A Liquid Biopsy Assay for Noninvasive Identification of Lymph Node Metastases in T1 Colorectal Cancer.
A Liquid Biopsy Assay for Noninvasive Identification of Lymph Node Metastases in T1 Colorectal Cancer.
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DOI：
10.1053/j.gastro.2021.03.062
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发表时间：
2021-07
期刊：
影响因子：
29.4
通讯作者：
中科院分区：
文献类型：
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我们最近报道了使用基于组织的转录组生物标志物（miRNA或mRNA）来识别浸润性粘膜下结直肠癌（T1 CRC）患者的淋巴结转移（LNM）。在这项研究中，我们将我们基于组织的生物标志物转化为基于血液的液体活检法，用于无创检测高风险T1 CRC患者的LNM。我们分析了330例来自高危T1型结直肠癌患者的标本，其中包括188份来自两个临床队列（训练队列：n=46，验证队列：n=142）和匹配的FFPE样本（n=142）的血清样本。我们通过RT-qPCR和logistic回归分析，结合临床危险因素，建立了一个综合转录组组，并建立了风险分层模型。我们对LNM阳性和阴性血清标本进行了综合表达谱分析，确定了一个优化的4种mirna （miR-181b, miR-193b, miR-195, miR-411）和5种mrna （AMT, FOXA1， PIGR, MMP1, MMP9）的转录组学小组，该小组可以可靠地识别LNM患者（曲线下面积[AUC]=0.86, 95% CI= 0.72-0.94）。我们在一个独立的验证队列中验证了面板性能（AUC=0.82, 95% CI= 0.74-0.88）。我们的风险分层模型比面板和独立预测器更准确（AUC=0.90，单因素：优势比[OR]=37.17, 95% CI=4.48 ~ 308.35， P< 0.001；多因素：OR=17.28, 95% CI=1.82 ~ 164.07， P= 0.013）。该模型将潜在的过度治疗限制在仅18%的所有患者中，这大大优于目前使用的病理特征（92%）。一种新的无创T1 CRC风险分层模型有可能避免传统风险分类标准中高风险患者的不必要手术。本研究报告了一种新的生物标志物特征，可以区分淋巴结转移的高危T1 crc患者和未转移的患者。
We recently reported use of tissue-based transcriptomic biomarkers (miRNA or 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 two clinical cohorts (training cohort: n=46, validation cohort: n=142) and matched FFPE samples (n=142). We performed RT-qPCR 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 -negative serum specimens to identify an optimized transcriptomic panel of four miRNAs (miR-181b, miR-193b, miR-195, miR-411) and five mRNAs (AMT, FOXA1, PIGR, MMP1, MMP9), which robustly identified patients with LNM (area under the curve [AUC]=0.86, 95% 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 currently used pathological features (92%). A novel risk-stratification model for noninvasive identification of T1 CRC has the potential to avoid unnecessary surgeries for patients classified as high-risk by conventional risk-classification criteria. This study reports a novel biomarker signature that can distinguish high-risk T1 CRCs patients wi th lymph node metastasis from those who do not.
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