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Analysis of somatic microsatellite indels identifies driver events in human tumors

Nature Biotechnology volume 35, pages 951–959 (2017)Cite this article

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Abstract

Microsatellites (MSs) are tracts of variable-length repeats of short DNA motifs that exhibit high rates of mutation in the form of insertions or deletions (indels) of the repeated motif. Despite their prevalence, the contribution of somatic MS indels to cancer has been largely unexplored, owing to difficulties in detecting them in short-read sequencing data. Here we present two tools: MSMuTect, for accurate detection of somatic MS indels, and MSMutSig, for identification of genes containing MS indels at a higher frequency than expected by chance. Applying MSMuTect to whole-exome data from 6,747 human tumors representing 20 tumor types, we identified >1,000 previously undescribed MS indels in cancer genes. Additionally, we demonstrate that the number and pattern of MS indels can accurately distinguish microsatellite-stable tumors from tumors with microsatellite instability, thus potentially improving classification of clinically relevant subgroups. Finally, we identified seven MS indel driver hotspots: four in known cancer genes (ACVR2A, RNF43, JAK1, and MSH3) and three in genes not previously implicated as cancer drivers (ESRP1, PRDM2, and DOCK3).

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Figure 1: Identifying somatic indels in microsatellites (MS indels): schematic description of MSMuTect.
Figure 2: Distribution of MS indels across 6,747 tumors from 20 tumor types.
Figure 3: Differences in mutation patterns and MS indel characteristics between MSI and MSS tumors.
Figure 5: Location of ACVR2A MS indel mutations in MSI-H STAD samples.
Figure 4: Transcriptional effects of the ESRP1 p.K511fs MS indel mutation.

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Acknowledgements

We thank C. Mayer for supplying and supporting the PHOBOS tool. G.G. was partially funded by the Paul C. Zamecnick, MD, Chair in Oncology at MGH and the NIH TCGA Genome Data Analysis Center (NIH U24CA143845). Y.E.M., P. Polak, and A. Kamburov were funded by G.G.'s start-up funds at Massachusetts General Hospital. K.W.M. was partially funded by an American Society of Radiation Oncology (ASTRO) Junior Faculty Career Research Training Award and a Harvard Catalyst KL2/CMeRIT Award. F.M. gratefully acknowledges support from the Dana-Farber Cancer Institute Physical Sciences Oncology Center (NIH U54CA193461). R.K. was supported by the European Commission Seventh Framework Programme (Integra-Life; grant 315997) and the Croatian Science Foundation (grant IP-2014-09-6400).

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Authors and Affiliations

  1. Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts, USA

    Yosef E Maruvka, Prasanna Parasuraman, Atanas Kamburov, Paz Polak, Nicholas J Haradhvala, Esther Rheinbay, Lior Z Braunstein, Michael S Lawrence, Andre Bernards & Gad Getz

  2. Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Yosef E Maruvka, Atanas Kamburov, Paz Polak, Nicholas J Haradhvala, Julian M Hess, Esther Rheinbay, Yehuda Brody, Lior Z Braunstein, Alan D'Andrea, Michael S Lawrence, Adam Bass, Franziska Michor & Gad Getz

  3. Harvard Medical School, Boston, Massachusetts, USA

    Kent W Mouw, Alan D'Andrea, Franziska Michor & Gad Getz

  4. Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Kent W Mouw & Alan D'Andrea

  5. Division of Molecular Biology,Department of Biology, Bioinformatics Group, Faculty of Science, University of Zagreb, Zagreb, Croatia

    Rosa Karlic

  6. Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA

    Amnon Koren

  7. Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Adam Bass

  8. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Franziska Michor

  9. Department of Stem Cells and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA

    Franziska Michor

  10. Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA

    Gad Getz

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  1. Yosef E Maruvka
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Contributions

Y.E.M., K.W.M., F.M., and G.G. devised the research strategy. Y.E.M. and G.G. developed the tools. Y.E.M., R.K., N.J.H., and J.M.H. performed analyses. Y.E.M., K.W.M., R.K., P. Parasuraman, A. Kamburov, P. Polak, N.J.H., J.M.H., E.R., Y.B., A. Koren, L.Z.B., A.D'A., M.S.L., A.J.B., A.B., F.M., and G.G. helped interpret results. Y.E.M., K.W.M., and G.G. wrote the manuscript. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Gad Getz.

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The Broad Institute has filed a patent application regarding the analysis of somatic microsatellite indels in cancers, as reported in this publication.

Integrated supplementary information

Supplementary Figure 1 Motif size distribution.

The number of MS loci per motif size across the whole genome (red), exome (green), and in an annotated set of cancer genes from Lawrence et at1 (blue). Mono- and di-repeats represent ~99% of all MS loci.

1. Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

Supplementary Figure 2 Sequencing coverage across motifs.

The number of MS loci per length for different motifs (A, C, AC, and AG) across the exome is shown in red while the average number of MS loci covered by at least 10 reads is shown in blue. The number of MS loci covered at 10x depth decreases more rapidly than the number of MS loci, demonstrating the difficulty in achieving sufficient coverage for longer repeat lengths. Together, the motifs A, C, AC, and AG represent 98% of MS loci in the exome.

Supplementary Figure 3 Comparison of accuracy of sequence-alignment tools at MS loci.

Noise is plotted as a function of the MS repeat length for the standard alignment (using Burrows-Wheeler Aligner, BWA2) versus the MS-specific alignment (adapted from lobSTR3). Data is shown for the AG motif. Noise was defined as the fraction of reads that differ from the modal number of repeats, aggregated over all the MS loci in the X-chromosome from normal male samples (which are assumed to be homozygous at each MS locus). On average, noise is reduced by approximately a factor of 5 using the MS-specific alignment method.

2. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma. Oxf. Engl. 25, 1754–1760 (2009).

3. Gymrek, M., Golan, D., Rosset, S. & Erlich, Y. lobSTR: A short tandem repeat profiler for personal genomes. Genome Res. 22, 1154–1162 (2012).

Supplementary Figure 4 Analysis of true-positive rates.

The number of detected simulated MS indels (out of 200) across repeat lengths (shown in different colors) and allele fractions. The sensitivity to detect MS indels decreases markedly at low allele fractions.

Supplementary Figure 5 False-positive rates.

False positive rates for the A and C motifs as a function MSMuTect parameters. Heat maps show the log10 false positive rate per MS locus (i.e. the fraction of false-called MS indels among all MS loci) for the A and C motifs. The y-axis is the threshold for the different AIC scores (Tr) and the x-axis is the threshold for the Kolmogorov-Smirnov (KS) filtering step.

Supplementary Figure 6 Distribution of MS indels and SNVs across cancer.

Comparison of the fraction of MS indels (upper panel) and number of SNVs (lower panel) across 4,041 tumors from 20 tumor types. Only samples with annotated MS indels and SNVs are shown. Red horizontal lines represent the mean number of MS indels in each tumor type.

Supplementary Figure 7 The number of MS indels for different changes in the number of alleles.

The number of MS indels for STAD samples (broken to MSI-H, MSI-L and MSS) plotted for different numbers of germline and tumor alleles. MSMuTect not only detects the presence of a somatic MS indel, but also infers the actual alleles in both the germline and tumor samples. The upper row shows the number of MS indels for loci that had one allele in the germline and the lower row for two alleles in the germline. The columns represent the number of somatic MS indels alleles in the tumor (range from one to four). For example, the plot in the third column of the second row shows cases in which the germline has two alleles (ie. heterozygous sites) but the tumor sample has 3 alleles. MS indels are more common in MSI-H tumors in all settings except when the germline has two alleles but the tumor has only a single allele (bottom left corner), which reflects loss-of-heterozygosity (LOH). MSI designations (MSI-H, MSI-L, or MSS) are based on Bethesda gel classification (taken from TCGA). The y-axis scale varies across panels. The significance of the difference was calculated using one tailed t-test (ns- p>0.05, * p<0.05, ** p<10-3, *** p<10-8, **** p<10-16).

Supplementary Figure 8 Correlation between germline variability and somatic MS indel frequency.

The x-axis represents the binned fraction of non-reference alleles at each MS locus (out of the 2*N alleles in our cohort, where N is the number of covered normal samples). The somatic MS indel frequency for each MS locus is plotted as blue dots. Black dots represent the mean of each bin. The upper panel shows germline variability of A8 in the range of germline variability between 0 to 0.1 and the lower panel in the range of 0 to 1. The effect of germline variability on the somatic rate is minor for germline variability <0.1.

Supplementary Figure 9 Distribution of MS indels in A8 in noncoding regions.

The observed frequency of mutated A8 loci per given number of indels are shown as black dots whereas the expected frequency using a fit based on a Binomial distribution is represented by the red line. The x-axis represents the number of MS indels and the y-axis represents the fraction of loci that have a particular number of MS indels.

Supplementary Figure 10 STAD quantile–quantile plot.

MSMutSig QQ plot for stomach adenocarcinoma (STAD). Quantile-quantile plot of observed vs. expected P-values under the negative binomial (also called gamma-Poisson) model. Significant MS loci (q<0.1) are shown in red.

Supplementary Figure 11 COAD quantile–quantile plot.

MSMutSig QQ plot for colon adenocarcinoma (COAD). Quantile-quantile plot of observed vs. expected P-values under the negative binomial (also called gamma-Poisson) model. Significant MS loci (q<0.1) are shown in red.

Supplementary Figure 12 UCEC quantile–quantile plot.

MSMutSig QQ plot for endometrial cancer (UCEC). Quantile-quantile plot of observed vs. expected P-values under the negative binomial (also called gamma-Poisson) model. Significant MS loci (q<0.1) are shown in red.

Supplementary Figure 13 PRDM2 transcript levels in WT versus mutant PRDM2 cases.

PRDM2 transcript levels (by RNAseq) was lower in cases with a PRDM2 p.K1489fs frameshift mutation than in PRDM2 WT cases (P=0.016, two tailed Mann-Whitney test).

Supplementary Figure 14 MutSig quantile–quantile plot for endometrial cancer (UCEC).

Quantile-quantile plot of observed vs. expected P-values for MSI-H cases using only previously identified mutations (red) and using previously identified mutations and MS indels (green). Using MutSig for datasets with large numbers of MS indels leads to an inflation in the number of significantly mutated genes.

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Life Sciences Reporting Summary (PDF 128 kb)

Supplementary Tables 1–5

Supplementary Tables 1–5 (XLSX 6429 kb)

Supplementary Software 1

MSMuTect (ZIP 167867 kb)

Supplementary Software 2

MSMutSig (ZIP 1362 kb)

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Maruvka, Y., Mouw, K., Karlic, R. et al. Analysis of somatic microsatellite indels identifies driver events in human tumors. Nat Biotechnol 35, 951–959 (2017). https://doi.org/10.1038/nbt.3966

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