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A molecular portrait of microsatellite instability across multiple cancers

Author

Listed:
  • Isidro Cortes-Ciriano

    (Harvard Medical School
    Ludwig Center at Harvard)

  • Sejoon Lee

    (Samsung Genome Institute, Samsung Medical Center)

  • Woong-Yang Park

    (Samsung Genome Institute, Samsung Medical Center)

  • Tae-Min Kim

    (College of Medicine, The Catholic University of Korea)

  • Peter J. Park

    (Harvard Medical School
    Ludwig Center at Harvard)

Abstract

Microsatellite instability (MSI) refers to the hypermutability of short repetitive sequences in the genome caused by impaired DNA mismatch repair. Although MSI has been studied for decades, large amounts of sequencing data now available allows us to examine the molecular fingerprints of MSI in greater detail. Here, we analyse ∼8,000 exomes and ∼1,000 whole genomes of cancer patients across 23 cancer types. Our analysis reveals that the frequency of MSI events is highly variable within and across tumour types. We also identify genes in DNA repair and oncogenic pathways recurrently subject to MSI and uncover non-coding loci that frequently display MSI. Finally, we propose a highly accurate exome-based predictive model for the MSI phenotype. These results advance our understanding of the genomic drivers and consequences of MSI, and our comprehensive catalogue of tumour-type-specific MSI loci will enable panel-based MSI testing to identify patients who are likely to benefit from immunotherapy.

Suggested Citation

  • Isidro Cortes-Ciriano & Sejoon Lee & Woong-Yang Park & Tae-Min Kim & Peter J. Park, 2017. "A molecular portrait of microsatellite instability across multiple cancers," Nature Communications, Nature, vol. 8(1), pages 1-12, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15180
    DOI: 10.1038/ncomms15180
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    Cited by:

    1. Xiaojing Wang & Anne-Marie Langevin & Peter J. Houghton & Siyuan Zheng, 2022. "Genomic disparities between cancers in adolescent and young adults and in older adults," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    2. Bruna Calsina & Elena Piñeiro-Yáñez & Ángel M. Martínez-Montes & Eduardo Caleiras & Ángel Fernández-Sanromán & María Monteagudo & Rafael Torres-Pérez & Coral Fustero-Torre & Marta Pulgarín-Alfaro & Ed, 2023. "Genomic and immune landscape Of metastatic pheochromocytoma and paraganglioma," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    3. Luan Nguyen & Arne Hoeck & Edwin Cuppen, 2022. "Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Zaoqu Liu & Long Liu & Siyuan Weng & Chunguang Guo & Qin Dang & Hui Xu & Libo Wang & Taoyuan Lu & Yuyuan Zhang & Zhenqiang Sun & Xinwei Han, 2022. "Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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