Evidence map›Paper›PMID 29504914›Full record

ArticleBMC medical genomics2018

Targeted sequencing reveals complex, phenotype-correlated genotypes in cystic fibrosis.

Maxim Ivanov, Alina Matsvay, Olga Glazova, Stanislav Krasovskiy, Mariya Usacheva, Elena Amelina, Aleksandr Chernyak, Mikhail Ivanov, Sergey Musienko, Timofey Prodanov and 3 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.8field-weighted citation impact, top 6% 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

18 citing papers in PubMed, 33 citations in OpenAlex.

  1. Article
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  4. Article
  5. Modulator Therapy in Cystic Fibrosis Patients withJournal of personalized medicine · 2022
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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

13 authors at 4 institutions in 2 countries.

Maxim IvanovMoscow Institute of Physics and Technology, Department of Biological and Medical Physics, Dolgoprudny, Moscow Region, Russian Federation, 141700. maksim.v.ivanov@phystech.edu.
Alina MatsvayMoscow Institute of Physics and Technology, Department of Biological and Medical Physics, Dolgoprudny, Moscow Region, Russian Federation, 141700.
Olga GlazovaMoscow Institute of Physics and Technology, Department of Biological and Medical Physics, Dolgoprudny, Moscow Region, Russian Federation, 141700.
Stanislav KrasovskiyFederal Pulmonology Research Institute, Moscow, Russian Federation, 105077.
Mariya UsachevaFederal Pulmonology Research Institute, Moscow, Russian Federation, 105077.
Elena AmelinaFederal Pulmonology Research Institute, Moscow, Russian Federation, 105077.
Aleksandr ChernyakFederal Pulmonology Research Institute, Moscow, Russian Federation, 105077.
Mikhail IvanovMoscow Institute of Physics and Technology, Department of Biological and Medical Physics, Dolgoprudny, Moscow Region, Russian Federation, 141700.
Sergey MusienkoAtlas Biomed Group, Moscow, Russian Federation, 121069.
Timofey ProdanovDepartment of Mathematics and Information Technology, St. Petersburg Academic University, St. Petersburg, Russian Federation, 195251.
Sergey KovalenkoThe Institute of Molecular Biology and Biophysics, Novosibirsk, Russian Federation, 630117.
Ancha BaranovaMoscow Institute of Physics and Technology, Department of Biological and Medical Physics, Dolgoprudny, Moscow Region, Russian Federation, 141700.
Kamil KhafizovMoscow Institute of Physics and Technology, Department of Biological and Medical Physics, Dolgoprudny, Moscow Region, Russian Federation, 141700.
Moscow Institute of Physics and Technology · RUInstitute of Molecular Biology and Biophysics · RUResearch Centre for Medical Genetics · RUSaint Petersburg Academic University · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCystic fibrosis (CF) is one of the most common life-threatening genetic disorders. Around 2000 variants in the CFTR gene have been identified, with some proportion known to be pathogenic and 300 disease-causing mutations have been characterized in detail by CFTR2 database, which complicates its analysis with conventional methods.

methodsWe conducted next-generation sequencing (NGS) in a cohort of 89 adult patients negative for p.Phe508del homozygosity. Complete clinical and demographic information were available for 84 patients.

resultsBy combining MLPA with NGS, we identified disease-causing alleles in all the CF patients. Importantly, in 10% of cases, standard bioinformatics pipelines were inefficient in identifying causative mutations. Class IV-V mutations were observed in 38 (45%) cases, predominantly ones with pancreatic sufficient CF disease; rest of the patients had Class I-III mutations. Diabetes was seen only in patients homozygous for class I-III mutations. We found that 12% of the patients were heterozygous for more than two pathogenic CFTR mutations. Two patients were observed with p.[Arg1070Gln, Ser466*] complex allele which was associated with milder pulmonary obstructions (FVC 107 and 109% versus 67%, CI 95%: 63-72%; FEV 90 and 111% versus 47%, CI 95%: 37-48%). For the first time p.[Phe508del, Leu467Phe] complex allele was reported, observed in four patients (5%).

conclusionNGS can be a more information-gaining technology compared to standard methods. Combined with its equivalent diagnostic performance, it can therefore be implemented in the clinical practice, although careful validation is still required.

Indexed as

Genetic Association StudiesMutationAdultBiomarkersCohort StudiesCystic FibrosisCystic Fibrosis Transmembrane Conductance RegulatorFemaleHigh-Throughput Nucleotide SequencingHumansMaleMiddle AgedYoung AdultBiomarkersCFTR protein, humanCystic Fibrosis Transmembrane Conductance RegulatorCFTR cystic fibrosisNGS next generation sequencing

Identifiers

PMID29504914
PMCPMC5836842
OpenAlexW2785839667

What OpenQuestion holds

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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.