Targeting multifactorial immune evasion mechanisms in ovarian cancer

Project summary

Cancer immunotherapy is a powerful treatment strategy that can reinvigorate the immune system against the patients’ tumor and lead to remarkable clinical outcomes, even in the face of metastatic late-stage disease. However, in ovarian cancer such therapies have, by and large, remained ineffective, and the specific mechanisms that allow ovarian cancer to escape the immune system are poorly understood. While there have been major advances in studying the biology of cancer cells, our ability to study how cancer cells interact with other cells, including immune cells, is still limited.

Here we will address this challenge with cutting-edge technologies, pairing genetic engineering (CRISPR) with machine learning, single-cell genomics, and advanced imaging. First, we will obtain molecular image-based maps of tumor tissues with far greater resolution and scope than ever before. We will measure thousands of molecules in millions of cells within both the primary and metastatic tumors and identify the unique features that mark immune exclusion and suppression in different patients and tissues. Following up on these findings, we will target tens of thousands of genes (one at a time) and identify those that can make ovarian cancer cells more (or less) sensitive to different types of immune attacks, focusing on Natural Killer (NK) and T cells. We will perform massively parallel experiments that will allow us to go beyond a binary output and track, in a high-throughput manner, how these novel interventions work at the molecular level. Of note, CRISPR is usually used to knockout (inactivate) genes to study their function. Here we will also use CRISPR to increasing/amplifying the expression of genes, targeting not only genes that encode for proteins, but also genes that encode for regulatory RNA molecules and have received less attention. As most drugs work by inactivating proteins, such interventions were, until recently, less clinically relevant. However, with RNA-delivery becoming clinically applicable (e.g., RNA COVID vaccines), we could potentially develop our findings into new forms of immunotherapies that will use RNA-delivery strategies. Lastly, we will repeat similar experiments in animal models and identify interventions that could not only make cancer cells more sensitive to T cells and NK cells’ attacks, but also allow these immune cells to enter the tumor better – this is absolutely critical because these immune cells need to have direct contact with the cancer cells in order to eliminate them; and, as we and others have shown, T cell exclusion confers resistance to immunotherapies.

In summary, this proposal offers a new path to disentangle and target immune evasion mechanisms in ovarian cancer. It will allow us to interrogate the cancer-immune interplay with unprecedented resolution and breadth both in patients and in experimental models, identify immunomodulating interventions at an accelerated pace, and pave the way to a new era of effective immunotherapies in ovarian cancer.