Compositions, methods and kits for diagnosis of lung cancer
    3.
    发明授权
    Compositions, methods and kits for diagnosis of lung cancer 有权
    用于诊断肺癌的组合物,方法和试剂盒

    公开(公告)号:US09588127B2

    公开(公告)日:2017-03-07

    申请号:US14926735

    申请日:2015-10-29

    Abstract: Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

    Abstract translation: 提供了用于鉴定在具有第一肺症状的受试者中呈现差异表达的生物标记蛋白的方法与健康受试者或具有第二肺病症的受试者的方法。 还提供了包含这些生物标记蛋白的组合物和使用这些生物标记蛋白或其面板的方法来诊断,分类和监测各种肺部病症。 本文提供的方法和组合物可以用于诊断或分类为具有肺癌或非癌性病症的受试者,并且区分不同类型的癌症(例如,恶性与良性,SCLC与NSCLC之间)。

    Compositions, methods and kits for diagnosis of lung cancer
    5.
    发明授权
    Compositions, methods and kits for diagnosis of lung cancer 有权
    用于诊断肺癌的组合物,方法和试剂盒

    公开(公告)号:US09201044B2

    公开(公告)日:2015-12-01

    申请号:US13724823

    申请日:2012-12-21

    Abstract: Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

    Abstract translation: 提供了用于鉴定在具有第一肺症状的受试者中呈现差异表达的生物标记蛋白的方法与健康受试者或具有第二肺病症的受试者的方法。 还提供了包含这些生物标记蛋白的组合物和使用这些生物标记蛋白或其面板的方法来诊断,分类和监测各种肺部病症。 本文提供的方法和组合物可以用于诊断或分类为具有肺癌或非癌性病症的受试者,并且区分不同类型的癌症(例如,恶性与良性,SCLC与NSCLC之间)。

    Compositions, Methods and Kits for Diagnosis of Lung Cancer
    6.
    发明申请
    Compositions, Methods and Kits for Diagnosis of Lung Cancer 审中-公开
    用于肺癌诊断的组合物,方法和试剂盒

    公开(公告)号:US20160161493A1

    公开(公告)日:2016-06-09

    申请号:US15041775

    申请日:2016-02-11

    Abstract: The present invention provides methods for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. The present invention also provides compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

    Abstract translation: 本发明提供用于鉴定在具有第一肺部病症的受试者中具有差异表达的健康受试者或具有第二肺部病症的受试者的生物标志物蛋白质的方法。 本发明还提供了包含这些生物标记蛋白的组合物和使用这些生物标记蛋白或其面板的方法来诊断,分类和监测各种肺部病症。 本文提供的方法和组合物可以用于诊断或分类为具有肺癌或非癌性病症的受试者,并且区分不同类型的癌症(例如,恶性与良性,SCLC与NSCLC之间)。

    Compositions, methods and kits for diagnosis of lung cancer
    8.
    发明授权
    Compositions, methods and kits for diagnosis of lung cancer 有权
    用于诊断肺癌的组合物,方法和试剂盒

    公开(公告)号:US09297805B2

    公开(公告)日:2016-03-29

    申请号:US14341245

    申请日:2014-07-25

    Abstract: The present invention provides methods for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. The present invention also provides compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

    Abstract translation: 本发明提供用于鉴定在具有第一肺部病症的受试者中具有差异表达的健康受试者或具有第二肺部病症的受试者的生物标志物蛋白质的方法。 本发明还提供了包含这些生物标记蛋白的组合物和使用这些生物标记蛋白或其面板的方法来诊断,分类和监测各种肺部病症。 本文提供的方法和组合物可以用于诊断或分类为具有肺癌或非癌性病症的受试者,并且区分不同类型的癌症(例如,恶性与良性,SCLC与NSCLC之间)。

    INTEGRATED QUANTIFICATION METHOD FOR PROTEIN MEASUREMENTS IN CLINICAL PROTEOMICS
    9.
    发明申请
    INTEGRATED QUANTIFICATION METHOD FOR PROTEIN MEASUREMENTS IN CLINICAL PROTEOMICS 有权
    蛋白质测量在临床应用中的综合定量方法

    公开(公告)号:US20150219666A1

    公开(公告)日:2015-08-06

    申请号:US14612959

    申请日:2015-02-03

    Abstract: Methods are provided for determining the expression level of target proteins in a subject. A plurality of respective peptide transitions are generated from a plurality of proteins obtained from a biological sample from the subject, wherein the plurality of proteins comprises both target and normalizing proteins. A mass spectroscopy (MS) signal intensity is measured from the plurality of respective peptide transitions and a plurality of corresponding stable isotope-labeled internal standard (SIS) peptide transitions. For each of the plurality of proteins, a response ratio is calculated between the MS signal intensity of the respective peptide transition and the corresponding SIS peptide transition. The response ratio for each target protein is normalized by a sample-dependent normalization factor calculated from the response ratio for each normalizing protein, wherein the normalized response ratios provide a determination of the expression level of the target proteins.

    Abstract translation: 提供了用于确定受试者中靶蛋白的表达水平的方法。 从从受试者的生物样品获得的多种蛋白质产生多个相应的肽转换,其中多个蛋白质包含靶标和归一化蛋白质。 从多个相应的肽转变和多个相应的稳定同位素标记的内标(SIS)肽转换测量质谱(MS)信号强度。 对于多个蛋白质中的每一个,在相应的肽转变的MS信号强度和相应的SIS肽转换之间计算响应比。 通过从每个归一化蛋白的响应比计算的样品依赖性归一化因子对每个靶蛋白的响应率进行归一化,其中归一化响应比提供了靶蛋白的表达水平的确定。

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