Evidence map›Paper›PMID 30613483›Full record

ArticleTranslational cancer research2017

Repurposing DNA repair factors to eradicate tumor cells upon radiotherapy.

Souparno Bhattacharya, Aroumougame Asaithamby

Abstract read
In one paragraph

Article in Translational cancer research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

2 authors.

Souparno BhattacharyaDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Aroumougame AsaithambyDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Funding

Molecular Roles for Werner Syndrome Protein in Replication Fork MaintenanceR01AG053341 · NIA · UT SOUTHWESTERN MEDICAL CENTER · PI AROUMOUGAME, ASAITHAMBY · 2016 to 2020
$1.7M
NIA NIH HHS R01 AG053341
6 · The paper itself

Abstract

Cancer is the leading cause of death worldwide. Almost 50% of all cancer patients undergo radiation therapy (RT) during treatment, with varying success. The main goal of RT is to kill tumor cells by damaging their DNA irreversibly while sparing the surrounding normal tissue. The outcome of RT is often determined by how tumors recognize and repair their damaged DNA. A growing body of evidence suggests that tumors often show abnormal expression of DNA double-strand break (DSB) repair genes that are absent from normal cells. Defects in a specific DNA repair pathway make tumor cells overly dependent on alternative or backup pathways to repair their damaged DNA. These tumor cell-specific abnormalities in the DNA damage response (DDR) machinery can potentially be used as biomarkers for treatment outcomes or as targets for sensitization to ionizing radiation (IR). An improved understanding of genetic or epigenetic alterations in the DNA repair pathways specific to cancer cells has paved the way for new treatments that combine pharmacological exploitation of tumor-specific molecular vulnerabilities with IR. Inhibiting DNA repair pathways has the potential to greatly enhance the therapeutic ratio of RT. In this review, we will discuss DNA repair pathways in active cells and how these pathways are deregulated in tumors. We will also describe the impact of targeting cancer-specific aberrations in the DDR as a treatment strategy to improve the efficacy of RT. Finally, we will address the current roadblocks and future prospects of these approaches.

Indexed as

cancercharged particle therapy (CPT)DNA damageDNA repairIonizing radiation (IR)radiotherapysynthetic lethality

Identifiers

PMID30613483
PMCPMC6319958

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Registered trials

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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.