Methylation-based droplet digital PCR testing for lung adenocarcinomas in tissue and plasma samples
DNA methylation refers to a type of chemical changes on the DNA molecule that affects how genetic information is interpreted and utilized. Since tissues in the body have different methylation patterns, these patterns can be used to identify where cancers originated from (e. g. , lung versus stomach). In tissue samples, methylation analyses have been used to assist in challenging cancer diagnoses. In blood samples, methylation patterns on fragments of circulating DNA from dying cancer cells could be used for early screening of cancers, and to monitor progress after treatment. To date, researchers in this area have mostly used comprehensive methods, for example, those involving the quantification of methylation levels at hundreds of thousands of different sites, to achieve desired results. While technically successful, these methods could prove too complex and expensive for routine clinical use. In a previously funded CPTRG project, we proposed that DNA methylation testing could be greatly simplified by reducing the number of methylation sites evaluated. As proof-of-principle, we developed a diagnostic test for the droplet digital PCR platform, which is comparatively simple and widely-available, to distinguish gastric from pancreatic cancers using information from only a single methylation site. For this Stream 4 application, we propose extend this “minimalist” approach to lung cancers by developing simple tests for routine pathology specimens and to look for lung cancer-specific signatures in the blood. The technical work will take place at the Advanced Molecular Diagnostics Laboratory at the University Health Network, which specializes in this type of translational research and in test implementation.
Research Team:
- Dr. Daniel Xia, University of Toronto
- Dr. Tracy Stockley, University of Toronto
STREAM 4 | ACTIVE
Application of the New Classification System of Papillary Renal Cell Carcinoma in Clinical Practice
Each month, more than 500 people are diagnosed with kidney cancer in Canada and it is estimated that 1 in 4 would die of the disease. However, not all kidney cancers behave the same. Currently tumor aggressive potential is assessed solely on how it looks under the microscope. That system is limited though and does not always accurately predict the tumor behavior and its response to therapies. In this study we assess a new combined morphological and biological/genomic system to better predict kidney cancer behavior. The new system has great potential to enhance kidney cancer patient care.
Research Team:
- Dr. Rola Saleeb, St. Michael’s Hospital
- Dr. Georg Bjarnason, Sunnybrook Health Sciences Centre
STREAM 4 | ACTIVE
Early hematologic cancer detection in the CanPath longitudinal population health cohort
The incidence of blood system cancers, such as myelodysplastic syndrome, acute myeloid leukemia (AML), and non-Hodgkin lymphoma are expected to increase with our aging Canadian population. Outcomes are especially dismal in older patients and new approaches to diagnosis and treatment are needed. Our group has pioneered discoveries of the earliest, pre-cancerous stage of blood cancers, known as clonal hematopoiesis (CH). We have shown CH can be detected up to 10 years in advance of AML, when patients have no signs or symptoms, using a simple genetic blood test. We have shown CH cells increase inflammation and organ damage throughout the body, contributing to other diseases of aging, like heart and lung disease. Targeting this pre-cancerous CH phase may contribute to healthy aging, and present a radical new prevention strategy for AML and other blood cancers. Here we will develop more sensitive genetic tests to detect CH, and better understand the lifestyle and environmental factors that contribute to blood cancer progression and inflammatory diseases. We will study cancer development over time in matched samples3 from patients who were healthy when they enrolled in a large Canadian population study and later presented to hospitals with blood cancers. Our work will facilitate the application of our novel tests to the most at-risk populations, including forging new relationships between hospital-based hematopathologists and primary care providers. Our proposal will set the stage for more standardized workup and earlier detection of blood cancers, and potentially novel blood cancer prevention trials.
Research Team:
- Dr. Michael Rauh, Queen’s University
- Dr. Sagi Abelson, OICR
STREAM 4 | ACTIVE
Unravelling the molecular mechanisms and prognostic significance of morphological heterogeneity in esophageal adenocarcinoma using tissue and organoid samples
Histological examination remains the gold standard in characterising and grading EAC tumour tissue, where EACs can be broadly divided into two types, (intestinal/diffuse) based on their ability to form glandular elements. Recently, we have observed an additional morphological pattern resembling NE tumours. However, the molecular makeup and therapeutic implications of these different patterns have not been explored. Moreover, the utility of patient derived organoids to model and characterize these features are unknown. Our goal in this study is to better understand the mechanism of development and prognostic significance of these different morphological patterns, which would permit us to better predict the course of disease development in patients and identify potential therapeutic vulnerabilities.
Research Team:
- Dr. Sangeetha Kalimuthu, University of Toronto
- Dr. Jonathan Yeung, University of Toronto
STREAM 1 | ACTIVE
Genomic profiling of Acute Myeloid Leukemia with BCR-ABL1 translocation
The Philadelphia chromosome is an abnormal chromosome which forms when chromosome 9 and chromosome 22 break and exchange portions. Ph is associated with almost all CML cases and many ALL cases, but in rare instances, in AML patients. Ph+ AML accounts for 0.5 to 3 percent of all AML cases, and the outcome is generally poor in these patients, with a median survival of 9 months. The pathophysiology and pathogenesis are largely unknown due to the nature of its rarity and lack of comprehensive genomic analyses. We have identified 9 Ph+ AML cases from our AML patient cohort. We will conduct a single cell sorting technique and a comprehensive cellwide profiling to demystify the cellular structure and clonal origin of this disease. Thus, we will be able to determine the best approaches to treatment and improve the survival outcomes of Ph+ AML.
Research Team:
- Dr. Hong Chang, Princess Margaret Cancer Centre
- Dr. Dennis Kim, Princess Margaret Cancer Centre
STREAM 1 | ACTIVE
Integrated Transcriptomic and Proteomic Analysis of Breast Cancer Brain Metastasis
Cancer from other parts of the body can spread to the brain. This is known as brain metastasis. Breast cancer is one of the most common cancers that spreads to the brain. Most breast cancer patients with brain metastasis are diagnosed quite late and with serious complications. Unfortunately, our overall understanding of the brain metastasis process is superficial, thus limiting our ability of early diagnosis and treatment. In our study, we will examine samples from breast cancer patients to identify changes in their cancer cells that allow the cells to spread and evade brain defense mechanisms. We will use cutting edge techniques known as NanoString Digital Spatial Profiling and mass spectrometry to identify the changes responsible for allowing cancer cells to flourish in the brain microenvironment. We hope that our work can identify key changes in that may serve as targets for the future treatment of breast cancer brain metastasis.
Research Team:
- Dr. Qi Zhang, Western University
- Dr. Shawn Li, Western University
- Dr. Parisa Shooshtari, Western University
STREAM 1 | ACTIVE
Understanding molecular determinants of metastasis in carcinomas of the prostate using a proteotranscriptomic approach
Prostate cancer is the most common non-skin cancer in men in North America. Some men have very aggressive cancers and require intensive therapy which can carry significant side effects. Determining who should receive maximal therapy vs. those that need less treatment is a challenge in the management of prostate cancer. Certain features of prostate cancer are now known to be associated with higher risk of cancer spread and recurrence. These features can be seen in tissue samples and are called “morphologic biomarkers”. In particular, cancers that grow in a sieve-like fashion or as solid sheets or single cells are recognized as being almost inevitable in patients whose cancers spread to their lymph nodes. These features cause concern for surgeons and oncologists who will tend to intensify treatment if they are present. However, not every patient with these findings will have cancer spread but may develop debilitating side effects from treatment. The goal of this project is to investigate the genes and proteins in these high-risk cancer patterns and determine how they associate with spread in3 the lymph nodes. Existing patient tissue samples with and without cancer spread to the nodes will be assessed to compare and contrast the gene and protein patterns. We think this will allow us to identify new markers that can be used to assess tissue samples so as to identify the amount of treatment a patient will require to individually manage their cancer.
Research Team:
- Dr. Michelle Downes, Sunnybrook Research Institute
- Dr. Thomas Kislinger, University of Toronto
STREAM 1 | ACTIVE
Validation of a Machine Learning Algorithm to Predict Tumour Mutational Burden Based on an H&E Morphologic Signature and Prediction of Response to Immunotherapy in Squamous Lung Cancer
Lung cancer is the most deadly cancer affecting Canadians and is responsible for on average 58 deaths every day. Recent advances in therapies have resulted in a significant improvement in survival for patients. One of the most promising therapies for many patients is immunotherapy, which works by activating the bodys own immune system to destroy the tumour cells. The problem is finding out which patients will benefit from these therapies. The rationale for this study is to find a better way to predict which patients will respond to immunotherapy. Currently, pathologists utilize a test that determines how many tumour cells express a protein known as PD-L1. In general tumours with high levels of PD-L1 are treated with immunotherapy alone and tumours with low levels or no PD-L1 require treatment with typical chemotherapy. There is another test known as tumour mutational burden (TMB) that counts the number of mutations in a patients tumour. The most accurate way to predict the response of tumours to immunotherapy is by combining both PD-L1 status and tumour mutational burden. However, TMB is challenging to perform, expensive and not routinely completed in Canada. This project seeks to validate an artificial intelligence test that can predict tumour mutational burden from the digital image of the tumour. If successful this test would provide a fast, accurate and effective means to improve our ability to predict response to immunotherapy at the time of diagnosis and help ensure that all patients with lung cancer receive the most effective therapies.
Research Team:
- Dr. Matthew Cecchini, Western University
- Dr. Aaron Ward, Western University
STREAM 1 | ACTIVE
Enabling Clinical Genomic Testing and Translational Research in Bone Marrow Core Biopsies
The diagnosis and treatment of blood cancers often starts with laboratory testing of blood or bone marrow. The latter is the blood making factory and can provide important information in a liquid form (the aspirate) and a solid form (the biopsy). Bone marrow biopsies have the benefit of preserving the structural relationship of different types of blood and their supporting cells and sometimes this is the only material available when an aspirate cannot be obtained. This situation often happens when the cancer burden is very high or the bone marrow is scarred. Studying bone marrow biopsies and eventually using geographical information for clinical purposes is important because how cancer cells grow in relation to other normal cells in their proximity is known to affect how cancer cells respond or resist therapies. Unfortunately, innovative methods for diagnosis, prognosis, treatment and research are limited in bone marrow biopsies by the bone softening procedure which renders genetic material depleted and damaged. In this proposal, we have deployed a gentler way of softening bone that potentially better preserves material for genomic clinical tests and translational research without affecting morphology assessments needed for routine clinical care. We propose to demonstrate the improved molecular assays by performing a variety of clinical and research genetic tests on bone marrow biopsies from various blood cancers including a subset where liquid bone marrow aspirates are especially limited by a cancer-induced scar forming process called myelofibrosis.
Research Team:
- Dr. Hubert Tsui, Sunnybrook Health Sciences Centre
- Dr. Arun Seth, Sunnybrook Health Sciences Centre
STREAM 1 | ACTIVE
Proteomic interrogation of HPV-associated endocervical adenocarcinomas to investigate tumour subclassification and categorization according to the patten-based (Silva) classification system
Adenocarcinomas of the endocervix, the canal which connects to outer cervix to the main part of the uterus, are tumours that are mostly caused by infection with high risk human papillomavirus infection. If possible, these tumours are treated by removing them, and pathologists are the doctors that examine them. At times, pathologists struggle with how to classify these tumours but, there have been recent developments in how a) the type of tumour (tumour subtype) is recognized and b) how aggressive the tumour looks (pattern of invasion). These recent developments are supported by different types of previous studies however, our goal in performing this study is to leverage a modern type of analysis (proteomics) to provide a novel type of information that will strengthen the appeal of these useful developments, by providing underlying biological evidence.
Research Team:
- Dr. Anjelica Hodgson, University of Toronto
- Dr. Thomas Kislinger, University of Toronto
STREAM 1 | ACTIVE
Uncovering the heterogeneity and prognostic power of tertiary lymphoid organs in pancreatic ductal adenocarcinoma via high-dimensional spatial profiling
Lymphocytes are cells present within the human body that help respond to infections. Recent studies have demonstrated the presence of lymphocytes and tertiary lymphoid organs (TLOs- aggregates of different types of lymphocytes) near or in between cancer cells can help predict better outcomes in patient survival. Here, we will examine TLOs in the setting of pancreatic ductal adenocarcinomas (PDACs). PDACs represent approximately 90% of all pancreatic cancers and are often characterized by their aggressive nature and exceptionally poor patient survival. While TLOs in the setting of PDACs have been associated with better prognostic outcomes, whether this is predictive across all patient stages and whether there is further variation in TLO types that would better predict patient outcomes remains unknown. Here, we plan to count TLOs in a large PDAC patient cohort and use a cutting-edge spatial technology called Nanostring DSP that will measure the expression of genes in both TLOs and tumour cells in a pilot cohort. Together, this will allow us to associate TLO status with patient outcomes across all stages of disease. This will further allow us to measure the gene expression patterns of each TLO and enable us to discern whether there are multiple subtypes of TLOs in patients and how they interact with the nearby tumour. We will then be able to re-score the large cohort of patients for these TLO subtypes and ascertain how predictive they are of patient outcomes. Together, this study will be the first to help answer long standing questions in how TLOs relate to the survival of patients with pancreatic cancer.
Research Team:
- Dr. Klaudia Nowak, University of Toronto
- Dr. Kieran Campbell, Lunenfeld-Tanenbaum Research Institute
STREAM 1 | ACTIVE
Developing a machine learning algorithm for differentiating pseudo- and true invasion in colorectal polyps
Polyps in large bowel are very common, affecting 1 in 4 people in general and 1 in 2 people in those older than 50 years. Most of them are harmless. However, a small proportion of them contain cancer. Colon cancer is the third most common cancer in North America. The diagnosis of malignancy in a polyp is made by pathologists identifying cancer cells in the deep layer of the bowel wall under the microscope: a process known as invasion. However, non-cancer cells can be misplaced in the same location which can mislead the pathologists to make the wrong diagnosis. Although there are a few features pathologists use to tell apart “true” and “pseudo” invasion, this is never an easy task. Often times a panel of expert pathologists cannot reach agreement. With the advanced technology in image analysis and machine learning, the artificial intelligence sometimes outperforms human’s eyes in imaging pattern recognition. We identified 100 cases of colorectal polyps with cancer (true invasion) and 100 cases of colorectal polyps with features mimicking cancer (pseudo-invasion), diagnosed with consensus by a panel of expert pathologists specialized in this field. We will develop a machine learning algorithm that can recognize cancer and its mimics using high resolution whole slide images of those cases. We will compare the diagnostic accuracy of the algorithm with pathologists. After the algorithm is fully validated, we will build a web application and publish it online to allow access by pathologists worldwide. Our ultimate goal is to aid pathologists in making an accurate diagnosis of colon cancer.
Research Team:
- Dr. Qi Zhang, Western University
- Dr. Charles Ling, Western University
IMAGE ANALYSIS
Applying Artificial Intelligence for Automated Diagnosis of Colorectal Polyps
Colorectal cancer (CRC) is 2nd most common cancer in Ontario. It has been shown that individuals at risk of developing CRC form small polyps in the colon that can then progress to become cancerous. Therefore, CRC surveillance is performed by various methods including colonoscopy in people over 50 years of age to identify and remove these polyps. The polyps removed during surveillance colonoscopy forms the bulk of workload of gastrointestinal pathologists, even though the diagnosis in majority of the cases is quite straight-forward. This will continue to increase with increasing age of the population. Hence, we are proposing an automated process to screen these polyps using artificial intelligence (AI) techniques. AI algorithms will be trained to screen and classify these polyps with high accuracy. Any polyps that can not be classified with confidence will be flagged for pathologist review, thus reducing the workload and allowing more time to devote to complex cases. AI will also serve as a ‘second reader’ to alert the pathologist to any discrepancy and as quality indicator.
Research Team:
- Dr. Sonal Varma, Queen’s University
- Dr. Konstantinos Plataniotis, University of Toronto
IMAGE ANALYSIS
p53 immunohistochemistry interpretation in AML and MDS: using image analysis to better predict mutation status
AML and MDS are rare blood cell cancers that become more common in older individuals, individuals exposed to carcinogens and in individuals with some inherited gene mutations. From many prior research studies, several gene mutations have been identified that contribute to the development and progression of AML and MDS. One important gene, P53, is involved in the repair of DNA damage and maintains the integrity of DNA. When p53 is lost or mutated, genetic “instability” ensues resulting in multiple gene mutations. Genetic instability allows the disease to progress despite our best efforts to treat it. Newer treatments are becoming available that may improve outcomes for such patients therefore knowing the P53 status at the time of diagnosis is critical in deciding on which treatment to choose. To identify a P53 mutation, DNA from bone marrow can be sequenced, however, this process is time-consuming and the result takes 3-4 weeks to be available. Staining for the P53 protein in the bone marrow biopsy may be another way to determine whether a mutation is present, however, the interpretation of protein staining in biopsies is subjective and depends on the pathologist looking at the tissue. Digital imaging is becoming more widespread in pathology and the development of computer algorithms to analyze biopsy samples may be a method that reduces the subjectivity in the interpretation of stains. In this project we propose to use digital imaging analysis of AML and MDS cases to determine whether P53 mutation status can be predicted.
Research Team:
- Dr. Larissa Liontos, Sunnybrook Health Sciences Centre
- Dr. Anne Martel, Sunnybrook Research Institute
IMAGE ANALYSIS
Development of deep learning tools for deciphering intra-tumoral heterogeneity and predicting drug response in High Grade Serous Ovarian Cancer
Ovarian cancer is an aggressive cancer of the female reproductive system that is most commonly diagnosed at advanced stage where only 30% of patients survive more than 5 years from diagnosis12. Despite spirited surgical and medical therapy, this poor outlook has remained fairly unchanged despite many breakthroughs in our understanding of cancer biology13. One recent explanation for these previous failures is that while we often think of a patient’s tumor as being a homogenous mass of identical cancer cells, there are in fact a number of tumor sub-clones within each cancer that respond differently to our conventional and traditional therapies10. This means that existing treatments may not equally target all tumor cells and allow resistant subclones to survive and drive disease recurrence and progression. To address this, this collaborative team plans to harness expertise in artificial intelligence and unique clinical cohorts to explore if this technology can help automate the detection of biologically distinct tumor subregions and understand the biological significance of these computer defined regions. First, they will assess if computer defined regions of variability do indeed show unique biological activity that may be driving partial responses to therapy. Secondly, they will examine if the degree of morphologic variation within these cancers can predict response to existing therapies. Routine detection and characterization of tumor subclones, within each individual patients’ tumor, could help propose personalized and effective drug combinations that together target a larger fraction of the overall tumor biology. This could ultimately provide more durable responses for patients.
Research Team:
- Dr. Phedias Diamandis, University of Toronto
- Dr. Stephanie Lheureux, University of Toronto
IMAGE ANALYSIS
Methylation-based classification of small B-cell lymphomas in bone marrows and tissue
Small B-cell lymphomas (SBCLs) are common cancers that arise from cells of the immune system. In order to determine the optimal management for each lymphoma, doctors must first assign it to a specific category, that is, classify or diagnose the disease. While traditional approaches for lymphoma diagnosis are very good, there remain significant numbers of cases that cannot be easily categorized. To address this, we plan to evaluate the diagnostic benefit of molecular technologies based on DNA methylation. Methylation markers on DNA act as “on” and “off” switches for the programming of unique cell types; as such, methylation signatures are useful for pinpointing the origins of cancers, thereby assisting with disease categorization. In this study, we will evaluate two complementary approaches of DNA methylation testing of SBCLs. The first is methylation profiling, which determines the statuses of hundreds of thousands of methylation markers in each cancer in a comprehensive fashion. The second is “minimalist” approaches to methylation testing, unique to our study. To explain, in the minimalist approach, we use data analysis reduce the number of methylation markers needed for accurate diagnosis in order to create less expensive and easier-to-implement tests for laboratories. As a multi-disciplinary collaboration, the overall goal is to generate new molecular tools to improve SBCL diagnostics.
Research Team:
- Dr. Daniel Xia, University of Toronto
- Dr. Tracy Stockley, University of Toronto
- Dr. Peter Sabatini, University of Toronto
STREAM 3
Investigating the Tumour Immune Microenvironment in BRCA1 and BRCA2- associated and Basal Breast Carcinomas by Immunohistochemistry and Imaging Mass Cytometry
Breast cancer is the most common cancer in women. Breast cancer can occur by chance (sporadic), or by genetic mutations (familial). The most common genes that cause familial breast cancer are BRCA1 and BRCA2. Cancers caused by mutations in these genes can be associated with distinct patterns of tumour cells and immune cells that interact with the tumour cells. Some of these immune cells are associated with a good prognosis in sporadic breast cancer when present, and some have been targeted by specific drugs. Little is currently known about these immune cells and their surrounding environment in familial breast cancers. This study aims to look at the immune cells in familial breast cancers and breast cancers that share features with familial cancers. This is done by determining which immune cells are present by labelling those cells with specific markers using immunohistochemistry techniques. This can be time consuming and requires multiple tissue samples. A novel alternative to this method uses a mass spectrometer, which can evaluate many markers at the same time, conserving tissue. This technique is very new and has not been used to evaluate familial breast cancers before. The two techniques will be used to identify the immune profile of breast cancers, to see which types may be associated with prognosis, and, at the same time, identifying a potential target(s) for future drug therapy.
Research Team:
- Dr. Phillip Williams, Mount Sinai Hospital
- Dr. Irene Andrulis, Lunenfeld-Tanenbaum Research Institute
STREAM 3
EMT and Immune Evasion Signatures as Biomarkers of BCG Response in high-risk NMIBC
Bladder cancer (BC) is one of the leading causes of cancer-related deaths worldwide. At diagnosis, most BC are confined to the inner lining of the bladder, and known as Non-Muscle Invasive BC (NMIBC). While most of these can be treated conservatively, a subset of these are inherently aggressive and will recur frequently and/or progress to the Muscle Invasive (MIBC) form. Given the variability in clinical behaviour, it is critically important to be able to identify which NMIBC are more aggressive cancers, so that they can be steered towards alternative treatment options. The tools available at present to predict aggressive behaviour are based on morphologic and clinical features, and are not very accurate, requiring patients to have to have life-long surveillance to look for tumour recurrence. An alternative approach exists, already validated in other cancer types, in which the changes at the molecular level are explored for features known as “biomarkers” of aggressiveness. In this study, we are specifically interested in two biology processes that are linked to aggressive tumour behaviour, which include epithelial-to-mesenchymal transition and tumour immune resistance. By profiling the status of the genes in these processes in NMIBC exhibiting aggressive behaviour with those that did not, we will derive a molecular signature that heralds an aggressive disease course. Such a signature can be an important clinical tool in the future to guide the therapy of BC according to the level of risk of invasiveness.
Research Team:
- Dr. Shamini Selvarajah, University of Toronto
- Dr. Theodorus Van Der Kwast, University of Toronto
STREAM 3
Improving DLBCL classification through disaggregated and integrated microRNA network analyses
Diffuse large B-cell lymphoma (DLBCL) is a common and aggressive cancer. Standard chemotherapy treatment is generally effective and most patients are cured. However, in approximately 40% of DLBCL patients, the disease either fails to respond to treatment or relapses later; patients with such “refractory” or relapse disease are at high risk of dying. Being able to predict the response to therapy from biopsied tumor tissues at the time of diagnosis would allow faster and more direct access to experimental therapies that could dramatically help these patients. microRNAs (miRNAs) are genetic control molecules that are present in all human cells including cancer cells. Some miRNAs are also excellent disease markers because they are only found in one disease type or stage. We believe that miRNAs can be used to distinguish patients who will do well with standard therapy from those who will have a poor outcome. In this proposal, we will combine their expertise in medical oncology, lymph node pathology, miRNA diagnostics, and computer science to identify miRNAs that predict treatment response. The project has a single aim: to assess the clinical utility of miRNAs in DLBCL classification and prediction of treatment response. Successful completion of this proposal will establish a simple test to inform treatment decisions and improve the clinical outcomes of a sizeable set of DLBCL patients.
Research Team:
- Dr. Neil Renwick, Queen’s University
- Dr. Katharin Tyryshkin, Queen’s University
STREAM 3
Distinguishing aerogenous metastasis from multiple primary adenocarcinomas: a multidisciplinary proof-of-concept study
In patients with multiple lung cancers, staging is guided by whether the tumours are thought to have arisen separately (multiple primary tumours; MPT), or if they are a result of spread within the lung (intrapulmonary metastasis), which confers poorer prognosis. The traditional routes of metastasis involve tumour cells travelling through the bloodstream or lymphatics (vascular/lymphatic intrapulmonary metastasis; VIM). However, the lung is composed of interconnected airway spaces that can potentially carry tumour cells to distant parts of the lung — a concept known as aerogenous intrapulmonary metastasis (AIM). Although the existence of AIM has been suggested by some researchers, it has not been confirmed by molecular studies, and is likely under-recognized and misclassified as MPT in practice. Our group has previously proposed a set of clinical, radiologic and pathologic criteria that can distinguish cases of AIM from VIM and MPT. In this study, we will examine cases of patients with multiple resected lung cancers that meet these criteria for AIM, VIM or MPT. We will analyze the genetic and molecular signatures in these tumours, which will enable us to determine whether paired tumours correspond to clonal neoplasms (intrapulmonary metastasis) or non-clonal neoplasms (multiple primary tumors). This will serve to confirm the existence of AIM, validate criteria in distinguishing AIM from VIM and MPT, and identify gene mutations associated with AIM that could be targeted in precision medicine treatments. Overall, our study will be the first to prove the concept of AIM and will help to understand its clinical significance.
Research Team:
- Dr. Marcio Gomes, University of Ottawa
- Dr. Bryan Lo, The Ottawa Hospital
STREAM 3
Prospective Molecular and Clinicopathologic Characterization of Progesterone Treatment Response in Grade 2 Endometrial Endometrioid Cancer
Treatment of endometrial (womb) cancers includes surgery (removal of the womb) or hormonal treatment, depending on the degree of severity and spread of disease. There is a growing interest in hormonal treatment for patients wishing to have children or patients that have other medical conditions that prevent them from having surgery. Hormonal treatment includes progesterone based therapy, which is currently reserved for precursor lesions and early low grade endometrial cancer (grade 1). Moreover, some women with cancers of intermediate grade (grade 2) are also interested in hormonal treatment in order to delay surgery and allow time for a successful pregnancy. However, not all cases respond to hormonal treatment or some tumors recur after treatment. A current challenge in management of these cases is to identify patients who would respond to hormonal treatment and for whom surgery can be safely delayed. Our aim is to recruit intermediate grade cancer patients awaiting surgery at the Sunnybrook gynecological cancer clinic to receive a 4-week hormonal treatment preoperatively and correlate therapy response with clinical information, microscopic tumor appearance and their molecular make-up. Based on our previous studies, we believe that a proportion of these intermediate grade cancers will successfully respond to treatment with progesterone. Through the currently proposed study we hope to be able to identify markers that would identify which patients would benefit from hormonal treatment and hence allow physicians to personalize their treatment.
Research Team:
- Dr. Bojana Djordjevic, Sunnybrook Health Sciences Centre
- Dr. Arun Seth, Sunnybrook Health Sciences Centre
STREAM 3
Next Generation Grading to Improve Research and Practice for early bladder cancer
In many fields, expert operators use precise quantitative measurements to guide decision making. Surprisingly, such measurements are rarely used by pathologists when they evaluate cancers and gauge their potential to invade and spread. By analogy to aviation, pathologists are stuck in the era before altimeters and air speed instruments, flying by sight alone. As a case in point, in early bladder cancer, cancer grade is an assessment by a pathologist of the shapes and arrangement of cancer cells. Cancer grade is the primary predictor of aggressive behavior. Current bladder cancer grading does not use any quantitative criteria. Its subjectivity and lack of reliability limit its utility in research and clinical practice. The advent of sophisticated image analysis programs and informatics techniques make it feasible to measure the size, shape, and arrangement of cancer cells, and to derive better evidence-based rules for grading. This project will use such techniques to better define objective grading criteria and ensure that they provide clinically useful information regarding the risk that a patient’s cancer will cause harm in the future. The result will not only improve decisions around monitoring and therapy, but also empower better research into the molecular events that drive aggressive behavior. Finally, by better defining rules for grading, this work will enable more effective and efficient training and more consistent practice by pathologists worldwide.
Research Team:
- Dr. David Berman, Queen’s University
- Dr. Robert Siemens, Queen’s University
- Dr. Amber Simpson, Queen’s University
STREAM 3