Evidence map›Paper›PMID 32192534›Full record

ReviewGenome medicine2020

Tracking cancer progression: from circulating tumor cells to metastasis.

Francesc Castro-Giner, Nicola Aceto

Abstract readReview
In one paragraph

Review in Genome medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 224 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
224citing papers in PubMed, 3 pooled it
–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

224 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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164 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

2 authors.

Francesc Castro-GinerDepartment of Biomedicine, Cancer Metastasis Laboratory, University of Basel and University Hospital Basel, 4058, Basel, Switzerland.
Nicola AcetoDepartment of Biomedicine, Cancer Metastasis Laboratory, University of Basel and University Hospital Basel, 4058, Basel, Switzerland. Nicola.Aceto@unibas.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The analysis of circulating tumor cells (CTCs) is an outstanding tool to provide insights into the biology of metastatic cancers, to monitor disease progression and with potential for use in liquid biopsy-based personalized cancer treatment. These goals are ambitious, yet recent studies are already allowing a sharper understanding of the strengths, challenges, and opportunities provided by liquid biopsy approaches. For instance, through single-cell-resolution genomics and transcriptomics, it is becoming increasingly clear that CTCs are heterogeneous at multiple levels and that only a fraction of them is capable of initiating metastasis. It also appears that CTCs adopt multiple ways to enhance their metastatic potential, including homotypic clustering and heterotypic interactions with immune and stromal cells. On the clinical side, both CTC enumeration and molecular analysis may provide new means to monitor cancer progression and to take individualized treatment decisions, but their use for early cancer detection appears to be challenging compared to that of other tumor derivatives such as circulating tumor DNA. In this review, we summarize current data on CTC biology and CTC-based clinical applications that are likely to impact our understanding of the metastatic process and to influence the clinical management of patients with metastatic cancer, including new prospects that may favor the implementation of precision medicine.

Indexed as

AnimalsBiomarkers, TumorHumansLiquid BiopsyNeoplasm MetastasisNeoplasmsNeoplastic Cells, CirculatingBiomarkers, Tumor

Identifiers

PMID32192534
PMCPMC7082968

What OpenQuestion holds

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LicenceCC BY
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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.