Evidence map›Paper›PMID 29208983›Full record

ArticleScientific reports2017

Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies.

Jie Liu, John T Halloran, Jeffrey A Bilmes, Riza M Daza, Choli Lee, Elisabeth M Mahen, Donna Prunkard, Chaozhong Song, Sibel Blau, Michael O Dorschner and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Jie LiuDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0002-9504-0587
John T HalloranDepartment of Electrical Engineering, University of Washington, Seattle, WA, USA.
Jeffrey A BilmesDepartment of Electrical Engineering, University of Washington, Seattle, WA, USA.
Riza M DazaDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Choli LeeDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0002-8901-1835
Elisabeth M MahenCenter for Cancer Innovation, University of Washington, Seattle, WA, USA.
Donna PrunkardDepartment of Pathology, University of Washington, Seattle, WA, USA.
Chaozhong SongCenter for Cancer Innovation, University of Washington, Seattle, WA, USA.
Sibel BlauCenter for Cancer Innovation, University of Washington, Seattle, WA, USA.
Michael O DorschnerCenter for Cancer Innovation, University of Washington, Seattle, WA, USA.
Vijayakrishna K GadiDepartment of Medicine/Oncology, University of Washington, Seattle, WA, USA.
Jay ShendureDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
C Anthony BlauCenter for Cancer Innovation, University of Washington, Seattle, WA, USA. tblau@uw.edu.
William S NobleDepartment of Genome Sciences, University of Washington, Seattle, WA, USA. william-noble@uw.edu.ORCID 0000-0001-7283-4715

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend.

Indexed as

Models, BiologicalAlgorithmsBayes TheoremBiopsyClonal EvolutionComputational BiologyDNA Copy Number VariationsFemaleHumansMutationNeoplasmsReproducibility of ResultsSequence Analysis, DNASingle-Cell AnalysisTranscriptomeTriple Negative Breast Neoplasms

Identifiers

PMID29208983
PMCPMC5717219

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.