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Enabling Precision Immuno-oncology in Colorectal cancer

Periodic Reporting for period 2 - EPIC (Enabling Precision Immuno-oncology in Colorectal cancer)

Reporting period: 2020-04-01 to 2021-09-30

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.
The bulk of the work performed from the beginning of the project was focusing on first three specific aims:

1) to investigate immunostimulatory effects of chemotherapy. We were able to collect patient samples from a cohort receiving chemotherapy of which one group had relapse and the other did not relapse. The samples are currently analyzed.

2) to investigate immunostimulatory of targeted drugs. We established a living biobank made of patient-derived tumor organoids and carried our deep characterization using selected drugs. This enabled us to generate a unique data set which we are currently exploiting. Most importantly, our conceptual advances and the proof-of-concept including logistics of sample handling, comprehensive molecular characterization, and in-depth computational analyses demonstrate the feasibility of the approach for developing strategies for tailoring combination cancer therapies.

3) to investigate metabolic reprogramming of T cells to enhance antitumor immunity. We used T cells from healthy donors at various differentiation stages and treated the cell with labelled glucose in order to chart the metabolic pathways involved in each stage. These results provides the basis for developing protocols for improving T cell fitness in autologous T cell therapies (CAR T cells, TCR-engineering T cells).

Additionally, we initiated the work on a reference databases and knowledge bank (aim 5) and used publicly available data from the TCGA database (see ). We downloaded and analyzed the molecular data and carried out data cleansing of the annotated clinical data.
We were able for the first time to demonstrate that a platform combining patient-derived tumor organoids, high-throughput and high-content data for testing drug combinations, and sophisticated computational analysis enables reconstruction of signaling networks in individual patients and identification of crosstalk with immune-related pathways.