352 research outputs found
A Medical Analysis for Colorectal Lymphomas using 3D MRI Images and Deep Residual Boltzmann CNN Mechanism
In this technological world the healthcare is very crucial and difficult to spend time for the wellbeing. The lifestyle disease can transform in to the life threating disease and lead to critical stages. Colorectal lymphomas are the 3rd most malignancy death in the entire world. The estimation of the volume of lymphomas is often used by Magnetic Resonance Imaging during medical diagnosis, particularly in advanced stages. The research study can be classified in multiple stages. In the initial stages, an automated method is used to calculated the volume of the colorectal lymphomas using 3D MRI images. The process begins with feature extraction using Iterative Multilinear Component Analysis and Multiscale Phase level set segmentation based on CNN model. Then, a logical frustum model is utilized for 3D simulation of colon lymphoma for rendering the medical data. The next stages is focused on tackling the matter of segmentation and classification of abnormality and normality of lymph nodes. A semi supervised fuzzy logic algorithm for clustering is used for segmentation, whereas bee herd optimization algorithm with scale down for employed to intensify corresponding classifier rate of detection. Finally, classification is performed using Deep residual Boltzmann CNN. Our proposed methodology gives a better results and diagnosis prediction for lymphomas for an accuracy 97.7%, sensitivity 95.7% and specify as 95.8% which is superior than the traditional approach
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Genomic Evolution of Glioblastoma
Understanding how tumors evolve and drive uncontrolled cellular growth may lead to better prognosis and therapy for individuals suffering from cancer. A key to understanding the paths of progression are to develop computational and experimental methods to dissect clonal heterogeneity and statistically model evolutionary routes.
This thesis contains results from analysis of genomic data using computational methods that integrate diverse next generation sequencing data and evolutionary concepts to model tumor evolution and delineate likely routes of genomic alterations.
First, I introduce some background and present studies into how tumor genomic sequencing tells us about tumor evolution. This will encompass some of the principles and practices related to tumor heterogeneity within the field of computional biology. Second, I will present a study of longitudinal sampling in Glioblastoma (GBM) in cohort of 114 individuals pre- and post-treatment. We will see how genomic alterations were dissected to uncover a diverse and largely unexpected landscape of recurrence. This details major observations that the recurrent tumor is not likely seeded by the primary lesion.
Second, to dissect heterogeneity from clonal evolution, multiple biopsies will be added to extend our longitudinal GBM cohort. This new data will introduce analyses to explicate inter and intra-tumor heterogeneity of GBM. Specifically, we identify a metric of intratumor heterogeneity able to identify multisector biopsies and propose a model of tumor growth in multiple GBM. These results will relate to clinical outcome and are in agreement with previously established hypotheses in truncal mutation targeting. Fourth, I will introduce new models of clonal growth applicable to 2 patient biopsies and then fit these to our GBM cohort. Simulations are used to verify models and a brief proof is presented
Computational Methods for the Analysis of Genomic Data and Biological Processes
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality
Redirection to the bone marrow improves T cell persistence and antitumor functions
A key predictor for the success of gene-modified T cell therapies for cancer is the persistence of transferred cells in the patient. The propensity of less differentiated memory T cells to expand and survive efficiently has therefore made them attractive candidates for clinical application. We hypothesized that redirecting T cells to specialized niches in the BM that support memory differentiation would confer increased therapeutic efficacy. We show that overexpression of chemokine receptor CXCR4 in CD8+ T cells (TCXCR4) enhanced their migration toward vascular-associated CXCL12+ cells in the BM and increased their local engraftment. Increased access of TCXCR4 to the BM microenvironment induced IL-15–dependent homeostatic expansion and promoted the differentiation of memory precursor–like cells with low expression of programmed death-1, resistance to apoptosis, and a heightened capacity to generate polyfunctional cytokine-producing effector cells. Following transfer to lymphoma-bearing mice, TCXCR4 showed a greater capacity for effector expansion and better tumor protection, the latter being independent of changes in trafficking to the tumor bed or local out-competition of regulatory T cells. Thus, redirected homing of T cells to the BM confers increased memory differentiation and antitumor immunity, suggesting an innovative solution to increase the persistence and functions of therapeutic T cells
ROLE OF EZH2 METHYLTRANSFERASE ACTIVITY IN THE MAINTENANCE OF MYC-DRIVEN B CELL LYMPHOMAS
The Polycomb group protein Ezh2 catalyzes the Histone H3 lysine-27 trimethylation (H3K27me3) within the Polycomb Repressive Complex 2 (PRC2). PRC2 exerts a critical control over the expression of a large set of target genes controlling important biological functions, including cell proliferation, differentiation and stem cell self-renewal.
Aberrant Ezh2 function is commonly observed in several cancer types and is due to deregulated enzymatic activity and/or expression of the Polycomb protein. Studies in preclinical models have started to reveal the importance of Ezh2 in B cell lymphomagenesis. In contrast, little is known about the effects of Ezh2 deregulated function/constitutive expression in B cell tumor maintenance and progression.
The present study addresses this issue taking advantage of a MYC-driven mouse lymphoma model, featuring high Ezh2 expression as a result of malignant B cell transformation. Conditional, genetic inactivation of Ezh2 methyltransferase activity in aggressive primary Burkitt-like mouse B cell lymphomas led to the identification of two classes of tumors, differentially responding to the loss of Polycomb function. In type-1 lymphomas, Ezh2 inactivation impaired clonal tumor growth starting from single lymphoma cells. Instead, type-2 lymphomas were largely resistant to the loss of Ezh2 catalytic function, giving rise to a substantial number of Ezh2 mutant clones. Transcriptome analyses allowed the identification of a molecular signature discriminating type-1 from type-2 lymphomas, including genes controlling cell cycle progression, DNA replication and cell survival, which were more expressed in type-2 tumors. These results correlated with a more aggressive behavior of type-2 lymphomas when transplantated into immunoproficient hosts.
The growth of rare Ezh2 mutant subclones, established from type-1 lymphomas, was impaired by the treatment with an Ezh1/2 small molecule inhibitor, identifying the Ezh2 paralog, Ezh1, as a determinant of resistance of tumor cells to Ezh2 inactivation. Ezh2 inhibition led to genome wide loss of H3K27me3, which was comparable between lymphoma types. However, while the loss of H3K27me3 at target genes in type-1 lymphomas failed to alter their expression, in type-2 lymphomas Ezh2 targets were in most cases deregulated following the loss of the histone mark. Based on these results, we propose that Ezh2 mutant subclones from type-1 lymphomas select an H3K27me3-independent mechanism to ensure correct regulation of Ezh2 target genes, which is needed for tumor growth. We also find that residual H3K27me3 is deposited at the promoter of new genes by a non-canonical PRC2/Ezh1, in Ezh2 mutant subclones from type-1 lymphomas. This activity alters the expression of target genes contributing to tumor growth.
We finally report the isolation of clonal variants from type-1 lymphomas that acquire secondary resistance to pharmacological Ezh1/2 inhibition. The latter tumors (together with type-2 lymphomas) will be instrumental to unravel the genetic bases of resistance of MYC-driven lymphomas to PRC2 inhibition.
Anti-Ezh2 inhibitors are currently being tested in phase-1 and -2 clinical trials for the treatment of both solid and blood cancers including B cell lymphomas. Our studies highlight the importance of understanding in more detail the mechanisms of action of Ezh2/PRC2 in tumors, in order to identify those that may benefit from anti-Ezh2 therapies. Our results also provide evidence for mechanisms of lymphoma resistance to Ezh2 inhibition and suggest strategies to circumvent such resistance
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Modelling timing in blood cancers
Dysregulation of biological processes in normal cells can lead to the abnormal growth of tumours. Oncogenesis requires the acquisition of advantageous mutations to expand in a fluctuating environment. Cancer cells gain these genetic and epigenetic alterations at different timing in their development, resulting in the formation of heterogeneous cell populations which interact and compete with each others inside tumours. At later stages, by escaping the immune system and acquiring malignant properties, some cancer cells manage to evade the primary tumour and spread in different organs to form metastases. Hence, tumour development in healthy tissues endure several biological changes whilst progressing and the order between these molecular and cellular events may modify prognosis.
This thesis addresses the influence of biological event timing on blood cancer progression and clinical outcomes. It first investigates the therapeutic efficacy of p53 restoration in a lymphoma mouse model. While several therapy schedules are tested, all fail due to resistance emergence. Computational modelling establishes the cell dynamics in these tumours and how to use it to propose alternative treatment strategies. Data availability leads this work to explore the impact of molecular evolution in myeloid malignancies. Notably, one study has found that Myeloproliferative Neoplasms patients with both JAK2 and TET2 mutations have different disease characteristics with distinct mutation order. My analyses identify HOXA9 as a potential prognosis marker and biological switch responsible for patient stratification in these patients and in Acute Myeloid Leukemia. Additionally, a molecular network identifies the hematopoietic regulators involved in the branching evolution of Myeloproliferative Neoplasms. Further investigations of the Acute Myeloid Leukemia data show the possible involvement of APP, a gene associated to Alzheimer disease, in early cell fate commitment in hematopoiesis and in poor survival prognosis in undifferentiated leukemia when lowly expressed. Finally, this thesis examines the regulatory dynamics behind three clusters of Acute Myeloid Leukemia patients with distinct levels of HOXA9 and APP expression. By building a program inferring molecular motifs from biological observations, genes which may interact with HOXA9 and APP are identified.Microsoft Research and the MRC Cancer Unit
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