Evidence map›Paper›PMID 32879317›Full record

ArticleNature communications2020

Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data.

Simone Zaccaria, Benjamin J Raphael

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed
5.9field-weighted citation impact, top 2% of its field
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

66 citing papers in PubMed, 135 citations in OpenAlex.

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6 more citing papers are in PubMed but not listed here.

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

2 authors at 1 institution in 1 country.

Simone ZaccariaDepartment of Computer Science, Princeton University, Princeton, NJ, 08540, USA.ORCID http://orcid.org/0000-0002-5265-7392
Benjamin J RaphaelDepartment of Computer Science, Princeton University, Princeton, NJ, 08540, USA. braphael@princeton.edu.ORCID http://orcid.org/0000-0003-1274-048X
Princeton University · US

Funding

Pathway and Network Integration of Cancer Genomics and Clinical DataU24CA211000 · NCI · PRINCETON UNIVERSITY · PI RAPHAEL, BENJAMIN, YEH, JEN JEN · 2016 to 2020
$1.7M
Computational Characterization of Genetic HeterogeneityR01HG007069 · NHGRI · PRINCETON UNIVERSITY · PI RAPHAEL, BENJAMIN · 2013 to 2015
$1.1M
NCI NIH HHS U24 CA211000NHGRI NIH HHS R01 HG007069
6 · The paper itself

Abstract

Copy-number aberrations (CNAs) and whole-genome duplications (WGDs) are frequent somatic mutations in cancer but their quantification from DNA sequencing of bulk tumor samples is challenging. Standard methods for CNA inference analyze tumor samples individually; however, DNA sequencing of multiple samples from a cancer patient has recently become more common. We introduce HATCHet (Holistic Allele-specific Tumor Copy-number Heterogeneity), an algorithm that infers allele- and clone-specific CNAs and WGDs jointly across multiple tumor samples from the same patient. We show that HATCHet outperforms current state-of-the-art methods on multi-sample DNA sequencing data that we simulate using MASCoTE (Multiple Allele-specific Simulation of Copy-number Tumor Evolution). Applying HATCHet to 84 tumor samples from 14 prostate and pancreas cancer patients, we identify subclonal CNAs and WGDs that are more plausible than previously published analyses and more consistent with somatic single-nucleotide variants (SNVs) and small indels in the same samples.

Indexed as

DNA Copy Number VariationsGene DuplicationBreast NeoplasmsDatasets as TopicExome SequencingFemaleHigh-Throughput Nucleotide SequencingHumansINDEL MutationMaleMutation RateNeoplasm MetastasisPancreatic NeoplasmsPolymorphism, Single NucleotideProstatic NeoplasmsSingle-Cell Analysis

Identifiers

PMID32879317
PMCPMC7468132
OpenAlexW3081916416

What OpenQuestion holds

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Registered trials

None linked

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.