Use of proteomic patterns in serum to identify ovarian cancer
Use of proteomic patterns in serum to identify ovarian cancer
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DOI：
10.1016/s0140-6736(02)07746-2
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发表时间：
2002-02-16
期刊：
影响因子：
168.9
通讯作者：
Liotta, LA
中科院分区：
文献类型：
--
作者：
Petricoin, EF;Ardekani, AM;Liotta, LA
研究背景：目前迫切需要新的技术来检测早期卵巢癌。器官内的病理变化可能反映在血清中的蛋白质组模式中。我们开发了一种生物信息学工具，并用它来识别血清中的蛋白质组模式，区分肿瘤性非肿瘤性疾病内ovaria.Methods蛋白质组谱产生的质谱（表面增强激光解吸和电离）。通过迭代搜索算法分析了来自50名未受影响的妇女和50名卵巢癌患者的血清的初步“训练”光谱集，该算法确定了完全区分癌症与非癌症的蛋白质组模式。发现的模式，然后被用来分类一个独立的一组116掩蔽血清样本：50名妇女卵巢癌，66名未受影响的妇女或那些与非恶性disorders.Findings算法确定了一个集群模式，在训练集中，完全隔离癌症从非癌症。判别模式正确识别了所有50例卵巢癌病例，包括所有18例I期病例。在66例非恶性疾病中，63例被认为不是癌症。该结果产生了100%的灵敏度（95%CI 93-100），特异性95%（87-99），阳性预测值94%（84-99）。解释这些发现证明了蛋白质组模式技术作为高危人群和一般人群中卵巢癌所有阶段的筛查工具的前瞻性人群为基础的评估。
Background New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary.Methods Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders.Findings The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93-100), specificity of 95% (87-99), and positive predictive, value of 94% (84-99).Interpretation These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.