Center for Theoretical Biological Physics, Rice University, Houston, TX, USA; Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA.
Center for Theoretical Biological Physics, Rice University, Houston, TX, USA; Department of Bioengineering, Northeastern University, Boston, MA, USA; Department of Physics, Northeastern University, Boston, MA, USA.
Trends Cancer. 2021 Apr;7(4):373-383. doi: 10.1016/j.trecan.2020.12.005. Epub 2021 Jan 11.
Cancer represents a diverse collection of diseases characterized by heterogeneous cell populations that dynamically evolve in their environment. As painfully evident in cases of treatment failure and recurrence, this general feature makes identifying long-term successful therapies difficult. It is now well-established that the adaptive immune system recognizes and eliminates cancer cells, and various immunotherapeutic strategies have emerged to augment this effect. These therapies, while promising, often fail as a result of immune-specific cancer evasion. Increasingly available empirical evidence details both cancer and immune system populations pre- and post-treatment, providing rich opportunity for mathematical models of the tumor-immune interaction and subsequent co-evolution. Integrated mathematical and experimental efforts bear immediate relevance for optimized therapies and will undoubtedly accelerate our understanding of this emergent field.
癌症是一组异质性细胞群体的疾病,其特征为在其环境中动态进化。在治疗失败和复发的情况下,这一普遍特征明显表现出来,这使得确定长期成功的治疗方法变得困难。现在已经确立,适应性免疫系统可以识别和消除癌细胞,并且已经出现了各种免疫治疗策略来增强这种效果。这些疗法虽然有希望,但往往由于免疫特异性的癌症逃避而失败。越来越多的经验证据详细描述了治疗前后的癌症和免疫系统群体,为肿瘤-免疫相互作用及其后续共同进化的数学模型提供了丰富的机会。数学和实验的综合努力对于优化治疗具有直接的意义,无疑将加速我们对这一新兴领域的理解。