Evidence map›Paper›PMID 35075163›Full record

ArticleScientific reports2022

Impact of hemolysis on multi-OMIC pancreatic biomarker discovery to derisk biomarker development in precision medicine studies.

Richard Searfoss, Punit Shah, Kennedy Ofori-Mensa, Valerie Bussberg, Vladimir Tolstikov, Bennett Greenwood, Hongyan Li, Kris Richardson, Gregory M Miller, Corinne DeCicco and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

17 authors.

Richard SearfossBERG, Framingham, MA, 01701, USA.
Punit ShahBERG, Framingham, MA, 01701, USA.
Kennedy Ofori-MensaBERG, Framingham, MA, 01701, USA.
Valerie BussbergBERG, Framingham, MA, 01701, USA.
Vladimir TolstikovBERG, Framingham, MA, 01701, USA.
Bennett GreenwoodBERG, Framingham, MA, 01701, USA.
Hongyan LiBERG, Framingham, MA, 01701, USA.
Kris RichardsonBERG, Framingham, MA, 01701, USA.
Gregory M MillerBERG, Framingham, MA, 01701, USA.
Corinne DeCiccoBeth Israel Deaconess, Harvard Medical School, Boston, MA, 02215, USA.
Elder GrangerBERG, Framingham, MA, 01701, USA.
Leonardo O RodriguesBERG, Framingham, MA, 01701, USA.
Eric M GrundBERG, Framingham, MA, 01701, USA.
A James MoserBeth Israel Deaconess, Harvard Medical School, Boston, MA, 02215, USA.
Rangaprasad SarangarajanBERG, Framingham, MA, 01701, USA.
Niven R NarainBERG, Framingham, MA, 01701, USA.
Michael A KiebishBERG, Framingham, MA, 01701, USA. michael.kiebish@berghealth.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer biomarker discovery is critically dependent on the integrity of biofluid and tissue samples acquired from study participants. Multi-omic profiling of candidate protein, lipid, and metabolite biomarkers is confounded by timing and fasting status of sample collection, participant demographics and treatment exposures of the study population. Contamination by hemoglobin, whether caused by hemolysis during sample preparation or underlying red cell fragility, contributes 0-10 g/L of extraneous protein to plasma, serum, and Buffy coat samples and may interfere with biomarker detection and validation. We analyzed 617 plasma, 701 serum, and 657 buffy coat samples from a 7-year longitudinal multi-omic biomarker discovery program evaluating 400+ participants with or at risk for pancreatic cancer, known as Project Survival. Hemolysis was undetectable in 93.1% of plasma and 95.0% of serum samples, whereas only 37.1% of buffy coat samples were free of contamination by hemoglobin. Regression analysis of multi-omic data demonstrated a statistically significant correlation between hemoglobin concentration and the resulting pattern of analyte detection and concentration. Although hemolysis had the greatest impact on identification and quantitation of the proteome, distinct differentials in metabolomics and lipidomics were also observed and correlated with severity. We conclude that quality control is vital to accurate detection of informative molecular differentials using OMIC technologies and that caution must be exercised to minimize the impact of hemolysis as a factor driving false discovery in large cancer biomarker studies.

Indexed as

HemolysisBiomarkersCase-Control StudiesFemaleHumansLipidomicsMaleMass SpectrometryPancreatic NeoplasmsPancreatitisPrecision MedicineProteomicsBiomarkers

Identifiers

PMID35075163
PMCPMC8786830

What OpenQuestion holds

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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.