A Metastasis or a Second Independent Cancer? Evaluating the Clonal Origin of Tumors Using Array-CGH Data

When a cancer patient develops a new tumor it is necessary to determine if this is a recurrence (metastasis) of the original cancer, or an entirely new occurrence of the disease. This is accomplished by assessing the histo-pathology of the lesions, and it is frequently relatively straightforward. However, there are many clinical scenarios in which this pathological diagnosis is difficult. Since each tumor is characterized by a genetic fingerprint of somatic mutations, a more definitive diagnosis is possible in principle in these difficult clinical scenarios by comparing the fingerprints. In this article we develop and evaluate a statistical strategy for this comparison when the data are derived from array comparative genomic hybridization, a technique designed to identify all of the somatic allelic gains and losses across the genome. Our method involves several stages. First a segmentation algorithm is used to estimate the regions of allelic gain and loss. Then the broad correlation in these patterns between the two tumors is assessed, leading to an initial likelihood ratio for the two diagnoses. This is then further refined by comparing in detail each plausibly clonal mutation within individual chromosome arms, and the results are aggregated to determine a final likelihood ratio. The method is employed to diagnose patients from several clinical scenarios, and the results show that in many cases a strong clonal signal emerges, occasionally contradicting the clinical diagnosis. The “quality” of the arrays can be summarized by a parameter that characterizes the clarity with which allelic changes are detected. Sensitivity analyses show that most of the diagnoses are robust when the data are of high quality

A classification model for distinguishing copy number variants from cancer-related alterations

<p>Abstract</p> <p>Background</p> <p>Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.</p> <p>Results</p> <p>We develop a prediction model that distinguishes CNVs from CNAs based on the information contained in the DGV and several other variables, including segment's length, height, closeness to a telomere or centromere and occurrence in other patients. The models are fitted on data from glioblastoma and their corresponding normal samples that were collected as part of The Cancer Genome Atlas project and hybridized to Agilent 244 K arrays.</p> <p>Conclusions</p> <p>Using the DGV alone CNVs in the test set can be correctly identified with about 85% accuracy if the outliers are removed before segmentation and with 72% accuracy if the outliers are included, and additional variables improve the prediction by about 2-3% and 12%, respectively. Final models applied to data from ovarian tumors have about 90% accuracy with all the variables and 86% accuracy with the DGV alone.</p

Prevalence and Co-Occurrence of Actionable Genomic Alterations in High-Grade Bladder Cancer

We sought to define the prevalence and co-occurrence of actionable genomic alterations in patients with high-grade bladder cancer to serve as a platform for therapeutic drug discovery

Identifying prognostic pairwise relationships among bacterial species in microbiome studies

Non UBCUnreviewedAuthor affiliation: Memorial Sloan-Kettering Cancer CenterResearche