Generation, Expansion, and Biobanking of Gastrointestinal Patient-Derived Organoids from Tumor and Normal Tissues.

Patient-derived organoids (PDOs) generated from adult stem cells present in tissues are invaluable tools for translational cancer research (Drost, Clevers, Nat Rev Cancer 18(7):407-418, 2018). The generation of this 3D cultures is not trivial and requires dedicated procedures. Despite the rapid increase in the use of organoids in cancer research, it is noteworthy that published procedures regarding their generation often lack critical information and standardized protocols remain elusive. Addressing these limitations, the protocol described in this chapter offers an in-depth and comprehensive guide to establishing, expanding, and freezing gastrointestinal PDOs obtained from normal and tumor tissue biopsies. Notably, it also provides valuable insights in the form of tips and tricks to guide and overcome potential challenges that may arise during the procedure.

How organoids can improve personalized treatment in patients with gastro-esophageal tumors.

Gastro-esophageal tumors constitute a big health problem. Treatment options still mainly rely on chemotherapy, and apart from human epidermal growth factor receptor 2 positive and microsatellite instable/Epstein-Barr Virus disease, there are no molecularly guided options. Therefore, despite the large number of identified molecular alterations, precision medicine is still far from the clinic. In this context, the recently developed technology of patient-derived organoids (PDOs) could offer the chance to accelerate drug development and biomarker discovery. Indeed, PDOs are 3D primary cultures that were shown to reproduce patient's tumor characteristics. Moreover, several reports indicated that PDOs can replicate patient's response to a given drug; therefore, they are one of the most promising tools for functional precision medicine.

"Proteotranscriptomic analysis of advanced colorectal cancer patient derived organoids for drug sensitivity prediction".

BACKGROUND: Patient-derived organoids (PDOs) from advanced colorectal cancer (CRC) patients could be a key platform to predict drug response and discover new biomarkers. We aimed to integrate PDO drug response with multi-omics characterization beyond genomics. METHODS: We generated 29 PDO lines from 22 advanced CRC patients and provided a morphologic, genomic, and transcriptomic characterization. We performed drug sensitivity assays with a panel of both standard and non-standard agents in five long-term cultures, and integrated drug response with a baseline proteomic and transcriptomic characterization by SWATH-MS and RNA-seq analysis, respectively. RESULTS: PDOs were successfully generated from heavily pre-treated patients, including a paired model of advanced MSI high CRC deriving from pre- and post-chemotherapy liver metastasis. Our PDOs faithfully reproduced genomic and phenotypic features of original tissue. Drug panel testing identified differential response among PDOs, particularly to oxaliplatin and palbociclib. Proteotranscriptomic analyses revealed that oxaliplatin non-responder PDOs present enrichment of the t-RNA aminoacylation process and showed a shift towards oxidative phosphorylation pathway dependence, while an exceptional response to palbociclib was detected in a PDO with activation of MYC and enrichment of chaperonin T-complex protein Ring Complex (TRiC), involved in proteome integrity. Proteotranscriptomic data fusion confirmed these results within a highly integrated network of functional processes involved in differential response to drugs. CONCLUSIONS: Our strategy of integrating PDOs drug sensitivity with SWATH-mass spectrometry and RNA-seq allowed us to identify different baseline proteins and gene expression profiles with the potential to predict treatment response/resistance and to help in the development of effective and personalized cancer therapeutics.

Will Organoids Fill the Gap towards Functional Precision Medicine?

Precision medicine approaches for solid tumors are mainly based on genomics. Its employment in clinical trials has led to somewhat underwhelming results, except for single responses. Moreover, several factors can influence the response, such as gene and protein expression, the coexistence of different genomic alterations or post-transcriptional/translational modifications, the impact of tumor microenvironment, etc., therefore making it insufficient to employ a genomics-only approach to predict response. Recently, the implementation of patient-derived organoids has shed light on the possibility to use them to predict patient response to drug treatment. This could offer for the first time the possibility to move precision medicine to a functional environment.

Integrative immune transcriptomic classification improves patient selection for precision immunotherapy in advanced gastro-oesophageal adenocarcinoma.

BACKGROUND: Advanced gastro-oesophageal cancer (GEA) treatment has been improved by the introduction of immune checkpoint inhibitors (CPIs), yet identifying predictive biomarkers remains a priority, particularly in patients with a combined positive score (CPS) < 5, where the benefit is less clear. Our study assesses certain immune microenvironment features related to sensitivity or resistance to CPIs with the aim of implementing a personalised approach across CPS < 5 GEA. DESIGN: Through integrative transcriptomic and clinicopathological analyses, we studied in both a retrospective and a prospective cohort, the immune tumour microenvironment features. We analysed the cell types composing the immune infiltrate highlighting their functional activity. RESULTS: This integrative study allowed the identification of four different groups across our patients. Among them, we identified a cluster whose tumours expressed the most gene signatures related to immunomodulatory pathways and immunotherapy response. These tumours presented an enriched immune infiltrate showing high immune function activity that could potentially achieve the best benefit from CPIs. Finally, our findings were proven in an external CPI-exposed population, where the use of our transcriptomic results combined with CPS helped better identify those patients who could benefit from immunotherapy than using CPS alone (p = 0.043). CONCLUSIONS: This transcriptomic classification could improve precision immunotherapy for GEA.