ArticleJournal of statistical physics2018
On statistical modeling of sequencing noise in high depth data to assess tumor evolution.
Article in Journal of statistical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Evolution of clonal hematopoiesis during cancer treatment and its impact on outcomes.The Journal of clinical investigation · 2026Article
- Clonal Hematopoiesis Dynamics and Evolutionary Fitness During Cancer Treatment Impact Clinical Outcomes.medRxiv : the preprint server for health sciences · 2025Article
- Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision.ArXiv · 2024Article
- Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules.Nature methods · 2024Article
- Understanding the natural language of DNA using encoder-decoder foundation models with byte-level precision.Bioinformatics advances · 2024Article
- A BAYESIAN NONPARAMETRIC MODEL FOR INFERRING SUBCLONAL POPULATIONS FROM STRUCTURED DNA SEQUENCING DATA.The annals of applied statistics · 2021Article
- Article
- Association of JAK2-V617F Mutations Detected by Solid Tumor Sequencing With Coexistent Myeloproliferative Neoplasms.JAMA oncology · 2019Article
- MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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 emerge 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 pre-treatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.
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Registered trials
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