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On statistical modeling of sequencing noise in high depth data to assess tumor evolution

Raul Rabadan, Gyan Bhanot, Sonia Marsilio, Nicholas Chiorazzi, Laura Pasqualucci, View ORCID ProfileHossein Khiabanian
doi: https://doi.org/10.1101/128587
Raul Rabadan
1Department of Systems Biology, Columbia University, New York, NY
2Center for Topology of Cancer Evolution and Heterogeneity, Columbia University, New York, NY
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Gyan Bhanot
3Department of Physics and Astronomy, Rutgers University, Piscataway, NJ
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Sonia Marsilio
4The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY
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Nicholas Chiorazzi
4The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY
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Laura Pasqualucci
5Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ
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Hossein Khiabanian
2Center for Topology of Cancer Evolution and Heterogeneity, Columbia University, New York, NY
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  • ORCID record for Hossein Khiabanian
  • For correspondence: h.khiabanian@rutgers.edu
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Abstract

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerges from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 04, 2017.
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On statistical modeling of sequencing noise in high depth data to assess tumor evolution
Raul Rabadan, Gyan Bhanot, Sonia Marsilio, Nicholas Chiorazzi, Laura Pasqualucci, Hossein Khiabanian
bioRxiv 128587; doi: https://doi.org/10.1101/128587
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On statistical modeling of sequencing noise in high depth data to assess tumor evolution
Raul Rabadan, Gyan Bhanot, Sonia Marsilio, Nicholas Chiorazzi, Laura Pasqualucci, Hossein Khiabanian
bioRxiv 128587; doi: https://doi.org/10.1101/128587

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