Lymphoid cancers arise from lymphocytes, a subset of white blood cells, and represent the fifth most common cause of cancer. Follicular lymphoma (FL) is a common subtype of lymphoid cancers. For ten percent of FL patients, the disease either does not respond to primary therapy or progresses early after treatment. These patients have poor outcomes and often require aggressive therapeutic interventions.
With regards to its origin, FL demonstrates how cancers arise through the successive acquisition of changes in their genomes. In his research, Dr. Kridel’s group has identified highly recurrent mutations in certain genes which delineate disease-initiating mechanisms. Yet, the biological underpinnings of treatment resistance and transformation in FL are only partially understood.
Dr. Kridel hypothesizes that both resistance to therapy and transformation can be explained by genetic alterations that are either present at diagnosis and get selected for during therapy, or that arise during the course of the disease. Thus, Dr. Kridel’s team will apply high-throughput genome sequencing technology to paired FL and progressed/transformed lymphoma samples. Dr. Kridel will leverage the availability of tumour specimens from the tissue repository of the Centre for Lymphoid Cancer at the BC Cancer Agency and collaborate with the Computational Biology group of Dr. Sohrab Shah, who has developed cutting-edge tools to examine genome-wide mutational changes.
The goal is to improve patient outcomes by understanding the genetic mechanisms that drive treatment resistance, early progression and transformation in FL.
Acute myeloid leukemia (AML) results from genetic defects. Recurrent variations in chromosomal structures are common in AML, and several genes have been identified to be recurrently mutated in AML. Identification of these genetic defects in AML patients has improved diagnosis and treatment. However, more than twenty-five percent of AML patients carry no mutations in the known leukemia-associated genes, and the heterogeneity of AML and various survival outcomes suggest that as yet, undiscovered genes and pathways contribute to AML.
Dr. Gerben Duns performed high-throughput RNA sequencing and resequenced whole exomes, a portion of the genome, on 92 AML clinical samples to discover novel genes involved in AML. Mutations were identified within a gene called SETD2 in 7.6 percent of samples, suggesting a role for SETD2 in a subset of AML samples. The nature of the identified mutations suggests that these mutations are inactivating, which is in concordance with the recent identification of inactivating SETD2 mutations in several other cancer types.
Through in vitro and in vivo studies, Dr. Duns will examine the effects of the inactivating and mutating gene SETD2 as it contributes to AML development. Bioinformatic approaches are also used to investigate the potential association between the presence of SETD2 mutations and the response to therapy and disease outcome.
This study will provide insights into the mechanisms of AML pathogenesis, and will potentially reveal novel diagnostic and prognostic markers, as well as therapeutical targets.
Recent evidence indicates that non-coding RNAs (NC-RNAs) play crucial functions in physiological and pathological cellular processes. Long non-coding RNAs (lncRNAs) are the most abundant NC-RNA class, accounting for 10–20,000 genes. Despite this, the role of only a few of them (approxim. 40) has been characterized. Many lncRNAs show a tissue-specific expression pattern and are altered in cancer cells. For this reason, it has been suggested that they may be useful as biomarkers in oncology.
We performed RNA-Seq. on non-metastatic and metastatic prostate cancer (PCa) tumor tissue xenografts. Our analysis revealed 159 up- and 77 down-regulated lncRNAs in the metastatic samples. We validated the differential expression of 7 up-regulated lncRNas in metastatic xenografts (QPCR). Using pooled plasma samples from 3 three patient groups (normal, localized PCa and metastatic PCa) one lncRNA (JUPITER) assayed to date differentiates amongst the three groups. We hypothesize that lncRNAs play critical roles in PCa progression and can be exploited as biomarkers and therapy targets. To address these hypotheses we will: 1: Characterize the function of selected lncRNAs. We will select the most up-regulated lncRNAs in metastatic vs. primary PCa xenografts and assay their expression in a panel of PCa cell lines. Once we identify 2-3 cell lines expressing the highest levels of a transcript, we will silence it using siRNAs. Silenced and control-treated cells will be assayed for proliferation, migration, invasion, apoptosis, and cell cycle progression. 2: Measure by QPCR the expression of selected lncRNas on RNA extracted from freshly frozen prostate samples (normal prostate, prostate intraepithelial neoplasia, local and metastatic PCa). For each gene, we will statistically compute correlations with clinico-pathological variables (grade, stage, PSA level). 3: Further analyze lncRNAs as biomarkers. The expression of JUPITER (and other differentially expressed lncRNAs) will be assayed in individual plasma samples from patients with different PCa stages, in order to estimate the optimal threshold values for early detection of metastatic PCa (ROC curve).
While localized PCa is a treatable disease, progression to a metastatic and drug-resistant cancer accounts for 4000 deaths annually in Canada. Understanding the mechanisms of Pca progression and identifying new molecular markers and therapeutic targets will allow better disease management and ultimately reduce deaths. In brief, we discovered a long non-coding RNA (PCAT18) that is expressed exclusively by prostate cancer cells and is required for prostate cancer cell growth and motility. This gene can be used as a biomarker and as a therapeutic target for metastatic prostate cancer.
Lung cancer is one of the largest health burdens worldwide: in Canada alone, lung cancer causes more cancer-related deaths than breast, colon, and prostate cancers combined. Smoking cessation programs have been highly successful, and the population of former smokers in Canada is well over seven million. Unfortunately, while quitting smoking is a proactive step, former smokers are still at risk for developing lung cancer. This cancer risk in former smokers will remain one of Canada’s most significant health concerns for the next 50 years. The molecular mechanisms responsible for the development of lung cancer in former smokers are not known. Recent studies have shown that although the majority of smoking-induced genetic damage returns to normal after smoking cessation, some genes are permanently damaged and never return to the pre-smoking state. Some of these irreversible genes are likely those that act as the gatekeepers for cancer development. Dr. Ewan Gibb’s research project will identify the genes in former smokers which do not return to normal after smoking cessation. He will be using integrative genomics to compare samples from former smokers with cancer and those without. This information will help Dr. Gibb understand why some former smokers go on to develop lung cancer while others remain cancer-free despite similar changes in lifestyle. This set of irreversibly damaged genes can serve as novel targets for anticancer therapies or may be developed as diagnostic markers for early detection of lung cancer while therapies are still effective.
Each human cell contains instructions — in the form of genetic material or the genome — to direct its growth, function and death. The genome is made up of three billion molecules called nucleotide pairs, which are joined in a specific sequence. Sometimes the nucleotide sequence in a cell’s genome can become altered, or mutated, and these mutations can lead to changes in the cell that cause cancer. The spread of cancer cells from the primary tumor is known as metastasis. Relatively little is known about the mutations in the genome that create, control and direct metastasis. Next-generation sequencing allows researchers to rapidly “read” the sequence of the three billion nucleotide pairs in the genome of cancer cells. Using this technology, Dr. Jill Mwenifumbo aims to identify the sequence mutations that are unique to, and perhaps essential for, colorectal cancer metastasis. Ultimately, discovering the genetic mutations that drive metastasis will help identify potential drug targets, which will lead to more effective treatments for this disease. Given that colorectal cancer is the second leading cause of cancer death in Canada, effective treatment has enormous potential to improve personal and population health.
A common method of testing new cancer drugs is to use human breast tumour cells that have been transplanted into mice. How this transplantation process and drug treatments affect the grafted cells is not known. In particular, we need to know if certain types of mutation within the tumour may survive the process of engraftment better than others, resulting in a transplanted tumour that has a different composition and different properties from the original human tumour. Dr. Peter Eirew's aim is to study in detail how the “landscape” of different gene mutations in the tumor evolves when tumour cells undergo transplantation and subsequent treatment with anti-cancer drugs. Dr. Eirew will sequence the entire DNA and RNA (a measure of the active genes in a cell) of breast cancer patients' tumours before and after transplantation into mice to see how the frequency of each mutation changes over time. Dr. Samuel Aparicio's group has already read the entire DNA sequence of human breast cancer — both the original tumour and a recurrence in a different part of the patient's body nine years later — and showed that the type and frequency of the mutations changed over time. In the second part of the study, he will sequence these human tumour cells before and after the drug treatment to determine the types of mutations that survive. This will set the stage for a follow-on clinical study to determine how closely the drug response of these human cells predict how tumours in patients respond to the same drugs. This study will be the first attempt to define how grafted breast cancer cells behave in mice and how this behaviour is affected by the choice of grafting methods and treatment with existing drugs. This information will be used to improve the methods that are currently used to test potential new cancer drugs, with the ultimate aim of bringing new breast cancer treatments into routine use more quickly than in the past. Knowing the types and combination of mutations that are present in a tumour and how this combination changes during treatment will be the key to developing new and more effective drugs. The study may also identify new mutations in breast tumours, which have the potential to answer more specific questions about how these cancers arise, progress and become resistant to treatment.
Lymphomas are cancers of the immune system. Canadian cancer statistics estimated around 8,100 newly diagnosed cases and 3,300 deaths from lymphoma in 2009. Lymphomas develop as the result of errors, or mutations, in the proteins that regulate the rate of cell division. These types of mutations are found in many different cancer types; however, certain mutations are found only in a specific cancer type. When the same mutation is found in several patients of a specific cancer type, it is likely to be a cancer-causing or cancer-driving mutation. The aim of Dr. Maria Mendez-Lago’s research is to investigate the impact of mutations found in the gene MLL2 on the formation and progression of lymphomas. Her research team discovered mutations in MLL2 by using next-generation sequencing of 127 non-Hodgkin lymphoma cases. Based on the pattern and distribution of the mutations, they believe MLL2 is a new tumour suppressor that might be acting through de-regulation of gene expression. Next-generation sequencing has allowed Dr. Mendez-Lago’s team to do whole genome, exome, and transcription sequencing using limited amounts of DNA from cancer tissues – an approach that was not possible only four years ago. They are applying this technology to different applications, such as the targeted sequencing approach used to detect mutations in MLL2. MLL2 has only recently been linked to cancer, so there is a great need to study the gene in further detail to understand how mutations in this gene promote cancer. To explore the impact of these mutations, Dr. Mendez-Lago’s team will culture and study all lines similar to the cancer cells from patients. Their findings will likely determine new candidates for designing drugs to treat cancers.
The Human Genome Project, which had the goal of sequencing the entire human genome, took more than 10 years, involved the work of thousands of people and cost more than $1 billion. Today, this same amount of work can be accomplished on a single machine in 10 days at a cost of $10,000, which halves every 18 months. The emergence of this "Next Generation Sequencing" (NGS) technology can reveal the precise genetic mutations that underlie how cancers develop, how they become more aggressive and how they acquire resistance to chemotherapy. A challenge of this technology is the data generated can be voluminous, complex and error prone; a single genome can produce over a terabyte of data.
Dr. Sohrab Shah is developing a new generation of computational tools using machine-learning approaches to improve accuracy and best interpret the large scale NGS data sets. With his clinically focused collaborators, he will then apply these technologies to sequence the tumour genomes from patients with triple negative breast cancer, ovarian clear cell carcinoma, and childhood osteosarcoma tumours — three cancer subtypes that do not respond to standard therapies. They hope to identify and profile unknown mutation patterns — or "mutation landscapes” — in each of these diseases.
These mutation landscapes will help Dr. Shah’s team further understand the biology of these tumours and provide a rational basis for the design of novel therapies to improve patient outcomes. His work will also include studying small populations of cancer cells to determine how they influence patients’ responses to treatment and how they become resistant to chemotherapy — two of the major issues facing oncologists today.
The Public Health Agency of Canada estimates that influenza infection currently results in an average of 20,000 hospitalizations and 4,000 deaths each year. Therefore, an influenza pandemic would have severe health, economic and social consequences. The Public Health Agency of Canada/Canadian Institutes of Health Research Influenza Research Network (PCIRN) was developed to identify research gaps in the country's pandemic influenza preparedness initiative. To facilitate the initiative, research will be done at various sites across the country, supported by a common information technology (IT) group. An essential mission of the IT support group is to develop standards ensuring proper communication and knowledge transmission amongst the different members of the network. Currently, differences in interpretation of the 'meaning' of data or semantic heterogeneity pose a significant challenge to combine information from multiple heterogeneous sources. In order to efficiently integrate information generated by the various centres constituting the network, a consistent representation of data must be adopted.
Mélanie Courtot's research centres on the development of a model to unambiguously interpret influenza data. Working in collaboration with Dr. Scheuermann, leader of the BioHealthBase project, the equivalent of the PCIRN network in the United States, Ms. Courtot will develop a guideline outlining the minimum information required, and derive a data model that captures the necessary elements and the semantic relationships between them, which will allow for the integration of Canadian and American data, thereby assisting in the development of a North American influenza data network. Establishment of standards for unambiguous data representation and investigation modeling will improve the integration and re-use of information produced, and ultimately increase the quality and re-usability of that information and decrease the cost of health care.