Paper reading (八十六)：Normalization of the microbiota in patients after treatment for colonic lesions

论文题目：Normalization of the microbiota in patients after treatment for colonic lesions

scholar 引用：10

页数：10

发表时间：2017.11

发表刊物：Microbiome

作者：Marc A. Sze, Nielson T. Baxter,..., Patrick D. Schloss

摘要：

Background
Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26).
Results
There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05).
Conclusions
By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota.

正文组织架构：

正文部分内容摘录：

1. Biological Problem: What biological problems have been solved in this paper?

identify groups of OTUs

2. Main discoveries: What is the main discoveries in this paper?

Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota.

3. ML(Machine Learning) Methods: What are the ML methods applied in this paper?

67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26).
Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups.
we generated random forest models to identify bacterial populations that were indicative of treatment for each diagnosis group.
We attempted to overcome these problems by using random forest models to identify collections of OTUs that would allow us to differentiate between pre- and post-treatment samples from each of the diagnosis groups.
We then constructed random forest models to classify the study samples, with the three diagnosis groups (adenoma, advanced adenoma, or carcinoma), or having a normal colon. The models performed moderately with CRC being the best (adenoma AUC range = 0.50 – 0.62, advanced adenoma AUC range = 0.53 – 0.67, carcinoma AUC range = 0.71 – 0.82)
The random forest algorithm was used to create the three models used to classify pre- and post-treatment samples by diagnosis (adenoma, advanced adenoma, or carcinoma) as well as to assess the probability that a sample was more similar to the patient’s original diagnosis or that of a disease-free patient.

4. ML Advantages: Why are these ML methods better than the traditional methods in these biological problems?

5. Biological Significance: What is the biological significance of these ML methods’ results?

6. Prospect: What are the potential applications of these machine learning methods in biological science?

7. Mine Question(Optional)

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Paper reading (八十六)：Normalization of the microbiota in patients after treatment for colonic lesions

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