Graphlet-adjacencies provide complementary views on the functional organisation of the cell and cancer mechanisms

Recent biotechnological advances have led to a wealth of biological network data. Topo- logical analysis of these networks (i.e., the analysis of their structure) has led to break- throughs in biology and medicine. The state-of-the-art topological node and network descriptors are based on graphlets, induced connected subgraphs of different shapes (e.g., paths, triangles). However, current graphlet-based methods ignore neighbourhood infor- mation (i.e., what nodes are connected). Therefore, to capture topology and connectivity information simultaneously, I introduce graphlet adjacency, which considers two nodes adjacent based on their frequency of co-occurrence on a given graphlet. I use graphlet adjacency to generalise spectral methods and apply these on molecular networks. I show that, depending on the chosen graphlet, graphlet spectral clustering uncovers clusters en- riched in different biological functions, and graphlet diffusion of gene mutation scores predicts different sets of cancer driver genes. This demonstrates that graphlet adjacency captures topology-function and topology-disease relationships in molecular networks. To further detail these relationships, I take a pathway-focused approach. To enable this investigation, I introduce graphlet eigencentrality to compute the importance of a gene in a pathway either from the local pathway perspective or from the global network perspective. I show that pathways are best described by the graphlet adjacencies that capture the importance of their functionally critical genes. I also show that cancer driver genes characteristically perform hub roles between pathways. Given the latter finding, I hypothesise that cancer pathways should be identified by changes in their pathway-pathway relationships. Within this context, I propose pathway- driven non-negative matrix tri-factorisation (PNMTF), which fuses molecular network data and pathway annotations to learn an embedding space that captures the organisation of a network as a composition of subnetworks. In this space, I measure the functional importance of a pathway or gene in the cell and its functional disruption in cancer. I apply this method to predict genes and the pathways involved in four major cancers. By using graphlet-adjacency, I can exploit the tendency of cancer-related genes to perform hub roles to improve the prediction accuracy

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Distribution bias analysis of germline and somatic single-nucleotide variations that impact protein functional site and neighboring amino acids

Single nucleotide variations (SNVs) can result in loss or gain of protein functional sites. We analyzed the effects of SNVs on enzyme active sites, ligand binding sites, and various types of post translational modification (PTM) sites. We found that, for most types of protein functional sites, the SNV pattern differs between germline and somatic mutations as well as between synonymous and non-synonymous mutations. From a total of 51,138 protein functional site affecting SNVs (pfsSNVs), a pan-cancer analysis revealed 142 somatic pfsSNVs in five or more cancer types. By leveraging patient information for somatic pfsSNVs, we identified 17 loss of functional site SNVs and 60 gain of functional site SNVs which are significantly enriched in patients with specific cancer types. Of the key pfsSNVs identified in our analysis above, we highlight 132 key pfsSNVs within 17 genes that are found in well-established cancer associated gene lists. For illustrating how key pfsSNVs can be prioritized further, we provide a use case where we performed survival analysis showing that a loss of phosphorylation site pfsSNV at position 105 in MEF2A is significantly associated with decreased pancreatic cancer patient survival rate. These 132 pfsSNVs can be used in developing genetic testing pipelines