Applications for the 2023-2024 period are now open!
CDI Spark Award
The goal of the CDI Spark Award is to support basic science research trainees in their projects by helping them leverage data science and machine learning expertise to uncover previously unseen trends in their data.
The CDI Spark Award will provide trainees with the expertise they need to conduct innovative research and create essential links within the scientific community, to continue to make impactful discoveries throughout their careers, and help fuel leaders of Canada’s scientific community.
The award aims to support basic research trainees across the UHN community by:
- Providing funding and resources to support their research projects
- Providing expertise and mentorship in data science and machine learning to better answer their research questions
- Fostering connections across research teams to provide guidance, build collaborations, and create a community
The CDI Spark Award will support four (4) trainees for the 2023-2024 academic year. Each successful trainee will receive $20,000 towards their yearly salary
FREQUENTLY ASKED QUESTIONS
The program consists of the following components:
- Funding – $20K per participant
- Group sessions with CDI specialist
- Individual sessions with a specialist
- Educational webinars
- Midpoint check-in
- Research day
- Project close-out presentation and report
The program will support trainees with $20,000 in salary funding provided in two instalments, first at program kick-off in September 2023 and second at the program midpoint in February 2024.
Trainees will have access to CDI specialists for group and individual training sessions. CDI has specialists across a variety of domains:
- Machine learning
- Computer science
- Data science
- Data governance and accessibility
- Navigating UHN Digital
- Service design
- Project management (waterfall/PMP, Lean Six Sigma, Agile)
There will be two group sessions with CDI specialists, where the specialists will provide an introduction on their domain and trainees will have an opportunity to ask questions.
Trainees can request individual sessions with specialists to do a deep-dive into their project.
How to Book a Specialist
There are two types of sessions available: a half-hour Q&A session (available monthly) and an extended 1-2 hour deep-dive session (available once). Request form coming soon.
- Trainees need to request the session two weeks in advance
- Trainees need to submit 2-3 questions that they want to discuss with the specialist in advance of the session
There will be three webinars throughout the program, presented by CDI specialists. Trainees will provide topic suggestions from specialist domains of expertise at the start of the program. The series will be tailored to their learning goals. The webinars are open to the lab of the trainees.
As part of project close-out, trainees will give a 15 minute presentation to discuss their project outcomes and lessons learned. These presentations will celebrate trainees’ skill development and accomplishments during the program. This session will be open to the labs of trainees as well as the CDI team.
CDI Spark Award Information Session
Recording of our information session for the 2022 application cycle.
- The award will target 2nd and 3rd year Ph.D. students or post-doctoral fellows in a lab of a Scientist or Clinician-scientist with an appointment at UHN or PM.
- Experience with computational data manipulation. Preference for people with formalized coding experience (i.e., R, Python, MatLab, etc)
- Domestic and international students are eligible to apply
- For more information, please refer to the FAQ linked below
- Research projects must be focused on pre-clinical or basic research
- Projects must be in the field of cancer research
How to Apply
Follow the notes below for your application. Please submit all documents via email in PDF format.
- Use the email subject “CDI Spark Award Registration [Last name, First name]” to submit the registration form
- Registration for the 2023-2024 cohort is now open!
- Use the email subject “ CDI Spark Award Full Application [Last name, First name]” to submit the full application form
- Applications for the 2023-2024 cohort are now open!.
Dr. Emma Bell is a postdoctoral research fellow at the Princess Margaret Cancer Centre in Toronto, Canada. As a bioinformatician, they combine biology, statistics, and information engineering, to ask biologically and clinically meaningful questions of genomics data. Their research uses cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) to detect evidence of cancer in blood plasma donated by ovarian cancer patients. This project aims to test the potential of cfMeDIP-seq as a liquid biopsy for this disease. Dr. Bell’s long-term career goal is to improve gynecologic healthcare outcomes for women, non-binary and trans people.
Most ovarian cancer patients receive their diagnosis when their cancer is already advanced. Only 30% of these patients survive beyond 5-years. In contrast, 92% of patients diagnosed with early-stage disease survive beyond 5-years. Early diagnosis would immediately improve the long-term survival of ovarian cancer patients. Thus, we must develop sensitive and specific diagnostic tools.
Ovarian cancer manifests as subtypes with distinct molecular drivers and differing prognoses. An effective diagnostic tool must distinguish these subtypes.
Our lab developed cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) to detect early-stage cancer. Cells methylate their DNA to control its expression. Each cell type does so following a unique pattern. Cancer cells highly methylate their DNA. This allows us to enrich for circulating tumour DNA (ctDNA) from the larger pool of cell-free DNA (cfDNA). Previous cfMeDIP-seq studies could discriminate not only between patients and disease-free controls, but also closely related cell types and early- and late-stage disease.
This project will evaluate cfMeDIP-seq as a potential diagnostic tool for ovarian cancer. I hypothesise ctDNA released from an ovarian tumour reflects the DNA methylation profile of its histological subtype. I will test this using the cfMeDIP-seq method to profile blood plasma donated by ovarian cancer patients.
Tina is a second-year PhD candidate at the University of Toronto, advised by Dr. Mathieu Lupien. Tina obtained her bachelor’s degree at the University of Toronto, specializing in Neuroscience with minors in Computer Science and Physiology. She loves using computers to understand biology. Her undergraduate thesis modelled neurodevelopmental trajectories in mice with autism-like behaviour. Currently, her doctoral thesis focuses on how DNA is used differently to make some cancers more aggressive than others. She attempts to integrate different layers of genetic information together to gain a more holistic understanding of cancer biology, which helps us develop better diagnostic tools and treatments. Besides research, Tina loves exercising and the outdoors. She’s part of the synchronized swimming team, trains for endurance sports, and loves backpacking and camping in the mountains.
The human body contains 37 trillion cells, each performing a very specific function. To do this, each cell carries out instructions from its genetic material stored as a 3 billion basepair long string of DNA. The DNA sequence is virtually identical in every cell, but the instructions carried out by each cell vastly differ. This is because different parts of the DNA are used to carry out these functions. Cancers can arise when normal cells lose control over which parts of the DNA are used. This can give rise to fast-growing cancer cells and potentially to cells that can migrate to other tissues and form metastatic tumours. Because metastasis is the reason for almost all cancer-related deaths, I intend to understand the parts of the DNA that are specifically used in metastatic cells to support their aggressive and invasive behaviour. Understanding the specific state of the 3-billion basepair DNA string in every single primary and metastatic cancer cells requires significant computational power and powerful statistical methods. Using technological advances in sequencing, we can start to pinpoint regions of the DNA that are used in metastasis, which will map the genetic basis of this deadly disease and open new avenues for diagnosis and intervention.
Sasha completed her undergraduate degree at Queen’s University, where she studied Biology with a Specialization in Mathematics. She is now a second-year PhD candidate in the laboratory of Dr. Scott Bratman within the department of Medical Biophysics (MBP) at the University of Toronto. Sasha’s doctoral research focuses on fragmentation characteristics of cell-free DNA in the bloodstream, and through liquid biopsy, using these features to reveal breast cancer biology and predict treatment response to targeted therapies. Sasha hopes to translate novel discoveries from the lab into clinically useful tools for the management of cancer patients. Outside of her research, Sasha enjoys traveling and spending time outdoors, namely hiking, camping, skiing, and cycling.
In our bodies, cells die and release fragments of their DNA into the bloodstream. In cancer patients, DNA fragments are shed from both normal healthy cells and tumour cells into the blood circulation. By testing a simple blood draw, we can use this circulating DNA to reveal tumour-specific information throughout a patient’s journey with cancer. We aim to advance existing blood test technologies by investigating characteristics related to how the DNA was fragmented to reveal more information about breast cancer, such as its location and behaviour. For example, I will explore the lengths of these DNA fragments, the types of DNA sequence at the fragments’ ends, and the abundance of these fragments across the human genome. These features are different between cancer patients and healthy individuals, and in cancer patients can provide helpful hints about a tumour’s biology. Specifically, we will explore whether these different DNA characteristics distinguish the breast cancer subtypes. Altogether, this research will uncover new tumour-specific information in circulating DNA that can one day be used to inform cancer management, ultimately improving the lives of cancer patients.
I joined the lab of Dr. Trevor Pugh in February of 2021 as a Post-doctoral fellow. My project is part of the Canada-wide CHARM consortium and involves investigating and developing blood-based cancer classifiers for the early detection of cancer in patients with hereditary cancer syndromes. Prior to this, I completed my PhD at the BC Cancer Research Center in Vancouver, BC with Dr. Stephen Yip where I investigated the proteogenomic landscape of low-grade gliomas. I am passionate about translational genomic research that can improve and impact patients using data. Outside of the lab, I enjoy rock climbing and visiting craft breweries.
As cells in your body grow and divide, they release DNA fragments into the blood. These DNA fragments are often non-random and provide clues to the type of cell that is releasing them (ie blood cell versus liver cell). Based on this, scientists are able to analyze the patterns of DNA fragments in the blood to determine where the DNA is coming from. In the case of cancer, each type of cancer (ie breast, colon etc) releases a unique pattern of DNA that is different than healthy cells and can be used to detect if and where the cancer is in a patient. This project is focused on using machine learning to properly and effectively classify these unique patterns of DNA fragments in order to predict if and what kind of cancer a patient may have. This tool will be especially helpful for detecting early cancer (when and where) in patients with hereditary cancer syndromes who are at a high-risk of developing cancer.