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Register a new userBACOM 2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor
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BACOM is a statistically principled and unsupervised method that detects copy number deletion types (homozygous versus heterozygous), estimates normal cell fraction, and recovers cancer specific copy number profiles, using allele specific copy number signals. In a subsequent analysis of TCGA ovarian cancer dataset, the average normal cell fraction estimated by BACOM was found higher than expected. In this letter, we first discuss the advantages of the BACOM in relation to alternative approaches. Then, we show that this elevated estimate of normal cell fraction is the combined result of inaccurate signal modeling and normalization. Lastly, we describe an allele specific signal modeling and normalization scheme that can enhance BACOM applications in many biological contexts. An open source MATLAB program was developed to implement our extended method and it is publically available.
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Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patients. Recent data collections provide a comprehensive characterization of cancer cell data, including genetic data on copy number alterations (CNAs). We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach that encodes each cancer sample as a persistence diagram of topological features, i.e., high-dimensional voids represented in the data. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data and demonstrate the viability of some applications on finding substructures in cancer data as well as comparing similarity of cancer types.
We develop a cross-platform open-source Java application (BACOM2) with graphic user interface (GUI), and users also can use a XML file to set the parameters of algorithm model, file paths and the dataset of paired samples. BACOM2 implements the new entire pipeline of copy number change analysis for heterogeneous cancer tissues, including extraction of raw copy number signals from CEL files of paired samples, attenuation correction, identification of balanced AB-genotype loci, copy number detection and segmentation, global baseline calculation and absolute normalization, differentiation of deletion types, estimation of the normal tissue fraction and correction of normal tissue contamination. BACOM2 focuses on the common tools for data preparation and absolute normalization for copy number analysis of heterogeneous cancer tissues. The software provides an additional choice for scientists who require a user-friendly, high-speed processing, cross-platform computing environment for large copy number data analysis.
The Kolmogorov-Arnold stochasticity parameter technique is applied for the first time to the study of cancer genome sequencing, to reveal mutations. Using data generated by next generation sequencing technologies, we have analyzed the exome sequences of brain tumor patients with matched tumor and normal blood. We show that mutations contained in sequencing data can be revealed using this technique thus providing a new methodology for determining subsequences of given length containing mutations i.e. its value differs from those of subsequences without mutations. A potential application for this technique involves simplifying the procedure of finding segments with mutations, speeding up genomic research, and accelerating its implementation in clinical diagnostic. Moreover, the prediction of a mutation associated to a family of frequent mutations in numerous types of cancers based purely on the value of the Kolmogorov function, indicates that this applied marker may recognize genomic sequences that are in extremely low abundance and can be used in revealing new types of mutations.
The proper functioning of mitochondria requires that both the mitochondrial and the nuclear genome are functional. To investigate the importance of the mitochondrial genome, which encodes only 13 subunits of the respiratory complexes, the mitochondrial rRNAs and a few tRNAs, we performed a comparative study on the 143B cell line and on its Rho-0 counterpart, i.e., devoid of mitochondrial DNA. Quantitative differences were found, of course in the respiratory complexes subunits, but also in the mitochondrial translation apparatus, mainly mitochondrial ribosomal proteins, and in the ion and protein import system, i.e., including membrane proteins. Various mitochondrial metabolic processes were also altered, especially electron transfer proteins and some dehydrogenases, but quite often on a few proteins for each pathway. This study also showed variations in some hypothetical or poorly characterized proteins, suggesting a mitochondrial localization for these proteins. Examples include a stomatin-like protein and a protein sharing homologies with bacterial proteins implicated in tyrosine catabolism. Proteins involved in apoptosis control are also found modulated in Rho-0 mitochondria.
Motivation: As cancer researchers have come to appreciate the importance of intratumor heterogeneity, much attention has focused on the challenges of accurately profiling heterogeneity in individual patients. Experimental technologies for directly profiling genomes of single cells are rapidly improving, but they are still impractical for large-scale sampling. Bulk genomic assays remain the standard for population-scale studies, but conflate the influences of mixtures of genetically distinct tumor, stromal, and infiltrating immune cells. Many computational approaches have been developed to deconvolute these mixed samples and reconstruct the genomics of genetically homogeneous clonal subpopulations. All such methods, however, are limited to reconstructing only coarse approximations to a few major subpopulations. In prior work, we showed that one can improve deconvolution of genomic data by leveraging substructure in cellular mixtures through a strategy called simplicial complex inference. This strategy, however, is also limited by the difficulty of inferring mixture structure from sparse, noisy assays. Results: We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures, with specific emphasis on genome-wide copy number variation (CNV) data. We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse, noisy data; and automated model inference methods for other key model parameters. We show that these improvements lead to more accurate inference of cell populations and mixture proportions in simulated scenarios. We further demonstrate their effectiveness in identifying mixture substructure in real tumor CNV data. Availability: Source code is available at http://www.cs.cmu.edu/~russells/software/WSCUnmix.zip
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