Goodman Jonathan R, Ashrafian Hutan
Leverhulme Centre for Human Evolutionary Studies, University of Cambridge, Cambridge, United Kingdom.
Institute of Global Health Innovation, Imperial College London, London, United Kingdom.
Front Oncol. 2020 Jan 20;9:1527. doi: 10.3389/fonc.2019.01527. eCollection 2019.
Theoretical and empirical work over the past several decades suggests that oncogenesis and disease progression represents an evolutionary story. Despite this knowledge, current anti-resistance strategies to drugs are often managed through treating cancers as independent biological agents divorced from human activity. Yet once drug resistance to cancer treatment is understood as a product of artificial or anthropogenic rather than unconscious selection, oncologists could improve outcomes for their patients by consulting evolutionary studies of oncology prior to clinical trial and treatment plan design. In the setting of multiple cancer types, for example, a machine learning algorithm can predict the genetic changes known to be related to drug resistance. In this way, a unity between technology and theory might have practical clinical implications-and may pave the way for a new paradigm shift in medicine.
过去几十年的理论和实证研究表明，肿瘤发生和疾病进展是一个进化的过程。尽管有了这一认识，但目前针对癌症药物的抗耐药策略往往是将癌症当作与人类活动无关的独立生物因子来处理。然而，一旦将癌症治疗耐药性理解为人工或人为选择而非无意识选择的产物，肿瘤学家就可以在临床试验和治疗方案设计之前参考肿瘤学的进化研究，从而改善患者的治疗效果。例如，在多种癌症类型的情况下，机器学习算法可以预测已知与耐药性相关的基因变化。通过这种方式，技术与理论的结合可能会产生实际的临床意义，并可能为医学领域的新范式转变铺平道路。
