A living biobank of matched pairs of patient-derived xenografts and organoids for cancer pharmacology

Biological Scaffolds in 3D Cell Models: Driving Innovation in Drug Discovery

The discipline of 3D cell modeling is currently undergoing a surge of captivating developments that are enhancing the realism and utility of tissue simulations. Using bioinks which represent cells, scaffolds, and growth factors scientists can construct intricate tissue architectures layer by layer using innovations like 3D bioprinting. Drug testing can be accelerated and organ functions more precisely replicated owing to the precise control that microfluidic technologies and organ-on-chip devices offer over the cellular environment. Tissue engineering is becoming more dynamic with materials that can modify their surroundings with the advent of hydrogels and smart biomaterials. Advances in spheroids and organoids are not only bringing us towards more effective and customized therapies, but they are also improving their ability to resemble actual human tissues. Confocal and two-photon microscopy are examples of advanced imaging methods that provide precise images of the functioning and interaction of cells. Artificial Intelligence models have applications for enhanced scaffold designs and for predicting the response of tissues to medications. Furthermore, via strengthening predictive models, optimizing data analysis, and simplifying 3D cell culture design, artificial intelligence is revolutionizing this field. When combined, these technologies are improving our ability to conduct research and moving us toward more individualized and effective medical interventions.

The use of patient-derived xenografts and patient-derived organoids in the search for new therapeutic regimens for pancreatic carcinoma. A review

Patient-derived organoids (PDOs) and xenografts (PDXs) are powerful tools for personalized medicine in pancreatic cancer (PC) research. This study explores the complementary strengths of PDOs and PDXs in terms of practicality, genetic fidelity, cost, and labor considerations. Among other models like 2D cell cultures, spheroids, cancer-on-chip systems, cell line-derived xenografts (CDX), and genetically engineered mouse models (GEMMs), PDOs and PDXs uniquely balance genetic fidelity and personalized medicine potential, offering distinct advantages over the simplicity of 2D cultures and the advanced, but often resource-intensive, GEMMs and cancer-on-chip systems. PDOs excel in high-throughput drug screening due to their ease of use, lower cost, and shorter experimental timelines. However, they lack a complete tumor microenvironment. Conversely, PDXs offer a more complex microenvironment that closely reflects patient tumors, potentially leading to more clinically relevant results. Despite limitations in size, number of specimens, and engraftment success, PDXs demonstrate significant concordance with patient responses to treatment, highlighting their value in personalized medicine. Both models exhibit significant genetic fidelity, making them suitable for drug sensitivity testing. The choice between PDOs and PDXs depends on the research focus, resource availability, and desired level of microenvironment complexity. PDOs are advantageous for high-throughput screening of a diverse array of potential therapeutic agents due to their relative ease of culture and scalability. PDXs, on the other hand, offer a more physiologically relevant model, allowing for a comprehensive evaluation of drug efficacy and mechanisms of action.

PDX models for functional precision oncology and discovery science

Precision oncology relies on detailed molecular analysis of how diverse tumours respond to various therapies, with the aim to optimize treatment outcomes for individual patients. Patient-derived xenograft (PDX) models have been key to preclinical validation of precision oncology approaches, enabling the analysis of each tumour's unique genomic landscape and testing therapies that are predicted to be effective based on specific mutations, gene expression patterns or signalling abnormalities. To extend these standard precision oncology approaches, the field has strived to complement the otherwise static and often descriptive measurements with functional assays, termed functional precision oncology (FPO). By utilizing diverse PDX and PDX-derived models, FPO has gained traction as an effective preclinical and clinical tool to more precisely recapitulate patient biology using in vivo and ex vivo functional assays. Here, we explore advances and limitations of PDX and PDX-derived models for precision oncology and FPO. We also examine the future of PDX models for precision oncology in the age of artificial intelligence. Integrating these two disciplines could be the key to fast, accurate and cost-effective treatment prediction, revolutionizing oncology and providing patients with cancer with the most effective, personalized treatments.

The SRC-family serves as a therapeutic target in triple negative breast cancer with acquired resistance to chemotherapy

BACKGROUND

Resistance to chemotherapy, combined with heterogeneity among resistant tumors, represents a significant challenge in the clinical management of triple negative breast cancer (TNBC). By dissecting molecular pathways associated with treatment resistance, we sought to define patient sub-groups and actionable targets for next-line treatment.

METHODS

Bulk RNA sequencing and reverse phase protein array profiling were performed on isogenic patient-derived xenografts (PDX) representing paclitaxel-sensitive and -resistant tumors. Pathways identified as upregulated in the resistant model were further explored as targets in PDX explants. Their clinical relevance was assessed in two distinct patient cohorts (NeoAva and MET500).

RESULTS

Increased activity in signaling pathways involving SRC-family kinases (SFKs)- and MAPK/ERK was found in treatment resistant PDX, with targeted inhibitors being significantly more potent in resistant tumors. Up-regulation of SFKs- and MAPK/ERK-pathways was also detected in a sub-group of chemoresistant patients after neoadjuvant treatment. Furthermore, High SFK expression (of either SRC, FYN and/or YES1) was detected in metastatic lesions of TNBC patients with fast progressing disease (median disease-free interval 27 vs 105 months).

CONCLUSIONS

Upregulation of SFK-signaling is found in a subset of chemoresistant tumors and is persistent in metastatic lesions. Based on pre-clinical results, these patients may respond favorably to treatment targeting SFKs.

Initiation of Cancer: The Journey From Mutations in Somatic Cells to Epigenetic Changes in Tissue-resident VSELs

Multiple theories exist to explain cancer initiation, although a consensus on this is crucial for developing effective therapies. 'Somatic mutation theory' suggests that mutations in somatic cells during DNA repair initiates cancer but this concept has several attached paradoxes. Research efforts to identify quiescent cancer stem cells (CSCs) that survive therapy and result in metastasis and recurrence have remained futile. In solid cancers, CSCs are suggested to appear during epithelial-mesenchymal transition by the dedifferentiation and reprogramming of epithelial cells. Pluripotent and quiescent very small embryonic-like stem cells (VSELs) exist in multiple tissues but remain elusive owing to their small size and scarce nature. VSELs are developmentally connected to primordial germ cells, undergo rare, asymmetrical cell divisions and are responsible for the regular turnover of cells to maintain tissue homeostasis throughout life. VSELs are directly vulnerable to extrinsic endocrine insults because they express gonadal and gonadotropin hormone receptors. VSELs undergo epigenetic changes due to endocrine insults and transform into CSCs. CSCs exhibit genomic instability and develop mutations due to errors during DNA replication while undergoing excessive proliferation and clonal expansion to form spheroids. Thus tissue-resident VSELs offer a connection between extrinsic insults and variations in cancer incidence reported in various body tissues. To conclude, cancer is indeed a stem cell disease with mutations occurring as a consequence. In addition to immunotherapy, targeting mutations, and Lgr5 + organoids for developing new therapeutics, targeting CSCs (epigenetically altered VSELs) by improving their niche and epigenetic status could serve as a promising strategy to treat cancer.

Humanized Mouse Models for Immuno-oncology Drug Discovery

Breakthroughs in cancer treatment with immunotherapeutics have provided long-term patient benefits for many different types of cancer. However, complete response is not achieved in many patients and tumor types, and the mechanisms underlying this lack of response are poorly understood. Despite this, numerous new targets, therapeutics, and drug combinations are being developed and tested in clinical trials. Preclinical models that recapitulate the complex human tumor microenvironment and the interplay between tumor and immune cells within the cancer-immunity cycle are needed to improve our understanding and screen new therapeutics for efficacy and safety/toxicity. Humanized mice, encompassing human tumors and human immune cells engrafted on immunodeficient mice, have been widely used for many years in immuno-oncology, with developments to improve both the humanization and the translational value central to the next generation of models. In this overview, we discuss recent advances in humanized models relevant to immuno-oncology drug discovery, the advantages and limitations of such models, the application of humanized models for efficacy and safety assessments of immunotherapeutics, and the potential opportunities. © 2023 Crown Bioscience. Current Protocols published by Wiley Periodicals LLC.

Viability Analysis and High-Content Live-Cell Imaging for Drug Testing in Prostate Cancer Xenograft-Derived Organoids

Tumor organoids have been pushed forward as advanced model systems for in vitro oncology drug testing, with the eventual goal to direct personalized cancer treatments. However, drug testing efforts suffer from a large variation in experimental conditions for organoid culturing and organoid treatment. Moreover, most drug tests are restricted to whole-well viability as the sole read-out, thereby losing important information about key biological aspects that might be impacted due to the use of administered drugs. These bulk read-outs also discard potential inter-organoid heterogeneity in drug responses. To tackle these issues, we developed a systematic approach for processing organoids from prostate cancer (PCa) patient-derived xenografts (PDXs) for viability-based drug testing and identified essential conditions and quality checks for consistent results. In addition, we generated an imaging-based drug testing procedure using high-content fluorescence microscopy in living PCa organoids to detect various modalities of cell death. Individual organoids and cell nuclei in organoids were segmented and quantified using a dye combination of Hoechst 33342, propidium iodide and Caspase 3/7 Green, allowing the identification of cytostatic and cytotoxic treatment effects. Our procedures provide important insights into the mechanistic actions of tested drugs. Moreover, these methods can be adapted for tumor organoids originating from other cancer types to increase organoid-based drug test validity, and ultimately, accelerate clinical implementation.

Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment

In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.