Abstract
Abstract Colorectal cancer (CRC), a cancer with 1.4 million new cases diagnosed annually worldwide, is refractory to immunotherapy (with the exception of a minority of tumors with microsatellite instability). This is somehow paradoxical as CRC is a cancer which we have shown is under immunologic control and that tumor-infiltrating lymphocytes represent a strong independent predictor of survival (1). Based on our previous work showing that the immunophenotypes are determined by the genotypes (2), we hypothesized that mutations are rewiring signaling pathways and thereby modulate the recognition of tumor cells by T cells. In order to investigate rewiring of signaling networks and their interference with immunity for individual patients, we developed an experimental-computational concept using perturbation experiments with patient-derived tumor organoids. A biobank of CRC organoids was generated from histologically verified tumor samples, normal tissue, and liver metastases obtained from CRC patients (n=22). Comprehensive characterization of the organoids (exome sequencing, RNA sequencing, and proteomics) and of the tumors (multiplexed immunofluorescence for 6 immune cell types) was carried out and the resulting data were used to prioritize perturbation experiments. Patient-derived organoids were then perturbed with kinase inhibitors (MEKi, PI3Ki, mTORi, TBK1i, IKKi, BRAFi, and TAKi) and large-scale phosphoproteomic profiling using data-independent acquisition (SWATH-MS) was carried out. Deep phosphoproteomic profiling of perturbed organoids enabled reconstruction of patient-specific signaling networks and revealed profound rewiring by targeted drugs and interference with immune-related pathways, suggesting possible pharmacologic modulation by approved targeted agents to induce immunogenic effects. We show for the first time that systematic and comprehensive analysis of the signaling rewiring can provide a mechanistic rationale for immunotherapy-based combination regimens in CRC. This work is an important step towards the development of a precision immuno-oncology platform that integrates tumor organoids with high-throughput and high-content data for making therapeutic recommendations for individual patients.
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