Characterizing cytotoxic therapy induced shifts in the cost-to-benefit ratio of high ploidy

摘要 传统的I期剂量发现策略仅监测两周的药物反应，基于以下假设 这将有助于ffice观察ff疗法如何影响2-4岁以上同质人群的加倍时间 几代人。但随着大多数癌症是异质性的范式转变，迫切需要考虑 这种治疗导致的人口构成变化在更长的时间范围内表现出来。我们之前创造了 “脱氧核糖核酸损伤治疗敏感性的翻转假说”，提出细胞毒治疗如果推动ff是有效的。 细胞的体细胞拷贝数改变(SCNA)加载到临界点以上。共存的可变接近度 肿瘤细胞达到这一临界点意味着剂量-反应关系不一定是单调的。细胞毒疗法可以 促使一个细胞进入凋亡，同时使另一个细胞迅速进入恶性增殖。作为开发人员， 广泛使用计算和数学方法，并建立了肿瘤研究计划 新陈代谢，并有建立动态过程模型和整合各种组学的广泛记录-以及 成像平台，我们的团队带来了互补的专业知识来开发个性化的细胞毒治疗策略 这限制了治疗诱导的抗性克隆的选择。我们将检测肿瘤细胞DNA含量和 DNTP底物的可用性用于预测肿瘤对SCNA比率增加的易感性。在此， 前述转折点不仅是由于SCNA负荷的升高，而且是由于组织的无力 微环境(TME)提供必要的资源。建议在胃和脑中进行实验 肿瘤--两种癌症类型，其TME可以“ffOrder”巨大不同数量的ff。我们的初步研究 表明在没有细胞毒性的情况下，75%的SCNA负载所需的DNA含量水平的能量成本不会 治疗，证明它们带来的掩饰益处是合理的。特别是，我们证明了限制dNTP浓度 放大高倍体细胞和低倍体细胞在S期持续时间的差异。我们的假设是细胞毒性 治疗导致超过SCNA临界点的肿瘤的适合性净增加。这一假设是 基于两个意想不到的最新发现：(I)整合的单细胞RNA和DNA测序分析 胃癌细胞提示，细胞死亡的风险在SCNA事件后立即发生，而不仅仅是SCNA 速率，影响克隆多样性。目标1将把单细胞测序与成像和数学相结合 对通过染色体错分离进化的异质种群进行建模，以检验观察到的 SCNA环境和错误分离耐受性，并预测有效的细胞毒治疗剂量。(Ii)甚至 同一胶质母细胞瘤中共存克隆之间DNA含量的微小变化可能会导致 明显更长的S阶段。目标2将评估氧、磷和葡萄糖作为限速底物 胃和脑组织环境中共同进化亚群的dNTP合成。这是第一次研究 研究临床决策是否以及如何从整合肿瘤环境的能量中受益 为癌细胞的基因组构成提供能量需求。

SUMMARY Traditional phase I dose-finding strategies monitor drug response only for two weeks, based on the assumption that it will suffice to observe how therapy affects doubling time of a homogeneous population over 2-4 generations. But with the paradigm shift that most cancers are heterogeneous comes an urgent need to consider that therapy-induced shifts in population composition manifest over longer time frames. We previously coined the “tip-over hypothesis of DNA damage therapy sensitivity”, proposing that cytotoxic therapy is effective if it pushes a cell’s somatic copy number alteration (SCNA) load above a tipping point. Variable proximity of co-existing tumor cells to this tipping point imply that dose-response relations need not be monotonic. Cytotoxic therapy can drive one cell into apoptosis, while skyrocketing another cell into malignant proliferation. As the developers of widely used computational and mathematical methods, with established research programs in tumor metabolism, and with a broad record of modeling dynamic processes and integrating various omics- and imaging platforms, our team brings complementary expertise to develop a personalized cytotoxic therapy strategy that confines therapy-induced selection of resistant clones. We will test the potential of tumor cell DNA content and dNTP substrate availability to predict a tumor’s vulnerability to increasing SCNA rate. Hereby, the aforementioned tipping point is accounted for not by elevated SCNA load alone, but by an inability of the tissue micro-environment (TME) to provide the necessary resources. Experiments are proposed in stomach and brain tumors—two cancer types whose TME can “afford” vastly different amounts of DNA. Our preliminary studies show that energetic costs of DNA content levels required for >75% SCNA load do not, in the absence of cytotoxic therapy, justify the masking benefits they bring. In particular, we showed that limiting dNTP concentrations amplify divergence in S-phase duration between high- and low-ploidy cells. Our hypothesis is that cytotoxic therapy causes a net-increase in fitness of tumors that exceed the SCNA tipping point. This hypothesis is founded on two unexpected recent findings: (i) integrated single-cell RNA- and DNA-sequencing analyses of stomach cancer cells suggests that the risk of cell death immediately after an SCNA event, rather than just SCNA rate, impacts clonal diversity. Aim 1 will integrate single cell sequencing with imaging and mathematical modeling of heterogeneous populations that evolve through chromosome missegregations, to examine observed SCNA landscapes and missegregation tolerances, and to predict effective cytotoxic therapy doses. (ii) Even minimal changes in DNA content among co-existing clones within the same Glioblastoma can result in significantly longer S-phases. Aim 2 will evaluate Oxygen, Phosphate and Glucose as rate-limiting substrates of dNTP synthesis of co-evolving subpopulations in stomach and brain tissue environments. This is the first study to investigate if and how clinical decisions can benefit from integrating a tumor environment’s energetic provision with the energetic demands of cancer cells’ genomic makeup.