Evidence map›Paper›PMID 36780195›Full record

ArticleClinical cancer research : an official journal of the American Association for Cancer Research2023

Cell-Free Urine and Plasma DNA Mutational Analysis Predicts Neoadjuvant Chemotherapy Response and Outcome in Patients with Muscle-Invasive Bladder Cancer.

Emil Christensen, Iver Nordentoft, Karin Birkenkamp-Demtröder, Sara K Elbæk, Sia V Lindskrog, Ann Taber, Tine G Andreasen, Trine Strandgaard, Michael Knudsen, Philippe Lamy and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
20.6field-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

47 citing papers in PubMed, 66 citations in OpenAlex.

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  4. ctDNA Dynamics and Recurrence Patterns after Organ-Sparing Trimodality Therapy for Bladder Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
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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

13 authors at 2 institutions in 1 country.

Emil Christensen *Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0001-7201-3933
Iver Nordentoft *Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0003-4856-4086
Karin Birkenkamp-DemtröderDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0001-8057-6277
Sara K ElbækDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark.ORCID 0000-0002-0135-6995
Sia V LindskrogDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0001-5280-7306
Ann TaberDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0001-8265-5315
Tine G AndreasenDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0002-9633-2987
Trine StrandgaardDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0001-8277-8155
Michael KnudsenDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0002-6294-2517
Philippe LamyDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0003-1347-7724
Mads AgerbækDepartment of Oncology, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0003-3923-6136
Jørgen B JensenDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark.ORCID 0000-0002-4347-739X
Lars DyrskjøtDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0001-7061-9851
Aarhus University · DKAarhus University Hospital · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo investigate the use of plasma and urine DNA mutation analysis for predicting neoadjuvant chemotherapy (NAC) response and oncological outcome in patients with muscle-invasive bladder cancer. EXPERIMENTAL

designWhole-exome sequencing of tumor and germline DNA was performed for 92 patients treated with NAC followed by radical cystectomy (RC). A custom NGS-panel capturing approximately 50 mutations per patient was designed and used to track mutated tumor DNA in plasma and urine. A total of 447 plasma samples, 281 urine supernatants, and 123 urine pellets collected before, during, and after treatment were analyzed. Patients were enrolled from 2013 to 2019, with a median follow-up time of 41.3 months after RC.

resultsWe identified tumor DNA before NAC in 89% of urine supernatants, 85% of urine pellets, and 43% of plasma samples. Tumor DNA levels were higher in urine supernatants and urine pellets compared with plasma samples (P < 0.001). In plasma, detection of circulating tumor DNA (ctDNA) before NAC was associated with a lower NAC response rate (P < 0.001). Detection of tumor DNA after NAC was associated with lower response rates in plasma, urine supernatant, and urine pellet (P < 0.001, P = 0.03, P = 0.002). Tumor DNA dynamics during NAC was predictive of NAC response and outcome in urine supernatant and plasma (P = 0.006 and P = 0.002). A combined measure from plasma and urine supernatant tumor DNA dynamics stratified patients by outcome (P = 0.003).

conclusionsAnalysis of tumor DNA in plasma and urine samples both separately and combined has a potential to predict treatment response and outcome.

Indexed as

Neoadjuvant TherapyUrinary Bladder NeoplasmsChemotherapy, AdjuvantCystectomyDNA Mutational AnalysisHumansMusclesNeoplasm InvasivenessRetrospective Studies

Identifiers

PMID36780195
PMCPMC10102843
OpenAlexW4320484973

What OpenQuestion holds

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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.