B056:一种新型的高灵敏度、多路生物标志物BlueSCAI技术推进胰腺癌的早期检测

IF 16.6 1区 医学 Q1 ONCOLOGY
Malcolm J. MacKenzie, Ilya Alexandrov, Matthew R. Preimesberger
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引用次数: 0

摘要

在早期、更可治疗的阶段检测胰腺导管腺癌(PDAC)的血液检测是一个未满足的需求。为了提供令人信服的临床价值并推动广泛采用,该检测必须:i.显示80%+灵敏度@ 99%+特异性;2。成本,lt; 500美元;ⅲ。以高危人群为目标,提高患病率并获得40%的PPV。不幸的是,由于假阳性和阴性率高、早期灵敏度低和成本高,现有的或正在开发的测试仍然不足。然而,某些不完善的循环标志物,如CA19-9,在许多PDAC患者中升高,提供了潜在的锚定标志物。利用正交的、互补的标记可能会增加不充分的锚标记性能。但需要一种更敏感的多重检测技术来利用高潜力但尚未开发的标记物。背景:我们使用MPAD (Multiplexed pair -antibody amplification Detection)平台和PanDx多标记面板检测PDAC。PanDx面板将CA19-9与其他因其非相关和互补信号谱而选择的蛋白质结合在一起。在先前的一项早期PDAC与非癌症对照的研究中,PanDx面板显示出76%的敏感性和99%的特异性。与CA19-9单独治疗相比,PanDx AUC提高20%,93% (95% CI: 89-97%) vs. 84% (95% CI: 74-90%);因此,支持一种多路正交生物标志物策略的潜力,用于PDAC的早期检测。方法和结果:我们开发了BlueSCAI(串行捕获,适配器插入),这是一种下一代分析技术,可大大提高MPAD的分析性能。BlueSCAI的工作原理是:1)显著降低分析背景,提高信噪比和目标LOD; 2)通过调节适配器浓度调节目标之间的相对信号强度。这种组合可以实现低丰度生物标志物的稳健多路分析(见海报),我们正在将其与一种新的胰腺源性细胞外囊泡(EV)富集策略相结合。随着EV的富集,灵敏度的提高,能够检测出具有生物信息性的EV相关蛋白靶标,通常丰度较低,低于传统多重方法的检测水平。我们还开发了一种快速的生物标志物筛选工作流程,使用来自PDAC和各种非PDAC对照队列的血浆样本。通过为每个候选生物标志物生成加权平均信号,该方法无需分析单个样本即可识别队列水平差异,并允许对多个靶点进行有效评估,并基于效应大小和与CA19-9的正交性进行高通量优先级排序。然后在单个样本中验证表现最好的标记,以便集成到PanDx面板中。结论:新颖的BlueSCAI技术、EV富集和生物标志物筛选平台共同提供了一个灵活、可扩展的系统,用于完善多标志物面板,支持开发临床有用的早期胰腺癌血液检测方法。引用格式:Malcolm J. MacKenzie, Ilya Alexandrov, Matthew R. Preimesberger。一种新型的高灵敏度、多路生物标志物技术BlueSCAI推进胰腺癌的早期检测[摘要]。摘自:AACR癌症研究特别会议论文集:胰腺癌研究进展-新兴科学驱动变革解决方案;波士顿;2025年9月28日至10月1日;波士顿,MA。费城(PA): AACR;癌症研究2025;85(18_Suppl_3): nr B056。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Abstract B056: Advancing early detection of pancreatic cancer with BlueSCAI, a novel high-sensitivity, multiplexed biomarker technology
Introduction: There is an unmet need for a blood-based assay to detect Pancreatic Ductal Adenocarcinoma (PDAC) at an earlier, more treatable stage. To provide compelling clinical value and drive broad adoption, the assay must: i. display 80%+ sensitivity @ 99%+ specificity; ii. cost <$500; and iii. target higher risk cohorts to raise prevalence and obtain PPV >40%. Unfortunately, tests available or in development still fall short due to high false positives and negative rates, low early-stage sensitivity, and high cost. However, certain imperfect circulating markers, like CA19-9, are elevated in many individuals with PDAC, offering potential anchor markers. Leveraging orthogonal, complementary markers may augment insufficient anchor marker performance. But a more sensitive multiplex assay technology is needed to harness high potential but untapped markers. Background: We used our MPAD (Multiplexed Paired-antibody Amplified Detection) platform and PanDx multi-marker panel for PDAC. The PanDx panel combines CA19-9 with additional proteins selected for their non-correlated and complementary signal profiles. In a prior study of early-stage PDAC vs. non-cancer controls, PanDx panel displayed sensitivity of 76% at 99% specificity, a >20% point improvement over CA19-9 alone, PanDx AUC: 93% (95% CI: 89–97%) vs. 84% (95% CI: 74–90%) for CA19-9 alone; thus supporting the potential of a multiplexed, orthogonal biomarker strategy for early detection of PDAC. Methods and Results: We developed BlueSCAI (Serial-Capture, Adapter-Insertion), a next-generation assay technology that substantially enhances MPAD analytical performance. BlueSCAI works by 1) dramatically reducing assay background, improving signal-to-noise ratios and target LOD, and 2) tuning relative signal intensity across targets by modulating adapter concentration. This combination enables robust multiplexed profiling of low-abundance biomarkers (see poster), which we are combining with a novel pancreas-derived extracellular vesicle (EV) enrichment strategy. Increased sensitivity, with EV enrichment, enables detection of biologically informative EV-associated protein targets, often in low abundance, and below detection levels with conventional multiplex approaches. We also developed a rapid biomarker screening workflow using pooled plasma samples from PDAC and various non-PDAC control cohorts. By generating a weighted average signal for each candidate biomarker, this approach enables identification of cohort-level differences without the need to analyze individual samples, and allows for efficient assessment of multiple targets, and high-throughput prioritization based on effect size and orthogonality to CA19-9. Top-performing markers are then validated in individual samples for integration into the PanDx panel. Conclusion: Together, novel BlueSCAI technology, EV enrichment, and the biomarker screening platform provide a flexible, scalable system for refining a multi-marker panel, supporting the development of a clinically useful, blood-based assay for early-stage pancreatic cancer. Citation Format: Malcolm J. MacKenzie, Ilya Alexandrov, Matthew R. Preimesberger. Advancing early detection of pancreatic cancer with BlueSCAI, a novel high-sensitivity, multiplexed biomarker technology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3): nr B056.
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来源期刊
Cancer research
Cancer research 医学-肿瘤学
CiteScore
16.10
自引率
0.90%
发文量
7677
审稿时长
2.5 months
期刊介绍: Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research. With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445. Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.
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