Getting More from Less: Multi-omic Capture and Analysis from Patient Samples

事半功倍：从患者样本中进行多组学捕获和分析

• 批准号: 9140592
• 负责人: Scott M Berry
• 金额: $ 24.07万
• 依托单位: SALUS DISCOVERY, LLC
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2017-08-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9140592
• 关键词: Address; Adopted; Advanced Malignant Neoplasm; Agreement; Androgen Receptor; Automation; Biological Assay; Biological Markers; Biological Models; Blood; CLIA certified; Cancer Center; Cancer Diagnostics; Cancer Patient; Caring; Cell Nucleus; Cells; Clinic; Clinical; Clinical Trials; Companions; Complement; Cyclic GMP; Cytoplasm; DNA; Data; Development; Devices; Diagnosis; Disease; Documentation; Drug Targeting; Exclusion; Foundations; Funding; Gene Expression; Genomics; Glean; Goals; Image; Industry; Laboratories; Letters; Licensing; Life; Ligand Binding; Malignant Neoplasms; Malignant neoplasm of prostate; Manuals; Measurement; Measures; Medicine; Messenger RNA; Methods; Modeling; Monitor; Mutation; Neoplasm Circulating Cells; Nuclear Translocation; Nucleic Acids; Patients; Pharmaceutical Preparations; Phase; Population; Positioning Attribute; Precision therapeutics; Preparation; Progressive Disease; Protein translocation; Proteins; RNA; RNA Splicing; Receptor Signaling; Recovery; Regulation; Resistance; Resistance development; Resources; Sampling; Small Business Innovation Research Grant; Staging; Staining method; Stains; TACSTD1 gene; Techniques; Technology; Testing; Time; Tube; Universities; Validation; Variant; Wisconsin; abiraterone; base; cancer therapy; carbonate dehydratase; clinical efficacy; clinical practice; clinically actionable; cohort; commercialization; design; effective therapy; improved; individual patient; inhibitor/antagonist; innovation; instrumentation; meetings; molecular marker; next generation sequencing; patient biomarkers; personalized medicine; predictive marker; prevent; programs; prospective; protein function; public health relevance; resistance mechanism; success; targeted treatment; therapy resistant; treatment choice; tumorigenesis

 DESCRIPTION (provided by applicant): Personalized medicine is predicated on having sufficient patient specific information to provide a diagnosis specific to that patient. Genomics (e.g. next generation sequencing) is now being adopted into clinical practice and is enabling improvements in patient specific care particularly in cancer. However, sequence information alone will not be sufficient to realize the full potential of personalized medicine. If we examine prostate cancer, more than half of patients do not benefit from new anti-cancer therapies such as Abiraterone and Enzalutamide and nearly all patients who initially benefit develop resistance within 1-2 years. Yet, for the clinician the treatment decisions are largely guesswork as he/she has little quantitative mechanistic data to guide treatment choices. Importantly, prostate cancer is driven in large part by the Androgen Receptor (AR) including ligand binding and translocation from the cytoplasm to the nucleus activating a transcriptional program critical to tumorigenesis. Multiple drugs are currently available that prevent nuclear translocation of the AR via different mechanisms, but sequencing alone will be insufficient to accurately guide therapy choice and to monitor the development of resistance (similar scenarios exist in other cancers and other diseases). Predicting and monitoring targeted therapies requires orthogonal multi-omic (i.e., protein, genomic, gene expression) endpoints. Circulating tumor cells (CTCs) have great potential as an accessible sample and many technologies are being developed for CTC analysis, but none have the ability to perform multi-omic analysis from a single sample. This proposal addresses these issues by leveraging and advancing Exclusion-based Sample Preparation (ESP) to enable the rapid and efficient isolation of multiple analytes (cells, proteins RNA, DNA) from a single precious sample with high recovery and purity. To efficiently move this platform into the clinic, we have signed licensing agreements with Gilson and Foundation Medicine (recently acquired by Roche) industry leaders in instrumentation/manufacturing, and advanced cancer diagnostics/clinical laboratory test development respectively. Additionally our clinical collaborator, Dr. Lang (University of Wisconsin), will enable us to directly demonstrate the clinical efficacy of our multi-omic approach during the SBIR proposal period, using advanced prostate cancer as a clinical model. As illustrated by the AR model system, assays capable of measuring multi-omic biomarkers would enable clinicians to make more informed decisions about what type of therapy to use and when to use it for patients with progressive disease, informing both choice of initial treatment as well as when to switch therapy as resistance occurs. We have chosen to submit a Fast-Track SBIR Proposal and have addressed the Fast-Track requirements of clear Phase I goals and clear evidence (e.g. letters) of additional funding & resource commitments that significantly enhance the likelihood of successful commercialization.

 描述（由适用提供）：预测个性化医学具有足够的患者特定信息，可以提供特定于该患者的诊断。基因组学（例如下一代测序）现在被采用到临床实践中，并且正在改善患者特定的护理，尤其是癌症。但是，仅序列信息就不足以实现个性化医学的全部潜力。如果我们检查前列腺癌，那么超过一半的患者不会受益于阿比罗酮和恩扎拉胺等新的抗癌疗法，以及几乎所有最初在1 - 2年内受益于发育耐药性的患者。但是，对于临床试验决定，他/她几乎没有定量的机械数据来指导治疗选择。重要的是，前列腺癌在很大程度上是由雄激素受体（AR）驱动的，包括配体结合和从细胞质到核激活转录程序至关重要的转录程序。目前有多种药物可以通过不同的机制来防止AR的核易位，但是单独的测序将不足以准确指导治疗选择并监测耐药性的发展（其他癌症和其他疾病中存在类似的情况）。预测和监测靶向疗法需要正交多摩尼克（即蛋白质，基因组，基因表达）终点。循环肿瘤细胞（CTC）作为可访问的样本具有巨大的潜力，并且正在开发许多技术进行CTC分析，但是没有一个能够从单个样本中进行多摩变分析。该提案通过利用和推进基于排除的样品制备（ESP）来解决这些问题，以便从具有高恢复和纯度的单个贵重样本中快速有效地分离多个分析物（细胞，蛋白质RNA，DNA）。为了有效地将该平台转移到诊所中，我们已与吉尔逊和基金会医学（最近被罗氏（Roche）获得的仪器/制造业领导者（最近被Roche）签署了许可协议，并分别分别是癌症诊断/临床实验室测试开发。此外，我们的临床合作者Lang博士（威斯康星大学）将使我们能够在SBIR提案期间直接证明我们的多摩变方法的临床效率，并使用晚期前列腺癌作为临床模型。正如AR模型系统所示，能够测量多摩变生物标志物的断言将使临床医生能够对要使用哪种类型的治疗以及何时将其用于进行性疾病的患者做出更明智的决定，从而告知初始治疗的选择以及何时将治疗切换为抵抗。我们选择提交快速的SBIR提案，并解决了清晰的I期目标的快速要求以及明确的证据（例如信件）额外的资金和资源承诺，从而大大提高了成功商业化的可能性。