EXPANDS: expanding ploidy and allele frequency on nested subpopulations

A general framework for analyzing tumor subclonality using SNP array and DNA sequencing data

Intra-tumor heterogeneity reflects cancer genome evolution and provides key information for diagnosis and treatment. When bulk tumor tissues are profiled for somatic copy number alterations (sCNA) and point mutations, it may be difficult to estimate their cellular fractions when a mutation falls within a sCNA. We present the Clonal Heterogeneity Analysis Tool, which estimates cellular fractions for both sCNAs and mutations, and uses their distributions to inform macroscopic clonal architecture. In a set of approximately 700 breast tumors, more than half appear to contain multiple recognizable aneuploid tumor clones, and many show subtype-specific differences in clonality for known cancer genes.

Towards accurate characterization of clonal heterogeneity based on structural variation

Background

Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal heterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of fractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as deletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate for allele fraction estimations. However, no systematic method exists that can support such analysis.

Results

In this paper, we present a novel maximum-likelihood method that estimates allele fractions of structural variants integratively from various forms of alignment signals. We develop a tool, BreakDown, to estimate the allele fractions of most structural variants including medium size (from 1 kilobase to 1 megabase) deletions and duplications, and balanced inversions and translocations.

Conclusions

Evaluation based on both simulated and real data indicates that our method systematically enables structural variants for clonal heterogeneity analysis and can greatly enhance the characterization of genomically instable tumors.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-299) contains supplementary material, which is available to authorized users.

An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples

Solid tumor samples typically contain multiple distinct clonal populations of cancer cells, and also stromal and immune cell contamination. A majority of the cancer genomics and transcriptomics studies do not explicitly consider genetic heterogeneity and impurity, and draw inferences based on mixed populations of cells. Deconvolution of genomic data from heterogeneous samples provides a powerful tool to address this limitation. We discuss several computational tools, which enable deconvolution of genomic and transcriptomic data from heterogeneous samples. We also performed a systematic comparative assessment of these tools. If properly used, these tools have potentials to complement single-cell genomics and immunoFISH analyses, and provide novel insights into tumor heterogeneity.

ASYMMETRIC CELL DIVISION: IMPLICATIONS FOR GLIOMA DEVELOPMENT AND TREATMENT

Glioma is a heterogeneous disease process with differential histology and treatment response. It was previously thought that the histological features of glial tumors indicated their cell of origin. However, the discovery of continuous neuro-gliogenesis in the normal adult brain and the identification of brain tumor stem cells within glioma have led to the hypothesis that these brain tumors originate from multipotent neural stem or progenitor cells, which primarily divide asymmetrically during the postnatal period. Asymmetric cell division allows these cell types to concurrently self-renew whilst also producing cells for the differentiation pathway. It has recently been shown that increased symmetrical cell division, favoring the self-renewal pathway, leads to oligodendroglioma formation from oligodendrocyte progenitor cells. In contrast, there is some evidence that asymmetric cell division maintenance in tumor stem-like cells within astrocytoma may lead to acquisition of treatment resistance. Therefore cell division mode in normal brain stem and progenitor cells may play a role in setting tumorigenic potential and the type of tumor formed. Moreover, heterogeneous tumor cell populations and their respective cell division mode may confer differential sensitivity to therapy. This review aims to shed light on the controllers of cell division mode which may be therapeutically targeted to prevent glioma formation and improve treatment response.