UCSF

Tumor progression is the major cause of death in cancer patients. Due to their higher chromosomal instability and other genomic alterations, tumors evolve rapidly in response to therapeutic interventions and other external pressures. The immune system is our first line of defense against cancer and its interaction with cancer cells can both constrain and promote tumor growth and metastasis. A heterogeneous tumor consists of subclones with different characteristics. During tumor progression the anti-tumor immune activity removes subclones that express highly immunogenic antigens and leaves behind cancer cells with high immune escape or immunosuppressive properties. This process is called tumor immunoediting. Studying tumor progression requires reliable in vivo models that effectively capture the intricacies and complexities of this process. During the 1970s, Isaiah Fidler demonstrated that repeated passaging of cancer cells in mice can be used to emulate metastatic progression. This in vivo selection model has been used by many different research groups (including us) to model tumor progression in a number of cancer models. Our group has utilized these in vivo selection models to study cell autonomous mechanisms of tumor progression. More recently, however, we have come to realize that by leveraging these in vivo-selection models we can focus on studying non-cell autonomous mechanisms. Building on this notion, here, we propose a generalization of in vivo selection that models the role of the immune system in shaping tumor evolution. Our “immune selection” model takes advantage of a panel of genetic mouse models with various degrees of immunocompetency to serve as hosts for established syngeneic tumor cell lines. We utilized these ‘immuno-selected’ derivatives, in conjunction with cutting-edge tools in genetic engineering and single-cell genomics, to study the tumor-immune co-evolution. We discovered that the interferon response pathway lies at the heart of tumor immune evasion. Additionally, we have uncovered novel molecular pathways responsible for conferring resistance to both antitumor immunity and immunotherapies. Targeting these pathways holds significant therapeutic potential, particularly when used in conjunction with immune checkpoint blockades (ICBs) and other forms of immunotherapy. The second part of this thesis is focused on utilizing machine learning and computational tools to identify important molecular mechanisms in small RNA secretion. We developed ExoGRU, a deep-learning model for predicting secretion probabilities of small RNAs based on their primary sequence. We used ExoGRU to (i) identify mutations that abrogate the secretion of known cell-free small RNAs, and (ii) predict high confidence sets of synthetic sequences that are secreted or retained. We also used independent experimental approaches to validate our model’s prediction abilities. We discovered that the molecular signature needed for small RNA secretion lies in its primary sequence. Furthermore, we identified both previously known and novel RNA binding proteins (RBPs) crucial for facilitating this secretion. In both projects discussed, we demonstrate the effectiveness of in vivo, high-throughput, multi-omics and computational tools in uncovering novel mechanisms, particularly in the evolution of tumor immunity and RNA secretion, areas traditionally challenging to explore with conventional methods.