Diversity Supplement: Identification of adaptive response mechanisms in breast cancer by information theory and proteomics

多样性补充：通过信息论和蛋白质组学鉴定乳腺癌的适应性反应机制

• 批准号: 10310720
• 负责人: Nataly Kravchenko-Balasha
• 金额: $ 11.28万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10310720
• 关键词: Apoptotic; Automobile Driving; Biological; Biological Models; Breast Cancer cell line; Bypass; Cancer cell line; Cell Line; Cell Survival; Cells; Clinic; Combined Modality Therapy; Coupled; Data; Data Set; Diagnostic; Disease; Dose; Drug Combinations; Drug resistance; Epigenetic Process; Future; Goals; Human; In Vitro; Informatics; Information Theory; Intervention; Knowledge; Lead; Ligands; Malignant Neoplasms; Mass Spectrum Analysis; Measurement; Mediating; Medical; Medicine; Molecular; Mutation; Oncogenic; Pathway interactions; Patient-derived xenograft models of breast cancer; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phase; Phenotype; Phosphoproteins; Phosphorylation; Physiological; Population; Primary Neoplasm; Process; Protein Analysis; Proteins; Proteomics; Research; Resistance; Resistance development; Resolution; Sampling; Signal Transduction; Specimen; Structure; System; Systems Development; Therapeutic; Thermodynamics; Time; Tissues; Translating; Tumor Tissue; base; cancer cell; cancer type; chemotherapy; comparative; data acquisition; design; disease phenotype; drug development; efficacious treatment; in vivo; individual patient; individualized medicine; insight; malignant breast neoplasm; migration; neoplastic cell; novel drug combination; patient derived xenograft model; phosphoproteomics; prevent; response; therapeutic development; therapeutic target; therapy resistant; tool; triple-negative invasive breast carcinoma; tumor; tumor xenograft

SUMMARY Over the past decade the accumulation of large-scale systems level data sets has occurred at an accelerating pace. Unfortunately, to date this massive accumulation of biological and medical information has rarely translated into truly efficacious therapies that dramatically alter the course of disease. Clearly new informatics approaches are needed that will enable the identification of transformative therapeutics. The central goal of this proposal is to develop an experimental-theoretical approach that defines, with high accuracy, the altered protein network structures present in each cancer malignancy. We propose to integrate quantitative mass spectrometry- based protein and protein phosphorylation measurements with surprisal analysis, a thermodynamic-based information theory approach, to resolve altered protein network structure in each malignancy. An altered network in each patients' tumor may comprise several distinct, sometimes rewired, protein subnetworks that drive the molecular imbalance in cancer tissue. Identification of unbalanced subnetworks will highlight molecular nodes that will be targeted in each patient to either restore the basal, non-transformed state or to decrease tumor cell viability. To demonstrate the ability of this approach to define unbalanced subnetworks and their associated therapeutic targets, the proposal is divided into three phases with increasing complexity and physiological relevance. In the first phase, RTK networks in breast cancer cell lines representing different subtypes will be stimulated with natural ligands to induce well characterized unbalanced processes to validate the ability of surprisal analysis to identify these networks. In the second phase, unbalanced processes present in the basal, unstimulated state of each cell line will be defined. Therapeutic targeting of these processes, alone or in combination, at high and low dose, will be performed to assess the effect of complete vs. incomplete inhibition. Unbalanced processes mediating the development of therapeutic resistance during long-term low- dose treatment will be quantified at various time points to predict combination therapies to abrogate resistance. Finally, surprisal analysis will be used to identify unbalanced processes associated with chemotherapeutic resistance in vivo in triple negative breast cancer patient derived xenograft tumors. Nodes in these imbalanced networks will be targeted to decrease tumor viability. Combination with chemotherapy may further sensitize tumor cells to treatment. Through these efforts we aim to demonstrate the ability of this combined proteomic- surprisal analysis strategy to rationally design, with high-precision, patient-specific drug cocktails that prevent drug resistance development.

总结 在过去的十年中，大规模系统级数据集的积累以加速的速度发生。 步伐。不幸的是，迄今为止，这种生物和医学信息的大量积累很少 转化为真正有效的治疗方法，大大改变了疾病的进程。显然，新信息学 需要能够识别变革性疗法的方法。这个项目的中心目标是 一个建议是开发一种实验-理论方法，以高精度定义改变的蛋白质 网络结构存在于每种癌症恶性肿瘤中。我们建议整合定量质谱- 基于蛋白质和蛋白质磷酸化测量的蛋白质分析， 信息理论方法，以解决改变蛋白质网络结构在每一个恶性肿瘤。改变的 每个患者肿瘤中的蛋白质网络可以包括几个不同的，有时重新连接的蛋白质子网络， 导致癌组织中的分子失衡。不平衡子网的识别将突出显示 这些分子节点将在每个患者中被靶向，以恢复基础的、非转化的状态，或者 降低肿瘤细胞活力。为了证明这种方法定义不平衡子网络的能力， 其相关的治疗目标，该提案分为三个阶段，复杂性不断增加， 生理相关性在第一阶段，RTK网络在乳腺癌细胞系代表不同的 将用天然配体刺激亚型，以诱导充分表征的不平衡过程， 通过网络分析来识别这些网络的能力。在第二阶段， 将定义每种细胞系的基础未刺激状态。这些过程的治疗靶向，单独或 将在高剂量和低剂量下联合进行，以评估完全与不完全的效应 抑制作用在长期低剂量治疗过程中介导治疗耐药性发展的不平衡过程 将在不同时间点定量剂量治疗以预测消除耐药性的联合疗法。 最后，将使用统计分析来识别与化疗相关的不平衡过程。 三阴性乳腺癌患者来源的异种移植肿瘤中的体内抗性。这些不平衡的节点 网络将被靶向以降低肿瘤活力。联合化疗可能会进一步增敏 肿瘤细胞进行治疗。通过这些努力，我们的目标是证明这种组合的蛋白质组学的能力， 合理设计具有高精度、患者特异性的药物鸡尾酒， 耐药性发展。