Thymic involution and rising disease incidence with age.
Thymic involution and rising disease incidence with age.
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DOI：
10.1073/pnas.1714478115
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发表时间：
2018-02-20
影响因子：
11.1
通讯作者：
Newman TJ
中科院分区：
文献类型：
--
作者：
Palmer S;Albergante L;Blackburn CC;Newman TJ
了解致癌的危险因素是生物医学研究的一个主要目标。从历史上看，人们一直关注体细胞突变的作用，而癌症通常发生在晚年，主要归因于这种突变的逐渐积累。我们挑战这一观点，并提出免疫系统的衰退是癌症是一种与年龄有关的疾病的主要原因。这里的免疫学模型捕获了许多癌症类型和传染病的风险概况，表明逆转T细胞衰竭或恢复T细胞产生的疗法将是有希望的治疗途径。对于许多类型的癌症，发病率随着年龄的增长而迅速上升，这是一个明显的幂律，支持了癌症是由基因突变逐渐积累引起的观点。同样，许多传染病的发病率也随着年龄的增长而急剧增加。在这里，结合免疫学和流行病学的数据，我们表明许多与年龄相关的发病率急剧增加可以基于免疫系统衰退而不是突变积累来建模。在人类中，胸腺从婴儿期开始萎缩，导致T细胞产量呈指数下降，半衰期为~ 16年，我们将其作为疾病发病率最小数学模型的基础。我们的模型在描述不同癌症的广泛范围内的癌症发病率数据方面优于具有相同数量拟合参数的幂律模型，并为传染病数据提供了出色的拟合。这一框架提供了癌症发生的机制，表明与年龄相关的T细胞输出下降是一个主要的危险因素。
Understanding the risk factors of carcinogenesis is a major goal of biomedical research. Historically, the focus has been on the role of somatic mutations, and the reason for cancer typically occurring late in life is predominantly attributed to a gradual accumulation of such mutations. We challenge that view and propose that the decline of the immune system is the primary reason why cancer is an age-related disease. The immunological model featured here captures risk profiles for many cancer types and infectious diseases, suggesting that therapies reversing T cell exhaustion or restoring T cell production will be promising avenues of treatment. For many cancer types, incidence rises rapidly with age as an apparent power law, supporting the idea that cancer is caused by a gradual accumulation of genetic mutations. Similarly, the incidence of many infectious diseases strongly increases with age. Here, combining data from immunology and epidemiology, we show that many of these dramatic age-related increases in incidence can be modeled based on immune system decline, rather than mutation accumulation. In humans, the thymus atrophies from infancy, resulting in an exponential decline in T cell production with a half-life of ∼16 years, which we use as the basis for a minimal mathematical model of disease incidence. Our model outperforms the power law model with the same number of fitting parameters in describing cancer incidence data across a wide spectrum of different cancers, and provides excellent fits to infectious disease data. This framework provides mechanistic insight into cancer emergence, suggesting that age-related decline in T cell output is a major risk factor.
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