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    • 9. 发明授权
    • Methods and compositions for diagnosing lung cancer with specific DNA methylation patterns
    • 用特定DNA甲基化模式诊断肺癌的方法和组合物
    • US08150627B2
    • 2012-04-03
    • US11259546
    • 2005-10-25
    • Jian-Bing FanMarina Bibikova
    • Jian-Bing FanMarina Bibikova
    • G01N33/48C12Q1/00
    • C12Q1/6827C12Q1/6886C12Q2600/112C12Q2600/118C12Q2600/154C12Q2600/16C12Q2523/125
    • The present invention provides a method for identification of differentially methylated genomic CpG dinucleotide sequences within genomic target sequences that are associated with cancer in an individual by obtaining a biological sample comprising genomic DNA from the individual measuring the level or pattern of methylated genomic CpG dinucleotide sequences for two or more of the genomic targets in the sample, and comparing the level of methylated genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level or pattern of methylation of the genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with cancer. As disclosed herein, the methods of the invention have numerous diagnostic and prognostic applications. The methods of the invention can be combined with a miniaturized array platform that allows for a high level of assay multiplexing and scalable automation for sample handling and data processing. Also provided by the invention are genomic targets and corresponding nucleic acid probes that are useful in the methods of the invention as they enable detection of differentially methylated genomic CpG dinucleotide sequences associated with adenocarcinomas of the lung.
    • 本发明提供了通过获得包含来自个体的基因组DNA的生物样品来鉴定与个体癌症相关的基因组靶序列内的差异甲基化基因组CpG二核苷酸序列的方法,所述生物样品包含测量甲基化基因组CpG二核苷酸序列的水平或模式, 样品中的两个或更多个基因组靶标,并将样品中甲基化的基因组CpG二核苷酸序列的水平与甲基化的基因组CpG二核苷酸序列的参考水平进行比较,其中基因组CpG二核苷酸的甲基化水平或模式的差异 与参考水平相比,样品中的序列鉴定与癌症相关的差异甲基化的基因组CpG二核苷酸序列。 如本文所公开的,本发明的方法具有许多诊断和预后应用。 本发明的方法可以与微型阵列平台组合,其允许用于样品处理和数据处理的高水平的测定复用和可扩展的自动化。 本发明还提供了可用于本发明方法的基因组靶标和相应的核酸探针,因为它们能够检测与肺腺癌相关的差异甲基化的基因组CpG二核苷酸序列。
    • 10. 发明申请
    • Expression Profiles to Predict Relapse of Prostate Cancer
    • 表达谱预测前列腺癌复发
    • US20110153534A1
    • 2011-06-23
    • US13035797
    • 2011-02-25
    • Eugene ChudinJean LozachJian-Bing FanMarina Bibikova
    • Eugene ChudinJean LozachJian-Bing FanMarina Bibikova
    • G06F19/00G06G7/60
    • C12Q1/6886C12Q2600/112C12Q2600/118C12Q2600/156C12Q2600/16G06F19/00
    • The present invention provides a method for preparing a reference model for cancer relapse prediction that provides higher resolution grading than Gleason score alone. The method encompasses obtaining from different individuals a plurality of prostate carcinoma tissue samples of known clinical outcome representing different Gleason scores; selecting a set of signature genes having an expression pattern that correlates positively or negatively in a statistically significant manner with the Gleason scores; independently deriving a prediction score that correlates gene expression of each individual signature gene with Gleason score for each signature gene in said plurality of prostate carcinoma tissue samples; deriving a prostate cancer gene expression (GEX) score that correlates gene expression of said set of signature genes with the Gleason score based on the combination of independently derived prediction scores in the plurality of prostate cancer tissue samples; and correlating said GEX score with the clinical outcome for each prostate carcinoma tissue sample. A set of signature genes is provided that encompasses all or a sub-combination of GI_2094528, KIP2, NRG1, NBL1, Prostein, CCNE2, CDC6, FBP1, HOXC6, MKI67, MYBL2, PTTG1, RAMP, UBE2C, Wnt5A, MEMD, AZGP1, CCK, MLCK, PPAP2B, and PROK1. Also provided a methods for predicting the probability of relapse of cancer in an individual and methods for deriving a prostate cancer gene expression (GEX) score for a prostate carcinoma tissue sample obtained from an individual.
    • 本发明提供了一种制备癌症复发预测的参考模型的方法,其提供比单独的格列森评分更高的分辨率分级。 该方法包括从不同个体获得表达不同格列森分数的已知临床结果的多个前列腺癌组织样品; 选择一组具有统计学显着的方式与Gleason评分正相关或负相关的表达模式的特征基因; 独立地导出将所述多个前列腺癌组织样品中每个特征基因的基因表达与每个特征基因的Gleason评分相关联的预测分数; 基于所述多个前列腺癌组织样本中独立衍生的预测分数的组合,得到使所述特征基因组的基因表达与格列森分数相关的前列腺癌基因表达(GEX)评分; 并将所述GEX评分与每个前列腺癌组织样品的临床结果相关联。 提供了一组签名基因,其包含GI_2094528,KIP2,NRG1,NBL1,Prostein,CCNE2,CDC6,FBP1,HOXC6,MKI67,MYBL2,PTTG1,RAMP,UBE2C,Wnt5A,MEMD,AZGP1的全部或亚组合, CCK,MLCK,PPAP2B和PROK1。 还提供了预测个体中癌症复发概率的方法以及从个体获得的前列腺癌组织样品的前列腺癌基因表达(GEX)得分的方法。