公开(公告)号：US10181009B2

公开(公告)日：2019-01-15

申请号：US11134688

申请日：2005-05-19

申请人： Timothy J. Yeatman ,  Steven Eschrich ,  Gregory C. Bloom

发明人： Timothy J. Yeatman ,  Steven Eschrich ,  Gregory C. Bloom

IPC分类号： G06F19/20 ,  C12Q1/6886 ,  G06F19/24

摘要： The invention provides a molecular marker set that can be used for prognosis of colorectal cancer in a colorectal cancer patient. The invention also provides methods and computer systems for evaluating prognosis of colorectal cancer in a colorectal cancer patient based on the molecular marker set. The invention also provides methods and computer systems for determining chemotherapy for a colorectal cancer patient and for enrolling patients in clinical trials.

公开(公告)号：US08642349B1

公开(公告)日：2014-02-04

申请号：US11837883

申请日：2007-08-13

申请人： Timothy J. Yeatman ,  Jeff Xiwu Zhou ,  Gregory C. Bloom ,  Steven A. Eschrich

发明人： Timothy J. Yeatman ,  Jeff Xiwu Zhou ,  Gregory C. Bloom ,  Steven A. Eschrich

IPC分类号： G01N33/00

CPC分类号： G01N33/6851 ,  G01N33/57419

摘要： Here the inventors describe a tumor classifier based on protein expression. Also disclosed is the use of proteomics to construct a highly accurate artificial neural network (ANN)-based classifier for the detection of an individual tumor type, as well as distinguishing between six common tumor types in an unknown primary diagnosis setting. Discriminating sets of proteins are also identified and are used as biomarkers for six carcinomas. A leave-one-out cross validation (LOOCV) method was used to test the ability of the constructed network to predict the single held out sample from each iteration with a maximum predictive accuracy of 87% and an average predictive accuracy of 82% over the range of proteins chosen for its construction.

摘要翻译： 这里发明人描述了基于蛋白质表达的肿瘤分类器。 还公开了使用蛋白质组学构建用于检测单个肿瘤类型的高度准确的人造神经网络（ANN）分类器，以及区分未知主要诊断设置中的六种常见肿瘤类型。 鉴定的蛋白质组也被鉴定，并被用作6个癌症的生物标志物。 使用一次性交叉验证（LOOCV）方法来测试构建的网络从每次迭代中预测单个保留样本的能力，其最大预测精度为87％，平均预测精度为82％，超过 为其构建选择的蛋白质范围。

公开(公告)号：US09057108B2

公开(公告)日：2015-06-16

申请号：US13611584

申请日：2012-09-12

申请人： Timothy Yeatman ,  Barbara A. Centeno ,  Gregory C. Bloom

发明人： Timothy Yeatman ,  Barbara A. Centeno ,  Gregory C. Bloom

IPC分类号： C12Q1/68 ,  G01N33/574 ,  G06F19/20 ,  G06F19/24

CPC分类号： C12Q1/6886 ,  C12Q2600/112 ,  C12Q2600/158 ,  G01N33/574 ,  G01N33/57484 ,  G06F19/20 ,  G06F19/24

摘要： A two-tiered classification system that can be integrated with the current algorithm used by pathologists for identification of the site of origin for ‘malignancy with unknown primary’ is presented. In use, morphology, immunohistochemical (IHC) studies, and microarray-based top tier gene expression classifiers first subclassify cytokeratin positive carcinomas into adenocarcinoma, squamous cell carcinoma, neuroendocrine carcinoma and urothelial carcinoma. Subsequently, organ-specific IHC-markers, if available, are used in conjunction with microarray-based second tier gene expression classifiers to assign the primary site of origin to the sample. This new hybrid approach combines IHC with a hierarchy of quantitative gene expression based classifiers into an algorithmic method that can assist pathologists to further refine and support their decision making process.

摘要翻译： 提出了一种双层分类系统，可与病理学家使用的当前算法结合，用于鉴定“未知原发性恶性肿瘤”的起源部位。 在使用中，形态学，免疫组化（IHC）研究和基于微阵列的顶层基因表达分类器首先将细胞角蛋白阳性癌分为腺癌，鳞状细胞癌，神经内分泌癌和尿路上皮癌。 随后，器官特异性IHC标记（如果可用）与基于微阵列的第二层基因表达分类器结合使用，以将原始起始位点分配给样品。 这种新的混合方法将IHC与基于定量基因表达的分类器的层次结合成一种算法方法，可以帮助病理学家进一步完善和支持他们的决策过程。

公开(公告)号：US20130172203A1

公开(公告)日：2013-07-04

申请号：US13611584

申请日：2012-09-12

申请人： Timothy Yeatman ,  Barbara A. Centeno ,  Gregory C. Bloom

发明人： Timothy Yeatman ,  Barbara A. Centeno ,  Gregory C. Bloom

IPC分类号： C12Q1/68 ,  G06F19/24 ,  G01N33/574

CPC分类号： C12Q1/6886 ,  C12Q2600/112 ,  C12Q2600/158 ,  G01N33/574 ,  G01N33/57484 ,  G06F19/20 ,  G06F19/24

摘要： A two-tiered classification system that can be integrated with the current algorithm used by pathologists for identification of the site of origin for ‘malignancy with unknown primary ’ is presented. In use, morphology, immunohistochemical (IHC) studies, and microarry-based top tier gene expression classifiers first subclassify cytokeratin positive carcinomas into adenocarcinoma, squamous cell carcinoma, neuroendocrine carcinoma and urothelial carcinoma. Subsequently, organ-specific IHC-markers, if available, are used in conjunction with microarray-based second tier gene expression classifiers to assign the primary site of origin to the sample. This new hybrid approach combines IHC with a hierarchy of quantitative gene expression based classifiers into an algorithmic method that can assist pathologists to further refine and support their decision making process.

摘要翻译： 提出了一种双层分类系统，可与病理学家使用的当前算法结合，用于鉴定“未知原发性恶性肿瘤”的起源部位。 在使用中，形态学，免疫组织化学（IHC）研究和基于微阵列的顶层基因表达分类器首先将细胞角蛋白阳性癌分类为腺癌，鳞状细胞癌，神经内分泌癌和尿路上皮癌。 随后，器官特异性IHC标记（如果可用）与基于微阵列的第二层基因表达分类器结合使用，以将原始起始位点分配给样品。 这种新的混合方法将IHC与基于定量基因表达的分类器的层次结合成一种算法方法，可以帮助病理学家进一步完善和支持他们的决策过程。