Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Background. A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. Results. We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. Conclusions. We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses

Mathematical modeling of the malignancy of cancer using graph evolution

Cataloged from PDF version of article.We report a novel computational method based on graph evolution process to model the malignancy of brain cancer called glioma. In this work, we analyze the phases that a graph passes through during its evolution and demonstrate strong relation between the malignancy of cancer and the phase of its graph. From the photomicrographs of tissues, which are diagnosed as normal, low-grade cancerous and high-grade cancerous, we construct cell-graphs based on the locations of cells; we probabilistically generate an edge between every pair of cells depending on the Euclidean distance between them. For a cell-graph, we extract connectivity information including the properties of its connected components in order to analyze the phase of the cell-graph. Working with brain tissue samples surgically removed from 12 patients, we demonstrate that cell-graphs generated for different tissue types evolve differently and that they exhibit different phase properties, which distinguish a tissue type from another. (C) 2007 Elsevier Inc. All rights reserved

Human Cancer Cell Radiation Response Investigated through Topological Analysis of 2D Cell Networks

Clonogenic assays are routinely used to evaluate the response of cancer cells to external radiation fields, assess their radioresistance and radiosensitivity, estimate the performance of radiotherapy. However, classic clonogenic tests focus on the number of colonies forming on a substrate upon exposure to ionizing radiation, and disregard other important characteristics of cells such their ability to generate structures with a certain shape. The radioresistance and radiosensitivity of cancer cells may depend less on the number of cells in a colony and more on the way cells interact to form complex networks. In this study, we have examined whether the topology of 2D cancer-cell graphs is influenced by ionizing radiation. We subjected different cancer cell lines, i.e. H4 epithelial neuroglioma cells, H460 lung cancer cells, PC3 bone metastasis of grade IV of prostate cancer and T24 urinary bladder cancer cells, cultured on planar surfaces, to increasing photon radiation levels up to 6 Gy. Fluorescence images of samples were then processed to determine the topological parameters of the cell-graphs developing over time. We found that the larger the dose, the less uniform the distribution of cells on the substrate—evidenced by high values of small-world coefficient (cc), high values of clustering coefficient (cc), and small values of characteristic path length (cpl). For all considered cell lines, >1 for doses higher or equal to 4 Gy, while the sensitivity to the dose varied for different cell lines: T24 cells seem more distinctly affected by the radiation, followed by the H4, H460 and PC3 cells. Results of the work reinforce the view that the characteristics of cancer cells and their response to radiotherapy can be determined by examining their collective behavior—encoded in a few topological parameters—as an alternative to classical clonogenic assays

Cooperation among cancer cells as public goods games on Voronoi networks

Cancer cells produce growth factors that diffuse and sustain tumor proliferation, a form of cooperation among cancer cells that can be studied using mathematical models of public goods in the framework of evolutionary game theory. Cell populations, however, form heterogeneous networks that cannot be described by regular lattices or scale-free networks, the types of graphs generally used in the study of cooperation. To describe the dynamics of growth factor production in populations of cancer cells, I study public goods games on Voronoi networks, using a range of non-linear benefits that account for the known properties of growth factors, and different types of diffusion gradients. e results are surprisingly similar to those obtained on regular graphs and different from results on scale-free networks, revealing that network heterogeneity per se does not promote cooperation when public goods diffuse beyond one-step neighbours. e exact shape of the diffusion gradient is not crucial, however, whereas the type of non-linear benefit is an essential determinant of the dynamics. Public goods games on Voronoi networks can shed light on intra-tumor heterogeneity, the evolution of resistance to therapies that target growth factors, and new types of cell therapy

Attenuation of hedgehog/GLI signaling by NT1721 extends survival in pancreatic cancer.

BackgroundPancreatic cancer is one of the most lethal malignancies due to frequent late diagnosis, aggressive tumor growth and metastasis formation. Continuously raising incidence rates of pancreatic cancer and a lack of significant improvement in survival rates over the past 30 years highlight the need for new therapeutic agents. Thus, new therapeutic agents and strategies are urgently needed to improve the outcome for patients with pancreatic cancer. Here, we evaluated the anti-tumor activity of a new natural product-based epidithiodiketopiperazine, NT1721, against pancreatic cancer.MethodsWe characterized the anticancer efficacy of NT1721 in multiple pancreatic cancer cell lines in vitro and in two orthotopic models. We also compared the effects of NT1721 to clinically used hedgehog inhibitors and the standard-of-care drug, gemcitabine. The effect of NT1721 on hedgehog/GLI signaling was assessed by determining the expression of GLI and GLI target genes both in vitro and in vivo.ResultsNT1721 displayed IC50 values in the submicromolar range in multiple pancreatic cancer cell lines, while largely sparing normal pancreatic epithelial cells. NT1721 attenuated hedgehog/GLI signaling through downregulation of GLI1/2 transcription factors and their downstream target genes, which reduced cell proliferation and invasion in vitro and significantly decreased tumor growth and liver metastasis in two preclinical orthotopic mouse models of pancreatic cancer. Importantly, treatment with NT1721 significantly improved survival times of mice with pancreatic cancer compared to the standard-of-care drug, gemcitabine.ConclusionsFavorable therapeutics properties, i.e. 10-fold lower IC50 values than clinically used hedgehog inhibitors (vismodegib, erismodegib), a 90% reduction in liver metastasis and significantly better survival times compared to the standard-of-care drug, gemcitabine, provide a rational for testing NT1721 in the clinic either as a single agent or possibly in combination with gemcitabine or other therapeutic agents in PDAC patients overexpressing GLI1/2. This could potentially result in promising new treatment options for patients suffering from this devastating disease

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Algebraic and Topological Indices of Molecular Pathway Networks in Human Cancers

Protein-protein interaction networks associated with diseases have gained prominence as an area of research. We investigate algebraic and topological indices for protein-protein interaction networks of 11 human cancers derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a strong correlation between relative automorphism group sizes and topological network complexities on the one hand and five year survival probabilities on the other hand. Moreover, we identify several protein families (e.g. PIK, ITG, AKT families) that are repeated motifs in many of the cancer pathways. Interestingly, these sources of symmetry are often central rather than peripheral. Our results can aide in identification of promising targets for anti-cancer drugs. Beyond that, we provide a unifying framework to study protein-protein interaction networks of families of related diseases (e.g. neurodegenerative diseases, viral diseases, substance abuse disorders).Comment: 15 pages, 4 figure

A Trib2-p38 axis controls myeloid leukaemia cell cycle and stress response signalling

Trib2 pseudokinase is involved in the etiology of a number of cancers including leukaemia, melanoma, ovarian, lung and liver cancer. Both high and low Trib2 expression levels correlate with different types of cancer. Elevated Trib2 expression has oncogenic properties in both leukaemia and lung cancer dependent on interactions with proteasome machinery proteins and degradation of transcription factors. Here, we demonstrated that Trib2 deficiency conferred a growth and survival advantage both at steady state and in stress conditions in leukaemia cells. In response to stress, wild type leukaemia cells exited the cell cycle and underwent apoptosis. In contrast, Trib2 deficient leukaemia cells continued to enter mitosis and survive. We showed that Trib2 deficient leukaemia cells had defective MAPK p38 signalling, which associated with a reduced γ-H2Ax and Chk1 stress signalling response, and continued proliferation following stress, associated with inefficient activation of cell cycle inhibitors p21, p16 and p19. Furthermore, Trib2 deficient leukaemia cells were more resistant to chemotherapy than wild type leukaemia cells, having less apoptosis and continued propagation. Trib2 re-expression or pharmacological activation of p38 in Trib2 deficient leukaemia cells sensitised the cells to chemotherapy-induced apoptosis comparable with wild type leukaemia cells. Our data provide evidence for a tumour suppressor role of Trib2 in myeloid leukaemia via activation of p38 stress signalling. This newly identified role indicates that Trib2 may counteract the propagation and chemotherapy resistance of leukaemia cells