Multi-Faceted Attributes of Salivary Cell-free DNA as Liquid Biopsy Biomarkers for Gastric Cancer Detection.

Cell-free DNA Fragmentomics Liquid Biopsy Salivary Cell-free DNA

Journal

Research square
Titre abrégé: Res Sq
Pays: United States
ID NLM: 101768035

Informations de publication

Date de publication:
14 Jul 2023
Historique:
pubmed: 28 7 2023
medline: 28 7 2023
entrez: 28 7 2023
Statut: epublish

Résumé

Recent advances in circulating cell-free DNA (cfDNA) analysis from biofluids have opened new avenues for liquid biopsy (LB). However, current cfDNA LB assays are limited by the availability of existing information on established genotypes associated with tumor tissues. Certain cancers present with a limited list of established mutated cfDNA biomarkers, and thus, nonmutated cfDNA characteristics along with alternative biofluids are needed to broaden the available cfDNA targets for cancer detection. Saliva is an intriguing and accessible biofluid that has yet to be fully explored for its clinical utility for cancer detection. In this report, we employed a low-coverage single stranded (ss) library NGS pipeline "Broad-Range cell-free DNA-Seq" (BRcfDNA-Seq) using saliva to comprehensively investigate the characteristics of salivary cfDNA (ScfDNA). The identification of cfDNA features has been made possible by applying novel cfDNA processing techniques that permit the incorporation of ultrashort, ss, and jagged DNA fragments. As a proof of concept using 10 gastric cancer (GC) and 10 noncancer samples, we examined whether ScfDNA characteristics, including fragmentomics, end motif profiles, microbial contribution, and human chromosomal mapping, could differentiate between these two groups. Individual and integrative analysis of these ScfDNA features demonstrated significant differences between the two cohorts, suggesting that disease state may affect the ScfDNA population by altering nuclear cleavage or the profile of contributory organism cfDNA to total ScfDNA. We report that principal component analysis integration of several aspects of salivary cell-free DNA fragmentomic profiles, genomic element profiles, end-motif sequence patterns, and distinct oral microbiome populations can differentiate the two populations with a p value of < 0.0001 (PC1). These novel features of ScfDNA characteristics could be clinically useful for improving saliva-based LB detection and the eventual monitoring of local or systemic diseases.

Sections du résumé

Background UNASSIGNED
Recent advances in circulating cell-free DNA (cfDNA) analysis from biofluids have opened new avenues for liquid biopsy (LB). However, current cfDNA LB assays are limited by the availability of existing information on established genotypes associated with tumor tissues. Certain cancers present with a limited list of established mutated cfDNA biomarkers, and thus, nonmutated cfDNA characteristics along with alternative biofluids are needed to broaden the available cfDNA targets for cancer detection. Saliva is an intriguing and accessible biofluid that has yet to be fully explored for its clinical utility for cancer detection.
Methods UNASSIGNED
In this report, we employed a low-coverage single stranded (ss) library NGS pipeline "Broad-Range cell-free DNA-Seq" (BRcfDNA-Seq) using saliva to comprehensively investigate the characteristics of salivary cfDNA (ScfDNA). The identification of cfDNA features has been made possible by applying novel cfDNA processing techniques that permit the incorporation of ultrashort, ss, and jagged DNA fragments. As a proof of concept using 10 gastric cancer (GC) and 10 noncancer samples, we examined whether ScfDNA characteristics, including fragmentomics, end motif profiles, microbial contribution, and human chromosomal mapping, could differentiate between these two groups.
Results UNASSIGNED
Individual and integrative analysis of these ScfDNA features demonstrated significant differences between the two cohorts, suggesting that disease state may affect the ScfDNA population by altering nuclear cleavage or the profile of contributory organism cfDNA to total ScfDNA. We report that principal component analysis integration of several aspects of salivary cell-free DNA fragmentomic profiles, genomic element profiles, end-motif sequence patterns, and distinct oral microbiome populations can differentiate the two populations with a p value of < 0.0001 (PC1).
Conclusion UNASSIGNED
These novel features of ScfDNA characteristics could be clinically useful for improving saliva-based LB detection and the eventual monitoring of local or systemic diseases.

Identifiants

pubmed: 37503289
doi: 10.21203/rs.3.rs-3154388/v1
pmc: PMC10371094
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIDCR NIH HHS
ID : R90 DE031531
Pays : United States
Organisme : NCI NIH HHS
ID : R21 CA239052
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA233370
Pays : United States
Organisme : NCI NIH HHS
ID : UH3 CA206126
Pays : United States
Organisme : NIDCR NIH HHS
ID : UH2 DE032208
Pays : United States

Commentaires et corrections

Type : UpdateIn

Déclaration de conflit d'intérêts

Competing interests Dr. David Wong is a consultant to GSK, Mars-Wrigley, and Colgate Palmolive and has equity in RNAmeTRIX and Liquid Diagnostics LLC. Dr. Liying Zhang reports that family members hold leadership positions and ownership interests in Decipher Medicine.

Auteurs

Neeti Swarup (N)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

Jordan Cheng (J)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

Irene Choi (I)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

You Jeong Heo (YJ)

The Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, Republic of Korea.

Misagh Kordi (M)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

Feng Li (F)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

Mohammad Aziz (M)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

David Chia (D)

Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

Fang Wei (F)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

David Elashoff (D)

Department of Medicine, Biostatistics and Computational Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA.

Liying Zhang (L)

Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

Sung Kim (S)

Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06355, South Korea.

Yong Kim (Y)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

David T W Wong (DTW)

School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

Classifications MeSH