Genome-wide mutational integration for ultra-sensitive plasma tumor burden monitoring in immunotherapy

项目总结 癌症诊断中的一个主要差距是最先进的成像和其他现有方法无法可靠地检测到 被称为微小残留病(MRD)的低水平癌症，手术切除后仍存在 早期肿瘤或晚期疾病的治疗。如果不进行治疗，MRD可能会扩散并导致致命 癌症复发。因此，迫切需要灵敏地检测MRD以优化佐剂 治疗或精确免疫疗法。液体活组织检查提供了通过检测 来源于癌细胞的循环肿瘤DNA(CtDNA)。尽管如此，ctdna的检测仍具有挑战性。 到极低水平的ctDNA在低负担疾病中。流行的范式主张深度定向 信息性基因座的测序。然而，我们已经表明，这种方法在以下方面面临着根本障碍 由于典型血浆样本中可用DNA的数量较少，因此具有敏感性，这对物理上限施加了限制 关于测序的深度。为了克服这一挑战，我们的跨学科遗传学家团队，计算机 科学家和肿瘤学家开发了MRDetect，这是一种用于ctDNA检测的正交方法，基于 单核苷酸变异体(SNV)和拷贝数变异体(CNV)的全基因组突变聚集 血浆全基因组测序。MRDetect使超敏感的MRD检测降至一个部件 我们已经证明了它有能力在手术或治疗后不久检测到MRD 结直肠癌、黑色素瘤和非小细胞肺癌(NSCLC)。我们在这个项目中的目标是开发 关键的进展将促进这项技术在癌症环境中的广泛采用。首先，我们 建议将高级机器学习(ML)框架(称为深度学习(DL))纳入 MRDetect平台在低肿瘤负荷环境下实现血浆WGS中SNV的识别(目标1)。这将是 Year MRDetect-DL，我们预计它将通过以下方式显著提高低肿瘤水平下的癌症检测 与核磁共振检测相比，A&gT；在信噪比增强方面提高了100倍。MRDetect-DL性能将 在术后高危黑色素瘤中进行测试，以确定辅助治疗的必要性，以及晚期黑色素瘤 精准免疫治疗应用免疫疗法治疗黑色素瘤。至关重要的是，MRDetect-DL将 消除了MRDetect对匹配肿瘤样本的需求，确保在不同的临床环境中广泛采用。 其次，我们假设，除了基于SNV的改进之外，MRDetect的灵敏度可以通过增强 CNV的检测，因为它们在实体肿瘤中被广泛观察到。我们建议开发MRDetect-CNV，以及 使用血浆WGS超灵敏检测微小CNV的ML去噪技术(目标2)。我们将测试 对接受新辅助免疫治疗的非小细胞肺癌患者血浆标本进行核磁共振检测以确定其 预测治疗反应的能力。影响：将MRDetect-DL与MRDetect-CNV配对将显著改进 在佐剂、新佐剂和系统免疫治疗中的低负担癌症检测，使广泛的临床 在肿瘤学中的应用。

PROJECT SUMMARY A major gap in cancer diagnostics is that state-of-the-art imaging and other existing methods fail to reliably detect low levels of cancer known as minimal residual disease (MRD), which remain following surgical resection of early-stage tumors or treatment of advanced disease. Left untreated, MRD can proliferate and result in lethal cancer recurrence. Hence, there is a critical need to sensitively detect MRD in order to optimize adjuvant therapies or precision immunotherapy. Liquid biopsy offers the ability to noninvasively monitor MRD by detecting circulating tumor DNA (ctDNA) originating from cancer cells. Nonetheless, detection of ctDNA is challenging due to extremely low levels of ctDNA in low-burden disease. The prevailing paradigm argues for deep targeted sequencing of informative loci. However, we have shown that this approach faces fundamental barriers to sensitivity due to the low amount of available DNA in typical plasma samples, which imposes a physical ceiling on depth of sequencing. To overcome this challenge, our interdisciplinary team of geneticists, computer scientists, and oncologists developed MRDetect, an orthogonal approach for ctDNA detection based on genome-wide mutation aggregation of single-nucleotide variants (SNVs) and copy number variants (CNVs) using whole-genome sequencing (WGS) of plasma. MRDetect enables ultra-sensitive MRD detection down to one part in a hundred thousand, and we have demonstrated its ability to detect MRD shortly after surgery or treatment in colorectal cancer, melanoma and non small-cell lung cancer (NSCLC). Our objective in this project is to develop crucial advances that will foster broad-based adoption of this technology across cancer settings. First, we propose to incorporate advanced machine learning (ML) framework known as ‘deep learning’ (DL) into the MRDetect platform to enable SNV identification in plasma WGS in low tumor burden settings (Aim 1). This will yield MRDetect-DL, which we anticipate will significantly improve cancer detection at low tumor levels through a >100-fold improvement in signal to noise enrichment compared to MRDetect. MRDetect-DL performance will be tested in high-risk post-operative melanoma to define the need for adjuvant therapy, as well as in advanced melanoma treated with immunotherapy for precision immunotherapy applications. Critically, MRDetect-DL will obviate MRDetect’s need for a matched tumor sample, ensuring broad adoption across different clinical settings. Second, we posit that in addition to SNV-based advances, MRDetect’s sensitivity can be increased by enhanced detection of CNVs, as these are broadly observed in solid tumors. We propose to develop MRDetect-CNV, an ML-denoising technique to ultra-sensitively detect small CNVs using plasma WGS (Aim 2). We will test MRDetect-CNV on NSCLC plasma samples from patients undergoing neoadjuvant immunotherapy to define its ability to predict treatment response. Impact: Pairing MRDetect-DL with MRDetect-CNV will significantly improve low burden cancer detection in adjuvant, neoadjuvant, and systemic immunotherapy, enabling broad clinical application in oncology.