{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/301149"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/301149","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"The Intra-Tumour Heterogeneity Landscape of Human Cancers","abstract":"Tumours accumulate many somatic mutations in their lifetime. Some of these mutations, drivers, convey a selective advantage and can induce clonal expansions. Incomplete clonal expansions give rise to intra-tumour heterogeneity. Somatic mutations can be measured through massively parallel sequencing, where mutations that are supporting incomplete expansions will appear as subclonal. These mutations can be used as a marker of the existence of the expansion and allow for a window into the clonal and subclonal architecture of the tumour at diagnosis. During my Ph.D. I have developed computational methods to infer intra-tumour hetero- geneity from massively parallel sequencing data and applied these to the 2,778 tumour whole genome sequences in the International Cancer Genome Consortium Pan-Cancer Analysis of Whole Genomes initiative to paint the pan-cancer landscape of intra-tumour heterogeneity. I will first introduce the methods; a method to call somatic copy number alterations (Battenberg) and a method to infer subclones from single nucleotide variants (DPClust). Both are extensively validated on simulated and on real data, and I describe a rigorous quality control procedure. The methods are then applied to a single sample to showcase what can be learned about the life history of a cancer, before introducing additional computational methods for a pan-cancer study of heterogeneity. Finally, I describe the findings. I find that nearly all cancers, for which there is sufficient power, contain at least one subclone (96.7% of 1,801 primary tumours). The subclones contain driver mutations that are under positive selection, and known cancer genes contain subclonal driver mutations in low proportions. 9.5% of tumours contain only subclonal drivers that are clinically actionable, suggesting that heterogeneity could inform treatment choices. Finally, the analysis reveals that activity of smoking and UV-light associated mutational signatures goes down as the tumour evolves, while activity of the APOBEC associated signatures goes up.","abstract_html":"Tumours accumulate many somatic mutations in their lifetime. Some of these mutations, drivers, convey a selective advantage and can induce clonal expansions. Incomplete clonal expansions give rise to intra-tumour heterogeneity. Somatic mutations can be measured through massively parallel sequencing, where mutations that are supporting incomplete expansions will appear as subclonal. These mutations can be used as a marker of the existence of the expansion and allow for a window into the clonal and subclonal architecture of the tumour at diagnosis. During my Ph.D. I have developed computational methods to infer intra-tumour hetero- geneity from massively parallel sequencing data and applied these to the 2,778 tumour whole genome sequences in the International Cancer Genome Consortium Pan-Cancer Analysis of Whole Genomes initiative to paint the pan-cancer landscape of intra-tumour heterogeneity. I will first introduce the methods; a method to call somatic copy number alterations (Battenberg) and a method to infer subclones from single nucleotide variants (DPClust). Both are extensively validated on simulated and on real data, and I describe a rigorous quality control procedure. The methods are then applied to a single sample to showcase what can be learned about the life history of a cancer, before introducing additional computational methods for a pan-cancer study of heterogeneity. Finally, I describe the findings. I find that nearly all cancers, for which there is sufficient power, contain at least one subclone (96.7% of 1,801 primary tumours). The subclones contain driver mutations that are under positive selection, and known cancer genes contain subclonal driver mutations in low proportions. 9.5% of tumours contain only subclonal drivers that are clinically actionable, suggesting that heterogeneity could inform treatment choices. Finally, the analysis reveals that activity of smoking and UV-light associated mutational signatures goes down as the tumour evolves, while activity of the APOBEC associated signatures goes up.","abstract_has_math":false,"creators":["Dentro, Stefan Christiaan"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Adams, David","Van Loo, Peter","Wedge, David"],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01-10","date_published":"2020-01-10","updated_at":"2026-07-22T22:24:01Z","subjects":["Cancer","Sequencing","Copy Number Alterations","Subclonal Architecture"],"languages":["en"],"rights":[],"rights_urls":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/418a7164-cb49-48bc-9c9e-0c9a345762f8/download","https://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.48225","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Adams, David","Van Loo, Peter","Wedge, David"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["This thesis was funded by the Wellcome Trust."]},{"key":"dc:creator","label":"Author","values":["Dentro, Stefan Christiaan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2020-01-10"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/301149"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cancer","Sequencing","Copy Number Alterations","Subclonal Architecture"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/418a7164-cb49-48bc-9c9e-0c9a345762f8/download","https://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.17863/CAM.48225"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/105edc86-8640-4f1c-8f27-e8e98f295c12/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Tumours accumulate many somatic mutations in their lifetime. 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I will first introduce the methods; a method to call somatic copy number alterations (Battenberg) and a method to infer subclones from single nucleotide variants (DPClust). Both are extensively validated on simulated and on real data, and I describe a rigorous quality control procedure. The methods are then applied to a single sample to showcase what can be learned about the life history of a cancer, before introducing additional computational methods for a pan-cancer study of heterogeneity. Finally, I describe the findings. I find that nearly all cancers, for which there is sufficient power, contain at least one subclone (96.7% of 1,801 primary tumours). The subclones contain driver mutations that are under positive selection, and known cancer genes contain subclonal driver mutations in low proportions. 9.5% of tumours contain only subclonal drivers that are clinically actionable, suggesting that heterogeneity could inform treatment choices. 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