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Using somatic variant richness to mine signals from rare variants in the cancer genome

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Listed:
  • Saptarshi Chakraborty

    (Memorial Sloan Kettering Cancer Center)

  • Arshi Arora

    (Memorial Sloan Kettering Cancer Center)

  • Colin B. Begg

    (Memorial Sloan Kettering Cancer Center)

  • Ronglai Shen

    (Memorial Sloan Kettering Cancer Center)

Abstract

To date, the vast preponderance of somatic variants observed in the cancer genome have been rare variants, and it is common in practice to encounter in a new tumor variants that have not been observed previously. Here we focus on probability estimation for encountering such hitherto unseen variants. We draw upon statistical methodology that has been developed in other fields of study, notably in species estimation in ecology, and word frequency estimation in computational linguistics. Analysis of whole-exome and targeted panel sequencing data sets reveal substantial variability in variant “richness” between genes that could be harnessed for clinically relevant problems. We quantify the variant-tissue association and show a strong gene-specific, lineage-dependent pattern of encountering new variants. This variability is largely determined by the proportion of observed variants that are rare. Our findings suggest that variants that occur at very low frequencies can harbor important signals that are clinically consequential.

Suggested Citation

  • Saptarshi Chakraborty & Arshi Arora & Colin B. Begg & Ronglai Shen, 2019. "Using somatic variant richness to mine signals from rare variants in the cancer genome," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13402-z
    DOI: 10.1038/s41467-019-13402-z
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    Cited by:

    1. Saptarshi Chakraborty & Colin B. Begg & Ronglai Shen, 2021. "Using the “Hidden” genome to improve classification of cancer types," Biometrics, The International Biometric Society, vol. 77(4), pages 1445-1455, December.
    2. Irina Ostrovnaya & Audrey Mauguen & Venkatraman E. Seshan & Colin B. Begg, 2021. "Testing tumors from different anatomic sites for clonal relatedness using somatic mutation data," Biometrics, The International Biometric Society, vol. 77(1), pages 283-292, March.

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