Evidence map›Paper›PMID 34321100›Full record

ArticleGenome medicine2021

Distinct signatures of codon and codon pair usage in 32 primary tumor types in the novel database CancerCoCoPUTs for cancer-specific codon usage.

Douglas Meyer, Jacob Kames, Haim Bar, Anton A Komar, Aikaterini Alexaki, Juan Ibla, Ryan C Hunt, Luis V Santana-Quintero, Anton Golikov, Michael DiCuccio and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

13 citing papers in PubMed, 27 citations in OpenAlex.

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  9. A statistical-physics approach for codon usage optimisation.Computational and structural biotechnology journal · 2024
    Article
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  11. Review
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  13. Pan-cancer analyses of synonymous mutations based on tissue-specific codon optimality.Computational and structural biotechnology journal · 2022
    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

11 authors at 5 institutions in 1 country.

Douglas Meyer *Hemostasis Branch, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, Center for Biologics Evaluation & Research, US Food and Drug Administration, Silver Spring, MD, USA.
Jacob Kames *Hemostasis Branch, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, Center for Biologics Evaluation & Research, US Food and Drug Administration, Silver Spring, MD, USA.
Haim BarDepartment of Statistics, University of Connecticut, Storrs, CT, USA.
Anton A KomarCenter for Gene Regulation in Health and Disease, Department of Biological, Geological and Environmental Sciences, Cleveland State University, Cleveland, OH, USA.
Aikaterini AlexakiHemostasis Branch, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, Center for Biologics Evaluation & Research, US Food and Drug Administration, Silver Spring, MD, USA.
Juan IblaDepartment of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
Ryan C HuntHemostasis Branch, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, Center for Biologics Evaluation & Research, US Food and Drug Administration, Silver Spring, MD, USA.
Luis V Santana-QuinteroHigh-performance Integrated Virtual Environment, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, 20993, USA.
Anton GolikovHigh-performance Integrated Virtual Environment, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, 20993, USA.
Michael DiCuccioNational Center of Biotechnology Information, National Institutes of Health, Bethesda, MD, USA.
Chava Kimchi-SarfatyHemostasis Branch, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, Center for Biologics Evaluation & Research, US Food and Drug Administration, Silver Spring, MD, USA. Chava.kimchi-sarfaty@fda.hhs.gov.ORCID 0000-0002-9355-8585
Center for Biologics Evaluation and Research · USBoston Children's Hospital · USCleveland State University · USNational Institutes of Health · USUniversity of Connecticut · US

Funding

Safer and more effective FIX therapeutics: impact of codon optimizationR01HL151392 · NHLBI · CLEVELAND STATE UNIVERSITY · PI KOMAR, ANTON A. · 2020 to 2023
$1.5M
NHLBI NIH HHS R01 HL151392
6 · The paper itself

Abstract

backgroundGene expression is highly variable across tissues of multi-cellular organisms, influencing the codon usage of the tissue-specific transcriptome. Cancer disrupts the gene expression pattern of healthy tissue resulting in altered codon usage preferences. The topic of codon usage changes as they relate to codon demand, and tRNA supply in cancer is of growing interest.

methodsWe analyzed transcriptome-weighted codon and codon pair usage based on The Cancer Genome Atlas (TCGA) RNA-seq data from 6427 solid tumor samples and 632 normal tissue samples. This dataset represents 32 cancer types affecting 11 distinct tissues. Our analysis focused on tissues that give rise to multiple solid tumor types and cancer types that are present in multiple tissues.

resultsWe identified distinct patterns of synonymous codon usage changes for different cancer types affecting the same tissue. For example, a substantial increase in GGT-glycine was observed in invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and mixed invasive ductal and lobular carcinoma (IDLC) of the breast. Change in synonymous codon preference favoring GGT correlated with change in synonymous codon preference against GGC in IDC and IDLC, but not in ILC. Furthermore, we examined the codon usage changes between paired healthy/tumor tissue from the same patient. Using clinical data from TCGA, we conducted a survival analysis of patients based on the degree of change between healthy and tumor-specific codon usage, revealing an association between larger changes and increased mortality. We have also created a database that contains cancer-specific codon and codon pair usage data for cancer types derived from TCGA, which represents a comprehensive tool for codon-usage-oriented cancer research.

conclusionsBased on data from TCGA, we have highlighted tumor type-specific signatures of codon and codon pair usage. Paired data revealed variable changes to codon usage patterns, which must be considered when designing personalized cancer treatments. The associated database, CancerCoCoPUTs, represents a comprehensive resource for codon and codon pair usage in cancer and is available at https://dnahive.fda.gov/review/cancercocoputs/ . These findings are important to understand the relationship between tRNA supply and codon demand in cancer states and could help guide the development of new cancer therapeutics.

Indexed as

CodonCodon UsageDatabases, GeneticBiomarkers, TumorComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticGenome-Wide Association StudyGenomicsHumansKaplan-Meier EstimateNeoplasmsPrognosisTranscriptomeBiomarkers, TumorCodonCancerCoCoPUTsCancer transcriptomeCodon pairCodon usageInvasive ductal carcinomaInvasive lobular carcinomaRelative synonymous codon usage (RSCU)Survival analysisSynonymous codonsThe Cancer Genome Atlas (TCGA)

Identifiers

PMID34321100
PMCPMC8317675
OpenAlexW3183518827

What OpenQuestion holds

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LicenceCC BY
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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.