{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/377666"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/377666","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Detecting tumour signals from multi-modal biological features of cell-free DNA fragments","abstract":"In this thesis I present my work to advance the tumour-naive methods for cancer detection using shallow whole genome sequencing (sWGS) data of cell-free DNA (cfDNA) isolated from plasma. cfDNAs in body fluids inform non-invasive cancer detection. Multimodal Artificial Intelligence (AI) can improve sensitivity by exploiting various biomarkers when cancer signal is sparse. Tumour-informed assays depending on mutations from solid tissue have limited practicality in cancer early detection. Emerging fragmentomic and epigenetic features underpin tumour-naive approaches to cancer screening for individuals with low tumour burden. cfDNA feature extraction depends on instrumental processes such as library preparation and feature extraction. First, I used sWGS data of ten different healthy donors using six library kits and ten data processing routes. Variations across different conditions were evaluated. I collected 430 healthy plasma samples from seven published studies for validation. Trim Align Pipeline (TAP) and cfDNAPro R package were developed to accommodate the specific properties of cfDNA and establish data analysis standards where fragmentomics has relevance in contrast to genomic DNA. Second, I designed the “UNIversal cfDNA feaTure Ensemble” (UNITE) framework, a scalable and sensitive method based on Convolutional Neural Network (CNN) to detect cancer signal from sWGS. By systematically evaluating both CNN and XGBoost models across multi-dimensional feature spaces and tumour fractions (TF), I found that multi-modal strategy predominately achieved optimal sensitivity. In samples with less than 3% TF, CNN based on UNITE framework is more sensitive than a model based on XGBoost (32.9% vs 25.3% at 99% specificity). In summary, the results clarify the fundamental issue of variations introduced by experimental and analytical methods. The UNITE framework can sensitively detect inherent cancer signal represented by cfDNA sequencing data. These results jointly provide a roadmap for better feature integration in cfDNA liquid biopsies.","abstract_html":"In this thesis I present my work to advance the tumour-naive methods for cancer detection using shallow whole genome sequencing (sWGS) data of cell-free DNA (cfDNA) isolated from plasma. cfDNAs in body fluids inform non-invasive cancer detection. Multimodal Artificial Intelligence (AI) can improve sensitivity by exploiting various biomarkers when cancer signal is sparse. Tumour-informed assays depending on mutations from solid tissue have limited practicality in cancer early detection. Emerging fragmentomic and epigenetic features underpin tumour-naive approaches to cancer screening for individuals with low tumour burden. cfDNA feature extraction depends on instrumental processes such as library preparation and feature extraction. First, I used sWGS data of ten different healthy donors using six library kits and ten data processing routes. Variations across different conditions were evaluated. I collected 430 healthy plasma samples from seven published studies for validation. Trim Align Pipeline (TAP) and cfDNAPro R package were developed to accommodate the specific properties of cfDNA and establish data analysis standards where fragmentomics has relevance in contrast to genomic DNA. Second, I designed the “UNIversal cfDNA feaTure Ensemble” (UNITE) framework, a scalable and sensitive method based on Convolutional Neural Network (CNN) to detect cancer signal from sWGS. By systematically evaluating both CNN and XGBoost models across multi-dimensional feature spaces and tumour fractions (TF), I found that multi-modal strategy predominately achieved optimal sensitivity. In samples with less than 3% TF, CNN based on UNITE framework is more sensitive than a model based on XGBoost (32.9% vs 25.3% at 99% specificity). In summary, the results clarify the fundamental issue of variations introduced by experimental and analytical methods. The UNITE framework can sensitively detect inherent cancer signal represented by cfDNA sequencing data. These results jointly provide a roadmap for better feature integration in cfDNA liquid biopsies.","abstract_has_math":false,"creators":["Wang, Haichao"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Markowetz, Florian"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-09-30","date_published":"2024-09-30","updated_at":"2026-07-22T22:24:13Z","subjects":["artificial intelligence","cell-free DNA","genomics","liquid biopsy","machine learning","multiomics"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/2a1a2897-f64d-4003-bdd5-552a7e186d0a/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["000000027648916X"],"render_values":[{"text":"0000-0002-7648-916X","href":"https://orcid.org/0000-0002-7648-916X","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.114408","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Markowetz, Florian"]},{"key":"dc:creator","label":"Author","values":["Wang, Haichao"]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["000000027648916X"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-09-30"]},{"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/1810/377666"]},{"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":["artificial intelligence","cell-free DNA","genomics","liquid biopsy","machine learning","multiomics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/2a1a2897-f64d-4003-bdd5-552a7e186d0a/download","http://purl.org/NET/rdflicense/allrightsreserved"]},{"key":"dc:rights.embargodate","label":"Dc Rights Embargodate","values":["2026-12-18"]},{"key":"dc:rights.embargotype","label":"Dc Rights Embargotype","values":["embargo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.114408"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/a7481f42-04ae-43a8-a8a4-22644a4ca443/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis I present my work to advance the tumour-naive methods for cancer detection using shallow whole genome sequencing (sWGS) data of cell-free DNA (cfDNA) isolated from plasma. cfDNAs in body fluids inform non-invasive cancer detection. Multimodal Artificial Intelligence (AI) can improve sensitivity by exploiting various biomarkers when cancer signal is sparse. Tumour-informed assays depending on mutations from solid tissue have limited practicality in cancer early detection. Emerging fragmentomic and epigenetic features underpin tumour-naive approaches to cancer screening for individuals with low tumour burden. cfDNA feature extraction depends on instrumental processes such as library preparation and feature extraction. First, I used sWGS data of ten different healthy donors using six library kits and ten data processing routes. Variations across different conditions were evaluated. I collected 430 healthy plasma samples from seven published studies for validation. Trim Align Pipeline (TAP) and cfDNAPro R package were developed to accommodate the specific properties of cfDNA and establish data analysis standards where fragmentomics has relevance in contrast to genomic DNA. Second, I designed the “UNIversal cfDNA feaTure Ensemble” (UNITE) framework, a scalable and sensitive method based on Convolutional Neural Network (CNN) to detect cancer signal from sWGS. By systematically evaluating both CNN and XGBoost models across multi-dimensional feature spaces and tumour fractions (TF), I found that multi-modal strategy predominately achieved optimal sensitivity. In samples with less than 3% TF, CNN based on UNITE framework is more sensitive than a model based on XGBoost (32.9% vs 25.3% at 99% specificity). In summary, the results clarify the fundamental issue of variations introduced by experimental and analytical methods. The UNITE framework can sensitively detect inherent cancer signal represented by cfDNA sequencing data. 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