Somatic microsatellite variability as a predictive marker for colorectal cancer and liver cancer progression

Microsatellite Instability and Loss of Heterozygosity as Prognostic Markers in Oral Squamous Cell Carcinoma: Molecular Mechanisms, Detection Techniques, and Therapeutic Strategies

The aim of this study was to conduct a systematic review of research investigating the potential role of microsatellite instability (MSI) and loss of heterozygosity (LOH) in oral squamous cell carcinoma (OSCC), with a focus on molecular mechanisms, detection methods, and therapeutic approaches. Search for articles involved the PubMed and Scopus. Previous retrospective and prospective studies identified variations between oral cancers that exhibit microsatellite stability and LOH. In this search, 294 articles were initially retrieved. Of these, 70 were excluded due to duplication, 106 were identified as ineligible by automated tools, and 24 were excluded as they were published in languages other than English. An additional 94 articles were excluded, 32 of which focused on head and neck cancers broadly, and 8 could not be accessed due to withdrawal. Ultimately, a systematic review was conducted based on 54 selected articles. Among the chromosomes analyzed for MSI and LOH, the highest frequency of LOH was observed on chromosome 9p. The MSI subtype is particularly susceptible to immunotherapeutic methods, such as the use of anti-PD-L1 and anti-CTLA4 antibodies, owing to its strong immunogenicity and ubiquitous expression of immune checkpoint ligands. Given the distinct characteristics and clinical behavior of oral cancer with MSI compared to microsatellite stable disease, it is advisable to incorporate MSI testing into the diagnostic process for all stages of tumor development. This ensured that each patient had received precise and effective treatment.

Germline microsatellite genotypes differentiate children with medulloblastoma

BACKGROUND

The germline genetic events underpinning medulloblastoma (MB) initiation, and therefore the ability to determine who is at risk, are still unknown for the majority of cases. Microsatellites are short repeated sequences that make up ~3% of the genome. Repeat lengths vary among individuals and are often nonrandomly associated with disease, including several cancers such as breast, glioma, lung, and ovarian. Due to their effects on gene function, they have been called the "tuning knobs of the genome."

METHODS

We have developed a novel approach for identifying a microsatellite-based signature to differentiate MB patients from controls using germline DNA.

RESULTS

Analyzing germline whole exome sequencing data from a training set of 120 MB subjects and 425 controls, we identified 139 individual microsatellite loci whose genotypes differ significantly between the groups. Using a genetic algorithm, we identified a subset of 43 microsatellites that distinguish MB subjects from controls with a sensitivity and specificity of 92% and 88%, respectively. This microsatellite signature was validated in an independent dataset consisting of 102 subjects and 428 controls, with comparable sensitivity and specificity of 95% and 90%, respectively. Analysis of the allele genotypes of those 139 informative loci demonstrates that their association with MB is a consequence of individual microsatellites' genotypes rather than their hypermutability. Finally, an analysis of the genes harboring these microsatellite loci reveals cellular functions important for tumorigenesis.

CONCLUSION

This study demonstrates that MB-specific germline microsatellite variations mark those at risk for MB development and suggests mechanisms of predisposition.

Correlations between microsatellite instability and the biological behaviour of tumours

Purpose

Microsatellites are widely distributed repetitive DNA motifs, accounting for approximately 3% of the genome. Due to mismatch repair system deficiency, insertion or deletion of repetitive units often occurs, leading to microsatellite instability. In this review, we aimed to explore the relationship between MSI and biological behaviour of colorectal carcinoma, gastric carcinoma, lymphoma/leukaemia and endometrial carcinoma, as well as the application of frameshift peptide vaccines in cancer therapy.

Methods

The relevant literature from PubMed and Baidu Xueshu were reviewed in this article. The ClinicalTrials.gov database was searched for clinical trials related to the specific topic.

Results

Microsatellite instability is divided into three subtypes: high-level, low-level microsatellite instability, and stable microsatellites. The majority of tumour patients with high-level microsatellite instability often show a better efficacy and prognosis than those with low-level microsatellite instability or stable microsatellites. In coding regions, especially for genes involved in tumourigenesis, microsatellite instability often results in inactivation of proteins and contributes to tumourigenesis. Moreover, the occurrence of microsatellite instability in coding regions can also cause the generation of frameshift peptides that are thought to be unknown and novel to the individual immune system. Thus, these frameshift peptides have the potential to be biomarkers to raise tumour-specific immune responses.

Conclusion

MSI has the potential to become a key predictor for evaluating the degree of malignancy, efficacy and prognosis of tumours. Clinically, MSI patterns will provide more valuable information for clinicians to create optimal individualized treatment strategies based on frameshift peptides vaccines.

High-depth, high-accuracy microsatellite genotyping enables precision lung cancer risk classification

There remains a large discrepancy between the known genetic contributions to cancer and that which can be explained by genomic variants, both inherited and somatic. Recently, understudied repetitive DNA regions called microsatellites have been identified as genetic risk markers for a number of diseases including various cancers (breast, ovarian and brain). In this study, we demonstrate an integrated process for identifying and further evaluating microsatellite-based risk markers for lung cancer using data from the cancer genome atlas and the 1000 genomes project. Comparing whole-exome germline sequencing data from 488 TCGA lung cancer samples to germline exome data from 390 control samples from the 1000 genomes project, we identified 119 potentially informative microsatellite loci. These loci were found to be able to distinguish between cancer and control samples with sensitivity and specificity ratios over 0.8. Then these loci, supplemented with additional loci from other cancers and controls, were evaluated using a target enrichment kit and sample-multiplexed nextgen sequencing. Thirteen of the 119 risk markers were found to be informative in a well powered study (>0.99 for a 0.95 confidence interval) using high-depth (579x±315) nextgen sequencing of 30 lung cancer and 89 control samples, resulting in sensitivity and specificity ratios of 0.90 and 0.94, respectively. When 8 loci harvested from the bioinformatic analysis of other cancers are added to the classifier, then the sensitivity and specificity rise to 0.93 and 0.97, respectively. Analysis of the genes harboring these loci revealed two genes (ARID1B and REL) and two significantly enriched pathways (chromatin organization and cellular stress response) suggesting that the process of lung carcinogenesis is linked to chromatin remodeling, inflammation, and tumor microenvironment restructuring. We illustrate that high-depth sequencing enables a high-precision microsatellite-based risk classifier analysis approach. This microsatellite-based platform confirms the potential to create clinically actionable diagnostics for lung cancer.