Evidence map›Paper›PMID 35102279›Full record

ReviewNature biomedical engineering2022

Limitations and opportunities of technologies for the analysis of cell-free DNA in cancer diagnostics.

Ping Song, Lucia Ruojia Wu, Yan Helen Yan, Jinny X Zhang, Tianqing Chu, Lawrence N Kwong, Abhijit A Patel, David Yu Zhang

Open access · greenAbstract readReview
In one paragraph

Review in Nature biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
176citing papers in PubMed, 5 pooled it
28.7field-weighted citation impact, top 1% 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

176 citing papers in PubMed, 5 syntheses or guidelines pooled it, 305 citations in OpenAlex.

  1. Pooled it
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  5. Pooled it
  6. Single-cell epigenomics of colorectal cancer.Experimental & molecular medicine · 2026
    Review
  7. Article
  8. Article
  9. Applications of circulating tumor DNA in unresectable and advanced non-small cell lung cancer immunotherapy: from stratification to therapeutic guidance.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  10. Review
  11. Article
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  13. Review
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  15. Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  16. Article
  17. Article
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  20. Article

116 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

8 authors at 5 institutions in 2 countries.

Ping Song *Department of Bioengineering, Rice University, Houston, TX, USA.
Lucia Ruojia Wu *Department of Bioengineering, Rice University, Houston, TX, USA.ORCID http://orcid.org/0000-0001-5051-9685
Yan Helen YanNuProbe USA, Houston, TX, USA.
Jinny X ZhangNuProbe USA, Houston, TX, USA.
Tianqing ChuDepartment of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Lawrence N KwongDepartment of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA.
Abhijit A PatelDepartment of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA.ORCID http://orcid.org/0000-0003-3943-5610
David Yu ZhangNuProbe USA, Houston, TX, USA. genomic.dave@gmail.com.ORCID http://orcid.org/0000-0002-0213-7663
Rice University · USGenomic Health (United States) · USShanghai Jiao Tong University · CNThe University of Texas MD Anderson Cancer Center · USYale University · US

Funding

Transcriptional mechanisms and melanomaP01CA163222 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI Kai W Wucherpfennig · 2013 to 2026
$25.3M
Lung Cancer Screening via Ultrasensitive and Cost-efficient Analysis of Tumor DNA Signatures in BloodU01CA233364 · NCI · YALE UNIVERSITY · PI PATEL, ABHIJIT, SKATES, STEVEN J · 2018 to 2022
$3.4M
Highly multiplexed and mutation-sensitive quantitative PCR for cancer diagnosticsR01CA203964 · NCI · RICE UNIVERSITY · PI ZHANG, DAVID YU · 2016 to 2020
$3.0M
Circulating DNA as a Marker of Treatment Efficacy and Failure in Lung CancerR01CA197486 · NCI · YALE UNIVERSITY · PI PATEL, ABHIJIT · 2016 to 2020
$1.9M
NCI NIH HHS P01 CA163222NCI NIH HHS R01 CA197486NCI NIH HHS R01 CA203964NCI NIH HHS U01 CA233364
6 · The paper itself

Abstract

Cell-free DNA (cfDNA) in the circulating blood plasma of patients with cancer contains tumour-derived DNA sequences that can serve as biomarkers for guiding therapy, for the monitoring of drug resistance, and for the early detection of cancers. However, the analysis of cfDNA for clinical diagnostic applications remains challenging because of the low concentrations of cfDNA, and because cfDNA is fragmented into short lengths and is susceptible to chemical damage. Barcodes of unique molecular identifiers have been implemented to overcome the intrinsic errors of next-generation sequencing, which is the prevailing method for highly multiplexed cfDNA analysis. However, a number of methodological and pre-analytical factors limit the clinical sensitivity of the cfDNA-based detection of cancers from liquid biopsies. In this Review, we describe the state-of-the-art technologies for cfDNA analysis, with emphasis on multiplexing strategies, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve cancer diagnostics and patient care.

Indexed as

Cell-Free Nucleic AcidsNeoplasmsBiomarkersHigh-Throughput Nucleotide SequencingHumansLiquid BiopsyBiomarkersCell-Free Nucleic Acids

Identifiers

PMID35102279
PMCPMC9336539
OpenAlexW4221030128

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.