Evidence map›Paper›PMID 31744086›Full record

ArticleInternational journal of molecular sciences2019

Defining Signatures of Arm-Wise Copy Number Change and Their Associated Drivers in Kidney Cancers.

Graeme Benstead-Hume, Sarah K Wooller, Jessica A Downs, Frances M G Pearl

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.4field-weighted citation impact, top 44% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

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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.

Graeme Benstead-HumeBioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QG, UK.
Sarah K WoollerBioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QG, UK.
Jessica A DownsDivision of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Road, London SW3 6JB, UK.
Frances M G PearlBioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QG, UK.
University of Sussex · GBInstitute of Cancer Research · GB

Funding

Cancer Research UK 25715Medical Research Council MR/N50189X/1
6 · The paper itself

Abstract

Using pan-cancer data from The Cancer Genome Atlas (TCGA), we investigated how patterns in copy number alterations in cancer cells vary both by tissue type and as a function of genetic alteration. We find that patterns in both chromosomal ploidy and individual arm copy number are dependent on tumour type. We highlight for example, the significant losses in chromosome arm 3p and the gain of ploidy in 5q in kidney clear cell renal cell carcinoma tissue samples. We find that specific gene mutations are associated with genome-wide copy number changes. Using signatures derived from non-negative factorisation, we also find gene mutations that are associated with particular patterns of ploidy change. Finally, utilising a set of machine learning classifiers, we successfully predicted the presence of mutated genes in a sample using arm-wise copy number patterns as features. This demonstrates that mutations in specific genes are correlated and may lead to specific patterns of ploidy loss and gain across chromosome arms. Using these same classifiers, we highlight which arms are most predictive of commonly mutated genes in kidney renal clear cell carcinoma (KIRC).

Indexed as

Area Under CurveCarcinoma, Renal CellChromosomesDNA Copy Number VariationsHumansKidney NeoplasmsMachine LearningMutationPloidiesROC CurveTumor Suppressor Protein p53Von Hippel-Lindau Tumor Suppressor ProteinTP53 protein, humanTumor Suppressor Protein p53VHL protein, humanVon Hippel-Lindau Tumor Suppressor Proteinaneuploidycopy numbermachine learningmutational signaturenon-negative matrix factorisation

Identifiers

PMID31744086
PMCPMC6887958
OpenAlexW2989336090

What OpenQuestion holds

Textmetadata
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.