Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing

Jiang, Y.; Qiu, Yu; Minn, Andy J.; Zhang, Nancy R.

doi:10.1073/pnas.1522203113

Cited by 211 publications

(245 citation statements)

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“…Canopy (Jiang, et al, 2016) identifies subclones within a tumor, determines the mutational profiles of these subclones, and infers the tumor's phylogenetic history by NGS data from temporally and/or spatially separated tumor resections from the same patient. Canopy jointly models somatic copy number changes and SNVs in a similar fashion to non-negative matrix factorization and adopts a Bayesian framework to reconstruct phylogeny with posterior confidence assessment.…”

Section: Methodsmentioning

confidence: 99%

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Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny

Jiang

Chen

Zhou

et al. 2017

Preprint

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Summary: Copy number variation is an important and abundant source of variation in the human genome, which has been associated with a number of diseases, especially cancer. Massively parallel next-generation sequencing allows copy number profiling with fine resolution. Such efforts, however, have met with mixed successes, with setbacks arising partly from the lack of reliable analytical methods to meet the diverse and unique challenges arising from the myriad experimental designs and study goals in genetic studies. In cancer genomics, detection of somatic copy number changes and profiling of allele-specific copy number (ASCN) are complicated by experimental biases and artifacts as well as normal cell contamination and cancer subclone admixture. Furthermore, careful statistical modeling is warranted to reconstruct tumor phylogeny by both somatic ASCN changes and single nucleotide variants. Here we describe a flexible computational pipeline, MARATHON, which integrates multiple related statistical software for copy number profiling and downstream analyses in disease genetic studies.

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Section: Methodsmentioning

confidence: 99%

“…In addition to somatic ASCN changes, single nucleotide variants (SNVs) also provide valuable information for the reconstruction of the tumor phylogeny. We show that Canopy (Jiang, et al, 2016) for tracking of longitudinal and spatial clonal evolution can be applied to…”

Section: Introductionmentioning

confidence: 99%

Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny

Jiang

Chen

Zhou

et al. 2017

Preprint

Self Cite

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“…The majority of tools focus on somatic point mutations, and either restrict the analysis to copy neutral regions of the genome or make the assumption that a single mutation will be present at the same copy number state in every cancer cell (Carter et al, 2012;Miller et al, 2014;Roth et al, 2014). While tools to infer copy number heterogeneity have also been developed (Ha et al, 2014;Shen and Seshan, 2016), more recent tools seek to combine copy number and mutational data (Fischer et al, 2014;Jiang et al, 2016) and furthermore, attempt to infer evolutionary relationships between subclonal populations (Deshwar et al, 2015;. Relatedly, orthogonal tools to dissect heterogeneity by using data from single-cell sequencing have also been developed (Roth et al, 2016).…”

Section: Heterogeneity Can Reveal a Tumor's Life Historymentioning

confidence: 99%

Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future

McGranahan

Swanton

2017

Cell

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1,636

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Intratumor heterogeneity, which fosters tumor evolution, is a key challenge in cancer medicine. Here we review data and technologies that have revealed intra-tumor heterogeneity across cancer types, and the dynamics, constraints and contingencies inherent to tumor evolution. We emphasize the importance of macro-evolutionary leaps, often involving large-scale chromosomal alterations, in driving tumor evolution and metastasis, and consider the role of the tumor microenvironment in engendering heterogeneity and drug-resistance. We suggest that bold approaches to drug development, harnessing the adaptive properties of the immunemicroenvironment whilst limiting those of the tumor, combined with advances in clinical trial-design, will improve patient outcome.

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“…http://dx.doi.org/10.1101/196915 doi: bioRxiv preprint first posted online Oct. Xu et al, 2015) . Whereas the tissue-type contamination will contain similar cell types in each tumor, the molecular alterations in tumors may be unique to each tumor (Jiang et al, 2016) . Assumptions about the evolutionary mechanisms of the accumulation of molecular alterations can be encoded in the factorization to model the resulting heterogeneity of these clones (Xie et al, 2017;Xu et al, 2015) .…”

Section: Cc-by 40 International License Peer-reviewed) Is the Authormentioning

confidence: 99%

Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

Stein-O’Brien¹,

Arora

Culhane

et al. 2017

Preprint

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SummaryHigh-dimensional data is currently standard for biological inquiry. Biological systems are comprised of interrelated gene regulatory mechanisms, gene-gene interactions, and cellular interactions. These interactions induce low-dimensional structure within the high-dimensional data. Matrix factorization, also known as compressed sensing, learns low-dimensional mathematical representations from high-dimensional data. These factorization techniques can embed assumptions about pleiotropy, epistasis, inter-relationships between complex traits, and context-dependent interactions. They have been applied to uncover new biological knowledge in a breadth of topics ranging from pathway discovery to time course analysis. These techniques have been applied to data from diverse high-throughput omics technologies, including bulk and single-cell data. There are numerous computational techniques within the class of matrix factorization, each of which provides a unique interpretation of the processes in high-dimensional data. We review the visualization and applications of matrix factorization to systems-level analyses, which are diverse and require standardization to enable biological interpretation. Codifying the techniques to decipher biologically relevant features with matrix factorization enables their broad application to discovery beyond the limits of current biological knowledge-answering questions from high-dimensional data that we have not yet thought to ask.

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Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing

Cited by 211 publications

References 45 publications

Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny

Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny

Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future

Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

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