{"id":{"repo_id":"toronto-retro","oai_identifier":"oai:utoronto.scholaris.ca:1807/97643"},"canonical_url":"https://search.dev.ndltd.org/etd/toronto-retro/oai:utoronto.scholaris.ca:1807/97643","repository":{"repo_id":"toronto-retro","name":"University of Toronto","base_url":"https://utoronto.scholaris.ca/server/oai/request"},"display":{"title":"Pan-Cancer Analysis of Non-Coding Driver Mutations","abstract":"Cancers are caused by genomic alterations known as drivers. As drivers have broad applications in precision oncology, their discovery has become one of the central motivations for cancer genomics. At present, the majority of drivers have been found in the ~2% protein-coding regions. Despite an intensive search for non-coding cancer drivers, however, only a few have been discovered to date. Here I describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify drivers within cancer whole genomes. Using 1,373 genomic features, DriverPower's background model explains up to 93% of the regional variance in mutation rates across multiple tumour types. By incorporating functional impact scores, I further increase the accuracy of driver discovery. Comparing to six published methods, DriverPower has the highest F1-score for both coding and non-coding driver discovery. Applied to 2,583 cancer genomes from public sources, DriverPower identifies 217 coding and 95 non-coding driver candidates in well-defined genomic regions, including novel candidates like the SGK1 splice site, GPR126 enhancer and ALB promoter. To test whether the surprisingly low number of non-coding drivers is related to missing drivers in poorly-defined genomic regions, I investigate non-coding spliceosomal RNAs since protein-coding splicing factors are frequently mutated in cancer. Indeed, I found a highly recurrent A>C somatic mutation at the third base of U1 spliceosomal RNA across several tumour types. This mutation changes the preferential A-U base-pairing between U1 and 5′ splice site to C-G base-pairing, thereby creating novel splice junctions and altering the splice pattern of multiple genes, including known cancer drivers. Clinically, the A>C mutation is associated with alcohol abuse in hepatocellular carcinoma and the aggressive subtype of chronic lymphocytic leukaemia (CLL). The mutation also confers an adverse prognosis to CLL patients independently. This finding demonstrates the first non-coding driver in spliceosomal RNAs, reveals a novel mechanism of aberrant splicing in cancer and may represent a new target for treatment. Together, my research indicates that non-coding mutations play crucial roles in cancer, and future studies should focus on completing the cancer driver catalog and using it for precision oncology.","abstract_html":"Cancers are caused by genomic alterations known as drivers. As drivers have broad applications in precision oncology, their discovery has become one of the central motivations for cancer genomics. At present, the majority of drivers have been found in the ~2% protein-coding regions. Despite an intensive search for non-coding cancer drivers, however, only a few have been discovered to date. Here I describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify drivers within cancer whole genomes. Using 1,373 genomic features, DriverPower's background model explains up to 93% of the regional variance in mutation rates across multiple tumour types. By incorporating functional impact scores, I further increase the accuracy of driver discovery. Comparing to six published methods, DriverPower has the highest F1-score for both coding and non-coding driver discovery. Applied to 2,583 cancer genomes from public sources, DriverPower identifies 217 coding and 95 non-coding driver candidates in well-defined genomic regions, including novel candidates like the SGK1 splice site, GPR126 enhancer and ALB promoter. To test whether the surprisingly low number of non-coding drivers is related to missing drivers in poorly-defined genomic regions, I investigate non-coding spliceosomal RNAs since protein-coding splicing factors are frequently mutated in cancer. Indeed, I found a highly recurrent A>C somatic mutation at the third base of U1 spliceosomal RNA across several tumour types. This mutation changes the preferential A-U base-pairing between U1 and 5′ splice site to C-G base-pairing, thereby creating novel splice junctions and altering the splice pattern of multiple genes, including known cancer drivers. Clinically, the A>C mutation is associated with alcohol abuse in hepatocellular carcinoma and the aggressive subtype of chronic lymphocytic leukaemia (CLL). The mutation also confers an adverse prognosis to CLL patients independently. This finding demonstrates the first non-coding driver in spliceosomal RNAs, reveals a novel mechanism of aberrant splicing in cancer and may represent a new target for treatment. Together, my research indicates that non-coding mutations play crucial roles in cancer, and future studies should focus on completing the cancer driver catalog and using it for precision oncology.","abstract_has_math":false,"creators":["Shuai, Shimin"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Molecular and Medical Genetics","school":null,"contributors":[],"advisors":["Stein, Lincoln D"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11","date_published":"2019-11","updated_at":"2026-07-27T21:28:01Z","subjects":["Cancer Driver","Cancer Genomics","RNA Splicing","Software"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1807/97643","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Stein, Lincoln D"]},{"key":"dc:contributor.department","label":"Department","values":["Molecular and Medical Genetics"]},{"key":"dc:creator","label":"Author","values":["Shuai, Shimin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-11-15T00:00:28Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-11-15T00:00:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-11"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cancer Driver","Cancer Genomics","RNA Splicing","Software"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1807/97643"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Cancers are caused by genomic alterations known as drivers. As drivers have broad applications in precision oncology, their discovery has become one of the central motivations for cancer genomics. At present, the majority of drivers have been found in the ~2% protein-coding regions. Despite an intensive search for non-coding cancer drivers, however, only a few have been discovered to date. Here I describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify drivers within cancer whole genomes. Using 1,373 genomic features, DriverPower's background model explains up to 93% of the regional variance in mutation rates across multiple tumour types. By incorporating functional impact scores, I further increase the accuracy of driver discovery. Comparing to six published methods, DriverPower has the highest F1-score for both coding and non-coding driver discovery. Applied to 2,583 cancer genomes from public sources, DriverPower identifies 217 coding and 95 non-coding driver candidates in well-defined genomic regions, including novel candidates like the SGK1 splice site, GPR126 enhancer and ALB promoter. To test whether the surprisingly low number of non-coding drivers is related to missing drivers in poorly-defined genomic regions, I investigate non-coding spliceosomal RNAs since protein-coding splicing factors are frequently mutated in cancer. Indeed, I found a highly recurrent A>C somatic mutation at the third base of U1 spliceosomal RNA across several tumour types. This mutation changes the preferential A-U base-pairing between U1 and 5′ splice site to C-G base-pairing, thereby creating novel splice junctions and altering the splice pattern of multiple genes, including known cancer drivers. Clinically, the A>C mutation is associated with alcohol abuse in hepatocellular carcinoma and the aggressive subtype of chronic lymphocytic leukaemia (CLL). The mutation also confers an adverse prognosis to CLL patients independently. This finding demonstrates the first non-coding driver in spliceosomal RNAs, reveals a novel mechanism of aberrant splicing in cancer and may represent a new target for treatment. Together, my research indicates that non-coding mutations play crucial roles in cancer, and future studies should focus on completing the cancer driver catalog and using it for precision oncology."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Pan-Cancer Analysis of Non-Coding Driver Mutations"]}]}],"canonical_facts":{"dc:contributor.advisor":["Stein, Lincoln D"],"dc:contributor.department":["Molecular and Medical Genetics"],"dc:creator":["Shuai, Shimin"],"dc:date":["2019-11"],"dc:date.accessioned":["2019-11-15T00:00:28Z"],"dc:date.available":["2019-11-15T00:00:28Z"],"dc:date.issued":["2019-11"],"dc:description.abstract":["Cancers are caused by genomic alterations known as drivers. As drivers have broad applications in precision oncology, their discovery has become one of the central motivations for cancer genomics. At present, the majority of drivers have been found in the ~2% protein-coding regions. Despite an intensive search for non-coding cancer drivers, however, only a few have been discovered to date. Here I describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify drivers within cancer whole genomes. Using 1,373 genomic features, DriverPower's background model explains up to 93% of the regional variance in mutation rates across multiple tumour types. By incorporating functional impact scores, I further increase the accuracy of driver discovery. Comparing to six published methods, DriverPower has the highest F1-score for both coding and non-coding driver discovery. Applied to 2,583 cancer genomes from public sources, DriverPower identifies 217 coding and 95 non-coding driver candidates in well-defined genomic regions, including novel candidates like the SGK1 splice site, GPR126 enhancer and ALB promoter. To test whether the surprisingly low number of non-coding drivers is related to missing drivers in poorly-defined genomic regions, I investigate non-coding spliceosomal RNAs since protein-coding splicing factors are frequently mutated in cancer. Indeed, I found a highly recurrent A>C somatic mutation at the third base of U1 spliceosomal RNA across several tumour types. This mutation changes the preferential A-U base-pairing between U1 and 5′ splice site to C-G base-pairing, thereby creating novel splice junctions and altering the splice pattern of multiple genes, including known cancer drivers. Clinically, the A>C mutation is associated with alcohol abuse in hepatocellular carcinoma and the aggressive subtype of chronic lymphocytic leukaemia (CLL). The mutation also confers an adverse prognosis to CLL patients independently. This finding demonstrates the first non-coding driver in spliceosomal RNAs, reveals a novel mechanism of aberrant splicing in cancer and may represent a new target for treatment. Together, my research indicates that non-coding mutations play crucial roles in cancer, and future studies should focus on completing the cancer driver catalog and using it for precision oncology."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["http://hdl.handle.net/1807/97643"],"dc:subject":["Cancer Driver","Cancer Genomics","RNA Splicing","Software"],"dc:title":["Pan-Cancer Analysis of Non-Coding Driver Mutations"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T21:28:01Z"}