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Accurate and Flexible Bayesian Mutation Call from Multi-regional Tumor Samples

  • Takuya Moriyama
  • Seiya Imoto
  • Satoru Miyano
  • Rui YamaguchiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11826)

Abstract

We propose a Bayesian method termed MultiMuC for accurate detection of somatic mutations (mutation call) from multi-regional tumor sequence data sets. To improve detection performance, our method is based on the assumption of mutation sharing: if we can predict at least one tumor region has the mutation, then we can be more confident to detect a mutation in more tumor regions by lowering the original threshold of detection. We find two drawbacks in existing methods for leveraging the assumption of mutation sharing. First, existing methods do not consider the probability of the “No-TP (True Positive)” case: we could expect mutation candidates in multiple regions, but actually, no true mutations exist. Second, existing methods cannot leverage scores from other state-of-the-art mutation calling methods for a single-regional tumor. We overcome the first drawback through evaluation of the probability of the No-TP case. Next, we solve the second drawback by the idea of Bayes-factor-based model construction that enables flexible integration of probability-based mutation call scores as building blocks of a Bayesian statistical model. We empirically evaluate that our method steadily improves results from mutation calling methods for a single-regional tumor, e.g., Strelka2 and NeuSomatic, and outperforms existing methods for multi-regional tumors through a real-data-based simulation study. Our implementation of MultiMuC is available at https://github.com/takumorizo/MultiMuC.

Notes

Acknowledgments

We used the supercomputers at Human Genome Center, the Institute of Medical Science, the University of Tokyo. This work has been supported by the Grant-in-Aid for JSPS Research Fellow (17J08884) and MEXT/JSPS KAKENHI Grant (15H05912, hp180198, hp170227, 18H03329, hp190158).

References

  1. 1.
    Koboldt, D.C., et al.: VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22(3), 568–576 (2012)CrossRefGoogle Scholar
  2. 2.
    Saunders, C.T., et al.: Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28(14), 1811–1817 (2012)CrossRefGoogle Scholar
  3. 3.
    Cibulskis, K., et al.: Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31(3), 213–219 (2013)CrossRefGoogle Scholar
  4. 4.
    Shiraishi, Y., et al.: An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data. Nucleic Acid Res. 41(7), e89 (2013)CrossRefGoogle Scholar
  5. 5.
    Usuyama, N., et al.: HapMuC: somatic mutation calling using heterozygous germ line variants near candidate mutations. Bioinformatics 30(23), 3302–3309 (2014)CrossRefGoogle Scholar
  6. 6.
    Kim, S., et al.: Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15(8), 591–594 (2018)CrossRefGoogle Scholar
  7. 7.
    Moriyama, T., et al.: A Bayesian model integration for mutation calling through data partitioning. Bioinformatics, btz233 (2019). https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz233/5423180
  8. 8.
    Sahraeian, S.M.E., et al.: Deep convolutional neural networks for accurate somatic mutation detection. Nat. Commun. 10(1), 1041 (2019)CrossRefGoogle Scholar
  9. 9.
    Poplin, R., et al.: A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36(10), 983–987 (2018)CrossRefGoogle Scholar
  10. 10.
    Reiter, J.G., et al.: Reconstructing metastatic seeding patterns of human cancers. Nature Commun. 8, 14114 (2017)CrossRefGoogle Scholar
  11. 11.
    Dorri, F., et al.: Somatic mutation detection and classification through probabilistic integration of clonal population information. Commun. Biol. 2(1), 44 (2019)CrossRefGoogle Scholar
  12. 12.
    van Rens, K.E., et al.: SNV-PPILP: refined SNV calling for tumor data using perfect phylogenies and ILP. Bioinformatics 31(7), 1133–1135 (2015)CrossRefGoogle Scholar
  13. 13.
    Salari, R., et al.: Inference of tumor phylogenies with improved somatic mutation discovery. J. Comput. Biol. 20(11), 933–944 (2013)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Josephidou, M., et al.: multiSNV: a probabilistic approach for improving detection of somatic point mutations from multiple related tumour samples. Nuclic Acids Res. 43(9), e61 (2015)CrossRefGoogle Scholar
  15. 15.
    Detering, H., et al.: Accuracy of somatic variant detection in multiregional tumor sequencing data. bioRxiv 655605 (2019)Google Scholar
  16. 16.
    Kass, R.E., et al.: Bayes factors. J. Am. Stat. Assoc. 90(430), 773–795 (1995)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Neal, R.M.: Probabilistic inference using Markov Chain Monte Carlo methods. Technical report, Department of Computer Science, University of Toronto (1993)Google Scholar
  18. 18.
    Koboldt, D.C., et al.: VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25(17), 2283–2285 (2009)CrossRefGoogle Scholar
  19. 19.
    Wilm, A., et al.: LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nuclic Acids Res. 40(22), 11189–11201 (2012)CrossRefGoogle Scholar
  20. 20.
    Narzisi, G., et al.: Genome-wide somatic variant calling using localized colored de Bruijn graphs. Commun. Biol. 1(1), 20 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Takuya Moriyama
    • 1
  • Seiya Imoto
    • 2
  • Satoru Miyano
    • 1
    • 2
  • Rui Yamaguchi
    • 1
    • 3
    • 4
    Email author
  1. 1.Human Genome Center, The Institute of Medical ScienceThe University of TokyoTokyoJapan
  2. 2.Health Intelligence Center, The Institute of Medical ScienceThe University of TokyoTokyoJapan
  3. 3.Division of Cancer Systems BiologyAichi Cancer Center Research InstituteNagoyaJapan
  4. 4.Department of Cancer InformaticsNagoya University Graduate School of MedicineNagoyaJapan

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