Periodic Reporting for period 1 - CROC (Unravelling ChemoResistance mechanisms and improving first-line therapeutic strategies in high-grade serous Ovarian Carcinoma using multi-culture patient-derived organoids)
Período documentado: 2023-01-01 hasta 2025-08-31
Ovarian cancer is the deadliest cancer of the female reproductive system. Even after undergoing surgery and anti-cancer therapy, most women recur in less than 3 years and eventually show resistance to the treatment. To develop better treatments for these women, we need to investigate and understand how treatment resistance occurs at the cellular level. And to conduct such investigations, the development of research models that better mimic the tumors from the patients is crucial.
Since tumors are not comprised of isolated cancer cells but these are rather in constant communication with other cell types (stromal cells), my first goal was to create a new research model incorporating both cancer cells (grown as stable organoids) and stromal cells from ovarian cancer patients. When this project started, such research model did not exist.
My second goal was to identify the cellular mechanisms of resistance to anti-cancer therapy in ovarian cancer patients and to design new treatments. To do so, I used a technique called single cell RNA sequencing and studied the changes that occur in the cancer cells and the stromal cells of my research models when they are exposed to the anti-cancer therapy given to patients. With this information, I found cellular processes that are unique to the cancer cells that survive the anti-cancer therapy and that are regulated by the stromal cells, and I investigated if such processes could be blocked with additional treatments.
Since tumors are not comprised of isolated cancer cells but these are rather in constant communication with other cell types (stromal cells), my first goal was to create a new research model incorporating both cancer cells (grown as stable organoids) and stromal cells from ovarian cancer patients. When this project started, such research model did not exist.
My second goal was to identify the cellular mechanisms of resistance to anti-cancer therapy in ovarian cancer patients and to design new treatments. To do so, I used a technique called single cell RNA sequencing and studied the changes that occur in the cancer cells and the stromal cells of my research models when they are exposed to the anti-cancer therapy given to patients. With this information, I found cellular processes that are unique to the cancer cells that survive the anti-cancer therapy and that are regulated by the stromal cells, and I investigated if such processes could be blocked with additional treatments.
First, I extracted different types of stromal cells from tumors obtained from ovarian cancer patients. I investigated some characteristics of these cells to assess which type of stroma they were, and I grew them to have enough cells to perform multiple experiments. Then I tested different experimental conditions to be able to combine the cancer organoids with the stromal cells, mimicking ovarian cancer tumors. By finding the best conditions, I completed the first goal of the project: to generate a new research model of ovarian cancer.
Then I performed single cell RNA sequencing of the research models treated with anti-cancer therapy and compared the changes in their transcriptome (which is the complete set of RNA molecules of a cell) to the control research models. After various bioinformatics analyses, I discovered some molecules that were present in higher amounts in the cancer cells that had survived the treatment compared to the cancer cells from the control models. I investigated in which cellular processes these molecules are involved, since they could protect the cancer cells from the anti-cancer therapy. In addition, I identified other treatments that could inhibit the function of some of these molecules, and I conducted experiments to test them in combination with the anti-cancer therapy. I am currently validating these results to be able to complete the second goal of the project: to identify cellular processes that confer treatment resistance and additional treatments to block them.
Then I performed single cell RNA sequencing of the research models treated with anti-cancer therapy and compared the changes in their transcriptome (which is the complete set of RNA molecules of a cell) to the control research models. After various bioinformatics analyses, I discovered some molecules that were present in higher amounts in the cancer cells that had survived the treatment compared to the cancer cells from the control models. I investigated in which cellular processes these molecules are involved, since they could protect the cancer cells from the anti-cancer therapy. In addition, I identified other treatments that could inhibit the function of some of these molecules, and I conducted experiments to test them in combination with the anti-cancer therapy. I am currently validating these results to be able to complete the second goal of the project: to identify cellular processes that confer treatment resistance and additional treatments to block them.
In this project, I have generated a research model of ovarian cancer that, for the first time, incorporates both cancer cell organoids and stromal cells. I have also written a detailed protocol that allows other researchers to recreate this unique model, and I plan to submit it for publication soon. If further refined for specific studies, this model could be useful to other researchers to investigate other types of cancer and to pharmaceutical companies to test new treatments before testing them in patients.
I also plan to publish the results of this project as a scientific article after the final validation experiments that I am currently performing. These results will help us to better understand the cellular mechanisms of resistance to anti-cancer therapy in ovarian cancer patients and to design new treatment combinations that improve the survival of women with ovarian cancer. In parallel, I will submit the sequencing data produced in this project to specific data repositories, so that other researchers can use it to generate new scientific hypotheses and validate their own results.
If some of the results from this project were validated by other researchers, they could have the potential to identify which patients will benefit from a specific treatment combination and eventually help towards preventing treatment resistance and cancer recurrence in ovarian cancer patients.
I also plan to publish the results of this project as a scientific article after the final validation experiments that I am currently performing. These results will help us to better understand the cellular mechanisms of resistance to anti-cancer therapy in ovarian cancer patients and to design new treatment combinations that improve the survival of women with ovarian cancer. In parallel, I will submit the sequencing data produced in this project to specific data repositories, so that other researchers can use it to generate new scientific hypotheses and validate their own results.
If some of the results from this project were validated by other researchers, they could have the potential to identify which patients will benefit from a specific treatment combination and eventually help towards preventing treatment resistance and cancer recurrence in ovarian cancer patients.