Evidence map›Paper›PMID 29554888›Full record

ArticleBMC biotechnology2018

Cell-based reference samples designed with specific differences in microRNA biomarkers.

P Scott Pine, Steven P Lund, Sanford A Stass, Debra Kukuruga, Feng Jiang, Lynn Sorbara, Sudhir Srivastava, Marc Salit

Open access · goldAbstract read
In one paragraph

Article in BMC biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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

8 authors at 3 institutions in 1 country.

P Scott PineJoint Initiative for Metrology in Biology, National Institute of Standards and Technology, 443 Via Ortega, Stanford, CA, 94305, USA. p.scott.pine@nist.gov.
Steven P LundStatistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.
Sanford A StassDepartment of Pathology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Debra KukurugaDepartment of Pathology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Feng JiangDepartment of Pathology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Lynn SorbaraDivision of Cancer Prevention, National Cancer Institute, Rockville, MD, 20850, USA.
Sudhir SrivastavaDivision of Cancer Prevention, National Cancer Institute, Rockville, MD, 20850, USA.
Marc SalitJoint Initiative for Metrology in Biology, National Institute of Standards and Technology, 443 Via Ortega, Stanford, CA, 94305, USA.
National Institute of Standards and Technology · USUniversity of Maryland, Baltimore · USNational Cancer Institute · US

Funding

University of Maryland Biomarker Reference LaboratoryU24CA115091 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI STASS, SANFORD A · 2005 to 2021
$7.1M
Division of Cancer Prevention, National Cancer Institute 2U24CA115091-06NCI NIH HHS U24 CA115091
6 · The paper itself

Abstract

backgroundWe demonstrate the feasibility of creating a pair of reference samples to be used as surrogates for clinical samples measured in either a research or clinical laboratory setting. The reference sample paradigm presented and evaluated here is designed to assess the capability of a measurement process to detect true differences between two biological samples. Cell-based reference samples can be created with a biomarker signature pattern designed in silico. Clinical laboratories working in regulated applications are required to participate in proficiency testing programs; research laboratories doing discovery typically do not. These reference samples can be used in proficiency tests or as process controls that allow a laboratory to evaluate and optimize its measurement systems, monitor performance over time (process drift), assess changes in protocols, reagents, and/or personnel, maintain standard operating procedures, and most importantly, provide evidence for quality results.

resultsThe biomarkers of interest in this study are microRNAs (miRNAs), small non-coding RNAs involved in the regulation of gene expression. Multiple lung cancer associated cell lines were determined by reverse transcription (RT)-PCR to have sufficiently different miRNA profiles to serve as components in mixture designs as reference samples. In silico models based on the component profiles were used to predict miRNA abundance ratios between two different cell line mixtures, providing target values for profiles obtained from in vitro mixtures. Two reference sample types were tested: total RNA mixed after extraction from cell lines, and intact cells mixed prior to RNA extraction. MicroRNA profiling of a pair of samples composed of extracted RNA derived from these cell lines successfully replicated the target values. Mixtures of intact cells from these lines also approximated the target values, demonstrating potential utility as mimics for clinical specimens. Both designs demonstrated their utility as reference samples for inter- or intra-laboratory testing.

conclusionsCell-based reference samples can be created for performance assessment of a measurement process from biomolecule extraction through quantitation. Although this study focused on miRNA profiling with RT-PCR using cell lines associated with lung cancer, the paradigm demonstrated here should be extendable to genome-scale platforms and other biomolecular endpoints.

Indexed as

Analysis of VarianceBiomarkers, TumorCell Line, TumorClinical Laboratory TechniquesGene ExpressionHumansMicroRNAsReference StandardsReproducibility of ResultsReverse Transcriptase Polymerase Chain ReactionRNA, Small UntranslatedBiomarkers, TumorMicroRNAsRNA, Small UntranslatedBiomarkersMeasurement assurancemicroRNA (miRNA)Performance assessmentReference samplesReverse transcription PCR (RT-PCR)

Identifiers

PMID29554888
PMCPMC5859499
OpenAlexW2793328998

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.