INDIVIDUALIZING COLON CANCER THERAPY USING HYBRID RNA AND DNA MOLECULAR SIGNATURE

利用混合 RNA 和 DNA 分子特征进行个体化结肠癌治疗

基本信息

批准号：
8894152
负责人：
TIMOTHY J YEATMAN
金额：
$ 13.47万
依托单位：
SPARTANBURG REGIONAL MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-19 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8894152
关键词：
Accounting Algorithms Alleles BRAF gene Biological Assay Cancer Patient Cetuximab Characteristics Clinical Clinical Data Clinical Trials Collaborations Colon Carcinoma Colorectal Cancer Complex DNA Data Development Diagnostic Ensure Environment Epidermal Growth Factor Receptor Experimental Designs Failure Formalin Freezing Future Gene Expression Profile Gene Mutation Genes Genetic Genetic Determinism Genome Genomics Goals Health Health Care Costs Healthcare Hybrids Investigational Therapies Laboratories Measures Molecular Molecular Profiling Mutate Mutation Nature Outcome Paraffin Embedding Pathway interactions Patients Pharmaceutical Preparations Pharmacologic Substance Population Prevalence Progression-Free Survivals Protocols documentation RNA Reading Regimen Reproducibility Resistance Risk Role Sampling Seasons Sensitivity and Specificity Sequence Analysis Signal Transduction Specimen System Testing Toxic effect Training Translations Validation base cancer care cancer therapy clinical application clinical phenotype cohort cost cost effective exome sequencing improved mutant novel oncology response success therapeutic target tumor tumor progression

项目摘要

DESCRIPTION (provided by applicant): Predicting which cancer patients will best respond to therapy is an enormous health care issue. It has been recently suggested, based on early reads of whole exome sequencing data, that there may only be ~12 molecular pathways that drive the development and progression of cancer. Many experimental therapies are now targeting these pathways. We believe that gene expression signatures may be one of the best ways to judge the activation of a particular molecular pathway, by providing a "molecular summary" of the activity of many genes. Moreover, we believe that the selective addition of mutational assessment may improve the resolving power of a hybrid, multi-analyte (DNA + RNA) test that may be used to guide patients to the most effective drugs. We have recently developed gene expression signatures to measure the activation of two of the most important pathways in colon cancer, RAS and PI3K, for which there is an increasing availability of pathway targeted therapeutics. Due to the complex nature of these pathways, simple analysis of canonical single gene mutations only identifies the response characteristics of a proportion (<30%) of the population. Here, we propose to technically validate the existing RAS and PI3K signatures and to refine their activity through novel mutational assessment. Multi-analyte signatures/ algorithms will be clinically validated in a CLIA environment with a cohort of colorectal cancer patients treated with cetuximab therapy. This approach will prepare signatures for clinical application in the near future.

描述（由申请人提供）：预测哪些癌症患者对治疗反应最好是一个巨大的医疗保健问题。最近，根据全外显子组测序数据的早期读取，有人提出，可能只有约 12 条分子途径驱动癌症的发生和进展。现在许多实验疗法都针对这些途径。我们相信，通过提供许多基因活性的“分子总结”，基因表达特征可能是判断特定分子途径激活的最佳方法之一。此外，我们相信选择性添加突变评估可能会提高混合多分析物（DNA + RNA）测试的分辨率，该测试可用于指导患者使用最有效的药物。我们最近开发了基因表达特征来测量结肠癌中两个最重要通路 RAS 和 PI3K 的激活，针对这两个通路靶向治疗的可用性越来越高。由于这些途径的复杂性，对典型单基因突变的简单分析只能识别一部分人群（<30%）的反应特征。在这里，我们建议从技术上验证现有的 RAS 和 PI3K 特征，并通过新的突变评估来完善其活性。多分析物特征/算法将在 CLIA 环境中对接受西妥昔单抗治疗的结直肠癌患者队列进行临床验证。这种方法将为不久的将来的临床应用准备签名。