Evidence map›Paper›PMID 29254223›Full record

ArticleOncotarget2017

Development and validation of a targeted next generation DNA sequencing panel outperforming whole exome sequencing for the identification of clinically relevant genetic variants.

Eirwen M Miller, Nicole E Patterson, Jenna Marcus Zechmeister, Michal Bejerano-Sagie, Maria Delio, Kunjan Patel, Nivedita Ravi, Wilber Quispe-Tintaya, Alexander Maslov, Nichelle Simmons and 6 more

Abstract read
In one paragraph

Article in Oncotarget, 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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  15. Genetic Variants Associated with FDNY WTC-Related Sarcoidosis.International journal of environmental research and public health · 2019
    Article
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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

16 authors.

Eirwen M MillerDepartment of Obstetrics & Gynecology and Women's Health, Division of Gynecologic Oncology, Montefiore Medical Center, Bronx, NY 10461, USA.
Nicole E PattersonDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Jenna Marcus ZechmeisterDepartment of Obstetrics & Gynecology and Women's Health, Division of Gynecologic Oncology, Montefiore Medical Center, Bronx, NY 10461, USA.
Michal Bejerano-SagieDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Maria DelioDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Kunjan PatelDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Nivedita RaviDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Wilber Quispe-TintayaDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Alexander MaslovDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Nichelle SimmonsDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Maria CastaldiDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Jan VijgDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Rouzan G KarabakhtsianDepartment of Pathology, Montefiore Medical Center, Bronx, NY 10461, USA.
John M GreallyDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Dennis Y S KuoDepartment of Obstetrics & Gynecology and Women's Health, Division of Gynecologic Oncology, Montefiore Medical Center, Bronx, NY 10461, USA.
Cristina MontagnaDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.

Funding

WORD PROCESSORP30CA013330 · NCI · YESHIVA UNIVERSITY · PI STEVEN C. ALMO · 1985 to 2026
$111.2M
Translational Research CoreP30DK020541 · NIDDK · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI STREAMSON C CHUA · 2015 to 2026
$27.7M
NCI NIH HHS P30 CA013330NIDDK NIH HHS P30 DK020541
6 · The paper itself

Abstract

Next generation sequencing (NGS) technologies have revolutionized our approach to genomic research. The use of whole genome sequencing (WGS), whole exome sequencing (WES), transcriptome profiling, and targeted DNA sequencing has exponentially improved our understanding of the human genome and the genetic complexities underlying malignancy. Yet, WGS and WES clinical applications remain limited due to high costs and the large volume of data generated. When utilized to address biological questions in basic science studies, targeted sequencing panels have proven extremely valuable due to reduced costs and higher sequencing depth. However, the routine application of targeted sequencing to the clinical setting is limited to a few cancer subtypes. Some highly aggressive tumor types, like type 2 endometrial cancer (EC), could greatly benefit from routine genomic analysis using targeted sequencing. To explore the potential utility of a mid size panel (~150 genes) in the clinical setting, we developed and validated a custom panel against WGS, WES, and another commercially available targeted panel. Our results indicate that a mid size custom designed panel is as efficient as WGS and WES in mapping variants of biological and clinical relevance, rendering higher coverage, at a lower cost, with fewer variants of uncertain significance. Because of the much higher sequencing depth that could be achieved, our results demonstrate that targeted sequencing outperformed WGS and WES in the mapping of pathogenic variants in a breast cancer case, as well as a case of mixed serous and high-grade endometrioid EC, the most aggressive EC subtype.

Indexed as

endometrial carcinomanext generation sequencingtarget sequencingtumor recurrence

Identifiers

PMID29254223
PMCPMC5731933

What OpenQuestion holds

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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.