Quantitative and functional characterization of therapeutic resistance in cancer

癌症治疗耐药性的定量和功能表征

• 批准号: 10162303
• 负责人: DOUGLAS A LAUFFENBURGER
• 金额: $ 197.6万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-07 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10162303
• 关键词: Acute leukemia; Address; Affect; Animals; Architecture; Award; Binding; Biological Assay; Cell Line; Cells; Cellular Assay; Clinical Research; Colon Carcinoma; DNA sequencing; Data; Decision Making; Docking; Epithelial; Exhibits; Failure; Genetic; Genetic Transcription; Genomics; Genotype; Goals; Hematopoietic; Heterogeneity; Immunophenotyping; In Situ; In Vitro; Individual; Malignant Neoplasms; Malignant neoplasm of pancreas; Measurement; Measures; Mediating; Minority; Molecular; Neoplasm Metastasis; Non-Malignant; Oncology; Organoids; Pathogenesis; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Population; Preclinical Testing; Prediction of Response to Therapy; Primary Neoplasm; Property; Protein Secretion; Research; Residual Neoplasm; Resistance; Sampling; Signal Pathway; Specimen; Surface; Systems Biology; Testing; Therapeutic; Time; Treatment Failure; Treatment-related toxicity; Xenograft procedure; base; cancer cell; career; cell killing; chemotherapy; clinically actionable; clinically relevant; cost; design; drug efficacy; drug sensitivity; drug testing; improved; in vivo; individual patient; inhibitor/antagonist; leukemia; mathematical model; molecular marker; neoplastic cell; new technology; novel; outreach program; pancreatic neoplasm; paracrine; population based; precision medicine; predicting response; preservation; resistance mutation; response; small molecule; targeted agent; therapeutically effective; therapy resistant; treatment response; tumor; tumor heterogeneity; tumor microenvironment

Overall – Project Summary Despite tremendous advances in our understanding of cancer pathogenesis, the treatment of individual patients with either conventional chemotherapy or targeted agents remains highly empiric. Current efforts to predict drug efficacy are generally focused on genetic and transcriptional markers of pathway activation or drug binding, such as resistance mutations that sterically hinder small molecule binding or activate parallel or orthogonal signaling pathways. These markers exist in a very small fraction of all cancers, such that most patients are treated with little or no understanding of whether they will respond to an individual therapy. This results in many patients receiving ineffective and/or unnecessarily toxic therapies. There is a desperate need to change this paradigm. The ideal for characterizing therapeutic sensitivity would allow for: real-time decision making, identification of rare subpopulations with therapeutic resistance, analysis of very small samples (e.g. MRD), and maintains viability individual cells for downstream assays to characterize phenotypic, genotypic, transcriptional and other determinants of sensitivity. The overall goal of our U54 application is to address this need using new strategies for predicting therapeutic response in which paired phenotypic and genomic properties are measured at the single-cell level. Phenotypic properties will include both physical parameters (e.g. mass, mass accumulation rate) and molecular markers (e.g. protein secretion, surface immunophenotype) that are rapidly affected by effective therapeutics and precede longer- term phenotypes (e.g. loss of viability). Because these properties are measured for each single cell, clonal architectures based on therapeutic response will be established across each tumor sample by incorporating molecular and physical parameter data from large numbers of cells. In settings of deep treatment response, pre-treatment and MRD samples will be compared to define the effects of therapy on clonal architecture. The cells that exhibit particular functional properties (e.g. phenotypic non-responders) will be isolated and analyzed for genomic determinants of these properties. These data will then be incorporated into mathematical models to design and optimize therapeutic approaches that overcome the heterogeneity within individual tumors responsible for treatment failure. By pursuing this approach, our center will establish a framework that enables an iterative cycle between novel single-cell measurements from clinically-relevant specimens and computational approaches that result in testable predictions.

总体 - 项目摘要 尽管我们对癌症发病机理的理解取得了巨大进步，但对个人的治疗 常规化学疗法或靶向药物的患者仍然高度经验。当前的努力 预测药物效率通常集中于途径激活或药物的遗传和转录标记 结合，例如在空间上阻碍小分子结合或激活平行或激活平行或激活的抗性突变 正交信号通路。这些标记存在于所有癌症中很小的一小部分，因此大多数 患者几乎没有理解他们是否会对个人做出反应的治疗 治疗。这导致许多患者接受无效和/或不必要的有毒疗法。有一个 迫切需要改变这种范式。表征治疗敏感性的理想选择将允许 对于：实时决策，鉴定具有热阻力的罕见亚群，分析 非常小的样品（例如MRD），并保持生存能力单元的下游测定法以表征 表型，基因型，转录和其他灵敏度的决定。我们U54的总体目标 应用是使用新的策略来预测治疗反应的新需求 配对的表型和基因组特性在单细胞水平上测量。表型特性 将包括物理参数（例如质量，质量积累率）和分子标记（例如蛋白质 分泌，表面免疫表型），受到有效疗法的迅速影响，先于更长的疗法 术语表型（例如生存能力丧失）。因为这些特性是针对每个单元的克隆测量的 通过编码，将在每个肿瘤样本中建立基于治疗反应的体系结构 来自大量细胞的分子和物理参数数据。在深度治疗反应的环境中 将比较预处理和MRD样品，以定义治疗对克隆结构的影响。这 将分离并分析存在特定功能特性（例如表型非反应器）的单元 用于基因组确定这些特性。然后将这些数据纳入数学模型 设计和优化克服各个肿瘤中异质性的治疗方法 负责治疗失败。通过采用这种方法，我们的中心将建立一个框架 在与临床相关标本的新型单细胞测量之间启用迭代循环 以及导致可检验预测的计算方法。

项目成果

PI3K activation allows immune evasion by promoting an inhibitory myeloid tumor microenvironment.

• DOI: 10.1136/jitc-2021-003402
• 发表时间: 2022-03
• 期刊: Journal for immunotherapy of cancer
• 影响因子: 10.9
• 作者: Collins NB;Al Abosy R;Miller BC;Bi K;Zhao Q;Quigley M;Ishizuka JJ;Yates KB;Pope HW;Manguso RT;Shrestha Y;Wadsworth M;Hughes T;Shalek AK;Boehm JS;Hahn WC;Doench JG;Haining WN
• 通讯作者: Haining WN

Targeting minimal residual disease: a path to cure?

• DOI: 10.1038/nrc.2017.125
• 发表时间: 2018-04
• 期刊: Nature reviews. Cancer
• 作者: Luskin MR;Murakami MA;Manalis SR;Weinstock DM
• 通讯作者: Weinstock DM

Alveolar macrophages in early stage COPD show functional deviations with properties of impaired immune activation.

• DOI: 10.3389/fimmu.2022.917232
• 发表时间: 2022
• 期刊: Frontiers in immunology
• 影响因子: 7.3

Oncogenic HSP90 Facilitates Metabolic Alterations in Aggressive B-cell Lymphomas.

• DOI: 10.1158/0008-5472.can-21-2734
• 发表时间: 2021-10-15
• 期刊: Cancer research
• 影响因子: 11.2
• 作者: Calvo-Vidal MN;Zamponi N;Krumsiek J;Stockslager MA;Revuelta MV;Phillip JM;Marullo R;Tikhonova E;Kotlov N;Patel J;Yang SN;Yang L;Taldone T;Thieblemont C;Leonard JP;Martin P;Inghirami G;Chiosis G;Manalis SR;Cerchietti L
• 通讯作者: Cerchietti L

Metabolic regulation of species-specific developmental rates.

• DOI: 10.1038/s41586-022-05574-4
• 发表时间: 2023-01
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Diaz-Cuadros, Margarete;Miettinen, Teemu P.;Skinner, Owen S.;Sheedy, Dylan;Diaz-Garcia, Carlos Manlio;Gapon, Svetlana;Hubaud, Alexis;Yellen, Gary;Manalis, Scott R.;Oldham, William M.;Pourquie, Olivier
• 通讯作者: Pourquie, Olivier