THESIS
2015
xiv, 187 pages : illustrations (some color) ; 30 cm
Abstract
AluScan is a pre-sequencing capture method with reduced costs and cancerous
DNA sample requirement, using inter-Alu PCR in conjunction with next-generation
sequencing (NGS). As an efficient solution to challenges in current cancer genome
studies, AluScan generates an Alu-anchored scan on human genome to achieve inter-Alu
sequences enriched in genic regions. With the built-up pipeline and developed programs,
AluScan sequences from cancer genomes were employed in analysis of genomic
alterations including single nucleotide variation (SNV), loss of heterozygosity (LOH) and
copy number variation (CNV) to reveal the potential underlying pathways and hence
improve the treatment of the tumors.
In this thesis, two programs developed for processing AluScan sequencing data
were introduce...[
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AluScan is a pre-sequencing capture method with reduced costs and cancerous
DNA sample requirement, using inter-Alu PCR in conjunction with next-generation
sequencing (NGS). As an efficient solution to challenges in current cancer genome
studies, AluScan generates an Alu-anchored scan on human genome to achieve inter-Alu
sequences enriched in genic regions. With the built-up pipeline and developed programs,
AluScan sequences from cancer genomes were employed in analysis of genomic
alterations including single nucleotide variation (SNV), loss of heterozygosity (LOH) and
copy number variation (CNV) to reveal the potential underlying pathways and hence
improve the treatment of the tumors.
In this thesis, two programs developed for processing AluScan sequencing data
were introduced, one was SAMSVM and the other was AluScanCNV.
For SAMSVM, it was developed as a tool for misalignment detection and filtration
on SAM-format sequences from Illumina platform with use of support vector machine
(SVM). Employing LIBSVM packages, SAMSVM performed misalignment detecting
with high accuracies ranged from 0.89 to 0.97 and F-scores ranging from 0.77 to 0.94 in
benchmarking of simulation data. Also, it increased mapping rate and on-target rate of
SNP calling on real data.
For AluScanCNV, it was developed as a tool for CNV calling on AluScan data.
Employing Geary-Hinkley transformation (GHT) and circular binary segmentation (CBS),
AluScan performed localized CNV calling and extended CNV calling in practical use.
The CNV calling result of liver cancers resembled the results obtained from whole
genome sequencing (WGS) study. Also, the validation test on existing dataset showed
high correlation (R = 0.935 in CNV loss calling and R = 0.776 in CNV gain calling) with
another CNV calling tool named FREEC.
In this thesis, AluScan sequencing data from ten hepatitis B virus (HBV) positive
and five non-viral hepatocellular carcinomas (HCCs) were subjected to comprehensive analysis to reveal genomic difference between viral and non-viral HCCs. Generally, non-viral
HCCs displayed far fewer SNVs than HBV-positive HCCs, whereas these two types
of HCCs showed similar patterns of LOH preferences and contained similar levels of
CNVs in comparable genic locations. Mutational signature analysis showed that the two
types of HCCs displayed specific signatures in base substitutions, suggesting that virus
infection could result in specific SNVs. Signature V1 enriched in C>T mutation at
NpC̲pG sites, suggesting that deamination of the methyl-5’ cytosine could be associated
with virus infection; the mutually reversible T>A mutations at ApT̲pC and GpT̲pT were
found in non-viral HCCs but only T>A mutations at GpT̲pT were observed in HBV-positive
HCCs. In addition, results of hierarchical clustering on samples and selected
functional events (SFEs) showed that non-viral HCCs belonged to C-class while HBV-positive HCCs belonged to M-C mixed class in terms of their dominant mutations. Lastly,
the cancer genes that were found to contain mutations were shown to be associated with
pathways of oncogenesis in both types of HCCs, while HBV-positive HCCs in addition
contained mutations in genes associated with pathways of virus infection. In conclusion,
the results suggest that the SNV-CNV mutational profiles of HBV-positive HCCs differ
from the profiles of non-viral HCCs. These differences could be important to both the
understanding and the therapeutic treatment of these two types of liver cancers.
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