Pancreatic ductal adenocarcinoma (PDAC) is an incurable disease characterized by poor survival, dense desmoplastic stroma and activating mutations in KRAS (>90%). These tumors are highly complex ecosystems composed of molecularly distinct sub-populations that exhibit a spectrum of genetic features and associated phenotypes. Despite recent advances in the transcriptomic characterization of PDAC into at least two tumor subtypes, this alone has been insufficient to define more specific patterns of oncogenic dependency. To fully leverage advancements in next generation sequencing and functional genomics, we have sought to establish computational methodologies to aid in refined target discovery, and to develop a novel platform to comprehensively characterize the transcriptional heterogeneity of PDAC. Specifically, focusing on a large PDAC PDX cohort, we focused on a) establishing a PDAC co-expression network to serve as a foundation for quantifying disease diversity within the cohort, while in parallel b) optimizing an analytical approach to allow for in vivo CRISPR-Cas9 functional genomics using select models from the cohort. Applying and integrating this novel computational methodology, we integrated CRISPR-based co-dependency annotations with a disease-specific co-expression network developed from patient-derived models to establish a framework to quantitatively associate gene-cluster patterns with genetic vulnerabilities. We defined multiple prominent anti-correlating gene-cluster signatures and pathway-specific dependencies, both across genetically distinct PDAC models and intratumorally at the single-cell level. This characterization of intratumoral cluster representation was accomplished through a novel adaptation of network signatures for single-cell analysis. Of these network-defined cluster trends, one differential signature recapitulated the characteristics of classical and basal-like PDAC molecular subtypes on a continuous scale, which we validated using direct capture Perturb-seq. Our results demonstrate the utility of this integrated platform as a quantitative approach for characterizing specific genetic dependencies within defined molecular contexts represented in PDAC, with the potential to guide future clinical positioning for targeted therapeutics while also considering a constantly evolving intratumoral heterogeneity.
","abstract_html":"<p>Pancreatic ductal adenocarcinoma (PDAC) is an incurable disease characterized by poor survival, dense desmoplastic stroma and activating mutations in <em>KRAS </em>(>90%). These tumors are highly complex ecosystems composed of molecularly distinct sub-populations that exhibit a spectrum of genetic features and associated phenotypes. Despite recent advances in the transcriptomic characterization of PDAC into at least two tumor subtypes, this alone has been insufficient to define more specific patterns of oncogenic dependency. To fully leverage advancements in next generation sequencing and functional genomics, we have sought to establish computational methodologies to aid in refined target discovery, and to develop a novel platform to comprehensively characterize the transcriptional heterogeneity of PDAC. Specifically, focusing on a large PDAC PDX cohort, we focused on a) establishing a PDAC co-expression network to serve as a foundation for quantifying disease diversity within the cohort, while in parallel b) optimizing an analytical approach to allow for <em>in vivo </em>CRISPR-Cas9 functional genomics using select models from the cohort. Applying and integrating this novel computational methodology, we integrated CRISPR-based co-dependency annotations with a disease-specific co-expression network developed from patient-derived models to establish a framework to quantitatively associate gene-cluster patterns with genetic vulnerabilities. We defined<strong> </strong>multiple<strong> </strong>prominent<strong> </strong>anti-correlating gene-cluster signatures and pathway-specific dependencies, both across genetically distinct PDAC models and intratumorally at the single-cell level. This characterization of intratumoral cluster representation was accomplished through a novel adaptation of network signatures for single-cell analysis. Of these network-defined cluster trends, one differential signature recapitulated the characteristics of classical and basal-like PDAC molecular subtypes on a continuous scale, which we validated using direct capture Perturb-seq. Our results demonstrate the utility of this integrated platform as a quantitative approach for characterizing specific genetic dependencies within defined molecular contexts represented in PDAC, with the potential to guide future clinical positioning for targeted therapeutics while also considering a constantly evolving intratumoral heterogeneity.</p>","abstract_has_math":false,"creators":["Srinivasan, Sanjana","0000-0002-1439-1052
"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation (PhD)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Giulio Draetta","Traver Hart","Swathi Arur"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-05-01T07:00:00Z","date_published":"2021-05-01T07:00:00Z","updated_at":"2026-07-24T05:49:41Z","subjects":["pancreatic cancer","network biology","functional genomics","CRISPR screens","co-expression networks","single cell sequencing","Bioinformatics","Computational Biology","Genomics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1070","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Giulio Draetta","Traver Hart","Swathi Arur"]},{"key":"dc:creator","label":"Author","values":["Srinivasan, Sanjana","0000-0002-1439-1052
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"]},{"key":"dc:title","label":"Title","values":["Computational Approaches to Delineate Transcriptional and Functional Heterogeneity In Pancreatic Cancer"]}]}],"canonical_facts":{"dc:contributor":["Giulio Draetta","Traver Hart","Swathi Arur"],"dc:creator":["Srinivasan, Sanjana","0000-0002-1439-1052
"],"dc:date.available":["2022-03-09T08:00:00Z"],"dc:description.abstract":["Pancreatic ductal adenocarcinoma (PDAC) is an incurable disease characterized by poor survival, dense desmoplastic stroma and activating mutations in KRAS (>90%). These tumors are highly complex ecosystems composed of molecularly distinct sub-populations that exhibit a spectrum of genetic features and associated phenotypes. Despite recent advances in the transcriptomic characterization of PDAC into at least two tumor subtypes, this alone has been insufficient to define more specific patterns of oncogenic dependency. To fully leverage advancements in next generation sequencing and functional genomics, we have sought to establish computational methodologies to aid in refined target discovery, and to develop a novel platform to comprehensively characterize the transcriptional heterogeneity of PDAC. Specifically, focusing on a large PDAC PDX cohort, we focused on a) establishing a PDAC co-expression network to serve as a foundation for quantifying disease diversity within the cohort, while in parallel b) optimizing an analytical approach to allow for in vivo CRISPR-Cas9 functional genomics using select models from the cohort. Applying and integrating this novel computational methodology, we integrated CRISPR-based co-dependency annotations with a disease-specific co-expression network developed from patient-derived models to establish a framework to quantitatively associate gene-cluster patterns with genetic vulnerabilities. We defined multiple prominent anti-correlating gene-cluster signatures and pathway-specific dependencies, both across genetically distinct PDAC models and intratumorally at the single-cell level. This characterization of intratumoral cluster representation was accomplished through a novel adaptation of network signatures for single-cell analysis. Of these network-defined cluster trends, one differential signature recapitulated the characteristics of classical and basal-like PDAC molecular subtypes on a continuous scale, which we validated using direct capture Perturb-seq. Our results demonstrate the utility of this integrated platform as a quantitative approach for characterizing specific genetic dependencies within defined molecular contexts represented in PDAC, with the potential to guide future clinical positioning for targeted therapeutics while also considering a constantly evolving intratumoral heterogeneity.
"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1070"],"dc:subject":["pancreatic cancer","network biology","functional genomics","CRISPR screens","co-expression networks","single cell sequencing","Bioinformatics","Computational Biology","Genomics"],"dc:title":["Computational Approaches to Delineate Transcriptional and Functional Heterogeneity In Pancreatic Cancer"],"thesis:degree_level":["Dissertation (PhD)"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T05:49:41Z"}