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

 描述（由申请人提供）：实体瘤是一种异质性细胞集合，具有非常不同的增殖、转移和抵抗治疗的能力。尽管肿瘤细胞个体间的功能多样性已被广泛认可，但我们并不知道真正有多少种功能状态，也不清楚如何在第一时间最好地对这些状态进行分类。由细胞表达的mRNA的全局谱可以表明其状态，只要测量是可靠的并且可以在其天然环境中以最小的细胞破坏获得。到目前为止，实体瘤还没有达到这两个标准。我们通过开发一种称为随机分析的新方法来规避这个问题，该方法测量原位显微切割的小10细胞池，通过统计分析收集单细胞信息。10个细胞池增加了起始材料，并允许用原位显微切割的样品实现可靠的表达谱。以前，我们已经使用随机分析来揭示乳腺癌3D器官型培养物中丰富的单细胞功能状态， 上皮细胞在我们对PQB 4的回答中，我们试图解决随机分析是否可以直接应用于人类或小鼠实体瘤，并产生有关癌症进展的有意义的信息。假设进展与调节状态的常见子集有关，随着实体瘤变得更晚期，调节状态的频率或特性发生变化。该建议的目的是：1）评估基因工程小细胞肺癌在不同进展阶段的离体调控异质性。我们将使用随机分析与癌前细胞和小细胞肺肿瘤球与Trp53和Rb有条件地删除在神经内分泌细胞。2)评估基因工程胶质瘤在不同进展阶段的体内调控异质性。我们将使用荧光引导的随机分析，以评估在小鼠的嗅球少突胶质细胞前体细胞中有条件地删除Trp53和Nf1的胶质瘤的发生。3)检测人类肿瘤中的调节异质性是否可以定量预测病理分期和分级。我们将结合联合收割机随机分析的乳腺肿瘤与偏最小二乘回归连接单细胞调节状态的临床参数。如果成功，该应用将为确定实体瘤中所有主要类别的调节异质性的长期目标奠定基础。为了表征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.