Anestis Touloumis

Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics

Glioblastoma (GB) is the most common and aggressive primary brain malignancy, with poor prognosis and a lack of effective therapeutic options. Accumulating evidence suggests that intratumor heterogeneity likely is the key to understanding treatment failure. However, the extent of intratumor heterogeneity as a result of tumor evolution is still poorly understood. To address this, we developed a unique surgical multisampling scheme to collect spatially distinct tumor fragments from 11 GB patients. We present an integrated genomic analysis that uncovers extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor. Moreover, we reconstructed the phylogeny of the fragments for each patient, identifying copy number alterations in EGFR and CDKN2A/B/p14ARF as early events, and aberrations in PDGFRA and PTEN as later events during cancer progression. We also characterized the clonal organization of each tumor fragment at the single-molecule level, detecting multiple coexisting cell lineages. Our results reveal the genome-wide architecture of intratumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design.

Nonparametric Stein-type shrinkage covariance matrix estimators in high-dimensional settings

Estimating a covariance matrix is an important task in applications where the number of variables is larger than the number of observations. Shrinkage approaches for estimating a high-dimensional covariance matrix are often employed to circumvent the limitations of the sample covariance matrix. A new family of nonparametric Stein-type shrinkage covariance estimators is proposed whose members are written as a convex linear combination of the sample covariance matrix and of a predefined invertible target matrix. Under the Frobenius norm criterion, the optimal shrinkage intensity that defines the best convex linear combination depends on the unobserved covariance matrix and it must be estimated from the data. A simple but effective estimation process that produces nonparametric and consistent estimators of the optimal shrinkage intensity for three popular target matrices is introduced. In simulations, the proposed Stein-type shrinkage covariance matrix estimator based on a scaled identity matrix appeared to be up to 80% more efficient than existing ones in extreme high-dimensional settings. A colon cancer dataset was analyzed to demonstrate the utility of the proposed estimators. A rule of thumb for adhoc selection among the three commonly used target matrices is recommended.

Contributions to drug resistance in glioblastoma derived from malignant cells in the sub-ependymal zone

Glioblastoma, the most common and aggressive adult brain tumor, is characterized by extreme phenotypic diversity and treatment failure. Through fluorescence-guided resection, we identified fluorescent tissue in the sub-ependymal zone (SEZ) of patients with glioblastoma. Histologic analysis and genomic characterization revealed that the SEZ harbors malignant cells with tumor-initiating capacity, analogous to cells isolated from the fluorescent tumor mass (T). We observed resistance to supramaximal chemotherapy doses along with differential patterns of drug response between T and SEZ in the same tumor. Our results reveal novel insights into glioblastoma growth dynamics, with implications for understanding and limiting treatment resistance.

Epigenetic Profile of Human Adventitial Progenitor Cells Correlates With Therapeutic Outcomes in a Mouse Model of Limb Ischemia

Objective— We investigated the association between the functional, epigenetic, and expressional profile of human adventitial progenitor cells (APCs) and therapeutic activity in a model of limb ischemia. Approach and Results— Antigenic and functional features were analyzed throughout passaging in 15 saphenous vein (SV)–derived APC lines, of which 10 from SV leftovers of coronary artery bypass graft surgery and 5 from varicose SV removal. Moreover, 5 SV-APC lines were transplanted (8×105 cells, IM) in mice with limb ischemia. Blood flow and capillary and arteriole density were correlated with functional characteristics and DNA methylation/expressional markers of transplanted cells. We report successful expansion of tested lines, which reached the therapeutic target of 30 to 50 million cells in ≈10 weeks. Typical antigenic profile, viability, and migratory and proangiogenic activities were conserved through passaging, with low levels of replicative senescence. In vivo, SV-APC transplantation improved blood flow recovery and revascularization of ischemic limbs. Whole genome screening showed an association between DNA methylation at the promoter or gene body level and microvascular density and to a lesser extent with blood flow recovery. Expressional studies highlighted the implication of an angiogenic network centered on the vascular endothelial growth factor receptor as a predictor of microvascular outcomes. FLT-1 gene silencing in SV-APCs remarkably reduced their ability to form tubes in vitro and support tube formation by human umbilical vein endothelial cells, thus confirming the importance of this signaling in SV-APC angiogenic function. Conclusions— DNA methylation landscape illustrates different therapeutic activities of human APCs. Epigenetic screening may help identify determinants of therapeutic vasculogenesis in ischemic disease.

Abstract 5016: The human sub-ependymal zone harbors glioblastoma precursors and represents a distinct therapeutic target.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC Glioblastoma (GB), the most common and aggressive adult brain tumor, is characterized by phenotypic diversity and ultimately treatment failure. Using fluorescence-guided sampling we integrated copy number aberration, gene expression and molecular clock data to show that the sub-ependymal zone (SEZ) contains precursor cells of the corresponding mass. The genetically distinct tumor margin contains non self-renewing cells that retain the potential to regenerate the tumor. Functional assays confirmed that SEZ and marginal disease were less clinically aggressive compared to the mass compartment in the same tumor. However, resistance to supra-maximal chemotherapy doses and differential patterns of drug response were observed between compartments. These data provide novel insights into the basis of clinical diversity, poor treatment response and emergence of resistant disease in GB patients. Citation Format: Sara Grazia Maria Piccirillo, Inma Spiteri, Andrea Sottoriva, Anestis Touloumis, Suzan Ber, Stephen J. Price, Richard Heywood, Nicola-Jane Francis, Vincent P. Collins, Ashok R. Venkitaraman, Christina Curtis, John C. Marioni, Simon Tavare’, Colin Watts. The human sub-ependymal zone harbors glioblastoma precursors and represents a distinct therapeutic target. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5016. doi:10.1158/1538-7445.AM2013-5016

Testing the mean matrix in high-dimensional transposable data

The structural information in high-dimensional transposable data allows us to write the data recorded for each subject in a matrix such that both the rows and the columns correspond to variables of interest. One important problem is to test the null hypothesis that the mean matrix has a particular structure without ignoring the dependence structure among and/or between the row and column variables. To address this, we develop a generic and computationally inexpensive nonparametric testing procedure to assess the hypothesis that, in each predefined subset of columns (rows), the column (row) mean vector remains constant. In simulation studies, the proposed testing procedure seems to have good performance and, unlike simple practical approaches, it preserves the nominal size and remains powerful even if the row and/or column variables are not independent. Finally, we illustrate the use of the proposed methodology via two empirical examples from gene expression microarrays.

Visual logics help people: An evaluation of diagrammatic, textual and symbolic notations

Our aim is to provide empirical evidence that diagrammatic logics are more effective than symbolic and textual logics in allowing people to better understand information. Ontologies provide an important focus for such an empirical study: people need to understand the axioms of which ontologies comprise. A between-groups study compared six frequently-used axiom types using the (textual) Manchester OWL Syntax (MOS), (symbolic) description logic (DL) and concept diagrams. Concept diagrams yielded significantly better task performance than DL for all six, and MOS for four, axiom types. MOS outperformed concept diagrams for just one axiom type and DL for only three axiom types. Thus diagrams could ensure ontologies are developed more robustly.

HDTD: analyzing multi-tissue gene expression data

Motivation: By collecting multiple samples per subject, researchers can characterize intra-subject variation using physiologically relevant measurements such as gene expression profiling. This can yield important insights into fundamental biological questions ranging from cell type identity to tumour development. For each subject, the data measurements can be written as a matrix with the different subsamples (e.g. multiple tissues) indexing the columns and the genes indexing the rows. In this context, neither the genes nor the tissues are expected to be independent and straightforward application of traditional statistical methods that ignore this two-way dependence might lead to erroneous conclusions. Herein, we present a suite of tools embedded within the R/Bioconductor package HDTD for robustly estimating and performing hypothesis tests about the mean relationship and the covariance structure within the rows and columns. We illustrate the utility of HDTD by applying it to analyze data generated by the Genotype-Tissue Expression consortium. Availability and Implementation: The R package HDTD is part of Bioconductor. The source code and a comprehensive user’s guide are available at http://bioconductor.org/packages/release/bioc/html/HDTD.html. Contact: A.Touloumis@brighton.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

The Efficacy of OWL and DL on User Understanding of Axioms and Their Entailments

OWL is recognized as the de facto standard notation for ontology engineering. The Manchester OWL Syntax (MOS) was developed as an alternative to symbolic description logic (DL) and it is believed to be more effective for users. This paper sets out to test that belief from two perspectives by evaluating how accurately and quickly people understand the informational content of axioms and derive inferences from them. By conducting a between-group empirical study, involving 60 novice participants, we found that DL is just as effective as MOS for people’s understanding of axioms. Moreover, for two types of inference problems, DL supported significantly better task performance than MOS, yet MOS never significantly outperformed DL. These surprising results suggest that the belief that MOS is more effective than DL, at least for these types of task, is unfounded. An outcome of this research is the suggestion that ontology axioms, when presented to non-experts, may be better presented in DL rather than MOS. Further empirical studies are needed to explain these unexpected results and to see whether they hold for other types of task.