Evidence map›Paper›PMID 37292608›Full record

ArticlebioRxiv : the preprint server for biology2023

Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.

Sasha Taluri, Vishal H Oza, Tabea M Soelter, Jennifer L Fisher, Brittany N Lasseigne

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Sasha TaluriDepartment of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Vishal H OzaDepartment of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Tabea M SoelterDepartment of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Jennifer L FisherDepartment of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Brittany N LasseigneDepartment of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
University of Alabama at Birmingham · US

Funding

Integrating multidimensional genomic data to discover clinically-relevant predictive models-Alzheimer's SupplementR00HG009678 · NHGRI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI LASSEIGNE, BRITTANY NICOLE · 2019 to 2021
$961k
NHGRI NIH HHS R00 HG009678
6 · The paper itself

Abstract

Background: Cancer is a complex disease that is the second leading cause of death in the United States. Despite research efforts, the ability to manage cancer and select optimal therapeutic responses for each patient remains elusive. Chromosomal instability (CIN) is primarily a product of segregation errors wherein one or many chromosomes, in part or whole, vary in number. CIN is an enabling characteristic of cancer, contributes to tumor-cell heterogeneity, and plays a crucial role in the multistep tumorigenesis process, especially in tumor growth and initiation and in response to treatment. Aims: Multiple studies have reported different metrics for analyzing copy number aberrations as surrogates of CIN from DNA copy number variation data. However, these metrics differ in how they are calculated with respect to the type of variation, the magnitude of change, and the inclusion of breakpoints. Here we compared metrics capturing CIN as either numerical aberrations, structural aberrations, or a combination of the two across 33 cancer data sets from The Cancer Genome Atlas (TCGA). Methods and results: Using CIN inferred by methods in the CINmetrics R package, we evaluated how six copy number CIN surrogates compared across TCGA cohorts by assessing each across tumor types, as well as how they associate with tumor stage, metastasis, and nodal involvement, and with respect to patient sex. Conclusions: We found that the tumor type impacts how well any two given CIN metrics correlate. While we also identified overlap between metrics regarding their association with clinical characteristics and patient sex, there was not complete agreement between metrics. We identified several cases where only one CIN metric was significantly associated with a clinical characteristic or patient sex for a given tumor type. Therefore, caution should be used when describing CIN based on a given metric or comparing it to other studies.

Identifiers

PMID37292608
PMCPMC10245901
OpenAlexW4377965823

What OpenQuestion holds

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LicenceCC BY-NC
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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.