Model-based clustering of array CGH data

Shah, Sohrab P.; Cheung, Katherine; Johnson, Nathalie A.; Alain, Guillaume; Gascoyne, Randy D.; Horsman, Douglas E.; Ng, Raymond T.; Murphy, Kevin

doi:10.1093/bioinformatics/btp205

Cited by 20 publications

(14 citation statements)

References 32 publications

(52 reference statements)

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“…WECCA was also found to define a common T-MF and SS group, a T-MF-only group, and a group composed of most CALCL cases, although some cases were not found in the relevant clinical category. Indeed, different algorithms were found to provide nearly identical results for clustering (Blaveri et al, 2005;Shah et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, clustering by giving weight to specific chromosomal regions, as with WECCA or other algorithms, would help to individualize prognostic groups by compiling data from different platforms. Such algorithms have been mainly applied to BAC array data with a uniform distribution of probes across the genome (Van Wieringen et al, 2008;Shah et al, 2009;Takeuchi et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Genome-Wide Analysis of Cutaneous T-Cell Lymphomas Identifies Three Clinically Relevant Classes

Laharanne

Oumouhou

Bonnet

et al. 2010

Journal of Investigative Dermatology

105

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This study was undertaken to identify recurrent genetic alterations of the three main types of cutaneous T-cell lymphomas (CTCLs): mycosis fungoides (MF), Sézary syndrome (SS), and cutaneous anaplastic large-cell lymphoma (CALCL). Using array-based comparative genomic hybridization, the molecular cytogenetic profiles of 72 samples obtained from 58 patients with CTCL corresponding to 24 transformed MF (T-MF), 16 SS, and 18 CALCLs were determined. T-MF was characterized by gains of 1q25-31, 7p22-11.2, 7q21, 7q31, and 17q12, and losses of 9p21, 10p11.2, and 10q26. SS exhibited gains of 8q23-24.3 and 17q23-24, as well as losses of 9p21, 10p12-11.2, 10q22-24, 10q25-26, and 17p13-q11.1. Finally, CALCL exhibited 6q27 and 13q34 losses. Such imbalances were statistically associated with one CTCL subtype. Unsupervised hierarchical clustering defined three categories of clinical relevance: (1) CALCL apart from epidermotropic-CTCL, (2) an SS-only category, and (3) a mixed category with T-MF and SS cases, with both primary and secondary SS cases. In rare cases, the genetic classification did not correspond to the inclusion diagnosis, possibly reflecting the association of two diseases in the same patient or initial misdiagnosis according to follow-up. Finally, different samples in the same patient clustered together, showing reproducibility of such a classifier.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Genome-Wide Analysis of Cutaneous T-Cell Lymphomas Identifies Three Clinically Relevant Classes

Laharanne

Oumouhou

Bonnet

et al. 2010

Journal of Investigative Dermatology

105

View full text Add to dashboard Cite

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“…Significant clusters were identified using a confidence level of 0.05. A second method was applied by running the hmmMix software [33] to identify three clusters with default parameters. Integer copy numbers were converted to log2(copy number/ploidy) values for each cytoband as input for hmmMix.…”

Section: Unsupervised Clustering Of Samplesmentioning

confidence: 99%

“…Similar results were obtained when the analysis was restricted to include only the subset of stage III and IV samples from the TCGA cohort (see Supplementary material, Figure S2), or using Euclidean rather than correlation-based distances (see Supplementary material, Figure S3). We repeated the analysis using a model-based clustering method (hmmMix [33]; see Materials and methods) to assess whether an independent, non-hierarchical approach method would produce comparable results. Analyses in which the algorithm was instructed to group samples into three to 10 clusters consistently revealed that spatially separated biopsies from the same tumour resided in different DNA copy number clusters.…”

Section: Intratumour Heterogeneity In Copy Number Profilesmentioning

confidence: 99%

Parallel evolution of tumour subclones mimics diversity between tumours

Martinez

Birkbak

Gerlinger

et al. 2013

The Journal of Pathology

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Intratumour heterogeneity (ITH) may foster tumour adaptation and compromise the efficacy of personalized medicine approaches. The scale of heterogeneity within a tumour (intratumour heterogeneity) relative to genetic differences between tumours (intertumour heterogeneity) is unknown. To address this, we obtained 48 biopsies from eight stage III and IV clear cell renal cell carcinomas (ccRCCs) and used DNA copy-number analyses to compare biopsies from the same tumour with 440 single tumour biopsies from the Cancer Genome Atlas (TCGA). Unsupervised hierarchical clustering of TCGA and multi-region ccRCC samples revealed segregation of samples from the same tumour into unrelated clusters; 25% of multi-region samples appeared more similar to unrelated samples than to any other sample originating from the same tumour. We found that the majority of recurrent DNA copy number driver aberrations in single biopsies were not present ubiquitously in late-stage ccRCCs and were likely to represent subclonal events acquired during tumour progression. Such heterogeneous subclonal genetic alterations within individual tumours may impair the identification of robust ccRCC molecular subtypes classified by distinct copy number alterations and clinical outcomes. The co-existence of distinct subclonal copy number events in different regions of individual tumours reflects the diversification of individual ccRCCs through multiple evolutionary routes and may contribute to tumour sampling bias and impact upon tumour progression and clinical outcome.

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“…Existing methods for aCGH analysis include algorithms for smoothing, segmentation and combined segmentation and classification of both single- [8], [9], [10], [11], [12], [13] and multi-sample data [14], [15], [16], [17], [18], [19], [20]. Such methods can be highly effective at identifying discrete copy number variations in such data, but are poorly suited to the problem of phylogenetic inference because they do not constrain solutions to common markers across tumor samples.…”

Section: Introductionmentioning

confidence: 99%

Novel Multi-sample Scheme for Inferring Phylogenetic Markers from Whole Genome Tumor Profiles

Subramanian

Shackney

Schwartz

2012

Bioinformatics Research and Applications

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Computational cancer phylogenetics seeks to enumerate the temporal sequences of aberrations in tumor evolution, thereby delineating the evolution of possible tumor progression pathways, molecular subtypes and mechanisms of action. We previously developed a pipeline for constructing phylogenies describing evolution between major recurring cell types computationally inferred from whole-genome tumor profiles. The accuracy and detail of the phylogenies, however, depends on the identification of accurate, high-resolution molecular markers of progression, i.e., reproducible regions of aberration that robustly differentiate different subtypes and stages of progression. Here we present a novel hidden Markov model (HMM) scheme for the problem of inferring such phylogenetically significant markers through joint segmentation and calling of multi-sample tumor data. Our method classifies sets of genome-wide DNA copy number measurements into a partitioning of samples into normal (diploid) or amplified at each probe. It differs from other similar HMM methods in its design specifically for the needs of tumor phylogenetics, by seeking to identify robust markers of progression conserved across a set of copy number profiles. We show an analysis of our method in comparison to other methods on both synthetic and real tumor data, which confirms its effectiveness for tumor phylogeny inference and suggests avenues for future advances.

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Model-based clustering of array CGH data

Cited by 20 publications

References 32 publications

Genome-Wide Analysis of Cutaneous T-Cell Lymphomas Identifies Three Clinically Relevant Classes

Genome-Wide Analysis of Cutaneous T-Cell Lymphomas Identifies Three Clinically Relevant Classes

Parallel evolution of tumour subclones mimics diversity between tumours

Novel Multi-sample Scheme for Inferring Phylogenetic Markers from Whole Genome Tumor Profiles

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