Crowdsourcing optimal cancer treatment strategies that maximize efficacy and minimize toxicity

众包最佳癌症治疗策略，最大限度地提高疗效并最大限度地降低毒性

• 批准号: 9078857
• 负责人: Alexander Robertson Allan Anderson
• 金额: $ 28.69万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-05 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9078857
• 关键词: Big Data; Biological; Blood flow; Cells; Combined Modality Therapy; Communities; Community Clinical Oncology Program; Complex; Cues; Data; Data Analyses; Development; Discipline; Disease; Drug resistance; Ecology; Ecosystem; Environment; Evolution; Failure; Future; Growth; Heart Research; Heterogeneity; Human; Hybrids; Immune; Individual; Intelligence; Knowledge; Lead; Malignant Neoplasms; Modeling; Mutation; Normal Cell; Nutrient; Organ; Patients; Pattern; Pharmacotherapy; Phase; Phenotype; Play; Population; Process; Recurrence; Regimen; Research; Resistance; Site; Solid Neoplasm; Stromal Cells; Survival Rate; Testing; Therapeutic; Time; Toxic effect; Treatment Failure; Treatment Protocols; Treatment-related toxicity; Variant; Visual; anticancer research; base; cancer cell; cancer therapy; crowdsourcing; effective therapy; follow-up; improved; in vivo; insight; mathematical model; multi-scale modeling; neoplastic cell; novel; public health relevance; resistance mechanism; response; success; therapy resistant; treatment strategy; tumor; tumor growth; tumor heterogeneity; tumor progression; virtual; visual feedback

 DESCRIPTION (provided by applicant): Understanding the complex spatial and temporal process by which tumors initiate, evolve and respond to therapy is a major focus of the oncology community and one that requires the integration of multiple disciplines. A diverse suite of therapies have been developed in the modern era, leading to significantly improved survival rates across many cancers. However, many treatments share a cycle of short-term success followed by recurrence, often of a more aggressive tumor. In the past decade, the cancer research community has begun to acknowledge the importance of heterogeneity across genotypic, phenotypic, and environmental scales as a key driver in drug resistance and treatment failure. The intricate dialogue between tumor cells and environment selects for clones that are best adapted phenotypically to survive, regardless of specific mutations that may facilitate tumor progression. These dynamics, occurring between a heterogeneous tumor and a heterogeneous environment (the cancer ecosystem) are almost impossible to dissect experimentally. Further, adding multiple treatments to the mix often leads to nonlinear and unintuitive dynamics. Therefore, understanding how tumor evolution and ecology changes with treatment is key to controlling the emergence of aggressive and resistant clones following therapy. Our central hypothesis here is that when treating cancer we should exploit heterogeneity, rather than ignore it, by developing crowdsourced sequential and combination therapies that steer tumor evolution and ecology producing more effective, less toxic and longer lasting responses. We plan to test this hypothesis through the development of a research game based on treating a heterogeneous evolving cancer. The core engine of the game will be a calibrated mathematical model of solid tumor growth, tailored to specific organ sites through different associated tumor phenotypes, environment and treatment options. Based on patterns observed while interacting with our research game, successful players will choose the follow-up treatments based on an understanding of the cancer's adaptive response to previous treatments as well as how the cancer is responding to the current therapy in real time. As a result of the power of crowdsourced computation and human intelligence we will derive a suite of optimal treatment strategies across a diverse set of cancer ecosystems.

 描述(由申请人提供)：了解肿瘤启动、演变和对治疗反应的复杂空间和时间过程是肿瘤学社区的一个主要焦点，需要多个学科的整合。现代已经开发了一套不同的治疗方法，大大提高了许多癌症的存活率。然而，许多治疗方法都有一个短期成功继而复发的周期，通常是更具侵袭性的肿瘤。在过去的十年中，癌症研究界已经开始认识到跨基因、表型和环境尺度的异质性的重要性，认为这是耐药和治疗失败的关键驱动因素。肿瘤细胞和环境之间错综复杂的对话选择了表型最适合生存的克隆，而不考虑可能促进肿瘤进展的特定突变。这些发生在异质肿瘤和异质环境(癌症生态系统)之间的动态几乎不可能通过实验进行剖析。此外，向混合物中添加多个处理通常会导致非线性和非直观的动力学。因此，了解肿瘤的进化和生态如何随着治疗的变化而变化，是控制治疗后出现侵袭性和抗药性克隆的关键。我们的中心假设是，在治疗癌症时，我们应该利用异质性，而不是忽视它，开发众包的顺序疗法和联合疗法，引导肿瘤的进化和生态，产生更有效、毒性更低和更持久的反应。我们计划通过开发一款基于治疗异质不断进化的癌症的研究游戏来检验这一假设。游戏的核心引擎将是一个经过校准的实体肿瘤生长数学模型，通过不同的相关肿瘤表型、环境和治疗选择，为特定的器官位置量身定做。根据在与我们的研究游戏互动时观察到的模式，成功的玩家将根据对癌症对先前治疗的适应性反应以及癌症对当前治疗的实时反应的了解来选择后续治疗。由于众包计算和人类智能的力量，我们将在一系列不同的癌症生态系统中得出一套最佳治疗策略。