Evidence map›Paper›PMID 31222134›Full record

ArticleScientific reports2019

High-Resolution Copy Number Patterns From Clinically Relevant FFPE Material.

Anastasia Filia, Alastair Droop, Mark Harland, Helene Thygesen, Juliette Randerson-Moor, Helen Snowden, Claire Taylor, Joey Mark S Diaz, Joanna Pozniak, Jérémie Nsengimana and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 45% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
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 1 institution in 2 countries.

Anastasia FiliaSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Alastair DroopMRC Medical Bioinformatics Centre, Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom.
Mark HarlandSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Helene ThygesenSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Juliette Randerson-MoorSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.ORCID http://orcid.org/0000-0003-0303-0072
Helen SnowdenSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Claire TaylorSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Joey Mark S DiazSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.ORCID http://orcid.org/0000-0002-3670-2288
Joanna PozniakSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Jérémie NsengimanaSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Jon LayeSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Julia A Newton-BishopSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
D Timothy BishopSection of Epidemiology and Biostatistics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom. D.T.Bishop@leeds.ac.uk.ORCID http://orcid.org/0000-0002-8752-8785
University of Leeds · GB

Funding

Cancer Research UK 10589Cancer Research UK 11963Cancer Research UK 19167Cancer Research UK C8216/A11963Medical Research Council MR/L01629X/1Medical Research Council MR/LO1629XMedical Research Council MR/S00386X/2
6 · The paper itself

Abstract

Systematic tumour profiling is essential for biomarker research and clinically for assessing response to therapy. Solving the challenge of delivering informative copy number (CN) profiles from formalin-fixed paraffin embedded (FFPE) material, the only likely readily available biospecimen for most cancers, involves successful processing of small quantities of degraded DNA. To investigate the potential for analysis of such lesions, whole-genome CNVseq was applied to 300 FFPE primary tumour samples, obtained from a large-scale epidemiological study of melanoma. The quality and the discriminatory power of CNVseq was assessed. Libraries were successfully generated for 93% of blocks, with input DNA quantity being the only predictor of success (success rate dropped to 65% if <20 ng available); 3% of libraries were dropped because of low sequence alignment rates. Technical replicates showed high reproducibility. Comparison with targeted CN assessment showed consistency with the Next Generation Sequencing (NGS) analysis. We were able to detect and distinguish CN changes with a resolution of ≤10 kb. To demonstrate performance, we report the spectrum of genomic CN alterations (CNAs) detected at 9p21, the major site of CN change in melanoma. This successful analysis of CN in FFPE material using NGS provides proof of principle for intensive examination of population-based samples.

Indexed as

DNA Copy Number VariationsParaffin EmbeddingHigh-Throughput Nucleotide SequencingHumansNeoplasmsReproducibility of ResultsTissue Fixation

Identifiers

PMID31222134
PMCPMC6586881
OpenAlexW2949311892

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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.