Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions.
Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions.
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DOI：
10.1186/s13073-016-0290-3
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发表时间：
2016-04-06
期刊：
影响因子：
12.3
通讯作者：
Schloss PD
中科院分区：
文献类型：
--
作者：
Baxter NT;Ruffin MT 4th;Rogers MA;Schloss PD
结直肠癌（CRC）是美国癌症中第二大死亡原因。尽管早期诊断的个体有超过 90% 的生存机会，但超过三分之一的个体不遵守筛查建议，部分原因是标准诊断、结肠镜检查和乙状结肠镜检查价格昂贵且具有侵入性。因此，非常需要提高非侵入性检测的灵敏度来检测早期癌症和腺瘤。许多研究已经确定了与结直肠癌进展相关的肠道微生物群组成的变化，这表明肠道微生物群可能代表了一个生物标志物库，可以补充现有的非侵入性方法，例如广泛使用的粪便免疫化学测试（FIT）。我们对 490 名患者粪便样本中的 16S rRNA 基因进行了测序。我们利用每个样本中细菌种群的相对丰度来开发随机森林分类模型，该模型利用肠道微生物群的相对丰度和粪便中血红蛋白的浓度来检测结肠病变。基于微生物群的随机森林模型检测出 91.7% 的癌症和 45.5% 的腺瘤，而仅 FIT 则分别检测出 75.0% 和 15.7%。在 FIT 漏掉的结肠病变中，该模型检测出了 70.0% 的癌症和 37.7% 的腺瘤。我们证实了解糖卟啉单胞菌、口腔消化链球菌、微单胞菌和具核梭杆菌与 CRC 的已知关联。然而，我们发现，与 FIT 联合使用时，潜在有益生物体（例如毛螺菌科成员）的丧失对于识别腺瘤患者更具预测性。这些发现表明微生物群分析有可能补充现有的筛查方法，以改善结肠病变的检测。本文的在线版本 (doi:10.1186/s13073-016-0290-3) 包含补充材料，可供授权用户使用。
Colorectal cancer (CRC) is the second leading cause of death among cancers in the United States. Although individuals diagnosed early have a greater than 90 % chance of survival, more than one-third of individuals do not adhere to screening recommendations partly because the standard diagnostics, colonoscopy and sigmoidoscopy, are expensive and invasive. Thus, there is a great need to improve the sensitivity of non-invasive tests to detect early stage cancers and adenomas. Numerous studies have identified shifts in the composition of the gut microbiota associated with the progression of CRC, suggesting that the gut microbiota may represent a reservoir of biomarkers that would complement existing non-invasive methods such as the widely used fecal immunochemical test (FIT). We sequenced the 16S rRNA genes from the stool samples of 490 patients. We used the relative abundances of the bacterial populations within each sample to develop a random forest classification model that detects colonic lesions using the relative abundance of gut microbiota and the concentration of hemoglobin in stool. The microbiota-based random forest model detected 91.7 % of cancers and 45.5 % of adenomas while FIT alone detected 75.0 % and 15.7 %, respectively. Of the colonic lesions missed by FIT, the model detected 70.0 % of cancers and 37.7 % of adenomas. We confirmed known associations of Porphyromonas assaccharolytica, Peptostreptococcus stomatis, Parvimonas micra, and Fusobacterium nucleatum with CRC. Yet, we found that the loss of potentially beneficial organisms, such as members of the Lachnospiraceae, was more predictive for identifying patients with adenomas when used in combination with FIT. These findings demonstrate the potential for microbiota analysis to complement existing screening methods to improve detection of colonic lesions. The online version of this article (doi:10.1186/s13073-016-0290-3) contains supplementary material, which is available to authorized users.