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

基本信息

批准号：
9254517
负责人：
Alexander Robertson Allan Anderson
金额：
$ 26.89万
依托单位：
H. LEE MOFFITT CANCER CTR & RES INST
依托单位国家：
美国
项目类别：
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/9254517
关键词：
Big Data Biological Blood flow Cells Combined Modality Therapy Communities Community Clinical Oncology Program Complex Cues Data Development Discipline Disease Drug resistance Ecology Ecosystem Environment Evolution Failure Future Genotype Growth Factor Heart Research Heterogeneity Human Hybrids Immune Individual Intelligence Knowledge Lead Malignant Neoplasms Modeling Modernization Mutation Normal Cell Nutrient Organ Patients Pattern Pharmacotherapy 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 Tumor Initiators 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 phase I trial public health relevance resistance mechanism response success therapy resistant treatment strategy tumor tumor growth tumor heterogeneity tumor progression virtual visual feedback

项目摘要

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、进化和对治疗作出反应的复杂空间和时间过程是肿瘤学界的一个主要焦点，并且需要整合多个学科已经开发了一套多样化的治疗方法。在现代，许多癌症的生存率显着提高。然而，许多治疗方法都有一个短期成功后复发的周期，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这样做。认识到异质性的重要性基因型、表型和环境尺度是耐药性和治疗失败的关键驱动因素，肿瘤细胞和环境之间的复杂对话会选择最适合表型生存的克隆，而不管可能促进肿瘤进展的特定突变如何。异质肿瘤和异质环境（癌症生态系统）之间发生的变化几乎不可能通过实验进行剖析，此外，添加多种治疗方法通常会导致非线性和不直观的动态变化。治疗带来的生态变化是控制治疗后侵袭性和耐药性克隆出现的关键，我们的中心假设是，在治疗癌症时，我们应该通过开发引导肿瘤进化和生态的众包序贯和联合疗法来利用异质性，而不是忽视它。我们计划通过开发一种基于治疗异质进化癌症的研究游戏来测试这一假设，该游戏的核心引擎将是定制的实体瘤生长的校准数学模型。通过特定的器官部位根据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如何反应的了解来选择后续治疗。借助众包计算和人类智能的力量，我们将在不同的癌症生态系统中得出一套最佳治疗策略。