Identify New Mutational Signature in Cancer Patients by Oncomine Panel

Abstract

With the invention and popularity of the next generation sequencing more and more researches that use whole genome sequencing and whole exome sequencing for analyzing are published However not all people afford to the cost of whole genome sequencing and whole exome sequences Besides In the aspect of mutation load Oncomine Panel can represent the whole exome sequencing to a high degree and the genes in Oncomine Panel are druggable Therefore it is possible to know the important genetic mutation information of the patient with reducing the cost This study designed a pipeline to find out the biological mechanisms underlying in cancer patients by cancer patient sequencing data First getting target sequencing and Whole-Genome Sequencing(WGS) by Next Generation Sequencing (NGS) technology and there are three cancer types including Colorectal cancer Endometrial cancer and ovarian cancer All patients are Taiwanese Then we extract Mutational signatures from all Single Nucleotide Variants(SNV) that in the target sequencing and use COSMIC signatures that are proposed and confirmed in previous studies (publish on the Catalogue Of Somatic Mutations In Cancer COSMIC) to help us analyze After that we find that there are some differences in the mutational load of our samples Then we analyze our samples at gene level[1] We use statistical methods to find out whether each gene has Single Nucleotide Variation or Copy Number Variation(CNV) or not can be the reason of separating patients The gene set obtain by the above method can determine the possible pathways and we can study these pathways for understanding the mechanism under the pathway Afterwards these pathways may be the reason why there are differences in mutational load At last we propose a mutational signature that extract from our dataset relate to higher mutational load on the target sequencing data of Oncomine Panel

Date of Award	2018 Aug 14
Original language	English
Supervisor	Jung-Hsien Chiang (Supervisor)