Zackular Joseph P, Baxter Nielson T, Chen Grace Y, Schloss Patrick D
Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.
Department of Internal Medicine, Division of Hematology and Oncology, University of Michigan, Ann Arbor, Michigan, USA.
mSphere. 2015 Nov 4;1(1). doi: 10.1128/mSphere.00001-15. eCollection 2016 Jan-Feb.
There is growing evidence that individuals with colonic adenomas and carcinomas harbor a distinct microbiota. Alterations to the gut microbiota may allow the outgrowth of bacterial populations that induce genomic mutations or exacerbate tumor-promoting inflammation. In addition, it is likely that the loss of key bacterial populations may result in the loss of protective functions that are normally provided by the microbiota. We explored the role of the gut microbiota in colon tumorigenesis by using an inflammation-based murine model. We observed that perturbing the microbiota with different combinations of antibiotics reduced the number of tumors at the end of the model. Using the random forest machine learning algorithm, we successfully modeled the number of tumors that developed over the course of the model on the basis of the initial composition of the microbiota. The timing of antibiotic treatment was an important determinant of tumor outcome, as colon tumorigenesis was arrested by the use of antibiotics during the early inflammation period of the murine model. Together, these results indicate that it is possible to predict colon tumorigenesis on the basis of the composition of the microbiota and that altering the gut microbiota can alter the course of tumorigenesis. IMPORTANCE Mounting evidence indicates that alterations to the gut microbiota, the complex community of bacteria that inhabits the gastrointestinal tract, are strongly associated with the development of colorectal cancer. We used antibiotic perturbations to a murine model of inflammation-driven colon cancer to generate eight starting communities that resulted in various severities of tumorigenesis. Furthermore, we were able to quantitatively predict the final number of tumors on the basis of the initial composition of the gut microbiota. These results further bolster the evidence that the gut microbiota is involved in mediating the development of colorectal cancer. As a final proof of principle, we showed that perturbing the gut microbiota in the midst of tumorigenesis could halt the formation of additional tumors. Together, alteration of the gut microbiota may be a useful therapeutic approach to preventing and altering the trajectory of colorectal cancer.
越来越多的证据表明,患有结肠腺瘤和癌的个体拥有独特的微生物群。肠道微生物群的改变可能会使诱导基因突变或加剧促肿瘤炎症的细菌种群过度生长。此外,关键细菌种群的丧失可能会导致微生物群通常提供的保护功能丧失。我们通过使用基于炎症的小鼠模型来探索肠道微生物群在结肠肿瘤发生中的作用。我们观察到,用不同抗生素组合干扰微生物群可减少模型末期的肿瘤数量。使用随机森林机器学习算法,我们基于微生物群的初始组成成功模拟了模型过程中发生的肿瘤数量。抗生素治疗的时机是肿瘤结果的重要决定因素,因为在小鼠模型的早期炎症期使用抗生素可阻止结肠肿瘤发生。总之,这些结果表明,根据微生物群的组成预测结肠肿瘤发生是可能的,并且改变肠道微生物群可以改变肿瘤发生的进程。重要性越来越多的证据表明,肠道微生物群(栖息在胃肠道中的复杂细菌群落)的改变与结直肠癌的发生密切相关。我们对炎症驱动的结肠癌小鼠模型使用抗生素干扰,以产生八个起始群落,这些群落导致了不同严重程度的肿瘤发生。此外,我们能够根据肠道微生物群的初始组成定量预测最终肿瘤数量。这些结果进一步支持了肠道微生物群参与介导结直肠癌发生的证据。作为最后的原理证明,我们表明在肿瘤发生过程中干扰肠道微生物群可以阻止额外肿瘤的形成。总之,改变肠道微生物群可能是预防和改变结直肠癌发展轨迹的一种有用治疗方法。