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Assessment of Microsatellite Instability from Next-Generation Sequencing Data

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Computational Methods for Precision Oncology

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1361))

Abstract

Microsatellite instability (MSI) is a genetic alteration due to a deficiency of the DNA mismatch repair system, where microsatellites accumulate insertions/deletions. This phenotype has been extensively characterized in colorectal cancer and is also sought in the context of Lynch syndrome diagnosis. It has recently been described in dozens of cancer types from whole genome/exome sequencing data, bearing some prognostic information. Moreover, MSI has also proven to be a major predicator of the response to immune checkpoint blockade therapy in solid cancer patients. Among the different methods developed for MSI detection in cancer, next-generation sequencing (NGS) is a promising and versatile technology offering many possibilities and advantages in diverse clinical applications compared to the gold standard PCR and capillary electrophoresis approach. NGS could notably increase the number of analyzed microsatellites and potentially be used to analyze other genetic alterations required for precision oncology. However, it requires the development of robust new computational algorithms for the analysis of NGS microsatellite data. In this chapter, we describe the different approaches developed for the assessment of MSI from NGS data in cancer, including the different microsatellite panels and computational algorithms proposed, highlighting their advantages and drawbacks, and their evaluation in different clinical applications.

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  1. Laboratory for Bioinformatics, Foundation Jean Dausset – CEPH, Paris, France

    Victor Renault & Emmanuel Tubacher

  2. Laboratory for Genomics, Foundation Jean Dausset – CEPH, Paris, France

    Alexandre How-Kit

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    Alessandro Laganà

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Renault, V., Tubacher, E., How-Kit, A. (2022). Assessment of Microsatellite Instability from Next-Generation Sequencing Data. In: Laganà, A. (eds) Computational Methods for Precision Oncology. Advances in Experimental Medicine and Biology, vol 1361. Springer, Cham. https://doi.org/10.1007/978-3-030-91836-1_5

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