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

• 批准号: 10272389
• 负责人: Christina N Curtis
• 金额: $ 26.33万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 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/10272389
• 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; 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; biomarker-driven; breast cancer progression; cohort; digital; genomic data; glycosyltransferase; high risk; hormone therapy; improved; in silico; in vivo; inhibitor/antagonist; macrophage; 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

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）的预后已得到显著改善，但20-30%的患者在术后复发。 最终死于疾病迄今为止，BC复发的空间和时间模式 很难预测此外，何时以及如何确定转移潜力在很大程度上取决于 未知为了解决这些问题，我们开发了空间计算和数学模型， 肿瘤进展来推断转移性种植的“时间”。将这些技术应用于配对的初级 具有匹配转移的乳腺癌，产生了早期转移播种的定量证据，通常为2- 可检测到原发性病变前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，我们在11个综合集群（IC）中定义了复发率和复发途径。 对2,000例早期BC进行长期随访分析（METABRIC队列：Nature 2019; 2012）。这些 包括具有不同复发轨迹两个三阴性BC亚组和四个ER+/HER 2-IC（1、2、6和 9)诊断后20年内复发的风险很高且持续存在。这些高风险的 这些亚组占所有ER+/HER 2-肿瘤的26%，并且大多数BC复发。复制模式 在这些高危IC中的CNA和过度表达与HER 2 + BC相似， 含有可药用克隆基因组驱动程序。这一突破性发现导致了生物标志物驱动的临床 在早期高风险BC患者中评估新靶向治疗的试验。然而，决定性的驱动因素和 这些亚组中的进展机制尚待表征。此外，局部组织如何 微环境（TME）在IC之间变化，并有助于免疫抑制，传播， 休眠和复发是未知的 我们假设，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： 决定免疫结构和重塑细胞表面糖蛋白质组，增强致耐受性细胞状态- 在项目2和3中，与Michael Angelo和Michael 巴西克此外，我们假设这些扩增子赋予内在内分泌抵抗力，因此需要 新的治疗策略。我们在以下具体目标中测试这些假设：目标1-表征 在纵向BC队列中进行TME，并评估IC、糖基转移酶表达， 对治疗的反应和复发。目标2 -量化BC复发的动态和对临床治疗的反应 在IC中进行治疗。目的3 -测量克隆动态并确定对靶向的抗性机制 和内分泌疗法治疗原发性和转移性病变的高风险ER+ BC患者源性类器官。