A Liquid Biopsy Assay for Noninvasive Identification of Lymph Node Metastases in T1 Colorectal Cancer.
Adult
Aged
Aged, 80 and over
Biomarkers, Tumor
/ blood
Colorectal Neoplasms
/ blood
Decision Support Techniques
Feasibility Studies
Female
Gene Expression Profiling
Hepatocyte Nuclear Factor 3-alpha
/ blood
Humans
Liquid Biopsy
Lymph Nodes
/ pathology
Lymphatic Metastasis
Male
Matrix Metalloproteinase 1
/ blood
Matrix Metalloproteinase 9
/ blood
MicroRNAs
/ blood
Middle Aged
Neoplasm Staging
Nomograms
Predictive Value of Tests
RNA, Messenger
/ blood
Receptors, Polymeric Immunoglobulin
/ blood
Reproducibility of Results
Retrospective Studies
Risk Assessment
Risk Factors
Transcriptome
Young Adult
Detection Biomarker
Noninvasive Assay
Risk-Stratification Model
Transcriptomic Panel
Journal
Gastroenterology
ISSN: 1528-0012
Titre abrégé: Gastroenterology
Pays: United States
ID NLM: 0374630
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
received:
04
09
2020
revised:
02
03
2021
accepted:
22
03
2021
pubmed:
6
4
2021
medline:
26
10
2021
entrez:
5
4
2021
Statut:
ppublish
Résumé
We recently reported use of tissue-based transcriptomic biomarkers (microRNA [miRNA] or messenger RNA [mRNA]) for identification of lymph node metastasis (LNM) in patients with invasive submucosal colorectal cancers (T1 CRC). In this study, we translated our tissue-based biomarkers into a blood-based liquid biopsy assay for noninvasive detection of LNM in patients with high-risk T1 CRC. We analyzed 330 specimens from patients with high-risk T1 CRC, which included 188 serum samples from 2 clinical cohorts-a training cohort (N = 46) and a validation cohort (N = 142)-and matched formalin-fixed paraffin-embedded samples (N = 142). We performed quantitative reverse-transcription polymerase chain reaction, followed by logistic regression analysis, to develop an integrated transcriptomic panel and establish a risk-stratification model combined with clinical risk factors. We used comprehensive expression profiling of a training cohort of LNM-positive and LMN-negative serum specimens to identify an optimized transcriptomic panel of 4 miRNAs (miR-181b, miR-193b, miR-195, and miR-411) and 5 mRNAs (AMT, forkhead box A1 [FOXA1], polymeric immunoglobulin receptor [PIGR], matrix metalloproteinase 1 [MMP1], and matrix metalloproteinase 9 [MMP9]), which robustly identified patients with LNM (area under the curve [AUC], 0.86; 95% confidence interval [CI], 0.72-0.94). We validated panel performance in an independent validation cohort (AUC, 0.82; 95% CI, 0.74-0.88). Our risk-stratification model was more accurate than the panel and an independent predictor for identification of LNM (AUC, 0.90; univariate: odds ratio [OR], 37.17; 95% CI, 4.48-308.35; P < .001; multivariate: OR, 17.28; 95% CI, 1.82-164.07; P = .013). The model limited potential overtreatment to only 18% of all patients, which is dramatically superior to pathologic features that are currently used (92%). A novel risk-stratification model for noninvasive identification of T1 CRC has the potential to avoid unnecessary operations for patients classified as high-risk by conventional risk-classification criteria.
Sections du résumé
BACKGROUND & AIMS
We recently reported use of tissue-based transcriptomic biomarkers (microRNA [miRNA] or messenger RNA [mRNA]) for identification of lymph node metastasis (LNM) in patients with invasive submucosal colorectal cancers (T1 CRC). In this study, we translated our tissue-based biomarkers into a blood-based liquid biopsy assay for noninvasive detection of LNM in patients with high-risk T1 CRC.
METHODS
We analyzed 330 specimens from patients with high-risk T1 CRC, which included 188 serum samples from 2 clinical cohorts-a training cohort (N = 46) and a validation cohort (N = 142)-and matched formalin-fixed paraffin-embedded samples (N = 142). We performed quantitative reverse-transcription polymerase chain reaction, followed by logistic regression analysis, to develop an integrated transcriptomic panel and establish a risk-stratification model combined with clinical risk factors.
RESULTS
We used comprehensive expression profiling of a training cohort of LNM-positive and LMN-negative serum specimens to identify an optimized transcriptomic panel of 4 miRNAs (miR-181b, miR-193b, miR-195, and miR-411) and 5 mRNAs (AMT, forkhead box A1 [FOXA1], polymeric immunoglobulin receptor [PIGR], matrix metalloproteinase 1 [MMP1], and matrix metalloproteinase 9 [MMP9]), which robustly identified patients with LNM (area under the curve [AUC], 0.86; 95% confidence interval [CI], 0.72-0.94). We validated panel performance in an independent validation cohort (AUC, 0.82; 95% CI, 0.74-0.88). Our risk-stratification model was more accurate than the panel and an independent predictor for identification of LNM (AUC, 0.90; univariate: odds ratio [OR], 37.17; 95% CI, 4.48-308.35; P < .001; multivariate: OR, 17.28; 95% CI, 1.82-164.07; P = .013). The model limited potential overtreatment to only 18% of all patients, which is dramatically superior to pathologic features that are currently used (92%).
CONCLUSIONS
A novel risk-stratification model for noninvasive identification of T1 CRC has the potential to avoid unnecessary operations for patients classified as high-risk by conventional risk-classification criteria.
Identifiants
pubmed: 33819484
pii: S0016-5085(21)00589-8
doi: 10.1053/j.gastro.2021.03.062
pmc: PMC10360659
mid: NIHMS1913336
pii:
doi:
Substances chimiques
Biomarkers, Tumor
0
FOXA1 protein, human
0
Hepatocyte Nuclear Factor 3-alpha
0
MicroRNAs
0
RNA, Messenger
0
Receptors, Polymeric Immunoglobulin
0
MMP9 protein, human
EC 3.4.24.35
Matrix Metalloproteinase 9
EC 3.4.24.35
MMP1 protein, human
EC 3.4.24.7
Matrix Metalloproteinase 1
EC 3.4.24.7
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
151-162.e1Subventions
Organisme : NCI NIH HHS
ID : R01 CA202797
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA184792
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA181572
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA072851
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA227602
Pays : United States
Commentaires et corrections
Type : CommentIn
Type : CommentIn
Informations de copyright
Copyright © 2021 AGA Institute. Published by Elsevier Inc. All rights reserved.
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