker panel for detection of MSI in murine tumors, the existence of cMNR instability in MSI murine tumors, the utility of mouse subspecies DNA for identification of polymorphic repeats, and repeat conservation among some orthologous human/mouse genes, two of them showing instability in human and mouse MSI intestinal tumors. MMR-deficient mice hence are a useful molecular model system for analyzing MSI intestinal carcinogenesis.

Microsatellite Instability Predicts Clinical Outcome in Radiation-Treated Endometrioid Endometrial Cancer

To elucidate whether microsatellite instability (MSI) predicts clinical outcome in radiation-treated endometrioid endometrial cancer (EEC). A consecutive series of 93 patients with EEC treated with extrafascial hysterectomy and postoperative radiotherapy was studied. The median clinical follow-up of patients was 138 months, with a maximum of 232 months. Five quasimonomorphic mononucleotide markers (BAT-25, BAT-26, NR21, NR24, and NR27) were used for MSI classification. Twenty-five patients (22%) were classified as MSI. Both in the whole series and in early stages (I and II), univariate analysis showed a significant association between MSI and poorer 10-year local disease-free survival, disease-free survival, and cancer-specific survival. In multivariate analysis, MSI was excluded from the final regression model in the whole series, but in early stages MSI provided additional significant predictive information independent of traditional prognostic and predictive factors (age, stage, grade, and vascular invasion) for disease-free survival (hazard ratio [HR] 3.25, 95% confidence interval [CI] 1.01-10.49; p = 0.048) and cancer-specific survival (HR 4.20, 95% CI 1.23-14.35; p = 0.022) and was marginally significant for local disease-free survival (HR 3.54, 95% CI 0.93-13.46; p = 0.064). These results suggest that MSI may predict radiotherapy response in early-stage EEC.

Differences in genomic instability between intestinal- and diffuse-type gastric cancer

Microsatellite instability (MSI) and loss of heterozygosity (LOH) are lesions in the genome found with different frequencies in gastric carcinomas (GCAs). Despite a great body of studies, no systematic approach to the detailed classification of MSI and LOH in the two major types of GCA has been published. Thirty-seven advanced GCAs, 25 intestinal-type (IGCAs) and 12 diffuse-type (DGCAs), were assayed with 15 autosomal tetranucleotide markers on 14 chromosomal arms. The observed frequencies and types of microsatellite alterations allowed stratification into subgroups, i.e., high- and low-grade MSI (MSI-H, MSI-L) or microsatellite-stable (MSS), and high- or low-grade, or non-detectable LOH (LOH-H, LOH-L, LOH-N). Collectively, the markers detected MSI-H tumors with sensitivity equal to that of BAT-26 (a single marker highly specific for MSI-H). Likewise, the markers detected LOH at chromosomal arms 5q, 18q, and 21q with a sensitivity equal to markers used previously. Seven (19%) MSI-H and six (16%) LOH-H tumors were found, with a significant association (P = 0.027) with IGCA: 92% of MSI-H and LOH-H occurred in IGCA patients only. Conversely, in DGCA, a significantly higher prevalence of a stable (LOH-N/MSS) phenotype was found as compared with IGCA (75.1% vs 28.0%; P = 0.035). The MSI-L phenotype was found in 57.9% of non-MSI-H IGCA tumors and was associated significantly (P = 0.015) with LOH-H. A clear difference in genomic instability between IGCA and DGCA was found. In IGCA, the MSI and LOH pathways were more commonly involved, whereas in DGCA, a stable phenotype was predominant. As a novel finding, MSI-L as a true phenomenon and its association with LOH was observed in IGCA.

Standardized approach for microsatellite instability detection in gastric carcinomas

Microsatellite instability (MSI) defines a specific type of genetic instability. Although consensus diagnostic criteria for MSI definition in colorectal cancer have been established, their utility in other tumor types remain to be proven. Previously we developed a mathematical model for MSI definition in colorectal cancer. The aim of this study was to establish diagnostic criteria for MSI evaluation in human gastric cancer. We designed an algorithm for the efficient characterization of MSI and used it to analyze data on 7 microsatellite markers in 35 gastric carcinomas. Theoretical models considering 1, 2, or 3 populations were tested against the data collected. Also, hypermethylation of hMLH1 gene promoter and hMLH1 protein expression were studied. The observed frequencies of MSI in our series of samples best fit a 2-population model: stable and unstable, defined by instability in 2 or more of a minimum of 7 markers analyzed. MSI was observed in 29% of the tumors. Misclassification rate was <4% when any 7 loci were analyzed. MSI(+) tumors inversely associated with p53 protein overexpression. A good correlation between hMLH1 status (either protein or promoter hypermethylation) and MSI classification was observed. We have developed a simple, sensitive, and specific approach to assess the presence of MSI in gastric cancer that may have clinical applications.