Evidence map›Paper›PMID 35708207›Full record

ArticleThoracic cancer2022

The clinical utility of cfRNA for disease detection and surveillance: A proof of concept study in non-small cell lung cancer.

Martin Metzenmacher, Balazs Hegedüs, Jan Forster, Alexander Schramm, Peter A Horn, Christoph A Klein, Nicola Bielefeld, Till Ploenes, Clemens Aigner, Jens T Siveke and 2 more

Abstract read
In one paragraph

Article in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
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

12 authors.

Martin MetzenmacherDepartment of Medical Oncology, West German Cancer Center, University Hospital Essen, Essen, Germany.ORCID 0000-0001-5395-9010
Balazs HegedüsDepartment of Thoracic Surgery, West German Cancer Center, University Medicine Essen Ruhrlandklinik, University Duisburg-Essen, Essen, Germany.ORCID 0000-0002-4341-4153
Jan ForsterGerman Cancer Consortium (DKTK), Partner site University Hospital Essen, Essen, Germany.
Alexander SchrammLaboratory for Molecular Oncology, Department of Medical Oncology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Peter A HornInstitute for Transfusion Medicine, University Hospital Essen, Essen, Germany.
Christoph A KleinExperimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany.
Nicola BielefeldGerman Cancer Consortium (DKTK), Partner site University Hospital Essen, Essen, Germany.
Till PloenesDepartment of Thoracic Surgery, West German Cancer Center, University Medicine Essen Ruhrlandklinik, University Duisburg-Essen, Essen, Germany.
Clemens AignerDepartment of Thoracic Surgery, West German Cancer Center, University Medicine Essen Ruhrlandklinik, University Duisburg-Essen, Essen, Germany.
Jens T SivekeGerman Cancer Consortium (DKTK), Partner site University Hospital Essen, Essen, Germany.
Martin SchulerDepartment of Medical Oncology, West German Cancer Center, University Hospital Essen, Essen, Germany.
Smiths S LueongGerman Cancer Consortium (DKTK), Partner site University Hospital Essen, Essen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCT scans are used in routine clinical practice for the diagnosis and treatment surveillance of non-small cell lung cancer (NSCLC). However, more sensitive methods are desirable. Liquid biopsy analyses of RNA and DNA can offer more sensitive diagnostic approaches. Cell-free RNA (cfRNA) has been described in several malignancies, but its clinical utility has not previously been explored.

methodsWe evaluated the clinical utility of cfRNA for early detection and surveillance of tumor disease in a proof-of-concept study. Using real-time-droplet digital polymerase chain reaction we characterized a candidate transcript (MORF4L2) in plasma samples from 41 advanced stage, 38 early stage NSCLC and 39 healthy samples. We compared its diagnostic performance with tumor markers and evaluated its utility for disease monitoring.

resultsMORF4L2 cfRNA was more abundant in patients than in healthy donors (p < 0.0001). Using the Youden index approach (cutoff value of 537 copies/ml was established) with a sensitivity of 0.73 (95% CI: 0.61-0.82) and a specificity of 0.87 (95% CI: 0.73-0.96). Positive and negative predictive values of 0.92 (95% CI: 0.83-0.95) and 0.59 (95% CI: 0.47-0.83) were achieved. Combination of cfRNA and Cyfra21-1 improved its predictive value from 89.5% to 94.7%. Low baseline MORF4L2 levels were associated with better overall survival (HR:0.25, 95% CI: 0.09-0.7, p = 0.009) and progression-free survival for patients treated with tyrosine kinase inhibitors (p = 0.011) and chemotherapy (p = 0.019). MORF4L2 profile between baseline and follow-up mirrored radiological response and tumor dynamics better than tumor markers. cfRNA transcripts allowed monitoring tumor dynamics in patients without tumor-reported genetic alterations.

conclusionOur data support clinical utility of cfRNA for detection and surveillance of NSCLC. Further studies with larger cohorts are required.

Indexed as

Carcinoma, Non-Small-Cell LungCell-Free Nucleic AcidsLung NeoplasmsAntigens, NeoplasmBiomarkers, TumorErbB ReceptorsHumansKeratin-19MutationProof of Concept StudyTranscription Factorsantigen CYFRA21.1Antigens, NeoplasmBiomarkers, TumorCell-Free Nucleic AcidsErbB ReceptorsKeratin-19MORF4L2 protein, humanTranscription FactorscfRNAddPCRliquid biopsyNGSNSCLC

Identifiers

PMID35708207
PMCPMC9346179

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.