Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.
Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features
Mayo Clinic（梅奥诊所）
癌症每年在全世界造成数百万人死亡。尽管癌症医学已经取得了显著的进展，但仍有许多问题有待解决，适当的癌症患者分层是选择适当治疗方案的前提，因为已知癌症患者具有异质性的遗传组成和表型差异。在这项研究中，建立在从梅奥诊所电子健康记录（EHR）和癌症患者遗传检测报告中提取的深度表型特征的基础上，我们评估了各种图神经网络（GNN），这些神经网络利用表型和遗传特征的联合进行癌症类型分类。在Mayo Clinic癌症疾病数据集上应用并微调模型。通过报告的准确度、精确度、召回率和F1值以及基于疾病类别的F1评分进行评估。根据我们的评估结果，GNN的平均表现优于基线模型，平均统计数据始终高于基线模型（准确率为0.849 vs 0.772，精确率为0.858 vs 0.794，召回率为0.843 vs 0.759，F1得分为0.843 vs 0.855）。在GNN中，ChebNet，GraphSAGE和TAGCN表现最好，而GAT表现最差。我们在Mayo Clinic癌症疾病数据集上应用并比较了八个GNN模型，包括AGNN，ChebNet，GAT，GCN，GIN，GraphSAGE，SGC和TAGCN，并评估了它们的性能，并将它们相互比较，并与我们用作基线的更传统的机器学习模型，如决策树，梯度提升，多层感知器，朴素贝叶斯和随机森林进行比较。
Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening
State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering（化学与生物传感国家重点实验室，计算机科学与电子工程学院；教育部长江超级计算与人工智能融合计算重点实验室）；The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence（湖南大学）；Hunan University（人工智能与计算机科学学院）；School of Artificial Intelligence and Computer Science（江南大学）；Jiangnan University（计算机科学系）；Department of Computer Science（筑波大学）；University of Tsukuba（中医学学院）；School of Chinese Medicine（香港 Baptist 大学）；Hong Kong Baptist University（信息学院，国家健康与医学数据科学研究院）；School of Informatics, National Institute for Data Science in Health and Medicine
Sensitivity analysis-guided model reduction of a mathematical model of pembrolizumab therapy for de novo metastatic MSI-H/dMMR colorectal cancer
School of Mathematics and Statistics, University of Sydney（悉尼大学数学与统计学院）；Department of Immuno-Oncology, Beckman Research Institute, City of Hope（城市之希望贝克曼研究所免疫肿瘤学系）
本文通过灵敏度分析指导的方法，对 pembrolizumab 治疗 de novo 转移性 MSI-H/dMMR 结直肠癌的数学模型进行简化，构建了两种模型以研究免疫检查点阻断的动态机制。
