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

摘要 在过去的十年中，大规模系统级数据集的积累正在加速进行 佩斯。不幸的是，到目前为止，这种大规模积累的生物和医学信息很少 转化为真正有效的疗法，戏剧性地改变了疾病的进程。显然是新的信息学 需要能够识别变革性疗法的方法。这样做的中心目标是 提议是开发一种实验-理论方法，以高精度定义改变的蛋白质 网状结构存在于每一种癌症恶性肿瘤中。我们建议将定量质谱学- 基于热力学的基于惊奇分析的蛋白质和蛋白质磷酸化测量 信息论方法，解决每个恶性肿瘤中蛋白质网络结构的变化。更改后的 每个患者肿瘤中的网络可能包括几个不同的、有时重新连接的蛋白质亚网络，这些亚网络 导致癌症组织中的分子失衡。识别不平衡的子网络将成为重点 分子结节将在每个患者中作为靶点，以恢复基础的、未转化的状态或 降低肿瘤细胞的存活率。要演示此方法定义不平衡子网络和 其相关的治疗目标，该提案分为三个阶段，越来越复杂和 生理上的相关性。在第一阶段，RTK网络在乳腺癌细胞系中代表不同的 亚型将被天然配体刺激，以诱导具有良好特征的不平衡过程来验证 令人惊讶的分析识别这些网络的能力。在第二阶段，不平衡的过程存在于 将定义每个细胞系的基础未受刺激状态。这些过程的治疗靶点，单独或 在高剂量和低剂量的组合下，将进行评估完全和不完全的效果。 抑制力。长期低剂量化疗中参与治疗耐药形成的不平衡过程 剂量治疗将在不同的时间点被量化，以预测消除耐药性的联合治疗。 最后，将使用令人惊讶的分析来确定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瘤的体内耐药性。这些不平衡中的节点 网络将成为降低肿瘤生存能力的目标。与化疗相结合可能会进一步增敏 肿瘤细胞的治疗。通过这些努力，我们的目标是展示这种组合蛋白质组的能力- 令人惊讶的分析策略，合理设计，高精度，针对患者的药物鸡尾酒，防止 耐药性的发展。

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.