EDUCATIONAL RESOURCE FOR TUMOR HETEROGENEITY

肿瘤学家认为肿瘤的自然史与 使用非正式推理的治疗，可能导致不正确的 结论.肿瘤学家们对癌症的生物学假设和假说 使用往往会产生反直觉的后果。了大量的 存在描述整个人的计算的研究文献 在细胞水平上的假设的后果，但这种知识 肿瘤学家基本上无法接触到，而且通常未能 影响他们的思维或提高他们的直觉。结果导致 肿瘤学家能够最佳利用现有的 抗癌药物已经受到阻碍。 因此，我们建议开发一套资源包， 生物数学建模软件，教程和文档。的 目标是培养当前和未来的研究肿瘤学家的理论 肿瘤细胞群体动力学应用于癌症治疗， 改进新假设的选择，以在临床和 实验室研究，以及改善结果的解释。 目标1：为培训医学生，编写一个简短的介绍性模块， 将开发用于课堂培训的肿瘤异质性研究。 该模块将包括讲座材料，印刷的学习材料， 模拟演习。目标2：为培训肿瘤学研究员， 将开发一套自主学习单元。目标是 一套教程的经验教训，开发的整体贡献，从 杰出的临床肿瘤学研究者图坦卡蒙将展示 在肿瘤细胞动力学的关键教训，而教学使用的程序 用于结合特定的生物现象（转移， 分化、非线性剂量反应和药物相互作用， 多效性抗性，多重抗性机制，血管生成， 治疗诱导的突变、毒性等）。目标3：积极 癌症研究人员，一个灵活的假设中立的建模资源将 发展。在为培训以下人员而编写的教程的基础上， 我们将提供一个软件套件， 面对一个特定的治疗问题来描述，在一个最自然的 方法，他们自己的疾病自然史的概念，并训练 他们自己，发展洞察力的后果， 假设

Oncologists think about the natural history of tumors in interaction with treatments using informal reasoning that can lead to incorrect conclusions. The biological assumptions and hypotheses that oncologists use often have counterintuitive consequences. A substantial body of research literature exists describing the calculation of the whole-person consequences of hypotheses at the cellular level, but this knowledge has been largely inaccessible to oncologists, and has generally failed to affect their thinking or improve their intuition. As a result, the ability of oncologists to make optimal use of currently available anticancer agents has been impeded. Consequently, we propose the development of a resource package consisting of biomathematical modelling software, tutorials, and documentation. The goal is to train current and future research oncologists in the theory of tumor cell population kinetics as applied to cancer treatment, leading to improved selection of new hypotheses to test in clinical and laboratory studies, as well as improved interpretation of results. Objective 1: For training medical students, a short introductory module on tumor heterogeneity will be developed for use in classroom training. The module will consist of lecture materials, printed study materials and simulation-based exercises. Objective 2: For training oncology fellows, a suite of self-directed study modules will be developed. The goal is a set of tutorial lessons developed with integral contributions from outstanding researchers in clinical oncology. Tutorials will demonstrate key lessons in tumor cell kinetics, while teaching the use of the program for incorporating specific biological phenomena (metastasis, differentiation, nonlinear dose-response and drug interactions, pleiotropic resistance, multiple resistance mechanisms, angiogenesis, treatment-induced mutation, toxicity, etc.). Objective 3: For active cancer researchers, a flexible hypothesis-neutral modeling resource will be developed. Building on the tutorials developed for training of fellows, we will provide a software suite which will allow researchers facing a particular treatment question to describe, in a most natural way, their own conceptions of the disease natural history, and to train themselves, developing insights into the consequences of their hypotheses.