SBIR Phase II: Early Detection of Pancreatic Cancer using Multiplex Protein Profiling

这项小型企业创新研究（SBIR）II期项目的更广泛影响/商业潜力是改善胰腺癌患者的临床结局和生活质量。只有10%的胰腺癌在诊断后五年内存活，因为大多数病例是在临床干预相对无效的晚期发现的。早期检测可改善干预措施，防止因不确定的诊断而出现不必要的程序，并节省卫生系统的费用。在美国，大约有500万人处于较高的风险中，但目前还没有针对早期阶段的筛查测试，监测市场估计为30 B。本项目将开发用于胰腺癌高危人群监测的诊断检测方法，其方法可能适用于其他类型的癌症和其他疾病。小企业创新研究（SBIR）II期项目将推进一项技术，该技术使用少量血液样本检测已知参与癌症发生和发展的多种生物信号通路的功能状态。该技术可以以低成本分析这些低丰度蛋白质，适用于广泛采用的监测测试。 一个专门构建的生物信息学系统分析和比较了许多个体的生物特征。 这个第二阶段项目将优化检测中的蛋白质靶点组，以检测疾病早期阶段的高性能差异生物特征，并将增强基于机器学习的匹配方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve clinical outcomes and quality-of-life for pancreatic cancer patients. Only 10% of pancreatic cancer survive five years after diagnosis because most cases are detected at later stages when clinical interventions are relatively ineffective. Earlier detection improves interventions, prevents unnecessary procedures arising from uncertain diagnosis, and leads to health system cost savings. Roughly 5 million individuals in the US are at higher risk, but there is no screening test available today for earlier stages, a surveillance market estimated at $3 B. This project will develop a diagnostic test for surveillance of people at high risk for developing pancreatic cancer, with methods potentially applicable to other types of cancer and other diseases.This Small Business Innovation Research (SBIR) Phase II project will advance a technology using a small blood sample to detect the functional state of multiple biological signaling pathways known to participate in cancer inception and progression. This technology can analyze these low abundance proteins at a low cost suitable for a widely adopted surveillance test. A purpose-built bioinformatic system analyses and compares the bio-signature identified by the assay across many individuals. This Phase II project will optimize the panel of protein targets in the assay to detect high-performing differential bio-signatures for early stages of the disease, and it will enhance the machine-learning-based matching methodology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.