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：在放射学检测极限以下监测恶性肿瘤负荷的能力仍然是一个挑战。 未满足的重大需求。循环肿瘤DNA（ctDNA）的液体活检提供了希望，然而， 测序方法-该领域的常规方法-在低体积癌症中面临敏感性平台 由于ctDNA信号的稀疏性。血浆全基因组测序（WGS）克服了这种敏感性 通过将信息位点的数量扩展到数千个体细胞单核苷酸变异来消除障碍 在实体瘤的基因组中观察到。我们用我们的肿瘤信息MRDetect框架表明， 血浆的WGS可以将液体活检灵敏度提高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， MRD-EDGE，一种仅限血浆的（从头）分类器，使用先进的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可以定量ctDNA肿瘤 在晚期黑色素瘤患者免疫治疗反应最低点期间的负担， 最早在首次输注后3周就显示出对治疗的反应。因此，MRD-EDGE能够 精确监测对单独使用标准WGS治疗的恶性疾病负荷。 在这个提议中，我的目标是首先通过包括表观遗传特征来从根本上提高MRD-EDGE灵敏度， 告知癌症诱变可能性，我假设这将允许前所未有的纯血浆液体 活检灵敏度然后，我将使用优化的MRD-EDGE平台来定义对以下问题的早期响应或抵抗： 转移性黑色素瘤的免疫治疗，这将建立ctDNA作为生物标志物，可以补充或 取代成像。最后，我将优化MRD-EDGE用于肺癌，并使用该平台监测 对新辅助免疫治疗的反应和检测术后微小残留病。我预计 在新辅助治疗期间对ctDNA动力学的超灵敏监测可以指导精确的辅助治疗， 从而对患者护理产生变革性影响。 候选人：我是纪念斯隆凯特琳癌症中心（MSK）的医学讲师，也是一名访问医生。 纽约基因组中心（NYGC）研究员。我已经制定了一个5年的职业发展计划， 过渡到一个独立的，终身跟踪物理学家，科学家调查固体的检测和监测 通过超灵敏液体活检来检测肿瘤。我将在博士的指导下进行这项研究。 丹朗道，国际公认的液体活检和癌症基因组学专家。我用K 08 该奖项旨在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是追求我的科学和职业目标的理想环境。两 机构拥有世界级的研究社区和独立培训的杰出记录 物理科学家