Evidence map›Paper›PMID 36849599›Full record

ReviewNature protocols2023

cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA.

Shuo Li, Ran Hu, Colin Small, Ting-Yu Kang, Chun-Chi Liu, Xianghong Jasmine Zhou, Wenyuan Li

Erratum issuedOpen access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

4 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Shuo LiDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-1960-6016
Ran HuDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-0563-8957
Colin SmallInstitute for Quantitative & Computational Biosciences, University of California at Los Angeles, Los Angeles, CA, USA.
Ting-Yu KangEarlyDiagnostics Inc., Los Angeles, CA, USA.
Chun-Chi LiuDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA.
Xianghong Jasmine ZhouDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA. XJZhou@mednet.ucla.edu.
Wenyuan LiDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA. WenyuanLi@mednet.ucla.edu.ORCID http://orcid.org/0000-0002-5029-8525
QB3 · USUniversity of California, Los Angeles · USVPDiagnostics (United States) · US

Funding

The UCLA Center in Early Detection of Liver CancerU01CA230705 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Vatche Agopian, Samuel Wheeler French · 2018 to 2026
$6.6M
Detecting and locating cancer for patients with CT-detected lung nodulesR01CA264864 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ZHOU, XIANGHONG JASMINE · 2022 to 2025
$3.2M
Novel Computation Methods for the Analysis of Cell-Free DNA Sequence DataR01CA246329 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M., LI, WENYUAN · 2019 to 2022
$2.2M
Informatics resources for liquid biopsy researchU01CA237711 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, WENYUAN · 2019 to 2021
$1.1M
NCI NIH HHS R01 CA246329NCI NIH HHS R01 CA264864NCI NIH HHS U01 CA230705NCI NIH HHS U01 CA237711
6 · The paper itself

Abstract

Cell-free DNA (cfDNA) in blood, viewed as a surrogate for tumor biopsy, has many clinical applications, including diagnosing cancer, guiding cancer treatment and monitoring treatment response. All these applications depend on an indispensable, yet underdeveloped task: detecting somatic mutations from cfDNA. The task is challenging because of the low tumor fraction in cfDNA. Recently, we developed the computational method cfSNV, the first method that comprehensively considers the properties of cfDNA for the sensitive detection of mutations from cfDNA. cfSNV vastly outperformed the conventional methods that were developed primarily for calling mutations from solid tumor tissues. cfSNV can accurately detect mutations in cfDNA even with medium-coverage (e.g., ≥200×) sequencing, which makes whole-exome sequencing (WES) of cfDNA a viable option for various clinical utilities. Here, we present a user-friendly cfSNV package that exhibits fast computation and convenient user options. We also built a Docker image of it, which is designed to enable researchers and clinicians with a limited computational background to easily carry out analyses on both high-performance computing platforms and local computers. Mutation calling from a standard preprocessed WES dataset (~250× and ~70 million base pair target size) can be carried out in 3 h on a server with eight virtual CPUs and 32 GB of random access memory.

Indexed as

Cell-Free Nucleic AcidsNeoplasmsHigh-Throughput Nucleotide SequencingHumansMutationSoftwareCell-Free Nucleic Acids

Identifiers

PMID36849599
PMCPMC10411976
OpenAlexW4322495060

What OpenQuestion holds

Textmetadata
LicenceTDM
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.