Prognostic analysis and progression modeling of basal-like breast cancer using multi-region sequencing

使用多区域测序对基底样乳腺癌进行预后分析和进展建模

• 批准号: 10586445
• 负责人: Steve Goodison
• 金额: $ 66.81万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-09 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10586445
• 关键词: Address; American Society of Clinical Oncology; Area; Automobile Driving; Bioinformatics; Biological Process; Cancer Patient; Cancer Prognosis; Cause of Death; Classification; Clinic; Clinical; Clinical Management; Clinical Research; Complex; Computational Technique; Computing Methodologies; DNA Sequence Alteration; DNA sequencing; Data; Data Set; Derivation procedure; Development; Disease; Disease-Free Survival; ERBB2 gene; Estrogen Receptors; Evaluation; Event; Evolution; Experimental Designs; Future; Genes; Genetic; Genetic Determinism; Genomic Instability; Guidelines; Institution; Interdisciplinary Study; Learning; Life; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Mammary Neoplasms; Methods; Modeling; Molecular; Molecular Profiling; Multiomic Data; Neoplasm Metastasis; Outcome; Pathology; Pathway interactions; Patients; Pattern; Prevalence; Primary Neoplasm; Process; Progesterone Receptors; Recurrence; Resolution; Role; Sampling; Series; Specimen; Structure; System; Technology; Therapeutic; Tissue Banks; Treatment Protocols; Tumor Biology; Tumor Tissue; Visualization; Woman; Work; anticancer research; chemotherapy; genetic association; high risk; indexing; insight; malignant breast neoplasm; molecular subtypes; multidisciplinary; next generation sequencing; novel; prognostic; prognostic assays; prognostic model; prognostication; prospective; targeted treatment; theories; therapeutic target; tissue mapping; transcriptome sequencing; tumor; tumor progression; tumorigenesis

Project Summary/Abstract Breast cancer is the most common cancer in women worldwide, and the fifth most common cause of death from cancer overall. As with many other cancers, breast cancer presents in a variety of forms and can be broadly divided into four molecular subtypes, including luminal A, luminal B, HER2+ and basal. Among them, basal cancer represents ~20% of primary breast tumors and is one of the most aggressive and deadly subtypes. While significant efforts have been made, the biological process of how basal cancer progresses to a malignant, life-threatening disease is not well understood, and the prognostication and treatment of basal cancer remain major challenges. Specifically, there are currently no prognostic tests available that can assist clinical management, and nearly all basal cancer patients are classified as having a high risk of recurrence. Moreover, as the majority of basal tumors lack expression of estrogen receptor (ER), progesterone receptor (PR) and HER2, there are presently no effective targeted treatment regimens available, and harsh, indiscriminate chemotherapy is the only treatment option. Consequently, a significant number of basal cancer patients are under- or over-treated. Built logically on our previous work, we propose a large-scale interdisciplinary research plan, in which we will use multi-region sequencing and advanced computational techniques to address some pressing issues and the aforementioned unmet clinical needs of basal breast cancer. Specifically, we will perform molecular profiling of 300 primary basal tumors and 50 matched metastatic tumors that we have identified from Mayo Clinic tissue banks. By using the obtained multi-region sequencing tumor tissue data, we will derive a prognostic evaluation system for basal cancer through multiple instance learning, and construct and validate a high-resolution progression model of basal cancer. We will also perform a large-scale analysis on a range of molecular data to systematically search for genetic determinants of basal cancer progression at both gene and pathway levels, which will provide a wealth of insights into molecular mechanisms of tumorigenesis and enable us to identify potential therapeutic targets for basal cancer. If successfully implemented, this work will significantly advance the basal cancer research, and pave the way for applying similar strategies to study other deadly cancers.

项目摘要/摘要 乳腺癌是全世界女性最常见的癌症，也是第五大最常见的死亡原因 从整体上看是癌症造成的。与许多其他癌症一样，乳腺癌的表现形式多种多样， 大致分为四种分子亚型，包括管腔A、管腔B、HER2+和碱基。其中， 基底癌约占乳腺原发肿瘤的20%，是最具侵袭性和致命性的肿瘤之一 子类型。虽然已经做出了重大努力，但基础癌如何进展为 恶性、危及生命的疾病尚不清楚，其预测和治疗基本 癌症仍然是主要的挑战。具体地说，目前还没有可用的预后测试来帮助 临床管理方面，几乎所有的基础癌症患者都被归类为具有高复发风险。 此外，由于大多数基底肿瘤缺乏雌激素受体(ER)、孕激素受体的表达 (PR)和HER2，目前没有有效的靶向治疗方案可用， 不分青红皂白的化疗是唯一的治疗选择。因此，相当数量的基底癌 患者治疗不足或过度。在我们以前工作的基础上，我们提出了一个大规模的 跨学科研究计划，其中我们将使用多区域排序和高级计算 解决一些紧迫问题和前述未得到满足的基础乳房临床需求的技术 癌症。具体地说，我们将对300个原发的基底肿瘤和50个匹配的肿瘤进行分子图谱分析 我们从梅奥诊所组织库中发现的转移性肿瘤。通过使用所获得的多区域 对肿瘤组织数据进行排序，我们将从多个角度推导出一个基底层癌预后评估系统 实例学习，并构建和验证一个高分辨率的基底肿瘤进展模型。我们还将 对一系列分子数据进行大规模分析，以系统地搜索遗传决定因素 在基因和途径水平上对基础癌症进展的研究，这将为 肿瘤发生的分子机制，并使我们能够确定潜在的治疗靶点 癌症。如果成功实施，这项工作将大大推进基础癌症的研究，并为 将类似的策略应用于研究其他致命癌症的方法。