MICROSCALED PROTEOGENOMICS FOR CANCER CLINICAL TRIALS

用于癌症临床试验的微观蛋白质组学

• 批准号: 9272692
• 负责人: STEVEN A CARR
• 金额: $ 145.25万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-07 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9272692
• 关键词: Aftercare; Aromatase Inhibitors; Automobile Driving; Beds; Biological; Biological Assay; Biology; Biopsy; Biopsy Specimen; Cancer Therapy Evaluation Program; Center for Translational Science Activities; Cessation of life; Chemotherapy-Oncologic Procedure; Clinical; Clinical Research; Clinical Trials; Clinical Trials Cooperative Group; Collaborations; Communities; Complex; Comprehensive Cancer Center; Core Biopsy; DNA sequencing; Data; Data Discovery; Development; Diagnostic; Drug Targeting; Drug resistance; ERBB2 gene; Enrollment; Evaluation; Event; Funding; Gene Expression; Gene Mutation; Genome; Human; Individual; Informatics; Institutes; Investigation; Link; Malignant Neoplasms; Mass Spectrum Analysis; Measures; Medicine; Mind; Modeling; Neoadjuvant Study; Neoadjuvant Therapy; Outcome; Pathogenesis; Pathologic; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phosphotransferases; Pre-Clinical Model; Preclinical Testing; Property; Protein Kinase; Proteins; Proteome; Proteomics; Relapse; Reproducibility; Research Personnel; Resistance; Resources; Sampling; Scheme; Selection for Treatments; Signal Transduction; Solid; Somatic Mutation; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Translating; Trastuzumab; Tumor Biology; Xenograft procedure; anticancer research; arm; assay development; base; bevacizumab; cancer clinical trial; cancer genome; cancer genomics; cancer proteomics; candidate marker; chromatin modification; clinical phenotype; cohort; college; drug efficacy; effective therapy; experience; improved; innovation; insight; iterative design; kinase inhibitor; lapatinib; malignant breast neoplasm; metastatic process; mortality; multidisciplinary; new therapeutic target; next generation; personalized diagnostics; phosphoproteomics; pre-clinical; pre-clinical research; predictive marker; progenitor; prognostic; prospective; proteogenomics; resistance mechanism; response; standard of care; therapy resistant; triple-negative invasive breast carcinoma; tumor; tumor microenvironment

Gene expression-based approaches to breast cancer are largely for prognostication, not prediction of individual drug responses. Furthermore, there are still no clinically validated somatic mutation-based approaches in breast cancer management based on next generation DNA sequencing (NGS). A major obstacle to progress in NGS-based diagnostics is a fundamental one: we poorly understand how complex cancer somatic genomes drive clinical phenotypes and drug vulnerabilities. Key issues such as therapeutic resistance, the contribution of the tumor microenvironment and the metastatic process belie single gene/mutation explanations. The new field of proteogenomics provides an opportunity to generate new insights by melding the complexity of cancer genomics with cancer proteomics to more completely understand how somatic genomes activate aberrant signal transduction events that drive cancer pathogenesis. To this end, we have formed a multi-institutional, multi-omics center to engage in collaborative studies under the aegis of the NCI-CPTAC PTRC initiative. Our core builds upon ongoing collaborations between the highly experienced and innovative teams at Baylor College of Medicine and the Broad Institute with complementary strengths in cancer genomics, precision diagnostics, proteogenomic technologies and decades of experience in breast cancer research. Our proposal leverages state-of-the-art quantitative discovery proteomics and phosphoproteomics as well as targeted assays to measure the kinome and chromatin modifications. These sensitive and reproducible pipelines will be used to analyze preclinical models, well-annotated cohorts and clinical trial samples in an iterative design. A robust proteogenomics pipeline developed by our group will be used to analyze and visualize the data. These analyses, together with the primary data generated by this multidisciplinary proposal will be made rapidly available to the scientific community. While the FOA envisioned that only targeted approaches could be applicable in the Clinical Research Arm, it did not anticipate the major advances already well underway in discovery proteomics. We show that the global, discovery-based proteome and phosphoproteome pipeline we have developed is already applicable to biopsy-scale tumor samples, providing deep and broad quantitative coverage of the proteome and phosphoproteome with excellent reproducibility and robustness. This key conceptual and practical advance enables us to employ discovery and targeted approaches in both Preclinical and Clinical arms thereby greatly increasing the power of our proposed studies to generate impactful insight into the causes of breast cancer mortality. We postulate that this integrated approach will provide new understanding of the biology of response and resistance to chemotherapeutics, sounder therapeutic hypotheses and identify more accurate predictive biomarkers for drug resistance and treatment selection that could be developed and deployed as clinical tests.

基于基因表达的乳腺癌治疗方法主要是为了证实，而不是预测乳腺癌的发生。 个体药物反应。此外，仍然没有临床验证的基于体细胞突变的 基于下一代DNA测序（NGS）的乳腺癌管理方法。一个主要 NGS诊断进展的一个基本障碍是：我们不太了解如何复杂 癌症体细胞基因组驱动临床表型和药物脆弱性。关键问题，如治疗 耐药性、肿瘤微环境的作用和转移过程与单一的耐药机制不同。 基因/突变的解释。蛋白质基因组学的新领域提供了一个机会， 通过融合癌症基因组学与癌症蛋白质组学的复杂性， 体细胞基因组如何激活驱动癌症发病机制异常信号转导事件。 为此，我们成立了一个多机构、多组学中心，在 NCI-CPTAC PTRC倡议的支持。我们的核心是建立在高度合作的基础上， 贝勒医学院和布罗德研究所的经验丰富的创新团队， 在癌症基因组学、精确诊断、蛋白基因组学技术方面的优势以及数十年的经验 在乳腺癌研究中。 我们的提案利用了最先进的定量发现蛋白质组学和磷酸化蛋白质组学， 靶向测定以测量激酶组和染色质修饰。这些敏感和可重复的 管道将用于分析临床前模型，注释良好的队列和临床试验样本， 迭代设计我们小组开发的一个强大的蛋白质基因组学管道将用于分析和 可视化数据。这些分析，以及由这一多学科提案产生的原始数据 将迅速提供给科学界。虽然FOA设想只针对 虽然这些方法可能适用于临床研究组，但它并没有预料到已经取得的重大进展， 在蛋白质组学研究中进展顺利。我们表明，全球，发现为基础的蛋白质组和 我们开发的磷酸化蛋白质组管道已经适用于活检规模的肿瘤样品， 蛋白质组和磷酸化蛋白质组的深度和广泛的定量覆盖，具有优异的重现性， 鲁棒性这一关键的概念和实践进步使我们能够采用发现和有针对性的 在临床前和临床两个分支的方法，从而大大增加了我们提出的研究的力量 对乳腺癌死亡率的原因产生有影响的见解。 我们假设，这种综合方法将提供对反应生物学的新理解， 化疗药物的耐药性，更合理的治疗假设，并确定更准确的预测 耐药性和治疗选择的生物标志物，可以开发和部署为临床测试。