A Liquid Biopsy Assay for Noninvasive Identification of Lymph Node Metastases in T1 Colorectal Cancer

Gastroenterology. 2021 Jul;161(1):151-162.e1. doi: 10.1053/j.gastro.2021.03.062. Epub 2021 Apr 2.

Abstract

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.

Keywords: Detection Biomarker; Noninvasive Assay; Risk-Stratification Model; Transcriptomic Panel.

Publication types

  • Research Support, N.I.H., Extramural
  • Validation Study

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biomarkers, Tumor / blood*
  • Biomarkers, Tumor / genetics
  • Colorectal Neoplasms / blood*
  • Colorectal Neoplasms / genetics
  • Colorectal Neoplasms / pathology
  • Decision Support Techniques*
  • Feasibility Studies
  • Female
  • Gene Expression Profiling*
  • Hepatocyte Nuclear Factor 3-alpha / blood
  • Hepatocyte Nuclear Factor 3-alpha / genetics
  • Humans
  • Liquid Biopsy
  • Lymph Nodes / pathology*
  • Lymphatic Metastasis
  • Male
  • Matrix Metalloproteinase 1 / blood
  • Matrix Metalloproteinase 1 / genetics
  • Matrix Metalloproteinase 9 / blood
  • Matrix Metalloproteinase 9 / genetics
  • MicroRNAs / blood*
  • MicroRNAs / genetics
  • Middle Aged
  • Neoplasm Staging
  • Nomograms
  • Predictive Value of Tests
  • RNA, Messenger / blood*
  • RNA, Messenger / genetics
  • Receptors, Polymeric Immunoglobulin / blood
  • Receptors, Polymeric Immunoglobulin / genetics
  • Reproducibility of Results
  • Retrospective Studies
  • Risk Assessment
  • Risk Factors
  • Transcriptome*
  • Young Adult

Substances

  • Biomarkers, Tumor
  • FOXA1 protein, human
  • Hepatocyte Nuclear Factor 3-alpha
  • MicroRNAs
  • RNA, Messenger
  • Receptors, Polymeric Immunoglobulin
  • MMP9 protein, human
  • Matrix Metalloproteinase 9
  • MMP1 protein, human
  • Matrix Metalloproteinase 1