Combating Subclonal Evolution of Resistant Cancer Phenotypes

对抗耐药癌症表型的亚克隆进化

• 批准号: 10207524
• 负责人: ANDREA Hope BILD
• 金额: $ 219.53万
• 依托单位: BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10207524
• 关键词: Address; Behavior; Biological Models; Biological Pharmacology; Biological Process; Breast; Cancer Patient; Cells; Chemoresistance; Clinical; Clinical Treatment; Clinical Trials; Clonal Evolution; Clonality; Collection; Communities; Community Outreach; Complex; DNA; Data; Development; Drug Targeting; Drug resistance; Education and Outreach; Educational Activities; Evolution; Faculty; Gene Expression Profile; Generations; Genetic; Genomics; Genotype; Goals; Heterogeneity; Individual; Infrastructure; Institutes; Knowledge; Malignant Neoplasms; Malignant neoplasm of ovary; Mathematics; Measures; Methodology; Modeling; Mutation; Neoplasm Metastasis; Oncogenic; Ovarian; Pathway interactions; Patients; Pharmacology; Pharmacology Study; Pharmacotherapy; Phenotype; Population Dynamics; Population Heterogeneity; Postdoctoral Fellow; Prevention program; Regimen; Research; Research Project Grants; Resistance; Resources; Sampling; Science; Scientific Advances and Accomplishments; Scientist; Signal Pathway; Signal Transduction; Site; Solid; Somatic Mutation; System; Systems Analysis; Systems Biology; Testing; Therapeutic; Translations; Variant; base; cancer cell; cohort; computational pipelines; computerized tools; data sharing; data tools; dynamic system; effective therapy; experimental study; improved; innovation; knowledge base; malignant breast neoplasm; member; multidisciplinary; neoplastic cell; next generation; novel drug combination; outreach program; personalized cancer therapy; predictive modeling; pressure; prevent; programs; refractory cancer; student training; synergism; targeted treatment; therapeutic target; tool; transcriptomics; treatment response; treatment strategy; tumor; tumor heterogeneity; tumor microenvironment

Overall abstract Our Cancer Systems Biology Center of HoPE (Heterogeneity of Phenotypic Evolution) will develop a suite of systems-based methodologies to understand how genomic diversity, clonal evolution, and phenotypic change . To evaluate their potential for translation, we will integrate these dynamic models with clinical trials that will evaluate whether these phenotypic changes can be targeted for therapy. We hypothesize that acquired resistance emerges from selection acting on phenotypes during tumor evolution, and that simultaneously measuring and modeling subclone genotypes and phenotypes will identify new, and testable, therapeutic targets. Selective pressures from therapy and the tumor microenvironment can propel subclones from every patient's tumor along an evolutionary trajectory that leads to resistance. Indeed, our data shows that both genetic and phenotypic diversity among tumor subclones evolves as cancer cells progress to a resistant state. However, it is not yet known the specific phenotypes that promote that resistant state, the interactions among them, and how they converge to common resistant phenotypes seen in late stage cancer. To address these and other questions, we will develop a new class of dynamical systems models of subclone evolution to characterize the changes and development of key cell states that arise during acquired chemo-resistance and metastasis using our unique patient cohorts. These mechanistic models will identify points of therapeutic vulnerability that we will test in clinical trials aimed at blocking evolution to a resistant state by targeting critical resistant phenotypes. Our Center is comprised of an Administrative, Education/Outreach, Translational, and Computational Cores, in addition to two complementary projects. The synergies are derived from: 1) the convergent parameterization of the evolutionary models drawn from deep longitudinal patient progression studies (Project 1) and broad multisite metastatic tumor analyses (Project 2), resulting in a robust model to identify resistant states for clinical targeting; and 2) an integrated computational and experimental framework and resources for dissecting tumor heterogeneity and evolution that will contribute to an improved capacity for personalized cancer therapy. Our multidisciplinary team of systems biologists, bioinformaticians, tumor biologists, pharmacologists, mathematical biologists, and clinicians will tackle these scientific challenges. We will create programs to educate the next generation of scientists in systems biology and inform the community about the latest scientific advances and their impact on treatment strategies. And we will provide state of the art tools for the analysis of patient samples and tumor genomic complexity. These studies move beyond prior research by integrating cell population dynamics and cellular phenotypes with cellular genotypes, and will deliver approaches and a knowledge base to block or reverse the transition to a resistant state for advanced stage cancer patients. interact in the progression toward chemoresistant breast and ovarian cancer

总体摘要 我们的癌症系统生物学中心HoPE（表型进化的异质性）将发展 一套 基于系统的方法来了解基因组多样性，克隆进化和表型变化 .评估他们的潜力， 翻译，我们将把这些动态模型与临床试验相结合，以评估这些模型是否 表型变化可以作为治疗的目标。我们假设获得性耐药性来自于 选择作用于肿瘤演变过程中的表型，同时测量和 模拟亚克隆基因型和表型将鉴定新的、可测试的治疗靶点。 来自治疗和肿瘤微环境的选择性压力可以推动每个患者的亚克隆 肿瘤沿着一条进化的轨迹，导致耐药性。事实上，我们的数据显示， 肿瘤亚克隆中的表型多样性随着癌细胞进展到耐药状态而演变。但 目前还不知道促进耐药状态的特定表型，它们之间的相互作用， 它们如何汇聚成晚期癌症中常见的耐药表型。为了解决这些和其他 问题，我们将开发一类新的动力系统模型的亚克隆进化的特点， 在获得性化疗耐药性和转移过程中出现的关键细胞状态的变化和发展， 我们独特的病人群体这些机制模型将确定治疗的弱点，我们将 临床试验中的一项测试，旨在通过靶向关键的耐药表型来阻断向耐药状态的演变。 我们的中心由行政、教育/外展、翻译和计算核心组成， 两个互补项目。协同作用来自：1）收敛参数化 从深度纵向患者进展研究（项目1）和广泛的 多中心转移性肿瘤分析（项目2），产生了一个稳健的模型，用于识别临床应用的耐药状态 靶向;和2）用于解剖肿瘤的综合计算和实验框架和资源 这将有助于提高个性化癌症治疗的能力。我们 系统生物学家、生物信息学家、肿瘤生物学家、药理学家、数学 生物学家和临床医生将解决这些科学挑战。我们将创建项目来教育下一个 系统生物学的一代科学家，并告知社会最新的科学进展， 对治疗策略的影响。我们将提供最先进的工具来分析病人 样本和肿瘤基因组复杂性。这些研究超越了先前的研究， 种群动态和细胞表型与细胞基因型，并将提供方法和 知识库，以阻止或逆转晚期癌症患者向耐药状态的转变。 在乳腺癌和卵巢癌化疗耐药的进展中相互作用

项目成果

Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives.

• DOI: 10.3934/mbe.2021315
• 发表时间: 2021-07-21
• 期刊: Mathematical biosciences and engineering : MBE
• 作者: Buhler CK;Terry RS;Link KG;Adler FR
• 通讯作者: Adler FR

Opportunities for improving cancer treatment using systems biology.

• DOI: 10.1016/j.coisb.2019.10.018
• 发表时间: 2019-10
• 期刊: Current opinion in systems biology
• 影响因子: 3.7
• 作者: J. Griffiths;A. Cohen;Veronica C Jones;R. Salgia;Jeffrey T. Chang;A. Bild

Phylogenetic inference from single-cell RNA-seq data.

• DOI: 10.1038/s41598-023-39995-6
• 发表时间: 2023-08-08
• 期刊: Scientific reports
• 影响因子: 4.6

ENDORSE: a prognostic model for endocrine therapy in estrogen-receptor-positive breast cancers.

• DOI: 10.15252/msb.202110558
• 发表时间: 2022-06
• 期刊: Molecular systems biology
• 影响因子: 9.9

Immune Phenotype and Response to Neoadjuvant Therapy in Triple-Negative Breast Cancer.

• DOI: 10.1158/1078-0432.ccr-21-0144
• 发表时间: 2021-10-01
• 期刊: Clinical cancer research : an official journal of the American Association for Cancer Research
• 作者: Yam C;Yen EY;Chang JT;Bassett RL;Alatrash G;Garber H;Huo L;Yang F;Philips AV;Ding QQ;Lim B;Ueno NT;Kannan K;Sun X;Sun B;Parra Cuentas ER;Symmans WF;White JB;Ravenberg E;Seth S;Guerriero JL;Rauch GM;Damodaran S;Litton JK;Wargo JA;Hortobagyi GN;Futreal A;Wistuba II;Sun R;Moulder SL;Mittendorf EA
• 通讯作者: Mittendorf EA