Evidence map›Paper›PMID 32282230›Full record

ArticleJCO clinical cancer informatics2020

Reliable Analysis of Clinical Tumor-Only Whole-Exome Sequencing Data.

Sehyun Oh, Ludwig Geistlinger, Marcel Ramos, Martin Morgan, Levi Waldron, Markus Riester

Abstract read
In one paragraph

Article in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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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

6 authors.

Sehyun OhGraduate School of Public Health and Health Policy, City University of New York, New York, NY.
Ludwig GeistlingerGraduate School of Public Health and Health Policy, City University of New York, New York, NY.
Marcel RamosGraduate School of Public Health and Health Policy, City University of New York, New York, NY.
Martin MorganRoswell Park Cancer Institute, Buffalo, NY.
Levi WaldronGraduate School of Public Health and Health Policy, City University of New York, New York, NY.
Markus RiesterNovartis Institutes for BioMedical Research, Cambridge, MA.

Funding

Implementing the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)U24HG010263 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI Enis Afgan, VINCENT JAMES CAREY · 2018 to 2026
$23.8M
Project-002U41HG004059 · NHGRI · ROSWELL PARK CANCER INSTITUTE CORP · PI MORGAN, MARTIN T · 2011 to 2020
$13.2M
Cancer Genomics:Integrative and Scalable Solutions in R / BioconductorU24CA180996 · NCI · ROSWELL PARK CANCER INSTITUTE CORP · PI MORGAN, MARTIN T, WALDRON, LEVI · 2014 to 2023
$6.9M
NCI NIH HHS U24 CA180996NHGRI NIH HHS U24 HG010263NHGRI NIH HHS U41 HG004059
6 · The paper itself

Abstract

purposeAllele-specific copy number alteration (CNA) analysis is essential to study the functional impact of single-nucleotide variants (SNVs) and the process of tumorigenesis. However, controversy over whether it can be performed with sufficient accuracy in data without matched normal profiles and a lack of open-source implementations have limited its application in clinical research and diagnosis.

methodsWe benchmark allele-specific CNA analysis performance of whole-exome sequencing (WES) data against gold standard whole-genome SNP6 microarray data and against WES data sets with matched normal samples. We provide a workflow based on the open-source PureCN R/Bioconductor package in conjunction with widely used variant-calling and copy number segmentation algorithms for allele-specific CNA analysis from WES without matched normals. This workflow further classifies SNVs by somatic status and then uses this information to infer somatic mutational signatures and tumor mutational burden (TMB).

resultsApplication of our workflow to tumor-only WES data produces tumor purity and ploidy estimates that are highly concordant with estimates from SNP6 microarray data and matched normal WES data. The presence of cancer type-specific somatic mutational signatures was inferred with high accuracy. We also demonstrate high concordance of TMB between our tumor-only workflow and matched normal pipelines.

conclusionThe proposed workflow provides, to our knowledge, the only open-source option with demonstrated high accuracy for comprehensive allele-specific CNA analysis and SNV classification of tumor-only WES. An implementation of the workflow is available on the Terra Cloud platform of the Broad Institute (Cambridge, MA).

Indexed as

AlgorithmsDNA Copy Number VariationsExomeMutationBiomarkers, TumorExome SequencingGene Expression Regulation, NeoplasticHigh-Throughput Nucleotide SequencingHumansNeoplasmsBiomarkers, Tumor

Identifiers

PMID32282230
PMCPMC7259876

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.