Genome-wide mutational integration for ultra-sensitive plasma tumor burden monitoring in immunotherapy
全基因组突变整合用于免疫治疗中超灵敏血浆肿瘤负荷监测
基本信息
- 批准号:10344658
- 负责人:
- 金额:$ 65.85万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-06-01 至 2027-05-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAdjuvantAdjuvant TherapyAdoptionAftercareArtificial IntelligenceBloodCancer BurdenCancer DetectionCancer DiagnosticsCancer PatientCellsClinicalColorectal CancerComputersCopy Number PolymorphismCustomDNADataDetectionDetection of Minimal Residual DiseaseDevelopmentDiseaseEngineeringEnsureEpigenetic ProcessExcisionFDA approvedFaceFosteringGenesGenomeGenomicsImageImmunotherapyIn complete remissionInterdisciplinary StudyJointsLeftLinkMachine LearningMalignant NeoplasmsMeasuresMedicineMethodsMonitorMutationNatureNeoadjuvant TherapyNoiseNon-Invasive Cancer DetectionNon-Small-Cell Lung CarcinomaOncologistOncologyOperative Surgical ProceduresPathologicPatientsPerformancePlasmaPostoperative PeriodPrediction of Response to TherapyProliferatingRecurrenceResidual NeoplasmSamplingScientistSignal TransductionSingle Nucleotide PolymorphismSiteSolid NeoplasmSyncopeTechniquesTechnologyTestingTissuesTumor BurdenTumor TissueTumor stageadvanced diseasebaseburden of illnesscancer cellcancer diagnosiscancer recurrencecancer therapycell free DNAcheckpoint inhibitionclinical applicationclinical carecomputerized toolsde novo mutationdeep learningdeep sequencingdenoisingdetection sensitivityempoweredgenome sequencinggenome-widehigh riskimaging biomarkerimprovedliquid biopsymachine learning frameworkmelanomamortalitymultidisciplinarynew technologynon-invasive monitorpersonalized immunotherapyprognosticrelapse riskresponsetargeted sequencingtumortumor DNAvariant detectionwhole genome
项目摘要
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.
项目摘要
癌症诊断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
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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Dan Landau其他文献
Dan Landau的其他文献
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{{ truncateString('Dan Landau', 18)}}的其他基金
Single-Cell Multi-omics to Link Clonal Mosaicism (CM) Genotypes with Chromatin, Epigenomic, Transcriptomic and Protein Phenotypes
单细胞多组学将克隆嵌合 (CM) 基因型与染色质、表观基因组、转录组和蛋白质表型联系起来
- 批准号:
10662879 - 财政年份:2023
- 资助金额:
$ 65.85万 - 项目类别:
Expanding the GoT toolkit to link single-cell clonal genotypes with protein, transcriptomic, epigenomic and spatial phenotypes
扩展 GoT 工具包,将单细胞克隆基因型与蛋白质、转录组、表观基因组和空间表型联系起来
- 批准号:
10698112 - 财政年份:2022
- 资助金额:
$ 65.85万 - 项目类别:
Genome-wide mutational integration for ultra-sensitive plasma tumor burden monitoring in immunotherapy
全基因组突变整合用于免疫治疗中超灵敏血浆肿瘤负荷监测
- 批准号:
10631872 - 财政年份:2022
- 资助金额:
$ 65.85万 - 项目类别:
Center for Integrated Cellular Analysis - Alanna Fields
综合细胞分析中心 - Alanna Fields
- 批准号:
10839068 - 财政年份:2020
- 资助金额:
$ 65.85万 - 项目类别:
Center for Integrated Cellular Analysis - Lina Habba
综合细胞分析中心 - Lina Habba
- 批准号:
10839082 - 财政年份:2020
- 资助金额:
$ 65.85万 - 项目类别:
Center for Integrated Cellular Analysis - Salma Amin
综合细胞分析中心 - Salma Amin
- 批准号:
10839076 - 财政年份:2020
- 资助金额:
$ 65.85万 - 项目类别:
Center for Integrated Cellular Analysis - Stephanie Figueroa Reyes
综合细胞分析中心 - Stephanie Figueroa Reyes
- 批准号:
10839077 - 财政年份:2020
- 资助金额:
$ 65.85万 - 项目类别:
Center for Integrated Cellular Analysis - Andrew Brown
综合细胞分析中心 - 安德鲁·布朗
- 批准号:
10839072 - 财政年份:2020
- 资助金额:
$ 65.85万 - 项目类别:
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