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

总结 传统的I期剂量探索策略仅监测两周的药物反应， 这将有助于观察治疗如何影响同质群体的倍增时间超过2-4 代但随着大多数癌症是异质性的范式转变，迫切需要考虑 治疗引起的人口组成变化在较长的时间范围内表现出来。我们之前创造了 “DNA损伤治疗敏感性的翻转假说”，提出细胞毒性治疗是有效的，如果它推动了 细胞的体细胞拷贝数改变（SCNA）负荷高于临界点。共存的可变接近度 肿瘤细胞达到这个临界点意味着剂量-反应关系不需要是单调的。细胞毒性治疗可以 使一个细胞凋亡，同时使另一个细胞恶性增殖。作为开发者， 广泛使用的计算和数学方法，建立了肿瘤研究计划 代谢，并与建模动态过程和整合各种组学的广泛记录-和 成像平台，我们的团队带来了互补的专业知识，以开发个性化的细胞毒性治疗策略 限制了治疗诱导的抗性克隆选择。我们将测试肿瘤细胞DNA含量的潜力， dNTP底物可用性来预测肿瘤对增加的SCNA速率的脆弱性。在此， 上述临界点不是由升高的SCNA负荷单独解释的，而是由组织不能 微环境（TME）提供必要的资源。实验拟在胃和脑中进行 肿瘤-两种癌症类型，其TME可以“改变”DNA的数量差异很大。我们的初步研究 表明> 75%SCNA负载所需的DNA含量水平的能量成本，在没有细胞毒性的情况下， 治疗，证明他们带来的掩盖好处。特别是，我们发现限制dNTP浓度 放大高倍性和低倍性细胞之间S期持续时间的差异。我们的假设是， 治疗导致超过SCNA临界点的肿瘤适应性的净增加。这种假设是 建立在两个意想不到的最近的发现：（i）整合单细胞RNA和DNA测序分析， 胃癌细胞表明，SCNA事件后立即发生细胞死亡的风险，而不仅仅是SCNA 影响克隆多样性。Aim 1将单细胞测序与成像和数学 通过染色体误分离进化的异质群体的建模，以检查观察到的 SCNA景观和错误隔离的宽容，并预测有效的细胞毒性治疗剂量。(ii)甚至 同一胶质母细胞瘤内共存克隆之间DNA含量的微小变化可导致 更长的S期。目标2将评估氧、磷酸盐和葡萄糖作为 胃和脑组织环境中共同进化亚群的dNTP合成。这是第一项研究 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， 提供癌细胞基因组组成的能量需求。

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.