Evidence map›Paper›PMID 31555592›Full record

ReviewFrontiers in oncology2019

Structural Variants as a Basis for Targeted Therapies in Hematological Malignancies.

Judith Schütte, Julia Reusch, Cyrus Khandanpour, Christine Eisfeld

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2019. 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
0.5field-weighted citation impact, top 32% 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, 15 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 2 institutions in 1 country.

Judith SchütteDepartment of Medicine A, Hematology, Oncology and Pneumology, University Hospital Münster, Münster, Germany.
Julia ReuschMedizinische Fakultät, Universität Münster, Münster, Germany.
Cyrus KhandanpourMedizinische Fakultät, Universität Münster, Münster, Germany.
Christine EisfeldMedizinische Fakultät, Universität Münster, Münster, Germany.
University of Münster · DEUniversity Hospital Münster · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural variants (SV) are changes in the genomic landscape that can alter gene expression levels and thus lead to disease development. The most common and best studied SVs in hematological malignancies are chromosomal translocations. Here, parts of two genes that are normally on different chromosomes come into close proximity due to a failure in DNA repair. As a consequence, fusion proteins which show a different function and/or cellular localization compared to the two original proteins are expressed, sometimes even at different levels. The identification of chromosomal translocations is often used to identify the specific disease a patient is suffering from. In addition, SVs such as deletions, duplications, inversions and single nucleotide polymorphisms (SNPs) can occur in hematopoietic cells and lead to their malignant transformations. Changes in the 3D genome structure have also recently been shown to impact disease development. In this review, we describe a variety of SVs occurring in different subtypes of hematological malignancies. Currently, most therapeutic approaches target fusion proteins which are the cellular product of chromosomal translocations. However, amplifications and SNPs also play a role in disease progression and can be targeted. We present some examples for different types of structural variants and how they are currently treated.

Indexed as

chromatin structurechromosomal rearrangementshematologystructural variant (SV)targeted therapy

Identifiers

PMID31555592
PMCPMC6722867
OpenAlexW2968680829

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.