Project 1:Evolutionary dynamics and drivers of breast cancer metastasis and relapse

项目1：乳腺癌转移和复发的进化动力学和驱动因素

基本信息

批准号：
10704684
负责人：
Christina N Curtis
金额：
$ 33.86万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-14 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10704684
关键词：
11q13 17q23 8q24 ATAC-seq Address Aftercare Bar Codes Biological Markers Breast Cancer Cell Breast Cancer Patient Breast cancer metastasis CCND1 gene CDK4 gene Cancer Relapse Cell surface Cells Cellular Indexing of Transcriptomes and Epitopes by Sequencing Clinical Clinical Data Clinical Trials Collaborations Colorectal Cancer Computer Models Cues Data Data Set Diagnosis Disease Distant EIF4EBP1 gene ERBB2 gene Endocrine Engineering Estrogen receptor positive FGF3 gene FGFR1 gene FRAP1 gene Fibroblast Growth Factor Receptors Genomics HBXAP gene Immune Immunosuppression In Situ In Vitro Link Longitudinal cohort Longterm Follow-up Lymphocyte MCF10A cells Macrophage Malignant neoplasm of lung Measures Metastatic breast cancer Modeling Nature Neoadjuvant Therapy Neoplasm Metastasis Oncogenic Organoids PVT1 gene Patients Pattern Polysaccharides Primary Lesion Primary Neoplasm Prognosis Progression-Free Survivals Proto-Oncogene Proteins c-akt RNA Recurrence Relapse Resistance Route Sampling Site Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Subgroup Techniques Testing Time Tissues Xenograft procedure biomarker driven breast cancer progression cohort digital genomic data glycosyltransferase high risk hormone therapy improved in silico in vivo inhibitor malignant breast neoplasm mathematical model monocyte nano-string neoplastic cell new therapeutic target novel therapeutic intervention overexpression predictive modeling prospective receptor expression relapse risk resistance mechanism response spatiotemporal targeted sequencing targeted treatment transcriptomics treatment response triple-negative invasive breast carcinoma tumor tumor progression

项目摘要

Abstract/Project Summary While prognosis for early stage breast cancer (BC) has improved dramatically, 20-30% of patients recur at distant sites and ultimately succumb to their disease. To date, the spatial and temporal patterns of BC relapse have been difficult to predict. Moreover, when and how metastatic potential is determined is largely unknown. To address these questions, we have developed spatial computational and mathematical models of tumor progression to infer the ‘time’ of metastatic seeding. Application of these techniques to paired primary breast cancers with matched metastases, yielded quantitative evidence for early metastatic seeding, often 2- 4 years before the primary lesion is detectable (Nat Gen 2020), consistent with experimental and clinical data indicating that BC cells can disseminate early and persist. In parallel, we defined the rates and routes of relapse across 11 Integrative Clusters (ICs) in an analysis of 2,000 early-stage BCs with long-term follow-up (METABRIC cohort: Nature 2019; 2012). These include two triple negative BC subgroups with distinct relapse trajectories and four ER+/HER2- ICs (1, 2, 6 and 9) with high and persistent risk of relapse up to 20 years after diagnosis. Collectively, these high-risk subgroups account for 26% of all ER+/HER2- tumors and the majority of BC relapses. The pattern of copy number amplification (CNA) and overexpression in these high-risk ICs echoes that seen for HER2+ BC, each harboring druggable clonal genomic drivers. This breakthrough discovery led to a biomarker-driven clinical trial evaluating new targeted therapies in early-stage high-risk BC patients. However, the definitive drivers and mechanisms of progression in these subgroups have yet to be characterized. Moreover, how the local tissue microenvironment (TME) varies across the ICs and contributes to immune suppression, dissemination, dormancy and relapse is unknown We hypothesize that the oncogenic drivers of the high-risk ICs drive tumor progression and relapse by dictating immune contexture and remodeling the cell surface glycoproteome, potentiating tolerogenic cell states– as further functionally evaluated in Projects 2 and 3 in collaboration with Michael Angelo and Michael Bassik. Additionally, we hypothesize that these amplicons confer intrinsic endocrine resistance, necessitating new therapeutic strategies. We test these hypotheses in the following Specific Aims: Aim 1- Characterizes the TME in longitudinal BC cohorts and evaluate the association between IC, glycosyltransferase expression, response to therapy and relapse. Aim 2 - Quantifies the dynamics of BC relapse and the response to clinical therapies across the ICs. Aim 3 - Measures clonal dynamics and identify mechanisms of resistance to targeted and endocrine therapies in high-risk ER+ BC patient-derived organoids from primary and metastatic lesions.

摘要/项目摘要虽然早期乳腺癌 (BC) 的预后已显着改善，但 20-30% 的患者在迄今为止，BC 复发的空间和时间模式。此外，转移潜力的确定时间和方式在很大程度上还很难预测。为了解决这些问题，我们开发了空间计算和数学模型。将这些技术应用于配对原发性肿瘤进展来推断转移播种的“时间”。具有匹配转移的乳腺癌，产生了早期转移播种的定量证据，通常是 2- 在可检测到原发病变之前 4 年 (Nat Gen 2020)，与实验和临床数据一致表明 BC 细胞可以早期传播并持续存在。与此同时，我们定义了 11 个综合集群 (IC) 的复发率和路径对 2,000 个早期 BC 进行长期随访分析（METABRIC 队列：Nature 2019；2012）。包括两个具有不同复发轨迹的三阴性 BC 亚组和四个 ER+/HER2- IC（1、2、6 和 9) 诊断后 20 年内具有高且持续的复发风险。亚组占所有 ER+/HER2- 肿瘤的 26%，并且大多数 BC 复发。这些高风险 IC 中的数字扩增 (CNA) 和过度表达与 HER2+ BC 中的情况相呼应，每个这一突破性的发现导致了生物标志物驱动的临床。评估早期高危 BC 患者新靶向疗法的试验然而，最终的驱动因素和。此外，这些亚组的进展机制尚未确定。不同 IC 的微环境 (TME) 各不相同，有助于免疫抑制、传播、休眠和复发未知我们追求高风险 IC 的致癌驱动因素通过以下方式驱动肿瘤进展和复发：决定免疫环境并重塑细胞表面糖蛋白组，增强耐受性细胞状态– 与 Michael Angelo 和 Michael 合作在项目 2 和 3 中进一步进行功能评估此外，我们认为这些扩增子具有内在的内分泌抵抗性，因此需要我们在以下具体目标中测试这些假设：目标 1 - 表征纵向 BC 队列中的 TME 并评估 IC、糖基转移酶表达、对治疗和复发的反应目标 2 - 量化 BC 复发的动态和对临床的反应。目标 3 - 测量克隆动态并确定靶向耐药机制。以及对来自原发性和转移性病变的高风险 ER+ BC 患者来源的类器官的内分泌治疗。