IIIT Hyderabad Publications |
|||||||||
|
Detection and Functional Characterization of Genetic Variations in Diffuse Large B-cell LymphomaAuthor: PRASHANTHI DHARANIPRAGADA Date: 2020-05-08 Report no: IIIT/TH/2020/35 Advisor:Nita Parekh AbstractIn the course of evolution, human genome has been disrupted, modified and rearranged as a consequence of various genetic events, such as single nucleotide mutations, insertions,deletions, copy number variations, translocations and inversions, making each individual’s genome unique. The accumulated genetic variations are known to play a significant role in shaping population-specific phenotypes and pre-disposition to various lethal diseases including cancer. Detection and annotation of these variants is the first step in understanding their role in disease initiation. Advancements in next generation sequencing (NGS) technologies have made it possible to carry out variant profiling of individuals, slowly replacing traditional and hybridization-based techniques. However, NGS techniques demonstrate inherent challenges due to short read length, incomplete coverage, GC composition, mappability bias, etc. To address some of these issues, we have developed two open-source, integrated platforms, SeqVItA and iCopyDAV, for the detection of small sequence variations (SSVs) and copy number variations (CNVs), respectively, in NGS data. Our tools are tailored to quickly and efficiently identify and functionally annotate genetic variations from large NGS data. In this thesis work we have carried out the detection and functional analysis of variants in Diffuse Large B-cell Lymphoma whole genome sequence data using these tools. Diffuse Large B-cell Lymphoma (DLBCL) is the most aggressive form of haematological malignancies and mostly develops as a result of accumulation of several genetic variations affecting important cellular processes and immuno-oncogenic pathways. The cell-of-origin classification system categorized DLBCL into Germinal Center B-cell (GCB) and Activated B-cell (ABC) subtypes, identified with distinct outcomes when treated with standard immunochemotherapy. Despite several studies carried out to characterize subtype-specific genetic variations, the complete spectrum of these alterations and their relationship with clinicopathological characteristics remains to be elucidated. Detection of subtype-specific genetic variations would assist in understanding the pathogenesis in each case and help in identifying molecular biomarkers for clinical testing, and novel targets for efficient therapy. In this study, we focus on genome-wide analysis of small sequence variants (SNVs and INDELs) and copy number variations (CNVs) in the two subtypes of DLBCL with the objective of identifying molecular markers implicated in the initiation and progression of tumour in these subtypes. The reason for considering these two category of genetic variants is that small sequence variants are widely studied in association studies for disease susceptibility and treatment outcome, while, CNVs affect a large fraction of the genome through focal or armlength amplifications and deletions contributing to the aggressiveness of the cancer. Detection, analysis and annotation of genetic variations is carried out in 12 DLBCL cell lines (7 GCB and 5 ABC subtype). We observe large differences in the variation profiles of the cell lines indicating differences in the genes and associated pathways affected across the cell lines. Detailed analysis revealed a novel set of recurrent genetic variations that may play a key role in lymphomagenesis in a subtype-specific manner. A set of novel genetic variations with potential prognosis are also uncovered. Integration of SSVs and CNVs revealed subgroups of shared variations among the cell lines analysed which help in understanding their shared effect on lymphomagenesis and prognosis. To summarize, in this thesis we present the workflow for detection and functional characterization of genetic variations in cancer genome and provide novel insights into the molecular nature of DLBCL tumorigenesis Full thesis: pdf Centre for Computational Natural Sciences and Bioinformatics |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |