Advanced machine learning to empower ultra-sensitive liquid biopsy in melanoma and non-small cell lung cancer

先进的机器学习使黑色素瘤和非小细胞肺癌的超灵敏液体活检成为可能

• 批准号: 10591304
• 负责人: Adam Widman
• 金额: $ 29.26万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-04 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591304
• 关键词: Adjuvant Therapy; Aftercare; Artificial Intelligence; Award; Biological Markers; Cells; Chromatin; Clinical; Clinical Trials; Collection; Colorectal Cancer; Communities; Complement; Complex; Data; Dependence; Detection; Development Plans; Disease; Disease-Free Survival; Dose; Early Diagnosis; Early identification; Environment; Epigenetic Process; Face; Genome; Genomics; Goals; Image; Immunotherapy; Infusion procedures; Institution; International; Length; Link; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measures; Medicine; Memorial Sloan-Kettering Cancer Center; Mentors; Mentorship; Metastatic Melanoma; Methods; Monitor; Mutagenesis; Mutation; Nature; Neoadjuvant Therapy; New York; Noise; Non-Small-Cell Lung Carcinoma; Nucleosomes; Outcome; Patient Care; Patients; Physicians; Plasma; Positioning Attribute; Postoperative Period; Precision therapeutics; Protocols documentation; Recurrent Malignant Neoplasm; Recurrent disease; Regimen; Relapse; Research; Residual Neoplasm; Resistance; Sampling; Scientist; Screening for cancer; Signal Transduction; Single Nucleotide Polymorphism; Site; Solid Neoplasm; Source; Syncope; Therapeutic; Tobacco; Toxic effect; Training; Tumor Burden; Tumor Tissue; Update; Validation; Vision; Visit; burden of illness; cancer genomics; cancer recurrence; cancer type; career; career development; cell free DNA; checkpoint inhibition; cohort; cost; deep learning; empowerment; falls; genome sequencing; improved; innovation; instructor; laboratory experience; learning classifier; liquid biopsy; melanoma; next generation sequencing; non-invasive monitor; predictive modeling; prognostic; prototype; radiological imaging; response; skills; targeted sequencing; tenure track; treatment response; tumor; tumor DNA; unnecessary treatment; variant detection; whole genome

Research: The ability to monitor malignant tumor burden below the limit of radiographic detection remains a major unmet need. Liquid biopsy for circulating tumor DNA (ctDNA) offers promise, however, deep targeted sequencing methods – the conventional approach in the field – face a sensitivity plateau in low volume cancer due to the sparsity of ctDNA signal. Whole genome sequencing (WGS) of plasma overcomes this sensitivity barrier by expanding the number of informative sites to the thousands of somatic single nucleotide variants observed across the genome in solid tumors. We showed with our tumor-informed MRDetect framework that WGS of plasma can increase liquid biopsy sensitivity by 1-2 orders of magnitude beyond deep targeted sequencing methods. To expand applicability and overcome MRDetect’s need for matched tumor tissue, I built MRD-EDGE, a plasma-only (de novo) classifier that uses advanced machine learning to increase error suppression and amplify ctDNA signal. My preliminary data shows that MRD-EDGE can quantify ctDNA tumor burden during the nadir of response to immunotherapy in patients with advanced melanoma, and can demonstrate a response to treatment as early as 3 weeks after first infusion. MRD-EDGE therefore enables precise monitoring of malignant disease burden in response to therapy using standard WGS alone. In this proposal, I aim to first radically improve MRD-EDGE sensitivity by including epigenetic features that inform likelihood of cancer mutagenesis, which I hypothesize will allow for unprecedented plasma-only liquid biopsy sensitivity. I will then use the optimized MRD-EDGE platform to define early response or resistance to immunotherapy in metastatic melanoma, which will establish ctDNA as a biomarker that can complement or replace imaging. Finally, I will optimize MRD-EDGE for use in lung cancer and use the platform to monitor response to neoadjuvant immunotherapy and detect postoperative minimal residual disease. I expect that ultra-sensitive monitoring of ctDNA dynamics in the neoadjuvant period can guide precision adjuvant therapy in the postoperative period and thereby provide a transformative impact on patient care. Candidate: I am an Instructor of Medicine at Memorial Sloan Kettering Cancer Center (MSK) and a Visiting Fellow at the New York Genome Center (NYGC). I have outlined a 5-year career development plan to transition to an independent, tenure-track physician-scientist investigating the detection and monitoring of solid tumors through ultrasensitive liquid biopsy. I will conduct the proposed research under the mentorship of Dr. Dan Landau, an internationally recognized expert in liquid biopsy and cancer genomics. I will use the K08 award to further develop skills in next-generation sequencing methods and analysis and advanced machine learning. MSK and the NYGC are ideal environments in which to pursue my scientific and career goals. Both institutions have world class research communities and an outstanding track record of training independent physician-scientists.

研究：监测低于放射学检测极限的恶性肿瘤负担的能力仍是一个问题 未得到满足的主要需求。循环肿瘤DNA(CtDNA)的液体活检提供了希望，然而，深度靶向 测序方法--该领域的传统方法--面临着低体积癌症的敏感性平台期 由于ctDNA信号的稀疏性。血浆的全基因组测序(Wgs)克服了这种敏感性。 通过将信息位点的数量扩大到数千个体细胞单核苷酸变体来设置障碍 在实体肿瘤的基因组中观察到。我们用我们的肿瘤信息核磁共振检测框架显示 血浆的WGS可以将液体活检的灵敏度提高1-2个数量级，超过深度靶向 测序方法。为了扩大适用性并克服MRDetect对匹配肿瘤组织的需求，我建立了 MRD-EDGE，一种仅使用等离子体(从头开始)的分类器，它使用高级机器学习来增加错误 抑制和放大ctDNA信号。我的初步数据显示mrd-edge可以对ctdna肿瘤进行量化。 晚期黑色素瘤患者免疫治疗反应最低点期间的负担，并且可以 最早在第一次注射后3周表现出对治疗的反应。因此，MRD-EDGE支持 对仅使用标准WGS的治疗反应的恶性疾病负担进行精确监测。 在这项提议中，我的目标是首先从根本上提高MRD-EDGE的敏感性，包括 告知癌症突变的可能性，我假设这将允许史无前例的纯血浆液体 活检敏感度。然后，我将使用优化的MRD-EDGE平台来定义早期响应或抵抗 转移性黑色素瘤的免疫治疗，这将建立ctDNA作为一种生物标记物，可以补充或 更换成像。最后，我将优化MRD-EDGE用于肺癌，并使用该平台进行监测 对新辅助免疫治疗的反应和术后微小残留病的检测。我希望如此 超灵敏监测新佐剂期ctDNA动态变化可指导临床精确辅助治疗 这可能会对患者的护理产生革命性的影响。 候选人：我是纪念斯隆-凯特琳癌症中心(MSK)的医学讲师和客座医生 纽约基因组中心(NYGC)研究员。我已经概述了一个5年的职业发展计划 过渡到独立的终身教职医生兼科学家，研究固体物质的检测和监测 肿瘤通过超灵敏的液体活组织检查。我将在博士的指导下进行这项拟议的研究。 丹·兰道，一位国际公认的液体活检和癌症基因组学专家。我要用K08 获奖以进一步发展下一代测序方法和分析以及先进机器的技能 学习。MSK和NYGC是我追求科学和职业目标的理想环境。两者都有 机构拥有世界级的研究社区和出色的独立培训记录 内科医生-科学家。