Cancer is a complex disease shaped by a heterogeneous landscape of inherited genetic variants and acquired somatic aberrations. Although specific patterns of somatic aberrations within key pathways are recognized as hallmarks of many cancers, and mounting evidence suggests a significant interplay between germline and somatic variants, the intricate relationship between germline predisposition and the disruption of these pathways remains poorly understood. Here, I present an integrative approach using multi-omics data to functionally characterize germline variants and explore the heterogeneous landscape of somatic mutations, with the aim of establish mechanistic links between functional variants and the disruption of cancer-related biological processes. To enable the identification of functional variants, I initially performed a comprehensive characterization of functionally annotated transcriptional regulatory elements, establishing a hierarchy of ‘consensus’ elements across multiple levels of abstraction. This analysis generated a vast collection of consensus promoters, enhancers, and active enhancers, spanning 198 cell lines and 38 tissue types, with aggregate data providing global consensus definitions for each element type. Additionally, ‘total binding affinity’ method was employed, integrating 1000 Genomes Project genotype data and thousands of transcription factor binding motifs, to further characterize and functionally annotate these regulatory elements. The results generated from this analysis can be interactively explored and visualized through the CONREL web application. To allow effective annotation of individual’s ancestry, I developed and successfully employed an improved version of EthSEQ (version 3), an R package that provides a rapid and reliable pipeline for ancestry annotation. Accurate stratification of individual ancestry is essential for correctly interpreting the impact of genomic variations in associations studies. EthSEQ version 3 was successfully utilized to determine the genetic ancestry of over 500 pediatric patients diagnosed with 11 different tumor types, enabling further investigation into the genetic landscape of patients confidently identified as of European ancestry.To further investigate into the interplay between germline and somatic variants, I conducted genome-wide association studies across 33 cancer types characterized by The Cancer Genome Atlas, using binary traits defined by somatic aberration profiles in ten oncogenic signaling pathways. Functional links between associated variants and somatic profiles were investigated through cis-eQTL data to identify regulatory interactions with pathway-related genes. Additionally, using GWAS summary statistics I employed polygenic scores to examine the contribution of germline genetic variation to somatic molecular profiles, tumor subtypes, and clinical outcomes such as patient survival and tumor aggressiveness. Polygenic scores were validated using external data from PCAWG and CCLE datasets. Lastly, to explore the heterogeneity of somatic mutational profiles, I employed a network-based approach to propagate somatic alterations through a molecular interaction network, aiming to reveal novel patterns of somatic alteration with potential significance in cancer. I then conducted a series of GWAS analyses, utilizing traits defined by combinations of these propagated somatic scores across genes involved in well-defined DNA repair pathways. Overall, I demonstrate that germline genetics can describe patients’ genetic liability to develop specific cancer molecular and clinical profiles. Understanding the functional roles of genetic variants can provide valuable insights into the biological mechanisms underlying a disease or trait.

Cancer development is driven by somatic genome alterations, ranging from single point mutations to larger structural variants (SV) affecting kilobases to megabases of one or more chromosomes. Studies of somatic rearrangement have previously been limited by a paucity of whole genome sequencing data, and a lack of methods for comprehensive structural classification and downstream analysis. The ICGC project on the Pan-Cancer Analysis of Whole Genomes provides an unprecedented opportunity to analyse somatic SVs at base-pair resolution in more than 2500 samples from 30 common cancer types. In this thesis, I build on a recently developed SV classification pipeline to present a census of rearrangement across the pan-cancer cohort, including chromoplexy, replicative two-jumps, and templated insertions connecting as many as eight distant loci. By identifying the precise structure of individual breakpoint junctions and separating out complex clusters, the classification scheme empowers detailed exploration of all simple SV properties and signatures. After illustrating the various SV classes and their frequency across cancer types and samples, Chapter 2 focuses on structural properties including event size and breakpoint homology. Then, in Chapter 3, I consider the SV distribution across the genome, and show patterns of association with various genome properties. Upon examination of rearrangement hotspot loci, I describe tissue-specific fragile site deletion patterns, and a variety of SV profiles around known cancer genes, including recurrent templated insertion cycles affecting TERT and RB1. Turning to co-occurring alteration patterns, Chapter 4 introduces the Hierarchical Dirichlet Process as a non-parametric Bayesian model of mutational signatures. After developing methods for consensus signature extraction, I detour to the domain of single nucleotide variants to test the HDP method on real and simulated data, and to illustrate its utility for simultaneous signature discovery and matching. Finally, I return to the PCAWG SV dataset, and extract SV signatures delineated by structural class, size, and replication timing. In Chapter 5, I move on to the complex SV clusters (largely set aside throughout Chapters 2—4) , and develop an improved breakpoint clustering method to subdivide the complex rearrangement landscape. I propose a raft of summary metrics for groups of five or more breakpoint junctions, and explore their utility for preliminary classification of chromothripsis and other complex phenomena. This comprehensive study of somatic genome rearrangement provides detailed insight into SV patterns and properties across event classes, genome regions, samples, and cancer types. To extrapolate from the progress made in this thesis, Chapter 6 suggests future strategies for addressing unanswered questions about complex SV mechanisms, annotation of functional consequences, and selection analysis to discover novel drivers of the cancer phenotype.

Pituitary tumor-transforming gene 1 (PTTG1) encodes a multifunctional protein that is involved in many cellular processes. However, the potential role of PTTG1 in tumor formation and its prognostic function in human pan-cancer is still unknown. The analysis of gene alteration, PTTG1 expression, prognostic function, and PTTG1-related immune analysis in 33 types of tumors was performed based on various databases such as The Cancer Genome Atlas database, the Genotype-Tissue Expression database, and the Human Protein Atlas database. Additionally, PTTG1-related gene enrichment analysis was performed to investigate the potential relationship and possible molecular mechanisms between PTTG1 and tumors. Overexpression of PTTG1 may lead to tumor formation and poor prognosis in various tumors. Consequently, PTTG1 acts as a potential oncogene in most tumors. Additionally, PTTG1 is related to immune infiltration, immune checkpoints, tumor mutational burden, and microsatellite instability. Thus, PTTG1 could be potential biomarker for both prognosis and outcomes of tumor treatment and it could also be a promising target in tumor therapy.