The problem we are addressing is the so-called colorectal cancer paradox: colorectal cancer is under surveillance of the immune system which is supported by a number of preclinical and clinical findings, and yet it is not responding to cancer immunotherapy with checkpoint blockers. A mentionable exception are colorectal cancers with high mutational rate (about 15%). Hence, the main issue is how can we provide a rationale for treating patients with a combination of immunotherapy and conventional drugs like chemotherapeutics or targeted drugs. Or in other words, can conventional drugs be used to sensitize the tumors for immunotherapy. However, addressing this problem is challenging due to several reasons. First, mutational profiles are patient-specific as there is almost no overlap between individual patients. Hence, treatment strategies need to be personalized. Second, the precise molecular mechanisms how anticancer agents mediate immunomodulatory effects are not known. Given the limited amount of material that can be obtained from the patients, comprehensive molecular analysis necessary to identify the mechanisms is not possible. And third, cell lines and mouse models have major limitations and cannot be applied to address this problem.
We therefore developed a strategy based on: 1) patient-derived tumor organoids, i.e. mini-organs with the same genetic fingerprint as the patient's tumor which can be grown in culture in large numbers and thereby enable numerous analyses; 2) cutting-edge molecular analysis of the organoids treated with different drugs including determination of the mutations, analysis of the expressed genes, and measurements of the phosphorylated peptides; and 3) novel computational tools for the analyses of heterogeneous data sets.
The strategy we developed can pave the way for informing precision cancer immunotherapy, i.e. cancer therapy based on combination of immunotherapy and conventional drugs tailored to the individual patient. By doing so, we envision that a large number of patients would benefit for a prolonged period of time.
The long-term goal is to establish a mechanistic rationale for immunotherapy-based combination regimens, and develop a precision immuno-oncology platform that integrates a living biobank with high-throughput and high-content data for testing drug combinations, and machine learning for making therapeutic recommendations for individual patients. The specific aims are:
1) to investigate immunostimulatory effects of chemotherapy;
2) to investigate immunostimulatory of targeted drugs;
3) to investigate metabolic reprogramming of T cells to enhance antitumor immunity;
4) to identify mechanisms of acquired resistance to immunotherapy in patients with high mutational rate;
5) to develop a diagnostic model for predicting response to combination therapy.
