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Hierarchical clustering of human papilloma virus genotype patterns in the ASCUS-LSIL triage study.

ABSTRACT: Anogenital cancers are associated with ?13 carcinogenic human papilloma virus (HPV) types in a broader group that cause cervical intraepithelial neoplasia (CIN). Multiple concurrent cervical HPV infections are common, which complicates the attribution of HPV types to different grades of CIN. Here we report the analysis of HPV genotype patterns in the atypical squamous cells of undetermined significance-low-grade squamous intraepithelial lesion triage study with the use of unsupervised hierarchical clustering. Women who underwent colposcopy at baseline (n = 2,780) were grouped into 20 disease categories based on histology and cytology. Disease groups and HPV genotypes were clustered with the use of complete linkage. Risk of 2-year cumulative CIN3+, viral load, colposcopic impression, and age were compared between disease groups and major clusters. Hierarchical clustering yielded four major disease clusters: cluster 1 included all CIN3 histology with abnormal cytology; cluster 2 included CIN3 histology with normal cytology and combinations with either CIN2 or high-grade squamous intraepithelial lesion cytology; cluster 3 included older women with normal or low-grade histology/cytology and low viral load; and cluster 4 included younger women with low-grade histology/cytology, multiple infections, and the highest viral load. Three major groups of HPV genotypes were identified: group 1 included only HPV16; group 2 included nine carcinogenic types, plus noncarcinogenic HPV53 and HPV66; and group 3 included noncarcinogenic types, plus carcinogenic HPV33 and HPV45. Clustering results suggested that colposcopy missed a prevalent precancer in many women with no biopsy/normal histology and high-grade squamous intraepithelial lesion. This result was confirmed by an elevated 2-year risk of CIN3+ in these groups. Our novel approach to study multiple genotype infections in cervical disease with the use of unsupervised hierarchical clustering can address complex genotype distributions on a population level.

SUBMITTER: Wentzensen N

PROVIDER: S-EPMC2970748 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Hierarchical clustering of human papilloma virus genotype patterns in the ASCUS-LSIL triage study.

Wentzensen Nicolas N Wilson Lauren E LE Wheeler Cosette M CM Carreon Joseph D JD Gravitt Patti E PE Schiffman Mark M Castle Philip E PE

Cancer research 20101019 21

Anogenital cancers are associated with ∼13 carcinogenic human papilloma virus (HPV) types in a broader group that cause cervical intraepithelial neoplasia (CIN). Multiple concurrent cervical HPV infections are common, which complicates the attribution of HPV types to different grades of CIN. Here we report the analysis of HPV genotype patterns in the atypical squamous cells of undetermined significance-low-grade squamous intraepithelial lesion triage study with the use of unsupervised hierarchic ...[more]

PMID: 20959485

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Project description:BackgroundA hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied.Methodology/principal findingsWe show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available.ConclusionsAlthough currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide insights in erroneous and missed annotations.

Dataset Information

Hierarchical clustering of human papilloma virus genotype patterns in the ASCUS-LSIL triage study.

Publications

Hierarchical clustering of human papilloma virus genotype patterns in the ASCUS-LSIL triage study.

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