Microsatellite instability (MSI) is a genomic alteration in which microsatellites, usually of one to four nucleotide repeats, accumulate mutations corresponding to deletions/insertions of a few nucleotides. The MSI phenotype has been extensively characterized in colorectal cancer and is due to a deficiency of the DNA mismatch repair system. MSI has recently been shown to be present in most types of cancer with variable frequencies (from <1 to 30%). It correlates positively to survival outcome and predicts the response to immune checkpoint blockade therapy. The different methods developed for MSI detection in cancer require taking into consideration two critical parameters which influence method performance. First, the microsatellite markers used should be chosen carefully to ensure they are highly sensitive and specific for MSI detection. Second, the analytical method used should be highly resolute to allow clear identification of MSI and of the mutant allele genotype, and should present the lowest limit of detection possible for application in samples with low mutant allele frequency. In this review, we describe all the different molecular and computational methods developed to date for the detection of MSI in cancer, how they have evolved and improved over the years, and their advantages and drawbacks.

DNA methylation has been identified as the most promising molecular biomarker for the prediction of age. Several DNA methylation-based models have been proposed for age prediction based on blood samples, using mainly pyrosequencing. These methods present different performances for age prediction and have rarely, if ever, been evaluated and intercompared in an independent validation study. Here, for the first time, we evaluate and compare six blood-based age prediction models (Bekaert¹, Park², Thong³, Weidner⁴, and the Zbiec-Piekarska 1⁵ and Zbiec-Piekarska 2⁶), using DNA methylation analysis by pyrosequencing on 100 blood samples from French individuals aged between 19–65 years. For each model, we perform correlation analysis and evaluate age-prediction performance (mean absolute deviation (MAD) and standard error of the estimate (SEE)). The best age-prediction performances were found with the Bekaert and Thong models (MAD of 4.5–5.2, SEE of 6.8–7.2), followed by the Zbiec-Piekarska 1 model (MAD of 6.8 and SEE of 9.2), while the Park, Weidner and Zbiec-Piekarska 2 models presented lower performances (MAD of 7.2–8.7 and SEE of 9.2–10.3). Given these results, we recommend performing systematic, independent evaluation of all age prediction models on a same cohort to validate the different models and compare their performance.

Aging is a progressive time-dependent biological process affecting differentially individuals, who can sometimes present exceptional longevity. Epigenetic alterations are one of the hallmarks of aging, which comprise the epigenetic drift and clock at DNA methylation level. In the present study, we estimated the DNA methylation-based age (DNAmage) using four epigenetic clocks based on a small number of CpGs in French centenarians and semi-supercentenarians (CSSC, n=214) as well as nonagenarians' and centenarians' offspring (NCO, n=143) compared to individuals from the French general population (CG, n=149). DNA methylation analysis of the nine CpGs included in the epigenetic clocks showed high correlation with chronological age (-0.66>R>0.54) and also the presence of an epigenetic drift for four CpGs that was only visible in CSSC. DNAmage analysis showed that CSSC and to a lesser extend NCO present a younger DNAmage than their chronological age (15-28.5 years for CSSC, 4.4-11.5 years for NCO and 4.2-8.2 years for CG), which were strongly significant in CSSC compared to CG (p-values<2.2e-16). These differences suggest that epigenetic aging and potentially biological aging are slowed in exceptionally long-lived individuals and that epigenetic clocks based on a small number of CpGs are sufficient to reveal alterations of the global epigenetic clock.

Microsatellites are polymorphic short tandem repeats of 1–6 nucleotides ubiquitously present in the genome that are extensively used in living organisms as genetic markers and in oncology to detect microsatellite instability (MSI). While the standard analysis method of microsatellites is based on PCR followed by capillary electrophoresis, it generates undesirable frameshift products known as ‘stutter peaks’ caused by the polymerase slippage that can greatly complicate the analysis and interpretation of the data. Here we present an easy multiplexable approach replacing PCR that is based on low temperature isothermal amplification using recombinase polymerase amplification (LT-RPA) that drastically reduces and sometimes completely abolishes the formation of stutter artifacts, thus greatly simplifying the calling of the alleles. Using HT17, a mononucleotide DNA repeat that was previously proposed as an optimal marker to detect MSI in tumor DNA, we showed that LT-RPA improves the limit of detection of MSI compared to PCR up to four times, notably for small deletions, and simplifies the identification of the mutant alleles. It was successfully applied to clinical colorectal cancer samples and enabled detection of MSI. This easy-to-handle, rapid and cost-effective approach may deeply improve the analysis of microsatellites in several biological and clinical applications.