改善的代谢生物标志物特征用于区分胰腺导管腺癌和慢性胰腺炎的独立验证和分析标准化。

Independent Validation and Assay Standardization of Improved Metabolic Biomarker Signature to Differentiate Pancreatic Ductal Adenocarcinoma From Chronic Pancreatitis.

机构信息

Department of Medicine II, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany; Bavarian Centre for Cancer Research (Bayerisches Zentrum für Krebsforschung), Munich, Germany.

Department of General and Visceral Surgery, Asklepios Klinikum Hamburg, Hamburg, Germany.

出版信息

Gastroenterology. 2022 Nov;163(5):1407-1422. doi: 10.1053/j.gastro.2022.07.047. Epub 2022 Jul 21.

DOI:10.1053/j.gastro.2022.07.047

PMID:35870514

Abstract

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature.

METHODS

We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography-tandem mass spectrometry platform. A machine learning-aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance.

RESULTS

The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively.

CONCLUSIONS

The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation.

摘要

背景与目的

胰腺导管腺癌（PDAC）是一种高度致命的恶性肿瘤，当原发肿瘤仍可切除时，需要进行有效的检测。我们之前开发了包含 9 种分析物和血清碳水化合物抗原 19-9（CA19-9）的 MxPancreasScore，其准确率达到 90.6%。然而，需要 5 种不同的分析平台和多次分析运行，这阻碍了其临床应用。因此，我们旨在开发一种更简单的单分析运行、单平台诊断特征。

方法

我们在 3 项多中心独立试验中评估了 941 名患者（PDAC，356 例；慢性胰腺炎[CP]，304 例；非胰腺疾病，281 例），并评估了鉴定（ID）和验证队列 1（VD1）和 2（VD2）。在液相色谱-串联质谱平台上进行靶向定量血浆代谢物分析。机器学习辅助算法确定了改进的（i-Metabolic）和简化的（m-Metabolic）代谢特征，并对其性能进行了比较。

结果

i-Metabolic 特征（12 种分析物加 CA19-9）在 ID、VD1 和 VD2 队列中分别以 97.2%（97.1%-97.3%）、93.5%（93.4%-93.7%）和 92.2%（92.1%-92.3%）的曲线下面积（95%置信区间）区分 PDAC 与 CP。在 VD2 队列中，m-Metabolic 特征（4 种分析物加 CA19-9）以 77.3%的敏感性和 89.6%的特异性区分 PDAC 与 CP，总体准确率为 82.4%。对于 45 例可切除 IA-IIB 期肿瘤的 PDAC 患者亚组，敏感性、特异性和准确性分别为 73.2%、89.6%和 82.7%；对于可检测到 CA19-9>2 U/mL 的患者，敏感性、特异性和准确性分别为 81.6%、88.7%和 84.5%；对于 CA19-9<37 U/mL 的患者，敏感性、特异性和准确性分别为 39.7%、94.1%和 76.3%。