Overview
Our team set out to create a biosensor that combined early detection with sensitivity to identify ovarian cancer at the earliest stages. There is currently no screening tool for ovarian cancer and it is often diagnosed in the later stages. Late stage diagnosis thoroughly worsens prognosis. Therefore, creating a biosensor that can detect early signs of cancer can significantly improve patient outcomes.
We consulted with many experts in the field. Dr. Brodeur is a gynecological oncologist who works at the Jewish General Hospital in Montreal, Quebec, one of the top hospitals in the world for cancer research. We consulted with Dr. Brodeur along with Dr. Mbarik, a researcher in the field. Dr. Brodeur informed us that the most fundamental aspect of any biosensor is that it can change outcomes for patients. Therefore, we knew we were on the right track by targeting early & sensitive detection, since this would improve prognosis.
Another important aspect is awareness of the issue. If vulnerable populations are not aware of the symptoms associated with ovarian cancer as well as the importance of early testing, our biosensor will have less of an impact. Of course, this is not something that will change rapidly. However, we place importance on raising public awareness and reducing stigma associated with ovarian cancer. So far, we have participated in club fairs at our university during ovarian cancer awareness month, September.
Wet Lab
The experts and stakeholders that we consulted also advised us on our experimental design in the wet lab. To begin, we addressed early diagnosis by targeting cancer stem cells.
Cancer stem cells are also known as tumor initiating cells (TICs). This is because they are thought to be present before cancer tumor cells. They also provide information about tumor progression and patient outcome. For example, the biomarkers we chose (CD44 and CD117) predict chemotherapy resistance and a higher chance of recurrence.
The first idea was to make a biosensor to detect any type of cancer. Since our goal was to detect early cancer biomarkers, we first explored the detection of very small embryonic-like stem cells (VSELs) and Cancer stem cells. We got feedback from our PI Dr. Kuzmin that the research on VSELs was not strong enough to use, so we decided on the detection of cancer stem cells. Dr. Kuzmin also mentioned that Montreal has the highest rates of ovarian cancer in our country, Canada. Therefore, to ensure that our project addressed a local issue, we chose to detect biomarkers for high grade serous ovarian cancer.
Although we consulted with professionals in the field, none of them had worked with cancer stem cells. So we made the decision to target CD44 and CD117 through literary research.
As mentioned, the second goal was sensitivity. We wanted to be able to detect very low levels of cancer stem cells. We addressed sensitivity through our method of detection.
The first design was a colorimetric assay that used conjugated gold nanoparticles and spectrophotometry for sensitive detection. The team consulted with Dr. Shi from Concordia university who is an expert in microfluidics. He encouraged us to make a lateral flow assay instead for its simplicity and use in Point-of-Care testing. We applied his feedback and changed our design to be a lateral flow assay. To increase sensitivity, we decided on using the thermal method, which uses a laser and IR camera to increase sensitivity and report concentration levels of the biomarkers in the blood.
Since our biosensor was lacking a synthetic biology aspect, our mentors and PIs advised us to incorporate CAR T cell therapy modelling into our project.
Following this, we consulted with Dr. Scott McComb who researches CAR T cell therapy at the National Research Institute for Cancer in Canada. He gave us information on CAR T cell therapy and suggested we incorporate Jurkat cells into our biosensor to create a more cohesive design. After consulting with our mentors, we decided to use yeast to express our antibodies and antigens instead of Jurkat cells. They explained that yeast is easy to use and we already have access to the colonies in the lab as well as expertise provided by mentors. Moreover, this would make our project more sustainable. For more about this, see the sustainability page.
Therefore, we started working on the yeast expression of antigen CD44 in Saccharomyces cerevisiae. The choice of yeast strain came at the advice of our mentors who are graduate students of synthetic biology. Since this project will continue into the next iGEM cycle, we plan to continue expressing proteins in yeast. To elaborate, we intend to express both the antigens CD44 and CD117 and their respective antibodies in Saccharomyces cerevisiae.
Dry Lab
Modelling
The experts and stakeholders that we consulted mentioned that is extremely important for us to work on the CAR-T cell modeling on our first iteration of the project so we can visualize the basic concept of CAR-T cells and how they work, that way we'll have enough time to improve it and make it more specified and more customizable for the user.
At first we had no knowledge on how modeling works or how to implement it into MATLAB or Python, so we had to do a lot of research to find the information needed and for that information to be related to CAR-T modeling specifically.
We found a research paper that explains the CAR-T cells behavior and they provided the differential equations they used for that experiment. Thanks to that we were able to understand how we can proceed with our work to model our scenarios.
After some meetings with our mentors, we were able to modify the equations so that they can serve our purpose of working with two different CAR-T cells and then the effect of injecting both CAR-T cells at the same time.
When writing the code for the modeling we consulted two of our engineering mentors so they can provide input on how to write a clean code and a fast code so that the simulations don't take too long to generate the results needed, after that we consulted our synthetic biology mentors to see if the outcome is what would they expect from a simulation software and what could be added to the simulation so it would further help the wet lab team in they're upcoming experiences.
Hardware
In our pursuit of working on the thermal method to enhance the sensitivity of the lateral flow assay, we encountered a significant obstacle: the need for a class 4 laser. Lasers are categorized into four classes, with class 4 being the highest and posing the greatest potential hazards. Due to their potent capabilities, class 4 lasers are usually found in laboratory and industrial settings, making their procurement challenging for our project.
To address this challenge, we sought guidance from Dr. Gurnam, a distinguished authority in Laser Safety at Concordia University. His expertise in laser safety regulations and access to specialized equipment made him an invaluable resource for our endeavor. Our first step in acquiring the required laser was to undergo a Laser Safety test, obtaining the necessary certification after successfully passing the examination. After acquiring the certification we were instructed to contact a series of Engineering doctors for the possibility to find the laser we are looking for.
We were able to get in contact with Dr. Zazubovits, an esteemed researcher in the field of engineering, who happened to possess a laser that precisely matched our project requirements. He granted us access to his laboratory, where we could work under his guidance and utilize the laser for our experiments.
Now for modeling the device that will host all the components ranging from electronics to lateral flow assay, we had to consult Dr. Gurnam again to see if any changes from color of the device to the angle of the laser is going to change the outcome of the results. And after gathering all the information needed we proceeded in creating a 3D model of the device.