MICROSCALED PROTEOGENOMICS FOR CANCER CLINICAL TRIALS

乳腺癌基于基因表达的方法主要是为了预测，而不是预测 个体药物反应。此外，目前还没有基于体细胞突变的临床验证方法。 基于下一代DNA测序(NGS)的乳腺癌治疗方法。一位少校 阻碍基于NGS的诊断取得进展的一个根本障碍是：我们很难理解复杂程度有多高 癌症体细胞基因组决定了临床表型和药物脆弱性。关键问题，如治疗 耐药、肿瘤微环境的贡献和转移过程是单一的 基因/突变解释。蛋白质组学的新领域提供了一个机会来产生新的 将癌症基因组学的复杂性与癌症蛋白质组学融合以更全面地理解 体细胞基因组如何激活驱动癌症发病机制的异常信号转导事件。 为此，我们成立了一个多机构、多组学的中心，在以下条件下开展合作研究 NCI-CPTAC临时技术合作中心倡议的支持。我们的核心建立在高度合作的 贝勒医学院和布罗德研究所经验丰富的创新团队 在癌症基因组学、精确诊断、蛋白质组学技术方面的优势和数十年的经验 在乳腺癌研究方面。 我们的建议利用最先进的定量发现蛋白质组学和磷酸蛋白质组学以及 有针对性的检测动态组和染色质的修饰。这些敏感的和可重现的 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。我们团队开发的强大的蛋白质组学流水线将用于分析和 将数据可视化。这些分析，以及这项多学科提案产生的主要数据 将迅速提供给科学界。虽然FOA设想只针对 方法可以适用于临床研究部门，它还没有预料到主要的进展 在发现蛋白质组学方面进展顺利。我们证明了基于发现的全球蛋白质组和 我们开发的磷酸蛋白质组流水线已经适用于活检规模的肿瘤样本，提供了 蛋白质组和磷酸蛋白质组的深度和广泛的定量覆盖，具有良好的重复性和 健壮性。这一关键的概念和实践进步使我们能够利用发现和定向 临床前和临床两个方面的方法，从而大大增加了我们建议的研究的力量 对乳腺癌死亡率的原因进行有影响力的洞察。 我们假设，这种综合方法将提供对反应和反应生物学的新理解 对化疗药物的抵抗力，更合理的治疗假设和更准确的预测 耐药和治疗选择的生物标记物，可以作为临床试验开发和部署。

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.