Predicting Cancer Cell Response to Endogenous and Exogenous Perturbations at the Single Cell Level

癌症的研究是建立在发现个体事件、机制和过程的基础上的 与正常细胞的恶性转化有关。人们的期望是学习如何 肿瘤的出现将教会我们如何通过提供药理上可操作的靶点来击败它们。 不幸的是，虽然许多癌症的病因已经被详细地阐明，但根治性疗法 对于更具攻击性的亚型，人们仍然难以捉摸。事实上，一旦他们的监管和信号逻辑 由于受到导致转化的事件的失调，细胞不再局限于明确定义的生理 而是过渡到一种新的失调和高度可塑性的格局，在这种格局中，去分化和 转分化事件变得常见，并与其他未转化的细胞群体产生新的协同效应 是伪造的，导致了真正的新器官的出现。因此，要研究癌症，我们必须产生 细胞网络模型完全能够概括细胞的动态行为和对药物的反应 肿瘤微环境(TME)中的扰动、突变和与细胞的相互作用。我们之前的研究 这表明，识别更普遍、更不可能被癌细胞击败的脆弱性 非凡的适应性将要求我们在剖析和询问内部和内部的能力方面有一个巨大的飞跃。 细胞网络模型可以预测癌症作为真正的新器官的动态行为。 为了应对这一挑战，我们将创建第一代基因组和蛋白质组范围的网络模型 可以有效地预测哺乳动物细胞对小分子和 基因扰动，以及它们在相对较少数量的 在几乎所有人类恶性肿瘤中都能检测到不同的分子状态。为了实现这些目标，我们将利用 一系列最先进的实验和计算进展，在过去六年中与 R35资金，用于创建系统的、大规模的转录调控基因敲除(TREK)单细胞 CRISPRi介导的调节蛋白沉默后多个时间点的图谱。由于他们的 新颖性和价值，这些试剂已经由Addgene分发给&gt；270个实验室。Trek Data， 由数十万到数百万个分子图谱和数十亿个分子组成 读数，将用于带有干预的因果网络学习，允许组装概率模型 它有效地概括了正常细胞和癌症相关细胞的动态行为，如 以及它们在TME内的相互作用。到R35融资周期结束时，我们预计能够推断 大多数调节蛋白和信号蛋白在任意遗传或药物作用下的时间依赖性活性 扰动，并根据小的(n≥6)的产生推断哪些蛋白质受到扰动的影响 纵向系列scRNA-seq图谱。这些新的蜂窝网络模型还将允许快速、系统地 阐明传统上通过低通量、假设驱动的分析来研究的机制。

The study of cancer has been predicated on the discovery of individual events, mechanisms, and processes that are implicated in the malignant transformation of normal cells. The expectation has been that learning how tumors arise would teach us how to defeat them, by providing pharmacologically actionable targets. Unfortunately, while the etiology of many cancers has been elucidated in painstaking detail, curative therapies for the more aggressive subtypes remain elusive. Indeed, once their regulatory and signaling logic is dysregulated by events leading to transformation, cells are no longer constrained to well-defined physiologic states but rather transition to a novel dysregulated and highly plastic landscape, where de-differentiation and trans-differentiation events become common and novel synergies with other non-transformed cell populations are forged, leading to the emergence of bona fide neomorphic organs. Thus, to study cancer, we must generate cellular network models that are fully capable of recapitulating dynamic cell behavior and response to drug perturbations, mutations, and interactions with cells in the tumor microenvironment (TME). Our prior research suggests that identifying vulnerabilities that are more universal and less likely to be defeated by the cancer cell’s remarkable adaptive nature will require a quantum leap in our ability to dissect and interrogate intra- and inter- cellular network models that are predictive of the dynamic behavior of cancer as a bona fide neomorphic organ. To address this challenge, we will create the first generation of genome- and proteome-wide network models that can effectively predict the probabilistic, time-dependent response of mammalian cells to small molecule and genetic perturbations, as well as their ability to plastically reprogram across the relatively small number of molecularly distinct states detected in virtually all human malignancies. To achieve these goals, we will leverage a repertoire of state-of-the-art experimental and computational advances, developed over the last six years with R35 funding, for the creation of systematic, large-scale Transcriptional Regulator Knock-down (TREK) single cell profiles at multiple time points following CRISPRi-mediated silencing of regulatory proteins. Owing to their novelty and value, these reagents have already been distributed by Addgene to >270 labs. TREK data, comprising hundreds of thousands to millions of individual molecular profiles and billions of individual molecular readouts, will be used for causal network learning with intervention, allowing the assembly of probabilistic models of cell regulation that effectively recapitulate the dynamic behavior of both normal and cancer-related cells, as well as their interactions within the TME. By the end of the R35 funding cycle, we expect to be able to infer the time-dependent activity of most regulatory and signaling proteins following arbitrary genetic or pharmacologic perturbations, and to infer which proteins were affected by a perturbation based on generation of a small (n ≥ 6) longitudinal series of scRNA-seq profiles. These new cellular network models will also allow rapid, systematic elucidation of mechanisms that have been traditionally studied via low-throughput, hypothesis-driven assays.