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Transcript
Microsatellite Instability
Detection by Next
Generation Sequencing
S.J. Salipante, S.M. Scroggins, H.L. Hampel,
E.H. Turner, and C.C. Pritchard
September 2014
www.clinchem.org/content/60/9/1192.full
© Copyright 2014 by the American Association for Clinical Chemistry
Introduction
 Microsatellite instability (MSI) is a clinically important molecular
phenotype used in the evaluation of tumors
 Provides information about prognosis, therapeutic choices,
familial cancer risk assessment.
 MSI is classically associated with colorectal cancers, but is
present in other cancer types
 Defined by the appearance of new microsatellite alleles that
are larger or smaller than those observed in matched germline
tissue
 Caused by dysregulation of mismatch repair (MMR) system
 Mutation in MLH1, MSH2, MSH6, EPCAM, or PMS2 are
common in hereditary cancer predisposition conditions
 Promoter methylation of MLH1 is common in sporadic disease
© Copyright 2009 by the American Association for Clinical Chemistry
Current detection methods
 PCR detection of instability at informative microsatellite
markers (MSI-PCR) is the chief DNA-based method in
current clinical use
 Small number (typically 5) of mononucleotide microsatellite
markers using fluorescently-labeled primers, and products
are resolved by capillary electrophoresis
 MSI is diagnosed if at least 2 of 5 markers are unstable
 Drawbacks:
 Requires matched normal tissue
 Requires subjective manual interpretation
© Copyright 2009 by the American Association for Clinical Chemistry
Question
 Can next-generation sequencing (NGS) data be used as
a clinical diagnostic for MSI?
 NGS is becoming increasingly used for molecular
oncology
 Due to prevalence of epigenetic causes of MSI, one
cannot simply look for mutations causing the phenotype
© Copyright 2009 by the American Association for Clinical Chemistry
Method (mSINGS)
1. Examine mononucleotide microsatellites incidentally captured during
pull-down enrichment of DNA

Typically intronic or intergenic DNA
2. Establish “baseline” statistics for the number of fragments observed at
each locus for a population of microsatellite stable samples


Even stable specimens will have multiple fragment lengths detectable
due to “stutter” artifact – template slippage during PCR
Calculate average and SD for number fragments at each locus
3. Sequence tumor DNA by targeted gene capture
4. Evaluate each locus compared to the baseline


Count number of different fragments
If significantly greater ( > baseline + 3 SD) than baseline number of
fragments, score locus as unstable
5. Interpret fraction of unstable loci to infer MSI status
© Copyright 2009 by the American Association for Clinical Chemistry
MSI-PCR compared to mSINGS
Figure 1. Detection of microsatellite instability by MSI-PCR and next-generation DNA sequencing.
Representative capillary electrophoresis results from MSI-PCR (left) and “virtual electropherograms” of next-generation
DNA sequencing data (right). The length (x-axis) and relative abundance (Y-axis) of variant repeats are plotted.
Columns represent individual loci and show paired data for MSI stable (top) and unstable (bottom) specimens. Different
microsatellite loci are shown for MSI-PCR and next-generation DNA sequencing, and are not directly comparable.
© Copyright 2009 by the American Association for Clinical Chemistry
Test Data
 Exome capture (44 Mbs, 30,000 genes)
 The Cancer Genome Atlas (TCGA) colorectal cancers
 2,957 loci included
 ColoSeq (1.4 Mbs, 50 whole genes with some flanking
sequence)
 Colorectal, endometrial, ovarian, breast, and prostate tumors
 146 loci included
 UW-OncoPlex (0.85 Mbs, 194 gene exons and select introns)
 Colon, endometrial, lung, breast, ovarian, melanoma, and other tumors
 15 loci included
Baseline established for each assay using data from peripheral blood DNA
(MSI negative, constitutionally)
© Copyright 2009 by the American Association for Clinical Chemistry
Results
Figure 2. Fraction of unstable microsatellite loci identified by next-generation sequencing predicts MSI
status. The fraction of unstable microsatellite loci (top) and cumulative distribution of that fraction (bottom) are
shown for A. Exome capture, B. ColoSeq, and C. UW-OncoPlex targeted gene capture designs. Results are
stratified by conventional MSI-PCR status, with the average fraction of unstable loci indicated by a solid
horizontal line. The threshold used for interpreting MSI status is indicated by a dashed line.
© Copyright 2009 by the American Association for Clinical Chemistry
Results
Table 1. Diagnostic sensitivity and specificity of NGS panels for MSI detection using
mSINGS. Results are shown for each of the three assays considered in this study, as well
as in aggregate. Only one false positive and one false negative result were encountered
(both in the ColoSeq capture design).
© Copyright 2009 by the American Association for Clinical Chemistry
Results
Figure 3. Quantitative correlation of MSI-PCR and next-generation sequencing
results. The fraction of unstable microsatellite markers as determined by next-generation
sequencing is plotted with relation to the fraction of unstable markers.
© Copyright 2009 by the American Association for Clinical Chemistry
Conclusions and possible advantages
 mSINGS can be used to detect the molecular phenotype
of MSI without special gene capture design
considerations
 mSINGS provides more comprehensive tumor
characterization from NGS data
 Compared to MSI-PCR
 Provides quantitative, digital information for identifying
unstable loci
 Statistical, rather than qualitative cutoffs for instability
 Allows far more microsatellites to be examined than
conventional MSI testing
 May eliminate the need for matched normal material
© Copyright 2009 by the American Association for Clinical Chemistry
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© Copyright 2009 by the American Association for Clinical Chemistry