A Liquid Biopsy Assay for Noninvasive Identification of Lymph Node Metastases in T1 Colorectal Cancer
Yuma Wada1, Mitsuo Shimada2, Tatsuro Murano3
1Center for Gastrointestinal Research, Baylor Scott & White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, Texas; Department of Surgery, Tokushima University, Tokushima, Japan; Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope Comprehensive Cancer Center, Duarte, California.
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.
