CN111712713A - 用于乳腺癌的早期检测的方法、装置和试剂盒 - Google Patents
用于乳腺癌的早期检测的方法、装置和试剂盒 Download PDFInfo
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Abstract
用于乳腺癌的早期检测的方法包含：(a)检测血液或尿液中的标志物8‑OHDG和EFGR；和(b)使用从检测阶段(a)的所述标志物中得到的数据对乳腺癌感染的概率进行统计计算，且所述方法的特征在于，对乳腺癌感染的概率的计算包括使用所述8‑OHDG、EGFR、NSE、CA 15.3和NGAL标志物的组合。
Description
本发明提及一种用于乳腺癌的早期检测的方法和装置，并涉及对多种血液标志物的分析。
背景技术
乳腺癌是沿乳腺导管和小叶排列的上皮细胞的恶性增殖。其是一种克隆性疾病，其中作为一系列体细胞或种系突变的产物的单个细胞获取了分裂自身的能力而不受控制或没有次序，使其繁殖直到其形成肿瘤。开始为轻微异常的这一肿瘤，侵入邻近的组织并最终扩散到身体的其它部分。因此，有效且早期的诊断对预防这一可能性是必要的。
乳腺癌有两种主要类型。开始于将乳汁从乳房运送到乳头的导管的浸润性导管癌是迄今为止最常见的—大约占病例的80％。排在第二位的是浸润性小叶癌—大约病例的10％开始于称为小叶的乳房部分，所述部分会产生母乳。整体来看，其余类型的乳腺癌不超过病例的10％。
感染乳腺癌的主要风险因素包括高龄、过早年龄时初次行经、高龄时初次怀孕或从未生育以及家庭背景。在病例的5％到10％之间，乳腺癌是由遗传性基因突变而引起的。
使用不同的测试来检测乳腺癌，如乳房X光摄影检查、使用高分辨率换能器的乳腺超声波—超声检查、雌激素及孕酮受体测试或磁共振成像。最后的乳腺癌诊断只能借助于乳房活检来确定。
在现有技术中，描述了借助于血液分析的乳腺癌诊断方法，所述血液分析检测与罹患乳腺癌相容的抗体。这些方法涉及非侵入性测试—血液分析，其中血清与血液分离，且一旦分离，就如文献EP2446272、WO9858978和WO2008032084中所描述的一样对所发现的抗体进行分析。
现有技术中描述的另一实例是文献US2015/0024960，其提及早期乳腺癌诊断。更具体地说来，其提及一组生物标志物，其被配置为在含有特异性识别乳腺癌的抗体的血液中诊断乳腺癌的出现。
尽管如此，根据本领域中的科学共识，不存在有效的标志物，也没有任何预测方法考虑使用所述标志物。现有技术仅仅考虑使用标志物CA15.3和CEA用于监测晚期疾病的治疗[Sturgeon C.M、Duffy M.J、Stenmam U-H等人，“国家临床生物化学研究院实验室对于在睾丸、前列腺、结肠直肠、乳房和卵巢癌症中使用肿瘤标志物的医学实践指南(NationalAcademy of Clinical Biochemistry Laboratory Medicine Practice Guidelines forUse of Tumor Markers in Testicular,Prostate,Colorectal,Breast,and OvarianCancers.)”，《临床化学(Clinical Chemistry)》(2008)12月；54(12)][KhatcheressianJ.L、Hurley P、Bantug E等人，“乳腺癌初始治疗后的随访和管理：美国临床肿瘤学会临床实践指南更新版(Breast Cancer Follow-Up and Management After PrimaryTreatment:American Society of Clinical Oncology Clinical Practice GuidelineUpdate)”，《临床肿瘤学杂志(Clin Oncol)》30，(2012)]y[Harris L、Fritsche H、Mennel R等人，“美国临床肿瘤学会对于在乳腺癌中使用肿瘤标志物的建议2007年更新版(AmericanSociety of Clinical Oncology 2007 update of recommendations for the use oftumor markers in breast cancer)”，《临床肿瘤学杂志》(2007)；25：5287-312]。
在文献[Bayo J、
Rivera F、Navarro F，“早期乳腺癌诊断的血液标志物分析(Analysis of blood markers for early breast cancer diagnosis)”，《临床与转化肿瘤学(Clin Transl Oncol)》，2017年8月14日]中已发布了一系列用于乳腺癌早期诊断的标志物，其被认为是与本发明最接近的现有技术。此文献及其描述的研究的目的是在诊断患有癌症的患者中确定可能存在的保持较高的任何血液指标，并因此可用作推定的乳腺癌预测标志物。
另一方面，在此工作中，标志物分为三组：
i.第一组标志物，从临床和科学的观点来看，其被现有技术中所描述的关于乳腺癌的大量工作认可。在此第一组中找到标志物CEA和CA 15.3。
ii.第二组标志物，尽管其与乳腺癌没有明显的关系，其在临床实践中系统地用于疾病诊断，例如CA 125、CYFRA 21.1、α-胎蛋白、CA 19.9和NSE(神经元特异性烯醇化酶)；和
iii.第三组实验标志物，如NGAL、EGFR和8-OHDG(分别为NGAL(中性粒细胞明胶酶相关脂质运载蛋白)、EGFR(表皮生长因子受体)和8-OHDG(8-羟基-2'-脱氧鸟苷)，其已在一些乳腺癌系列中进行了研究但并非出于早期诊断的特定目的。
在文献[Bayo J、
Rivera F、Navarro F，“早期乳腺癌诊断的血液标志物分析”，《临床与转化肿瘤学》，2017年8月14日]中已使用病例设计和对照设计了分析性观察流行病学研究，所述对照研究包括63例病例和63例对照。这些病例是被诊断患有局部乳腺癌(cT1-2和cN0)而等待进行手术的患者。这些患者的选择标准是：(a)被诊断患有可手术的乳腺癌；(b)先前未曾罹患别的肿瘤；(c)所述疾病未处于晚期或转移期；(d)先前未曾接受过新辅助癌症治疗；以及(e)接受被包括于所述研究。
关于对照组，其特征为健康的女性，其选择标准为其没有罹患慢性病或没有任何癌症病史。从逻辑上说，其还必须接受被包括于所述研究。
关于定量和描述性特征二者的统计分析；基于卡方检验(chi-square test)的比例比较和基于斯图登氏t检验(Student's t-test)的均值比较用于解决主要目标。然后使用瓦尔德(Wald)方法进行二元逻辑回归分析，然后采用ROC曲线方法以结束。
这两个系列证明是相似的。尽管如此，对照系列的平均年龄较低(45岁相较于57岁)，并且因此，与家庭主妇的组相比，所述对照系列的闭经比例较低且此外，就业率较高。然而，病例系列的维生素D含量较低，而BMI(体重指数)较高。其余特征都很均衡。
对[Bayo J、
Rivera F、Navarro F，“早期乳腺癌诊断的血液标志物分析”，《临床与转化肿瘤学》，2017年8月14日]的结果的描述可以被概括为：首先，对标志物的分析反映了两组中六个标志物的显著差异：四个常规标志物(CYFRA、NSE、CEA和CA 15.3)和两个实验标志物(EGFR和8-OHDG)。然而，当在标志物中应用具有正常范围的截止点时，仅有CA15.3被证明是重要的。敏感性极低(11％)，并因此排除了其单独在早期诊断中的有用性。
如先前现有技术公开案中所指示，对照中的EGFR明显更高。然而，在病例中，8-OHDG标志物明显更高，这是第一次在早期乳腺癌诊断中对此标志物进行了研究。此外，借助于逻辑回归分析和ROC曲线的构建，获得了由五个标志物(即CA 15.3、NSE、NGAL、EGFR和8-OHDG)构成的数学方程式，其达成91.8％乳腺癌诊断的正确概率。
发明内容
本发明的目的是一种用于乳腺癌的早期诊断的方法、装置和试剂盒，其从文献[Bayo J、
Rivera F、Navarro F，“早期乳腺癌诊断的血液标志物分析”《临床与转化肿瘤学》，2017年8月14日]中进行的实验的科学发现出发，提高了诊断效率并加强了诊断的简便性。
本发明的另一目的是提高典型乳腺癌诊断标志物的成功率。如现有技术中所指示，迄今为止，还不存在在这种临床情形下显示为有效的标志物。尽管如此，本发明显示出实验标志物8-OHDG/EGFR之间的关系在乳腺癌的早期诊断中是有效的。通过权利要求1所述的方法达到这一目的。在从属权利要求中，描述了加强本发明方法的有效性的其它标志物的使用。
本发明方法、此方法使用的装置和诊断试剂盒借助于包括计算算法来帮助支持不同的临床病例，所述算法通过简单的血液提取就能够以高于90％的成功率使得多种肿瘤标志物(存在或不存在所述疾病)被确定。
本发明中描述的方法、装置和试剂盒可用于不同年龄人群的较大高风险群组，以补充或替代乳房X光摄影检查，这是一种非侵入性方法，不会诱发医源性辐射，并且因此可以常常根据需要而重复进行，而且此外适合于所有年龄。
最后，对于本领域的技术人员来说，值得指示的是，本发明的其它目的、益处和特征将出自本说明书、附图和权利要求书。此外，本发明覆盖了此处所指示的具体和优选实施例的所有可能组合。
附图说明
此处下文是对一系列附图的极为简要的描述，所述附图有助于更好地理解本发明，并且其明确地涉及借助于非限制性实例来说明的所述发明的实施例。
图1示出了本发明的实际实施例的第一实例的ROC曲线。
图2示出了本发明的实际实施例的第二实例的第二ROC曲线。
图3示出了本发明的实际实施例的第三实例的第三ROC曲线。
具体实施方式
从[Bayo J、
Rivera F、Navarro F，“早期乳腺癌诊断的血液标志物分析”《临床与转化肿瘤学》，2017年8月14日]中所进行并描述的工作出发，已重复进行了所描述的试验并分析了所有可能的相关性，已令人惊讶地确认8-OHDG和EGFR标志物的反关系行为(inverse behaviour)使得其商数能够在乳腺癌的早期诊断中进行评估。本发明方法使得能够在乳腺癌的初始阶段中确定8-OHDG/EFGR商数以及其它肿瘤标志物的临床效用。
关于借助于[Bayo J、
Rivera F、Navarro F，“早期乳腺癌诊断的血液标志物分析”，《临床与转化肿瘤学》，2017年8月14日]描述的试验所获得的分析，进行了一项新的横断面描述性研究，其中有62位患有局部乳腺癌而等待手术干预的患者和62位健康女性。
一旦CA 15.3、CEA、CA125、CA 19.9、NSE、CYFRA 21.1、α-胎蛋白和实验性乳腺癌标志物(NGAL，EGFR y 8-OHDG)已被确定，就定义了比率(8-OHDG)/(EGFR)*100。为了按组比较含量，将曼-惠特尼U检验(Mann-Whitney U test)和多元逻辑回归(此后为瓦尔德(Wald))用于根据8-OHDG/EGFR比率单独地以及与所评估的其余标志物一起预测癌症的概率。已借助于ROC曲线面积(图1到3)对诊断性能进行了评估。
在对照中检测到较高含量的EGFR(5.097对比5.81ng/ml，其中p<0.001)，并且在病例中检测到较高含量的8-OHDG(9.85对比7.37ng/ml，其中p<0.001)，8-OHDG/EFGR比率的差值更明显(198对比122，其中p<0.001)。
患者中的CEA、CYFRA、NSE和NGAL标志物较高(p<0.05)，尽管其单独状态下的行为显示出不足的诊断敏感性。此外，如年龄、闭经、工作、BMI和低含量的维生素D的因素被证明是疾病的风险变量。对所评估比率的逻辑回归分析得出了单独状态下的82.4％的性能(参见实例1)，通过将上述商数与其它标志物组合从而获得包括NSE和NGAL的多元预测方程式来将所述性能提高到91.2％(参见实例2)，由于不同标志物的协同相互作用来将所述性能提高到高达92.8％(参见实例3)。
实例1.8-OHDG/EGFR比率的临床意义.双变量分析.
下表显示了对早期乳腺癌诊断试验结果的分析的结果。图1示出了试验的ROC曲线。
曼-惠特尼检验(针对独立样本)：
图1的逻辑回归和ROC曲线面积：
因此，对所评估比率的逻辑回归分析得出了82.4％的性能，其中p<0.0001，在乳腺癌的早期诊断中单独地使用8-OHDG/EGFR比率的成本明显低于[Bayo J、
Rivera F、Navarro F，“早期乳腺癌诊断的血液标志物分析”，《临床与转化肿瘤学》，2017年8月14日]中所提出的方法。
实例2.8-OHDG/EGFR比率的临床意义.多变量分析.
下表显示了对早期乳腺癌诊断试验结果的分析的结果。图2示出了试验的ROC曲线。在此实例中，预测值在非迭代模型中提高到91.2％，其中在乳腺癌的早期检测中使用8-OHDG/EGFR比率以及NSE和NGAL标志物的成本要明显低于文献[Bayo J、
Rivera F、Navarro F，“早期乳腺癌诊断的血液标志物分析”，《临床与转化肿瘤学》，2017年8月14日]中提出的方法，因为它比前述分析少用了一个标志物(CA 15.3)。
借助于多元逻辑回归估算参数：
其中B是估算的参数，ET是典型误差，GL是自由度，且SIGMA是标准偏差。将考虑具有最低标准偏差的三个参数(8-OHDG/EGFR、NSE和NGAL比率)。
所述模型的概述如下：
在此情况下，应该考虑到估算在第6次迭代时结束，因为参数的估算值变化小于0.001。因此，公式如下：
图2的逻辑回归面积和ROC曲线：
因此，对所评估比率的逻辑回归分析得出了91.2％的性能，其中p<0.0001。
实例3.8-OHDG/EGFR比率的临床意义.多变量分析.
下表显示了对早期乳腺癌诊断试验结果的分析的结果。图3示出了试验的ROC曲线。在此实例中，预测值在非迭代模型中提高到92.8％，其中在乳腺癌的早期检测中使用8-OHDG/EGFR比率和NSE、NGAL和NGAL*CA15.3标志物具有更大的诊断精确度。
以下示出了在迭代11中借助于多元逻辑回归所估算的参数，其提供了逻辑回归计算的最佳结果：
其中B是估算的参数，ET是典型误差，GL是自由度，且SIGMA是标准偏差。关于所述参数，其为RATIO(即8-OHDG/EGFR之间的关系)，NSE(标志物NSE)，RATIONGAL(即RATIO(其为标志物8-OHDG/EGFR之间的关系)与标志物NGAL之间的乘积)，CYFRANSE(即标志物CYFRA和标志物NSE之间的关系)和NGALCA153(即标志物NGAL和标志物CA 15.3之间的关系)。
将考虑具有最低标准偏差的三个参数(不考虑CYFRANSE)。因此，公式如下：
图3的逻辑回归和ROC曲线的面积：
因此，对所评估比率的逻辑回归分析得出了92.8％的性能，其中p<0.0001。
应注意的是，在试验期间，已显示出一些标志物之间存在相互作用，并且对在有相互作用情况下的方程式(实例3)和在没有相互作用情况下的方程式(实例2)进行了试验，并观察到，考虑到标志物之间的相互作用(协同关系)，所述关于预测的结果更好(92.8％相较于91.2％)，这一事实是现有技术中没有描述的。
本发明方法可以用不同的方式实现。因此，例如，其可在装置中实施，所述装置包含配置为执行本发明方法或可借助于包含任何实施例(实例1到3)中所指示的标志物的试剂盒来分布的构件和执行所描述的方法的构件，所述描述方法逻辑上包含进行血液或尿液分析的构件以及根据所描述的实例而计算乳腺癌感染的概率的构件，并且所述方法可例如在具有足够计算能力的IT系统中容易地实施。
以非限制性方式，此IT系统可以是来自可在计算机、平板电脑或移动电话到专用电子装置上执行的应用程序的任何事物，唯一所需条件是其借助于可由处理器执行的指令来实施每一个实例中指示的公式。
Claims (3)
1.一种用于乳腺癌的早期检测的方法，其由以下组成：(a)检测血液或尿液中的标志物8-OHDG、EGFR、NSE、NGAL和CA 15.3的存在并定量；和(b)根据与以下公式一致的血液或尿液中所检测并定量的所述标志物8-OHDG、EGFR、NSE、CA 15.3和NGAL的组合的值的逻辑回归，计算乳腺癌感染的概率：
2.一种用于乳腺癌的早期检测的装置，其特征在于，其包含一个处理器或多个处理器，所述处理器包含多个指令，所述多个指令在由所述一个处理器或多个处理器执行时使得所述装置执行根据权利要求1所述的方法。
3.一种用于乳腺癌的早期检测的试剂盒，其包含：(a)由8-OHDG、EGFR、NSE、NGAL和CA15.3标志物组成的多种标志物；(b)用于检测和定量血液或尿液中的所述8-OHDG、EGFR、NSE、NGAL和CA 15.3标志物的构件；以及(c)被配置为由包含多个指令的一个处理器或多个处理器执行的软件产品，以根据与以下公式一致的在血液或尿液中所检测并定量的所述标志物8-OHDG、EGFR、NSE、CA 15.3和NGAL的组合的值的逻辑回归来计算乳腺癌感染的概率：
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