Genetics and Genomics Commons^™

Estimating The Probability Of Clonal Relatedness Of Pairs Of Tumors In Cancer Patients, Audrey Mauguen, Venkatraman E. Seshan, Irina Ostrovnaya, Colin B. Begg

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

Next generation sequencing panels are being used increasingly in cancer research to study tumor evolution. A specific statistical challenge is to compare the mutational profiles in different tumors from a patient to determine the strength of evidence that the tumors are clonally related, i.e. derived from a single, founder clonal cell. The presence of identical mutations in each tumor provides evidence of clonal relatedness, although the strength of evidence from a match is related to how commonly the mutation is seen in the tumor type under investigation. This evidence must be weighed against the evidence in favor of independent tumors …

Sparse Integrative Clustering Of Multiple Omics Data Sets, Ronglai Shen, Sijian Wang, Qianxing Mo

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation, and gene expression associated with a disease. An integrated genomic profiling approach measuring multiple omics data types simultaneously in the same set of biological samples would render an integrated data resolution that would not be available with any single data type. In a previous publication (Shen et al., 2009), we proposed a latent variable regression with a lasso constraint (Tibshirani, 1996) for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient …

Integrative Clustering Of Multiple Genomic Data Types Using A Joint Latent Variable Model With Application To Breast And Lung Cancer Subtype Analysis, Ronglai Shen, Adam Olshen, Marc Ladanyi

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic, and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment. We developed a joint latent variable model for integrative clustering. We call the resulting methodology iCluster. iCluster incorporates flexible modeling of the associations …

A Classification Model For Distinguishing Copy Number Variants From Cancer-Related Alterations, Irina Ostrovnaya, Gouri Nanjangud, Adam Olshen

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

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 (Iafrate et al., 2004) contains a list of all known CNVs, there is no standard methodology to use the database effectively.

We develop a prediction model that distinguishes CNVs from CNAs based on the information contained in the Database and several other variables, including potential CNV’s length, height, closeness to …

Statistical Evaluation Of Evidence For Clonal Allelic Alterations In Array-Cgh Experiments, Colin B. Begg, Kevin Eng, Adam Olshen, E S. Venkatraman

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

In recent years numerous investigators have conducted genetic studies of pairs of tumor specimens from the same patient to determine whether the tumors share a clonal origin. These studies have the potential to be of considerable clinical significance, especially in clinical settings where the distinction of a new primary cancer and metastatic spread of a previous cancer would lead to radically different indications for treatment. Studies of clonality have typically involved comparison of the patterns of somatic mutations in the tumors at candidate genetic loci to see if the patterns are sufficiently similar to indicate a clonal origin. More recently, …

Sequential Quantitative Trait Locus Mapping In Experimental Crosses, Jaya M. Satagopan, Saunak Sen, Gary A. Churchill

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

The etiology of complex diseases is heterogeneous. The presence of risk alleles in one or more genetic loci affects the function of a variety of intermediate biological pathways, resulting in the overt expression of disease. Hence, there is an increasing focus on identifying the genetic basis of disease by sytematically studying phenotypic traits pertaining to the underlying biological functions. In this paper we focus on identifying genetic loci linked to quantitative phenotypic traits in experimental crosses. Such genetic mapping methods often use a one stage design by genotyping all the markers of interest on the available subjects. A genome scan …

A Faster Circular Binary Segmentation Algorithm For The Analysis Of Array Cgh Data, E S. Venkatraman, Adam Olshen

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number (Olshen {\it et~al}, 2004). The algorithm tests for change-points using a maximal $t$-statistic with a permutation reference distribution to obtain the corresponding $p$-value. The number of computations required for the maximal test statistic is $O(N^2),$ where $N$ is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the …