OBJECTIVE: 

To evaluate the detection of malignancy in women with a pelvic mass by using multiplexed gene expression analysis of cells captured from peripheral blood.

METHODS: 

This was an IRB-approved, prospective clinical study. Eligible patients had a pelvic mass and were scheduled for surgery or biopsy. Rare cells were captured from peripheral blood obtained preoperatively by using a microfluidic cell capture device. Isolated mRNA from the captured cells was analyzed for expression of 72 different gene transcripts. Serum levels for several commonly assayed biomarkers were measured. All patients had a tissue diagnosis. Univariate and multivariate logistic regression analyses for the prediction of malignancy using gene expression and serum biomarker levels were performed, and receiver operating characteristic curves were constructed and compared.

RESULTS: 

A total of 183 evaluable patients were enrolled (average age 56 years, range 19–91 years). There were 104 benign tumors, 17 low malignant potential tumors, and 62 malignant tumors. Comparison of the area under the receiver operating characteristic curve for individual genes and various combinations of genes with or without serum biomarkers to differentiate between benign conditions (excluding low malignant potential tumors) and malignant tumors showed that a multivariate model combining the expression levels of eight genes and four serum biomarkers achieved the highest area under the curve (AUC) (95.1%, 95% CI 92.0–98.2%). The MAGIC (Malignancy Assessment using Gene Identification in Captured Cells) algorithm significantly outperformed all individual genes (AUC 50.2–65.2%; all P<.001) and a multivariate model combining 14 different genes (AUC 88.0%, 95% CI 82.9–93.0%; P=.005). Further, the MAGIC algorithm achieved an AUC of 89.5% (95% CI 81.3–97.8%) for stage I–II and 98.9% (95% CI 96.7–100%) for stage III–IV patients with epithelial ovarian cancer.

CONCLUSION: 

Multiplexed gene expression evaluation of cells captured from blood, with or without serum biomarker levels, accurately detects malignancy in women with a pelvic mass.

CLINICAL TRIAL REGISTRATION: 

ClinicalTrials.gov, NCT02781272.

FUNDING SOURCE: 

This study was funded by ANGLE Europe Limited (Surrey Research Park, Guildford, Surrey, United Kingdom).

中文翻译：

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使用捕获细胞算法中的基因鉴定预测盆腔肿块女性恶性肿瘤的恶性肿瘤评估

客观的： 

通过对从外周血捕获的细胞进行多重基因表达分析，评估对盆腔肿块女性恶性肿瘤的检测。

方法： 

这是一项 IRB 批准的前瞻性临床研究。符合条件的患者有盆腔肿块，并被安排进行手术或活检。使用微流体细胞捕获装置从术前获得的外周血中捕获稀有细胞。从捕获的细胞中分离出的 mRNA 被分析用于 72 种不同基因转录物的表达。测量了几种常用测定的生物标志物的血清水平。所有患者均进行了组织诊断。对使用基因表达和血清生物标志物水平预测恶性肿瘤进行了单变量和多变量逻辑回归分析，构建并比较了接受者操作特征曲线。

结果： 

共招募了 183 名可评估患者（平均年龄 56 岁，范围 19-91 岁）。良性肿瘤104个，低度恶性肿瘤17个，恶性肿瘤62个。比较单个基因的受试者工作特征曲线下的面积和具有或不具有血清生物标志物的各种基因组合以区分良性病症（不包括低度恶性潜在肿瘤）和恶性肿瘤表明，结合八个基因表达水平的多变量模型四种血清生物标志物的曲线下面积 (AUC) 最高 (95.1%, 95% CI 92.0–98.2%)。MAGIC（使用捕获细胞中的基因识别进行恶性评估）算法显着优于所有单个基因（AUC 50.2–65.2%；所有P<.001) 和一个结合了 14 个不同基因的多变量模型 (AUC 88.0%, 95% CI 82.9–93.0%; P =.005)。此外，MAGIC 算法对于 I-II 期上皮性卵巢癌患者的 AUC 达到 89.5%（95% CI 81.3-97.8%），对于 III-IV 期上皮性卵巢癌患者达到 98.9%（95% CI 96.7-100%）。

结论： 

对从血液中捕获的细胞进行多重基因表达评估，无论是否具有血清生物标志物水平，都可以准确地检测出患有盆腔肿块的女性的恶性肿瘤。

临床试验注册： 

ClinicalTrials.gov，NCT02781272。

资金来源： 

本研究由 ANGLE Europe Limited（英国萨里吉尔福德萨里研究园）资助。