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Abstract Background Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor’s clonal composition. Results To overcome these challenges, we formulate the identification of clones inmore »
Abstract Motivation We propose Meltos, a novel computational framework to address the challenging problem of building tumor phylogeny trees using somatic structural variants (SVs) among multiple samples. Meltos leverages the tumor phylogeny tree built on somatic single nucleotide variants (SNVs) to identify high confidence SVs and produce a comprehensive tumor lineage tree, using a novel optimization formulation. While we do not assume the evolutionary progression of SVs is necessarily the same as SNVs, we show that a tumor phylogeny tree using high-quality somatic SNVs can act as a guide for calling and assigning somatic SVs on a tree. Meltos utilizesmore »
Abstract Motivation Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivationsmore »
Despite widespread interest in next-generation sequencing (NGS), the adoption of personalized clinical genomics and mutation profiling of cancer specimens is lagging, in part because of technical limitations. Tumors are genetically heterogeneous and often contain normal/stromal cells, features that lead to low-abundance somatic mutations that generate ambiguous results or reside below NGS detection limits, thus hindering the clinical sensitivity/specificity standards of mutation calling. We applied COLD-PCR (coamplification at lower denaturation temperature PCR), a PCR methodology that selectively enriches variants, to improve the detection of unknown mutations before NGS-based amplicon resequencing.
We used both COLD-PCR and conventional PCR (for comparison) tomore »
In agreement with recent findings, sequencing errors by conventional targeted-amplicon approaches dictated a mutation-detection limit of approximately 1%–2%. Conversely, COLD-PCR amplicons enriched mutations above the error-related noise, enabling reliable identification of mutation abundances of approximately 0.04%. Sequencing depth was not a large factor in the identification of COLD-PCR–enriched mutations. For the clinical samples, several missense mutations were not called with conventional amplicons, yet they were clearly detectable with COLD-PCR amplicons. Tumor heterogeneity for the TP53 gene was apparent.
As cancer care shifts toward personalized intervention based on each patient's unique genetic abnormalities and tumor genome, we anticipate that COLD-PCR combined with NGS will elucidate the role of mutations in tumor progression, enabling NGS-based analysis of diverse clinical specimens within clinical practice.
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