Modeling Colon Cancer, Intervention and Prevention

描述(由申请人提供)：癌症发生的数学模型旨在捕捉从正常细胞到中间细胞种群的扩大，再到恶性肿瘤的出现的多阶段过程的重要特征，这些模型提供了更详细地研究影响癌症发生所涉及的关键生物过程的特定干预措施的效果的机会。因此，这项建议的主要重点是开发工具和方法，对可能的预防和干预策略进行定量评估，并将其应用于结直肠癌。这些工具和方法将基于癌症发生的随机模型，这些模型反映了过去十年来对结肠癌发病机制的洞察，以及这些模型的扩展，允许纳入与基因组不稳定相关的额外(假定)途径。为了有信心地实现这一目标，重要的是为此目的使用的模型对普通人群中结肠癌的发病率进行令人满意的描述。到目前为止尚未满足的一个挑战是，这些模型也与对腺瘤性息肉的患病率和大小分布的观察一致，腺瘤性息肉是结直肠肿瘤的病理特征良好的前驱病变。这些息肉也是特定癌症筛查和干预策略的对象。这推动了统计和计算工具的数学发展，以分析相关数据集：(1)SEER关于结直肠癌(CRC)发病率的登记数据，(2)一大批女性预期队列中的结直肠癌发病率(上海研究)，(3)明尼苏达州癌症预防研究部(CPRU)的息肉数据，以及(4)弗雷德·哈钦森癌症研究中心进行的一项关于结直肠癌和乙状结肠镜检查的病例对照研究的数据。从这些分析中得出的模型和开发的计算工具将有助于预测与具体的筛查、预防和干预战略相关的益处。

DESCRIPTION (provided by applicant): Mathematical models of carcinogenesis that are designed to capture important features of the multistage process, from normal cells, via expanding intermediate cell populations, to the appearance of malignant tumors, offer the opportunity to study in greater detail the effects of specific interventions that affect critical biological processes involved in carcinogenesis. The main focus of this proposal is therefore the development of tools and methods for the quantitative evaluation of possible prevention and intervention strategies and to apply them to colorectal cancer. These tools and methods will be based on stochastic models of carcinogenesis that reflect insights gained into the pathogenesis of colon cancer over the past decade, as well as extensions of these models that allow for the incorporation of additional (putative) pathways associated with genomic instability. To achieve this goal with confidence, it is important that the models used for this purpose give satisfactory descriptions of the incidence of colon cancer in the general population. A challenge, so far unmet, is that these models also be consistent with observations on the prevalence and size distribution of adenomatous polyps, pathologically well-characterized precursor lesions to colorectal tumors. These polyps are also the subject of specific cancer screening and intervention strategies. This motivates the mathematical development of statistical and computational tools to analyze relevant data sets: (1) the SEER registry data on colorectal cancer (CRC) incidence, (2) CRC incidence in a large prospective cohort of women (the Shanghai study), (3) the Minnesota Cancer Prevention Research Unit (CPRU) polyp data, and (4) data from a case-control study of CRC and the role of sigmoidoscopy conducted at the Fred Hutchinson Cancer Research Center. Models derived from these analyses and the computational tools developed will facilitate the prediction of benefits associated with specific screening, prevention and intervention strategies.