(PQB4) Stochastic Profiling of Functional Single-Cell States Within Solid Tumors

 描述(申请人提供)：实体瘤是一种异质的细胞集合，具有非常不同的增殖、转移和抵抗治疗的能力。尽管单个肿瘤细胞的功能多样性得到了广泛的认识，但我们不知道真正有多少种功能状态，也不清楚如何最好地在第一个PLCE中对这些状态进行分类。一个细胞表达的mRNAs的全球图谱可以暗示它的状态，前提是测量是可靠的，并且可以在细胞本身的环境中以最小的干扰获得。到目前为止，实体瘤的这两个标准都没有达到。我们开发了一种名为随机图谱的新方法，通过测量原位显微解剖的10个细胞的小池，通过统计分析收集单细胞信息，从而绕过了这个问题。10细胞池增加了起始材料，并允许通过原位显微解剖的样本获得可靠的表达谱。在此之前，我们已经使用随机图谱在3D器官型乳房培养中发现了丰富的单细胞功能状态 上皮细胞。在我们对PQB4的回答中，我们寻求解决随机图谱是否可以直接应用于人类或小鼠实体肿瘤，并产生关于癌症进展的有意义的信息。假说是，进展与一组常见的调节状态有关，随着实体肿瘤变得更加晚期，这些状态的频率或身份会发生变化。这项建议的目的是：1)评估不同进展阶段的基因工程小细胞肺癌的体外调控异质性。我们将对神经内分泌细胞中有条件缺失Trp53和Rb的小鼠的癌前细胞和小细胞肺肿瘤球体进行随机分析。2)评价不同进展阶段基因工程胶质瘤体内调控的异质性。我们将使用荧光引导的随机图谱来评估在嗅球的少突胶质前体细胞中有条件地缺失Trp53和Nf1的小鼠的胶质瘤形成。3)检验人类肿瘤的调控异质性是否可以定量预测肿瘤的病理分期和分级。我们将结合乳腺肿瘤的随机图谱和偏最小二乘回归，将单细胞调节状态与临床参数联系起来。如果成功，这项应用将为确定实体肿瘤中所有主要类别的调控异质性的长期目标奠定基础。要在上下文中描述单个肿瘤细胞的功能状态，答案可能是避免完全测量单个细胞。

 DESCRIPTION (provided by applicant): Solid tumors are a heterogeneous collection of cells with vastly different capacities to proliferate, metastasize, and resist therapy. Although functionl diversity among individual tumor cells is widely recognized, we do not know how many functional states there truly are, nor is it clear how best to catalog those states in the first plce. The global profile of mRNAs expressed by a cell can suggest its state, provided that the measurements are reliable and can be obtained with minimal disruption of the cell in its native context. To date, neither of these criteria has been achieved for solid tumors. We circumvented the problem by developing a new method, called stochastic profiling, which measures small 10-cell pools of cells microdissected in situ to glean single- cell information through statistical analysis. The 10-cell pools increase the starting material and allow reliable expression profiles to be achieved with samples microdissected in situ. Previously, we have used stochastic profiling to uncover a wealth of single-cell functional states in 3D organotypic cultures of breast epithelial cells. In our answer to PQB4, we seek to address whether stochastic profiling can be directly applied to human or murine solid tumors and yield meaningful information about cancer progression. The hypothesis is that progression is linked to a common subset of regulatory states that change in frequency or identity as solid tumors become more advanced. The aims of this proposal are: 1) To evaluate ex vivo regulatory heterogeneities within genetically engineered small-cell lung cancers at various stages of progression. We will use stochastic profiling with premalignant cells and small-cell lung tumorspheres from mice with Trp53 and Rb conditionally deleted in neuroendocrine cells. 2) To evaluate in vivo regulatory heterogeneities within genetically engineered gliomas at various stages of progression. We will use fluorescence-guided stochastic profiling to evaluate gliomagenesis in mice with Trp53 and Nf1 conditionally deleted in oligodendrocyte precursor cells of the olfactory bulb. 3) To test whether regulatory heterogeneities in human tumors are quantitatively predictive of pathologic stage and grade. We will combine stochastic profiling of breast tumors with partial least squares regression to link single-cell regulatory states to clinical parameters. If successful, this application would set the stage for a long-term goal of identifying all major categories of regulatory heterogeneity in solid tumors. To characterize the functional state of individual tumor cells in context, the answer may be to avoid measuring single cells entirely.