Evidence map›Paper›PMID 34680298›Full record

ReviewCancers2021

New Advances in Liquid Biopsy Technologies for Anaplastic Lymphoma Kinase (ALK)-Positive Cancer.

Matteo Villa, Geeta G Sharma, Chiara Manfroni, Diego Cortinovis, Luca Mologni

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

6 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
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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

5 authors at 3 institutions in 2 countries.

Matteo VillaDepartment of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0002-6534-642X
Geeta G SharmaDepartment of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0003-4144-2799
Chiara ManfroniDepartment of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.
Diego CortinovisDepartment of Oncology, San Gerardo Hospital, 20900 Monza, Italy.ORCID 0000-0001-7611-7369
Luca MologniDepartment of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.
University of Milano-Bicocca · ITAzienda Ospedaliera San Gerardo · ITCity Of Hope National Medical Center · US

Funding

Italian Association for Cancer Research IG-24828
6 · The paper itself

Abstract

Cancer cells are characterized by high genetic instability, that favors tumor relapse. The identification of the genetic causes of relapse can direct next-line therapeutic choices. As tumor tissue rebiopsy at disease progression is not always feasible, noninvasive alternative methods are being explored. Liquid biopsy is emerging as a non-invasive, easy and repeatable tool to identify specific molecular alterations and monitor disease response during treatment. The dynamic follow-up provided by this analysis can provide useful predictive information and allow prompt therapeutic actions, tailored to the genetic profile of the recurring disease, several months before radiographic relapse. Oncogenic fusion genes are particularly suited for this type of analysis. Anaplastic Lymphoma Kinase (ALK) is the dominant driver oncogene in several tumors, including Anaplastic Large-Cell Lymphoma (ALCL), Non-Small Cell Lung Cancer (NSCLC) and others. Here we review recent findings in liquid biopsy technologies, including ctDNA, CTCs, exosomes, and other markers that can be investigated from plasma samples, in ALK-positive cancers.

Indexed as

ALKliquid biopsylung cancer

Identifiers

PMID34680298
PMCPMC8534237
OpenAlexW3205265917

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.