131 research outputs found
TrAp: a Tree Approach for Fingerprinting Subclonal Tumor Composition
Revealing the clonal composition of a single tumor is essential for
identifying cell subpopulations with metastatic potential in primary tumors or
with resistance to therapies in metastatic tumors. Sequencing technologies
provide an overview of an aggregate of numerous cells, rather than
subclonal-specific quantification of aberrations such as single nucleotide
variants (SNVs). Computational approaches to de-mix a single collective signal
from the mixed cell population of a tumor sample into its individual components
are currently not available. Herein we propose a framework for deconvolving
data from a single genome-wide experiment to infer the composition, abundance
and evolutionary paths of the underlying cell subpopulations of a tumor. The
method is based on the plausible biological assumption that tumor progression
is an evolutionary process where each individual aberration event stems from a
unique subclone and is present in all its descendants subclones. We have
developed an efficient algorithm (TrAp) for solving this mixture problem. In
silico analyses show that TrAp correctly deconvolves mixed subpopulations when
the number of subpopulations and the measurement errors are moderate. We
demonstrate the applicability of the method using tumor karyotypes and somatic
hypermutation datasets. We applied TrAp to SNV frequency profile from Exome-Seq
experiment of a renal cell carcinoma tumor sample and compared the mutational
profile of the inferred subpopulations to the mutational profiles of twenty
single cells of the same tumor. Despite the large experimental noise, specific
co-occurring mutations found in clones inferred by TrAp are also present in
some of these single cells. Finally, we deconvolve Exome-Seq data from three
distinct metastases from different body compartments of one melanoma patient
and exhibit the evolutionary relationships of their subpopulations
Characterizing disease states from topological properties of transcriptional regulatory networks
BACKGROUND: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. RESULTS: We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. CONCLUSION: Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states
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