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A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer.

ABSTRACT: Colorectal cancer is a common and deadly disease in the United States accounting for over 50,000 deaths in 2020. This progressive disease is highly preventable with early detection and treatment, but many people do not comply with the recommended screening guidelines. The gut microbiome has emerged as a promising target for noninvasive detection of colorectal cancer. Most microbiome-based classification efforts utilize taxonomic abundance data from operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) with the goal of increasing taxonomic resolution. However, it is unknown which taxonomic resolution is optimal for microbiome-based classification of colorectal cancer. To address this question, we used a reproducible machine learning framework to quantify classification performance of models based on data annotated to phylum, class, order, family, genus, OTU, and ASV levels. We found that model performance increased with increasing taxonomic resolution, up to the family level where performance was equal (P > 0.05) among family (mean area under the receiver operating characteristic curve [AUROC], 0.689), genus (mean AUROC, 0.690), and OTU (mean AUROC, 0.693) levels before decreasing at the ASV level (P < 0.05; mean AUROC, 0.676). These results demonstrate a trade-off between taxonomic resolution and prediction performance, where coarse taxonomic resolution (e.g., phylum) is not distinct enough, but fine resolution (e.g., ASV) is too individualized to accurately classify samples. Similar to the story of Goldilocks and the three bears (L. B. Cauley, Goldilocks and the Three Bears, 1981), mid-range resolution (i.e., family, genus, and OTU) is "just right" for optimal prediction of colorectal cancer from microbiome data. IMPORTANCE Despite being highly preventable, colorectal cancer remains a leading cause of cancer-related death in the United States. Low-cost, noninvasive detection methods could greatly improve our ability to identify and treat early stages of disease. The microbiome has shown promise as a resource for detection of colorectal cancer. Research on the gut microbiome tends to focus on improving our ability to profile species and strain level taxonomic resolution. However, we found that finer resolution impedes the ability to predict colorectal cancer based on the gut microbiome. These results highlight the need for consideration of the appropriate taxonomic resolution for microbiome analyses and that finer resolution is not always more informative.

SUBMITTER: Armour CR

PROVIDER: S-EPMC8749421 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The advent of high throughput sequencing has enabled in-depth characterization of human and environmental microbiomes. Determining the taxonomic origin of microbial sequences is one of the first, and frequently only, analysis performed on microbiome samples. Substantial research has focused on the development of methods for taxonomic annotation, often making trade-offs in computational efficiency and classification accuracy. A side-effect of these efforts has been a reexamination of the bacterial taxonomy itself. Taxonomies developed prior to the genomic revolution captured complex relationships between organisms that went beyond uniform taxonomic levels such as species, genus, and family. Driven in part by the need to simplify computational workflows, the bacterial taxonomies used most commonly today have been regularized to fit within a standard seven taxonomic levels. Consequently, modern analyses of microbial communities are relatively coarse-grained. Few methods make classifications below the genus level, impacting our ability to capture biologically relevant signals. Here, we present ATLAS, a novel strategy for taxonomic annotation that uses significant outliers within database search results to group sequences in the database into partitions. These partitions capture the extent of taxonomic ambiguity within the classification of a sample. The ATLAS pipeline can be found on GitHub [https://github.com/shahnidhi/outlier_in_BLAST_hits]. We demonstrate that ATLAS provides similar annotations to phylogenetic placement methods, but with higher computational efficiency. When applied to human microbiome data, ATLAS is able to identify previously characterized taxonomic groupings, such as those in the class Clostridia and the genus Bacillus. Furthermore, the majority of partitions identified by ATLAS are at the subgenus level, replacing higher-level annotations with specific groups of species. These more precise partitions improve our detection power in determining differential abundance in microbiome association studies.

Project description:BACKGROUND:Microbiome studies often involve sequencing a marker gene to identify the microorganisms in samples of interest. Sequence classification is a critical component of this process, whereby sequences are assigned to a reference taxonomy containing known sequence representatives of many microbial groups. Previous studies have shown that existing classification programs often assign sequences to reference groups even if they belong to novel taxonomic groups that are absent from the reference taxonomy. This high rate of "over classification" is particularly detrimental in microbiome studies because reference taxonomies are far from comprehensive. RESULTS:Here, we introduce IDTAXA, a novel approach to taxonomic classification that employs principles from machine learning to reduce over classification errors. Using multiple reference taxonomies, we demonstrate that IDTAXA has higher accuracy than popular classifiers such as BLAST, MAPSeq, QIIME, SINTAX, SPINGO, and the RDP Classifier. Similarly, IDTAXA yields far fewer over classifications on Illumina mock microbial community data when the expected taxa are absent from the training set. Furthermore, IDTAXA offers many practical advantages over other classifiers, such as maintaining low error rates across varying input sequence lengths and withholding classifications from input sequences composed of random nucleotides or repeats. CONCLUSIONS:IDTAXA's classifications may lead to different conclusions in microbiome studies because of the substantially reduced number of taxa that are incorrectly identified through over classification. Although misclassification error is relatively minor, we believe that many remaining misclassifications are likely caused by errors in the reference taxonomy. We describe how IDTAXA is able to identify many putative mislabeling errors in reference taxonomies, enabling training sets to be automatically corrected by eliminating spurious sequences. IDTAXA is part of the DECIPHER package for the R programming language, available through the Bioconductor repository or accessible online ( http://DECIPHER.codes ).

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A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer.

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