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: 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
• 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

 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.