Workshop: Exploring Quantitative Approaches to Clinical Cancer Data; February 13-18, 2015, Princeton, New Jersey

过去五到十五年，应用于生物问题的技术取得了显着进步。这导致了基因组、RNA种类、蛋白质功能和修饰以及细胞功能的表观遗传调节的定量测量。利用这些新技术已经建立了非常大的信息数据库。在生物学领域中，应用这些技术最快的领域之一是癌症研究。基于定量的分析对于了解癌症的原因、途径、诊断、预后和治疗变得越来越重要。通常，生物学家和临床医生缺乏从这些大型数据集中提取有用信息所需的复杂分析水平所需的数学，物理，计算机科学和化学方面的广泛技能。来自一系列学科的定量研究人员已经进入生物学领域，探索这些基础科学挑战，但他们通常很少或根本没有在癌症生物学领域接受过培训，并且对该领域的重要生物学问题知之甚少。在癌症研究中，这两个群体之间合作的好处开始得到解决和观察，但协同合作的步伐和充分承诺仍然是一个挑战。SU 2C将汇集理论物理学家，数学家和计算机科学家，他们在生物学研究和临床肿瘤学家，两组的研究领域传统上并不相交的强大背景，以开发一个研究项目集成数学和计算方法和临床癌症研究。SU 2C将组织一个想法实验室，将这些研究人员带到住宅研究环境中，进行为期多日的想法，方法和知识交流，以开发此类综合研究项目。目标是组建3到4个临床肿瘤学家和定量科学家团队，以解决临床癌症研究数据中出现的重要问题或疑问。这些方法将由想法实验室的科学家团队或小组在简短的初步建议中阐述。然后，将对这些建议进行完善，并可能要求小组的其他成员参加。然后，可以提交提案，争取公共和私人来源提供可能的资金。因此，Ideas Lab既有智力成果，也有潜在的实践成果，从而利用临床研究数据探索最佳想法和定量方法。将上述两个小组的高度成就的个人聚集在一起，共同教育两个小组，并在定量和临床癌症研究的融合中开发研究项目，可以预期，这将导致癌症研究中基础科学问题的新方法的发展。这一努力有可能创造一个新的研究领域，可以解决我们对疾病起源，进展，诊断，预后，治疗和患者疾病结局的理解。这反过来又有可能降低开发有效治疗方法的社会成本，提高癌症患者的生活质量和结果。可以实现的益处类型包括以下可能性：1）改进技术工具以生成稳健的定量生物学数据; 2）使用严格的数学方法捕获和表示高维基因组数据-这些表示可以帮助识别标记以将患者分层并识别潜在的治疗靶点; 3）整合不同类型的大规模癌症数据; 4）生成工具以在大数据集中识别潜在的治疗靶标，而不是单个基因改变的复发;例如，使用大规模基因组数据识别不同基因之间的协同活性，以及5）使用基因组数据提供关于肿瘤进化的定量可测试模型;特别是从纵向和横截面基因组数据重建肿瘤的进化历史。

The past five to fifteen years have seen remarkable advances in the technologies applied to biological problems. This has resulted in quantitative measurements of genomes, RNA species, protein functions and modifications, and epigenetic regulation of cellular functions. Extremely large databases of information have been assembled using these new technologies. Among the most rapid areas of biology to apply these technologies has occurred in cancer research. Quantitative based analysis is becoming increasingly important to understand the causes, pathways, diagnosis, prognosis and treatment of cancer. Commonly, biologists and clinicians lack the extensive skills in mathematics, physics, computer science, and chemistry that are required for the sophisticated levels of analysis that are needed to extract useful information from these large datasets. Quantitative researchers from an array of disciplines have entered the field of biology to explore these basic science challenges, but they often have little or no prior training in the field of cancer biology, and little understanding of the important biological questions in the field. The benefits of collaboration between these two groups are starting to be addressed and observed in cancer research, but the pace and full promise of synergistic collaboration remains a challenge. SU2C will bring together theoretical physicists, mathematicians, and computer scientists with strong backgrounds in biological research and clinical oncologists, two groups whose research fields do not traditionally intersect, in order to develop a research project integrating mathematical and computational approaches and clinical cancer research. SU2C will organize an Ideas Lab to bring these researchers to a residential research setting for a multi-day exchange of ideas, methods, and knowledge, to develop such integrated research projects. The goal is to assemble 3 to 4 teams of clinical oncologists and quantitative scientists to address important problems or questions that arise from clinical cancer research data. These approaches will be articulated in short preliminary proposals by teams or groups of scientists at the Ideas Lab. The proposals will then be refined and additional members of a team may be asked to participate. The proposals may then be submitted for possible funding from public and private sources. Thus, the Ideas Lab has both an intellectual and potential practical set of outcomes, resulting in the best ideas and quantitative approaches being explored employing clinical research data.Bringing together highly accomplished individuals from the two groups outlined above, for the purpose of jointly educating both groups and developing research projects at the convergence of quantitative and clinical cancer research, can be expected to lead to the development of novel approaches to fundamental science questions in cancer research. This effort has the potential to create a new field of research that can address our understanding of disease origin, progression, diagnosis, prognosis, treatment and the outcome of disease in patients. This, in turn, has the potential to lower the costs to society of developing effective therapeutic treatments, of enhancing quality of life and the outcome for cancer patients. The types of benefit that may be realized include the possibility of: 1) refining the technologicaltools to generate robust quantitative biological data; 2) capturing and representing high dimensional genomic data using rigorous mathematical approaches - these representations can help to identify markers to stratify patients and to identify potential therapeutic targets; 3) integrating different types of large scale cancer data; 4) generating tools to identify potential therapeutic targets in large datasets beyond recurrence of single gene alterations; for instance, identifying synergistic activities between different genes using large scale genomic data, and 5) providing quantitative testable models about the evolution of tumors using genomic data; in particular, reconstructing the evolutionary history of tumors from longitudinal and cross sectional genomic data.