A MicroRNA Signature Identifies Pancreatic Ductal Adenocarcinoma Patients at Risk for Lymph Node Metastases
A MicroRNA Signature Identifies Pancreatic Ductal Adenocarcinoma Patients at Risk for Lymph Node Metastases
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DOI：
10.1053/j.gastro.2020.04.057
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发表时间：
2020-08-01
期刊：
影响因子：
29.4
通讯作者：
Goel, Ajay
中科院分区：
文献类型：
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作者：
Nishiwada, Satoshi;Sho, Masayuki;Goel, Ajay
背景与目的：胰腺导管腺癌（PDAC）经常转移到淋巴结;需要策略来识别淋巴结转移风险最高的患者。我们对手术或内镜超声引导细针抽吸（EUS-FNA）过程中收集的PDAC标本进行了全基因组表达谱分析，以确定与淋巴结转移相关的microRNA（miRNA）特征。方法：为了发现生物标志物，我们分析了来自3个公开数据集（癌症基因组图谱，GSE 24279和GSE 32688）的原发性胰腺肿瘤的miRNA表达谱。然后，我们分析了2001年至2017年从日本收集的157份PDAC标本（83份来自淋巴结转移患者，74份无淋巴结转移患者）用于训练队列，并分析了2002年至2016年从日本不同医疗中心收集的107份PDAC标本（63份来自淋巴结转移患者，44份无淋巴结转移患者）用于验证队列。我们还分析了术前通过EUS-FNA从47例患者（22例淋巴结转移患者和25例无淋巴结转移患者; 17例训练队列和30例验证队列）和62例接受新辅助化疗患者（9例淋巴结转移患者和53例无淋巴结转移患者）的任何治疗前标本进行额外验证。采用多变量logistic回归分析评估有转移与无转移患者之间miRNA表达的统计学差异。结果：我们鉴定了与PDAC淋巴结转移诊断相关的miRNA表达模式。使用逻辑回归分析，我们优化并训练了训练队列的6-miRNA风险预测模型;该模型以0.84的曲线下面积（AUC）（95%置信区间[CI]，0.77-0.89）区分有淋巴结转移的患者与无淋巴结转移的患者。在验证队列中，该模型确定了有与无淋巴结转移的患者，AUC为0.73（95% CI，0.64-0.81）。在EUS-FNA活检样本中，该模型确定了有与无淋巴结转移的患者，AUC为0.78（95% CI，0.63-0.89）。miRNA表达模式是EUS-FNA队列（优势比，17.05; 95%CI，2.43-119.57）和新辅助治疗前EUS-FNA队列（95%CI，0.65-0.87）中PDAC淋巴结转移的独立预测因子。结论：使用来自4个独立队列的数据和肿瘤样本，我们确定了一个miRNA标签，该标签可识别有PDAC转移至淋巴结风险的患者。该特征在分析切除的肿瘤标本和EUS-FNA活检标本中具有相似的准确度水平。该模型可用于选择PDAC患者的治疗和管理策略。
BACKGROUND & AIMS: Pancreatic ductal adenocarcinomas (PDACs) frequently metastasize to the lymph nodes; strategies are needed to identify patients at highest risk for lymph node metastases. We performed genome-wide expression profile analyses of PDAC specimens, collected during surgery or endoscopic ultrasound-guided fine-need aspiration (EUS-FNA), to identify a microRNA (miRNA) signature associated with metastasis to lymph nodes. METHODS: For biomarker discovery, we analyzed miRNA expression profiles of primary pancreatic tumors from 3 public data sets (The Cancer Genome Atlas, GSE24279, and GSE32688). We then analyzed 157 PDAC specimens (83 from patients with lymph node metastases and 74 without) from Japan, collected from 2001 through 2017, for the training cohort and 107 PDAC specimens (63 from patients with lymph node metastases and 44 without) from a different medical center in Japan, from 2002 through 2016, for the validation cohort. We also analyzed samples collected by EUS-FNA before surgery from 47 patients (22 patients with lymph node metastases and 25 without; 17 for the training cohort and 30 from the validation cohort) and 62 specimens before any treatment from patients who received neoadjuvant chemotherapy (9 patients with lymph node metastasis and 53 without) for additional validation. Multivariate logistic regression analyses were used to evaluate the statistical differences in miRNA expression between patients with vs without metastases. RESULTS: We identified an miRNA expression pattern associated with diagnosis of PDAC metastasis to lymph nodes. Using logistic regression analysis, we optimized and trained a 6-miRNA risk prediction model for the training cohort; this model discriminated patients with vs without lymph node metastases with an area under the curve (AUC) of 0.84 (95% confidence interval [CI], 0.77-0.89). In the validation cohort, the model identified patients with vs without lymph node metastases with an AUC of 0.73 (95% CI, 0.64-0.81). In EUS-FNA biopsy samples, the model identified patients with vs without lymph node metastases with an AUC of 0.78 (95% CI, 0.63-0.89). The miRNA expression pattern was an independent predictor of PDAC metastasis to lymph nodes in the EUS-FNA cohort (odds ratio, 17.05; 95% CI, 2.43-119.57) and in the preneoadjuvant therapy EUS-FNA cohort (95% CI, 0.65-0.87). CONCLUSIONS: Using data and tumor samples from 4 independent cohorts, we identified an miRNA signature that identifies patients at risk for PDAC metastasis to lymph nodes. The signature has similar levels of accuracy in the analysis of resected tumor specimens and EUS-FNA biopsy specimens. This model might be used to select treatment and management strategies for patients with PDAC.