Mutational patterns in oncogenes and tumour suppressors

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. Knowledge of these mutations is crucial to understanding the biology of cancer initiation and progression, and to the development of targeted therapeutic strategies. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer, comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. In this paper we examine the mutation patterns observed in oncogenes and tumour suppressors, and discuss different approaches that have been developed to identify driver mutations within cancers that contribute to the disease progress. We also discuss the MOKCa database where we have developed an automatic pipeline that structurally and functionally annotates all proteins from the human proteome that are mutated in cancer

Squamous Cell Cancers: A Unified Perspective on Biology and Genetics.

Squamous cell carcinomas (SCCs) represent the most frequent human solid tumors and are a major cause of cancer mortality. These highly heterogeneous tumors arise from closely interconnected epithelial cell populations with intrinsic self-renewal potential inversely related to the stratified differentiation program. SCCs can also originate from simple or pseudo-stratified epithelia through activation of quiescent cells and/or a switch in cell-fate determination. Here, we focus on specific determinants implicated in the development of SCCs by recent large-scale genomic, genetic, and epigenetic studies, and complementary functional analysis. The evidence indicates that SCCs from various body sites, while clinically treated as separate entities, have common determinants, pointing to a unified perspective of the disease and potential new avenues for prevention and treatment

Discerning Drivers of Cancer: Computational Approaches to Somatic Exome Sequencing Data

Paired tumor-normal sequencing of thousands of patient’s exomes has revealed millions of somatic mutations, but functional characterization and clinical decision making are stymied because biologically neutral ‘passenger’ mutations greatly outnumber pathogenic ‘driver’ mutations. Since most mutations will return negative results if tested, conventional resource-intensive experiments are reserved for mutations which are observed in multiple patients or rarer mutations found in well-established cancer genes. Most mutations are therefore never tested, diminishing the potential to discover new mechanisms of cancer development and treatment opportunities. Computational methods that reliably prioritize mutations for testing would greatly increase the translation of sequencing results to clinical care. The goal of this thesis is to develop new approaches that use datasets of protein-coding somatic mutations to identify putative cancer-causing genes and mutations, and to validate these predictions in silico and experimentally. This effort will be split among several inter-related efforts, which taken together will help experimental biologists and clinicians focus on hypotheses that can yield novel insights into cancer biology, development, and treatment

Computational methods for personalized cancer genomics

In recent years, cancer treatment strategies have moved towards personalized approaches, specifically tailoring cancer treatments on a single-patient basis using molecular profiles from the patients’ tumor genomes. Knowledge of a patient’s molecular profile can be used to 1) identify the disease mechanisms and underlying cause of a single patient’s cancer, 2) assign patients into treatment groups based on the molecular prognosis, and 3) recommend potential treatments for individual patients based on the patient’s molecular signature data. However, the bottleneck of the personalized medicine approach lies in the challenge of translating the vast amount of sequencing data to meaningful clinical insights. This dissertation explores several computational methods that utilize molecular signature data to understand disease mechanisms of cancer, categorize patients into biologically relevant subtypes, and recommend drug treatments to patients. In the dissertation, we present a method, DawnRank, a patient-specific method that determines the potential driving genomic alterations (the drivers) of cancer. We expand on DawnRank’s capabilities by using the DawnRank scores in key driver mutations and copy number variants (CNVs) to identify breast cancer subtypes. We found 5 alternative subtypes based on potentially clinically relevant driver genes, each with unique defining target features and pathways. These subtypes correspond to and build upon our previous knowledge of breast cancer subtypes. We also identify disease mechanisms in identifying key novel cancer pathways in which driver genes interact. We developed a method, C3, which pinpoints patterns of cancer mutations in a pathway context from a patient population to detect novel cancer pathways that consist of significant driver genes. C3 improves on current methods in driver pathway detection both on a technical aspect and a results-oriented aspect. C3 can detect larger and more consistent pathways than previous methods as well as discovering more biologically relevant drivers. Finally, we address the issue of drug recommendation in the wake of molecular signature data. We develop a method, Scattershot, which combines genomic information along with biological insights on cancer disease mechanisms to predict drug response and prioritize drug treatments. Scattershot outperforms previous methods in predicting drug response and produces recommendations that largely comply with known medical treatment protocols.Scattershot recommends drugs to cancer patients that are in line with the actual drugs prescribed by the physician

Mutations in regulators of the epigenome and their effects on the DNA methylome

Genome-wide profiling for genetic alterations in cancer has identified mutations in genes that are associated with epigenetic programming of genomes for DNA methylation patterns, histone modifications patterns and the positioning of nucleosomes. Here a systematic evaluation of the available cancer genome profiling data established by large international consortia, in order to identify recurrently mutated genes or pathways was described. Using curated list of approximately 700 epigenetic regulators and currently available genome-wide datasets on genetic and epigenetic alterations in cancers, the distribution of alterations in epigenetic regulators was described. Epigenetic genes were classified as potential oncogenic or those with tumor-suppressor function based on the location of mutations relative to functional domains and their frequencies. A panel of 50 epigenetic genes, including: DNMTs, histones (H3F3A, HIST1H3B), histone editors (KDM5C, KDM6A) and writers (MLLs, SETD2, EZH2, ATM) that can promote epigenetic changes in cancer was identified. Using correlative analysis of publicly available methylation data with information on deregulated epigenetic driver genes, many identified subtype-specific methylation clusters were correlated with groups of up to 3 epigenetic regulators. This analysis provides a source for the identification and link between methylation groups and deregulated epigenetic genes. Major cancer specific methylation changes have been observed in promoters and gene bodies. Tissue-specific cancer methylation differences have been located in enhancers and regulatory regions of non-coding RNAs. Based on identified results, the major mechanism of non-coding RNA deregulation in cancer has been investigated on independent data cohort. Using integrative analysis of non-coding RNA in early-onset prostate cancer, non-coding RNAs were classified as tumor-suppressive and oncogenic. About 120 novel prostate cancer specific non-coding RNAs that have been epigenetically deregulated have been identified. Our study on the defects in regulators of the epigenome will help to understand mechanisms leading to distinct epigenetic patterns and will allow the molecular validation of defined correlations in experimental settings

Identifying driver mutations in cancers

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects, I analysed over one million somatic mutations. I identified mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domains. I found that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain. Next, I investigated the ability of seven prediction algorithms to discriminate between driver missense mutations in oncogenes and tumour suppressors. Using 19 features to describe these mutations, I then developed a random forest classifier, MOKCaRF, to distinguish between gain of function and loss of function missense mutations in cancer. MOKCaRF performs significantly better than existing algorithms. I then evaluated the ability of six existing prediction tools to distinguish between pathogenic and neutral mutations for both inframe insertion and inframe deletion mutations. I developed my own classifiers using 11 features that perform better than the current algorithms. Finally, using the algorithms that I developed, as well as changes in copy number and expression data for each gene, I analysed samples from 50 lung cancer patients to identify the actionable targets and potential new drug targets for each tumour

Genetic Drivers and Clonal Heterogeneity of Lethal Breast Cancer

Breast cancer remains the second leading cause of cancer related death in women in the United States. Despite great advances in both early detection and treatment for primary breast cancer, 40,000 women die of breast cancer each year. Metastasis, namely when cancer spreads beyond the original site, is the main cause of breast cancer mortality. A lack of understanding of metastasis continues to thwart prevention and treatment of lethal breast cancer. Genome-wide comparisons of both the genetic composition (DNA) and expression (RNA) of primaries and metastases in multiple patients could help elucidate the underlying mechanisms causing breast cancer metastasis. In this thesis, next-generation sequencing was performed on a dataset of patients with both primary breast cancers and multiple distant metastases. DNA and RNA sequencing were performed on 16 breast cancer patients with 86 matched tumors (primary + multiple metastases). We confirmed previous work that the primary cancer is extremely diverse with multiple distinct populations of cells. Comparisons of these populations in the original tumor and the distant metastases demonstrates that in some instances, it is likely that a clump of cells containing multiple different genetic populations together leave the breast and seed distant sites. Finally, a novel computational method integrating RNA gene expression, somatic copy number alterations, and somatic mutations identifies drivers of breast cancer in matched primaries, metastases, and in the broader context of breast cancer as a whole. We show that a majority of the drivers of breast cancer are established in the original cancer and maintained in metastasis. This work asks clinically impactful questions of the biology of breast cancer metastasis through multiple genomic approaches. The body of knowledge presented here demonstrates that the complex heterogeneity in primary breast cancer is maintained throughout metastasis while also proving that the majority of genetic drivers in metastasis are established in the original breast cancer. Finally, we demonstrate that common mechanisms driving breast cancer are utilized across the previously-described molecular and clinical subgroups of breast cancer, offering novel, tractable therapeutic targets. These findings contribute significantly to our understanding of the genetic diversity and drivers of lethal breast cancer metastasis.Doctor of Philosoph

Genetic studies into rare diseases and cancer using next generation sequencing technologies

Rare genetic diseases (RGDs) and rare sporadic cancers are often considered as two separate groups of diseases. Nevertheless, both groups share the same burden: their rarity and the challenges in diagnosis and treatment, thus affecting the wellbeing of many patients and their families around the world. Although next generation sequencing (NGS) technologies have revolutionised the genetic landscape of RGDs and cancers, many patients with these diseases are still without a definitive molecular diagnosis. In this thesis, NGS was conducted on congenital hypothyroidism (CHT) families (an example of an RGD) and three rare bone cancers, aiming to expand the understanding of the genetic and pathogenic mechanisms of these diseases. To identify known or novel disease-causing genes, WES was conducted on four families with CHT. In one family, a homozygous candidate variant in SIX2 was identified, and subsequent functional characterisation experiments and family segregation analyses were performed. After more family members were included, the SIX2 variant did not segregate with the disease in the family and, therefore, was classified as unlikely disease causing. WES and RNA sequencing (RNA-Seq) were conducted on three rare bone tumours: undifferentiated pleomorphic sarcoma of bone (UPSb), adamantinoma and osteofibrous dysplasia (OFD)-like adamantinoma. In UPSb tumours, 31 genes were recurrently mutated, including TP53 in 4/14 samples (29%), and chromatin remodelling genes (ATRX, H3F3A, DOT1L) in 5/14 samples (36%). In addition, two previously reported gene fusions (CLTC-VMP1 and FARP1-STK24) were identified in these tumours. In adamantinoma tumours, KMT2D, a histone methyltransferase, was recurrently mutated in 2/8 adamantinomas (25%). In addition, a cancer predisposing germline fusion (KANSL1-ARL17A) was identified in 4/6 adamantinoma (66.7%) and in 3/4 OFD-like adamantinoma (75%) tumours. This thesis is a practical example demonstrating how rare diseases and cancers can be investigated using the same high-throughput techniques. Moreover, the three bone tumour studies represent the first comprehensive WES and RNA-Seq analyses conducted on these tumours, revealing novel molecular insights that can be translated into clinical practices to enhance the diagnosis, prognosis and the outcomes of patients with these diseases

COMPUTATIONAL FRAMEWORKS FOR THE IDENTIFICATION OF SOMATIC AND GERMLINE VARIANTS CONTRIBUTING TO CANCER PREDISPOSITION AND DEVELOPMENT

The most recent cancer classification from NIH includes ~200 types of tumor that originates from several tissue types (http://www.cancer.gov/types). Although macroscopic and microscopic characteristics varies significantly across subtypes, the starting point of every cancer is believed to be a single cell that acquires DNA somatic alterations that increases its fitness over the surrounding cells and makes it behave abnormally and proliferate uncontrollably. Somatic mutations are the consequence of many possible defective processes such as replication deficiencies, exposure to carcinogens, or DNA repair machinery faults. Mutation development is a random and mostly natural process that frequently happens in every cell of an individual. Only the acquisition of a series of subtype-specific alterations, including also larger aberrations such as translocations or deletions, can lead to the development of the disease and this is a long process for the majority of adult tumor types. However, genetic predisposition for certain cancer types is epidemiologically well established. In fact, several cancer predisposing genes where identified in the last 30 years with various technologies but they characterize only a small fraction of familial cases. This work will therefore cover two main steps of cancer genetics and genomics: the identification of the genes that somatically changes the behavior of a normal human cell to a cancer cell and the genetic variants that increase risk of cancer development. The use of publicly available datasets is common to all the three results sections that compose this work. In particular, we took advantage of several whole exome sequencing databases (WES) for the identification of both driver mutations and driver variants. In particular, the use of WES in cancer predisposition analysis represents one of the few attempts of performing such analysis on genome-wide sequencing germline data