SyncOva

The Issue

In 2022, the Terry Fox Research Institute reported that approximately 3000 women in Canada were diagnosed with ovarian cancer and over half of these women were predicted to pass due to this deadly disease. According to Navaneelan and Ellison (2015), ovarian cancer is the ninth most common cancer, and the fifth most common cause of death from cancer among women in Canada. It also accounts for the most deaths among all gynecological cancers. To elaborate, ovarian cancer refers to a group of cancers that begin in or near the ovaries. This type of cancer can develop in the surface epithelium, germ cells of stromal cells. Women of all ages can develop ovarian cancer, but those who have undergone menopause are more at risk.Currently, only about 20% of ovarian cancers are found at an early stage, which greatly increases the survival rate. In fact, with early diagnosis, 94% patients live longer than 5 years post-diagnosis. Conversely, Death from ovarian cancer is highest following the first year of diagnosis and chances of surviving another year decrease to 75.3% after the first year.

Furthermore, ovarian cancers vary in severity, with high-grade serous ovarian cancer (HGSOC) ranking as the deadliest form of this disease. This form of ovarian cancer originates in ovarian epithelial cells . The main reason for the low survival rate of patients with ovarian cancer is the late cancer diagnosis as well as acquisition of therapy resistance by relapsed tumors (Lheureux et al., 2019). It has been recently shown that chemotherapy can provoke the spread of therapy-resistant clones or activation of compensatory signaling pathways, which allows the tumor cells to cope with damage and initiate tumor repopulation (Izar et al., 2020; Shnaider et al., 2020; Kan et al., 2022; Zhang et al., 2022). Thus, there is a need to develop new methods for prediction of tumor aggressiveness and reliable early detection of cancer progression along with new chemotherapy regimens.
There is currently no standard screening test available to identify ovarian cancer and it is typically found through regular women’s health exams. The two most common methods used for screening are transvaginal ultrasounds (TVUS) and the CA-125 blood test. The former involves an invasive examination while the latter is not an accurate predictor of ovarian cancer, since it can be present in other conditions, such as endometriosis. It is also a cancer tumor marker, meaning that it is not the earliest marker of ovarian cancer. This brings us to cancer stem cell markers and our solution.

Our Solution: coupling sensitive & early detection for the ultimate screening test

Our solution is to create a blood test that screens for the presence of early ovarian cancer and has high sensitivity. Firstly, we chose to detect cancer stem cells instead of cancer tumor cells. This is because current research indicates that cancer stem cells (CSCs) may be present before cancer tumor cells. These cells can also drive tumor initiation and cause relapses. The CSC biomarkers we chose are CD117 and CD44, for multiple reasons. Both of these biomarkers are expressed in ovarian cancer stem cells and can indicate the state of the disease. For example, the CD117 biomarker is associated with tumor aggressiveness, prediction of treatment success and prognosis. It is also the most commonly used biomarker for the detection of ovarian cancer stem cells. Furthermore, coupling multiple biomarkers together leads to a more targeted detection method for ovarian cancer.

In order to create a method of detection that is sensitive, simple, rapid, low cost and specific, our team decided to create a lateral flow assay to detect CD117 and CD44. Gold nanoparticles have unique optical properties due to surface plasmon resonance (SPR) that allow for the detection of our selected biomarkers with high sensitivity. This means that they can detect low concentrations of ovarian cancer stem cells. We decided to couple our lateral flow assay with the thermal method of detection. This method uses a laser to heat the antigen bound gold nanoparticle-antibody complexes and this heated area is subsequently read by an IR camera. This method increases the sensitivity of lateral flow assays and can therefore ensure detection of lower levels of biomarkers. This is relevant to early diagnosis.

Synbio Component

The construction of a lateral flow assay requires three antibodies: detection, capture and control antibodies. These are costly and require shipping for a small number of antibodies, increasing the carbon footprint of the user. However, the cost to pay for the antibodies is not only monetary: many animals are used and killed regularly in the process of creating antibodies for research and the creation of diagnostic materials. Commercial antibodies are created through the injection of the target antigen, in our case CD44 or CD117, into an animal and subsequently collecting a significant amount of blood that contains the antibody. Overall, it is an expensive process that borders on cruelty.

In addition to this, in order to test the validity, reliability and sensitivity of a lateral flow assay, researchers need to use the target antigen that the lateral flow assay is meant to detect. This protein is also purchased commercially and comes with similar issues as the antibodies.

Therefore, our design incorporates the use of yeast to express the CD44 and CD117 antigens as well as their antibodies. Currently, we are working on the expression of CD44 antigen in Saccharomyces cerevisiae.

The End Goal: CAR T-cell therapy and detection to target the biomarkers found by our biosensor

As previously mentioned, the hallmark of a responsible and useful biosensor is that it changes the outcome of patients. In our case, early detection can significantly increase survival in ovarian cancer patients. Furthermore, we can consider an additional aspect in relation to patient outcome: treatment efficacy. The CD44 and CD117 biomarkers indicate tumors that are likely to resist chemotherapy. Therefore, if our biosensor detects early stage HGSOC and the presence of either biomarker, this cancer may require additional treatment beyond chemotherapy.

Chimeric antigen receptor (CAR) T-cell therapy is a last line immunotherapy that extracts T cells from the cancer patient and modifies them to express proteins that target the cancer itself. With this said, once the cancer stem cells have been detected through their biomarkers, the second phase of our project involves bioengineering multivalent chimeric antigen receptor (CAR) T-cells that target the biomarkers CD117 and CD44. For this iGEM cycle, this part of the project consists of modeling CAR T-cells that targets the ovarian cancer stem cell biomarkers CD44 and CD117. Cancer stem cell biomarkers, such as tumor-associated antigens (TAAs), are potent tools for CAR T-cell therapy. In fact, CSC markers have been used as targets for CAR-T cells in previous research, with CD44 and CD117 being most commonly used. Our goal is to model a CAR T-cell that targets CD117 and CD44, while including safety measures to prevent side effects. Since we plan on continuing with this project into the next iGEM cycle, we plan on developing the CAR T-cells in the lab in the future.

It is important to note that CAR T-cell therapy can come with side effects, especially when treating solid tumors such as ovarian cancer. The two common side effects of CAR T-cell treatment are off-tumor toxicity, which can result in the damage of healthy tissues, and antigen escape, which happens when cancer cells evade CAR T-cells. Our team is addressing tumor resistance to CAR T-cell therapy through the design of a multivalent CAR T-cell. Tumors are more responsive to and less likely to escape multivalent CAR T-cells. In regards to off-tumor toxicity, the Munich iGEM 2022 team focused on addressing off-tumor toxicity in CAR T-cell therapy of solid tumors. They did so by adding a pH-sensitive linker to their CAR T-cell that would increase its specificity to only target the cancer cells by unmasking in their acidic environment. We plan on testing this strategy with our model. Also, in order to further address off-tumor toxicity, we plan on targeting areas with high expression of folate receptor alpha. Multiple studies have demonstrated that the folate receptor alpha is overexpressed in ovarian epithelial cancers. Furthermore, recent research indicates that targeting these areas can increase efficacy of treatment for ovarian cancer. Therefore, incorporating this into our CAR T-cell model and subsequent experimentation will increase the efficacy of our therapy. Source 1, source 2, source 3 - cite in APA style + add to list of sources.

Project design - Wet Lab

The initial goal for our project was to create a sensitive, specific, simple and rapid detection method for ovarian cancer, specifically the biomarkers CD44 and CD117 that are found in the presence of Cancer Stem Cells (CSCs). In alignment with this goal, our team decided to build a Lateral Flow Assay (LFA) that detects the presence of the biomarkers mentioned above. Access to an early detection method for ovarian cancer can improve the prognosis of patients. Therefore, this design may be able to save lives in the future, and provide more information about the disease to the general public.
The final goal for our project is to use the information that we gained from the LFA biosensor to develop a CAR-T cell therapy and detection method to target CD117 and CD44 biomarkers.
The Lateral Flow Assay was tested using the biomarker CD117, which was ordered from a reputable source. This was done to ensure that the device and the materials worked well. Biomarker CD44 was grown in E.coli bacteria, expressed in yeast and then extracted from it.

The Design of the Biosensor

We wanted to get a colored output from a positive test result with the lateral flow assay in order to employ the thermal method. Therefore, our lateral flow assay was designed with the sandwich method because this method allows for an output of color when the test is positive, as opposed to the competitive method. The blood sample containing the ovarian cancer biomarkers should be placed on the sample pad, from which it would go through the conjugate pad and then nitrocellulose membrane through capillary action. On the conjugate pad we can find the gold nanoparticles bound to detect anti-mouse in rat antibodies (ex. AuNP-CD117).

When the sample reaches the conjugate pad, the AuNP-CD117 complex connects to the human CD117 biomarker, and moves it along the nitrocellulose membrane. On the nitrocellulose membrane, a test dot and a control dot can be found. Our team used dots in the design instead of lines because we must place the solutions on the nitrocellulose by hand. We attempted to place lines on the nitrocellulose membranes using micropipettes, but the results were unsuccessful. This is because a specific machine is required in order to place lines on the nitrocellulose membrane. Since we didn’t have access to such a machine, we chose to use test and control dots instead of lines. After some experimenting in the lab as well as reading established LFA protocols, we decided to use 0.5 microliters of capture antibodies for each dot.
The test dot has anti-human in rat CD117 immobilized on it, and the control dot has the anti-rat in goat IgG immobilized on it. When the AuNP-anti-CD117 complex moves by capillary action along the nitrocellulose membrane, the one that contains the human CD117 biomarker connects to the immobilized anti-human CD117 in rat (test dot), as can be seen in diagram 2. This is the sandwich assay. The AuNP-anti-CD117 complex that hasn't bonded to the human CD117 biomarker is the one that bonds to the immobilized IgG anti-rat in goat (control dot). Once the rest of the sample reaches the end of the nitrocellulose membrane, it is absorbed by the absorption pad.

For more details on the creation of the project design, visit the engineering cycle page.

For the sake of simplicity, only CD117 antibodies and antigen are mentioned in the diagrams and design. However, the same information and diagrams can be used for CD44 antigen and antibodies, by simply swapping them with CD117 antigen and antibodies. To explain, in the interest of sustainability and a simple engineering cycle, we chose to focus on one biomarker at a time when working on gold nanoparticle conjugation for the lateral flow assay. Our team began by experimenting with CD117 antibodies and gold nanoparticles. However, we will be doing further work with CD44 antibodies and antigen as well as we continue our lab work for this two-year project.

CAR-T cell Modelling

The goal for the Modeling was to create a modelling tool that can simulate the basic interaction of CAR-T cells with Tumour cells based on different parameters, the tool will save the wet lab a lot of time and resources so they won’t have to go and do each experiment in the lab to get the outcome of it.

The simulation focuses on the interplay of effector CAR-T cells, Memory CAR-T cells and Tumour cells inside of an immunodeficient mouse model of haematological cancers.

The code for the modelling was done in MATLAB but then we migrated to Python so we can create a more user-friendly user interface that can be easy to use for the wet lab and fast to modify since they will need to constantly change values to see the different outcome of their experiment.

The current differential equations used to model the CAR-T cells are straight forward, meaning working with one antigen on each CAR-T cell and neglecting any effect on the CAR-T cells inside the host. This is only the first step of the modelling simulation, later on it will be improved so we can model a multivalent CAR-T cell with multiple scenarios and challenges inside the host.

For more information visit the Engineering section, Modelling page.

Dry lab & Hardware

The goal for the thermal method is to increase the sensitivity of the LFA and to get quantitative data out of it. Since the Wet Lab is working to get an answer from the lfa test, the answer can only be true or false. Although it gives you the results, it is not a very detailed answer and you can’t track the patient’s progress through it. Meaning if the patient came back to do the test again you won’t be able to know if the tumour cells concentration is still the same or not.

For that reason the Dry Lab is working on creating a device that will give you the possibility to get a quantitative answer from the lfa test, through that method you’ll be able to get the concentration of tumour cells of the patient from the solution that is on the test line of the LFA.

The thermal method works by heating up the Gold Nanoparticles that are on the test line of the lateral flow assay, there are multiple ways of heating the Gold Nanoparticles but in this situation we are using a laser. After getting the Gold Nanoparticles heated we will detect the heat with an IR sensor that can store this information, which will later on allow us to use it so we can calculate the concentration of Gold Nanoparticles.

The tasks were split between small subteams ranging from mechanical engineers, computer engineers and computer science undergrads.

For more information Mechanical engineering subteam : worked on creating the 3D design of the device and how it’ll host the different components like microcontrollers, IR sensor and leds. As well as taking into consideration how the laser will be hitting the test line of the lfa inside the 3D printed box.visit the Engineering section, Modelling page.

Computer engineering/science subteam: worked on getting the microcontroller (ESP32) to work well with the IR sensor and getting accurate reading of the heat dissipation and heat change induced by the laser on the Gold Nanoparticles. As well as researching how to get the concentration from the heat generated from the Gold Nanoparticles in play.

For more details on how the Thermal method is being implemented, visit the engineering section, Hardware pageHeader

Project Description