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

项目摘要 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癌症。 低水平的癌症称为微小残留病（MRD），在手术切除后仍然存在， 早期肿瘤或晚期疾病的治疗。如果不进行治疗，MRD可以增殖并导致致命的 癌症复发因此，迫切需要灵敏地检测MRD以优化佐剂组合物。 精准免疫疗法或精准免疫疗法。液体活检提供了无创监测MRD的能力， 源自癌细胞的循环肿瘤DNA（ctDNA）。尽管如此，ctDNA的检测是具有挑战性的， 低负担疾病中ctDNA水平极低。流行的范式主张深度定向 信息位点的测序。然而，我们已经表明，这种方法面临着根本性的障碍， 由于典型血浆样品中可用DNA的量低，这施加了物理上限， 测序的深度。为了克服这一挑战，我们的跨学科团队的遗传学家，计算机 科学家和肿瘤学家开发了MRDetect，这是一种基于 单核苷酸变异（SNV）和拷贝数变异（CNV）的全基因组突变聚集， 血浆的全基因组测序（WGS）。MRDetect可实现超灵敏的MRD检测，最小可检测到一个零件 我们已经证明它能够在手术或治疗后不久检测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 关键的进步，将促进这种技术在癌症环境中的广泛采用。一是 建议将被称为“深度学习”（DL）的高级机器学习（ML）框架纳入 MRDetect平台能够在低肿瘤负荷环境中识别血浆WGS中的SNV（目标1）。这将 产生MRDecet-DL，我们预计它将显著提高低肿瘤水平的癌症检测， a与MRDetect相比，信噪比富集提高>100倍。MRDetect-DL性能将 在高风险的术后黑色素瘤中进行测试，以确定是否需要辅助治疗，以及在晚期黑色素瘤中 用于精确免疫治疗应用的免疫疗法治疗黑素瘤。重要的是，MRDetect-DL将 MRDetect需要匹配的肿瘤样本，确保在不同的临床环境中广泛采用。 其次，我们认为，除了基于SNV的进步，MRDetect的灵敏度可以通过增强 检测CNV，因为这些在实体瘤中广泛观察到。我们建议开发MRDetect-CNV， ML去噪技术使用血浆WGS超灵敏地检测小CNV（目标2）。我们将测试 对接受新辅助免疫治疗患者的NSCLC血浆样本进行MRDetect-CNV检测，以确定其 预测治疗反应的能力。影响：将MRDecet-DL与MRDecet-CNV配对将显著改善 辅助、新辅助和全身免疫治疗中的低负担癌症检测， 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.