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.

Ovarian cancer (OC) remains a significant challenge in oncology due to high rates of drug resistance and disease relapse following standard treatment with surgery and platinum-based chemotherapy. Despite the widespread use of these treatments, no effective biomarkers currently exist to identify which patients will respond favorably to therapy. This study introduces a zebrafish patient-derived xenograft (PDX) system, capable of replicating both the carboplatin response and metastatic behavior observed in OC patients, within a rapid 3-day assay period.

Two OC cell lines: carboplatin-sensitive (A2780) and resistant (OVCAR8) were used to assess differential responses to treatment in murine and zebrafish xenograft models. Tumor tissues from 16 OC patients were implanted into zebrafish embryos to test carboplatin responses and predict metastasis. Additionally, eight clinical OC samples were directly implanted into zebrafish embryos as part of a proof-of-concept demonstration.

The zebrafish xenografts accurately reflected the carboplatin sensitivity and resistance patterns seen in in vitro and murine models. The zebrafish PDX model demonstrated a 67 % success rate for implantation and a 100 % success rate for engraftment. Notably, the model effectively distinguished between metastatic and non-metastatic disease, with an area under the ROC curve (AUC) of 0.818. Furthermore, the zebrafish PDX model showed a high concordance with patient-specific responses to carboplatin.

This zebrafish PDX model offers a fast, accurate, and clinically relevant platform for evaluating carboplatin response and predicting metastasis in OC patients. It holds significant potential for advancing personalized medicine, allowing for more precise therapeutic outcome predictions and individualized treatment strategies.

Cancer patients often undergo rounds of trial-and-error to find the most effective treatment because there is no test in the clinical practice for predicting therapy response. Here, we conduct a clinical study to validate the zebrafish patient-derived xenograft model (zAvatar) as a fast predictive platform for personalized treatment in colorectal cancer. zAvatars are generated with patient tumor cells, treated exactly with the same therapy as their corresponding patient and analyzed at single-cell resolution. By individually comparing the clinical responses of 55 patients with their zAvatar-test, we develop a decision tree model integrating tumor stage, zAvatar-apoptosis, and zAvatar-metastatic potential. This model accurately forecasts patient progression with 91% accuracy. Importantly, patients with a sensitive zAvatar-test exhibit longer progression-free survival compared to those with a resistant test. We propose the zAvatar-test as a rapid approach to guide clinical decisions, optimizing treatment options and improving the survival of cancer patients.

Epithelial ovarian cancer (EOC) is the gynaecological malignancy with highest mortality. Although adjuvant treatment with carboplatin and paclitaxel leads to an objective response in ~80% of these patients, a majority will relapse within two years. Better methods for assessing long-term treatment outcomes are needed. To address this, we established safe and efficacious doses of carboplatin and paclitaxel using IGROV-1 zebrafish-CDX models. Then fluorescently-labelled cell suspensions from 83 tumour biopsies collected at exploratory laparotomy of women with suspected EOC were generated and 37 (45%) were successfully implanted in zebrafish larvae. Among these 19 of 27 pathology-confirmed EOC samples (70%) engrafted. These zebrafish patient-derived tumour xenograft (ZTX) models were treated with carboplatin or paclitaxel and tumour growth/regression and metastatic dissemination were recorded. In a subgroup of nine patients, four ZTX models regressed during carboplatin treatment. All four corresponding patients had >24 months PFS. Furthermore, both ZTX models established from two patients having <24 months PFS failed to regress during carboplatin treatment. Seven of eight models seeding <6 metastatic cells were established from patients having >24 months PFS. In eleven of fourteen patients, FIGO stage I + II or III tumours gave rise to ZTX models seeding <4 or >4 metastatic cells, respectively. In conclusion, ZTX models predicted patients having >24 or <24 months PFS, based on response/no response to carboplatin. Furthermore, high metastatic dissemination in ZTX models correlated to shorter PFS and more advanced disease at diagnosis. These preliminary results suggest that ZTX models could become a useful prognostic tool in EOC treatment planning.

The development of new anticancer therapies tends to be very slow. Although their impact on potential candidates is confirmed in preclinical studies, ∼95 % of these new therapies are not approved when tested in clinical trials. One of the main reasons for this is the lack of accurate preclinical models. In this context, there are different patient-derived models, which have emerged as a powerful oncological tool: patient-derived xenografts (PDXs), patient-derived organoids (PDOs), and patient-derived cells (PDCs). Although all these models are widely applied, PDXs, which are created by engraftment of patient tumor tissues into mice, is considered more reliable. In fundamental research, the PDX model is used to evaluate drug-sensitive markers and, in clinical practice, to select a personalized therapeutic strategy. Melatonin is of particular importance in the development of innovative cancer treatments due to its oncostatic impact and lack of adverse effects. However, the literature regarding the oncostatic effect of melatonin in patient-derived tumor models is scant. This review aims to describe the important role of patient-derived models in the development of anticancer treatments, focusing, in particular, on PDX models, as well as their use in cancer research. This review also summarizes the existing literature on the anti-tumoral effect of melatonin in patient-derived models in order to propose future anti-neoplastic clinical applications.

Gastric cancer (GC) is one of the most common malignant tumors with poor prognosis in terms of advanced stage. However, the survival-associated biomarkers for GC remains unclear.

To investigate the potential biomarkers of the prognosis of patients with GC, so as to provide new methods and strategies for the treatment of GC.

RNA sequencing data from The Cancer Genome Atlas (TCGA) database of STAD tumors, and microarray data from Gene Expression Omnibus (GEO) database (GSE19826, GSE79973 and GSE29998) were obtained. The differentially expressed genes (DEGs) between GC patients and health people were picked out using R software (x64 4.1.3). The intersections were underwent between the above obtained co-expression of differential genes (co-DEGs) and the DEGs of GC from Gene Expression Profiling Interactive Analysis database, and Gene Ontology (GO) analysis, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), Protein-protein Interaction (PPI) analysis and Kaplan-Meier Plotter survival analysis were performed on these DEGs. Using Immunohistochemistry (IHC) database of Human Protein Atlas (HPA), we verified the candidate Hub genes.

With DEGs analysis, there were 334 co-DEGs, including 133 up-regulated genes and 201 down-regulated genes. GO enrichment analysis showed that the co-DEGs were involved in biological process, cell composition and molecular function pathways. KEGG enrichment analysis suggested the co-DEGs pathways were mainly enriched in ECM-receptor interaction, protein digestion and absorption pathways, etc. GSEA pathway analysis showed that co-DEGs mainly concentrated in cell cycle progression, mitotic cell cycle and cell cycle pathways, etc. PPI analysis showed 84 nodes and 654 edges for the co-DEGs. The survival analysis illustrated 11 Hub genes with notable significance for prognosis of patients were screened. Furtherly, using IHC database of HPA, we confirmed the above candidate Hub genes, and 10 Hub genes that associated with prognosis of GC were identified, namely BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN.

The 10 Hub genes may be the potential biomarkers for predicting the prognosis of GC, which can provide new strategies and methods for the diagnosis and treatment of GC.

Bacillus Calmette-Guérin (BCG) immunotherapy is the standard-of-care adjuvant therapy for non-muscle-invasive bladder cancer in patients at considerable risk of disease recurrence. Although its exact mechanism of action is unknown, BCG significantly reduces this risk in responding patients but is mainly associated with toxic side-effects in those facing treatment resistance. Methods that allow the identification of BCG responders are, therefore, urgently needed.

Fluorescently labelled UM-UC-3 cells and dissociated patient tumor samples were used to establish zebrafish tumor xenograft (ZTX) models. Changes in the relative primary tumor size and cell dissemination to the tail were evaluated via fluorescence microscopy at three days post-implantation. The data were compared to the treatment outcomes of the corresponding patients. Toxicity was evaluated based on gross morphological evaluation of the treated zebrafish larvae.

BCG-induced toxicity was avoided by removing the water-soluble fraction of the BCG formulation prior to use. BCG treatment via co-injection with the tumor cells resulted in significant and dose-dependent primary tumor size regression. Heat-inactivation of BCG decreased this effect, while intravenous BCG injections were ineffective. ZTX models were successfully established for six of six patients based on TUR-B biopsies. In two of these models, significant tumor regression was observed, which, in both cases, corresponded to the treatment response in the patients.

The observed BCG-related anti-tumor effect indicates that ZTX models might predict the BCG response and thereby improve treatment planning. More experiments and clinical studies are needed, however, to elucidate the BCG mechanism and estimate the predictive value.

Previous studies have shown that PRDX5 and Nrf2 are antioxidant proteins related to abnormal reactive oxidative species (ROS). PRDX5 and Nrf2 play a critical role in the progression of inflammations and tumors. The combination of PRDX5 and Nrf2 was examined by Co-immunoprecipitation, western blotting and Immunohistochemistry. H2O2 was applied to affect the production of ROS and induced multi-resistant protein 1 (MRP1) expression in NSCLC cells. The zebrafish models mainly investigated the synergistic effects of PRDX5 and Nrf2 on lung cancer drug resistance under oxidative stress. We showed that PRDX5 and Nrf2 form a complex and significantly increase the NSCLC tissues compared to adjacent tissues. The oxidative stress improved the combination of PRDX5 and Nrf2. We demonstrated that the synergy between PRDX5 and Nrf2 is positively related to the proliferation and drug resistance of NSCLC cells in the zebrafish models. In conclusion, our data indicated that PRDX5 could bind to Nrf2 and has a synergistic effect with Nrf2. Meanwhile, in the zebrafish models, PRDX5 and Nrf2 have significant regulatory impacts on lung cancer progression and drug resistance activities under oxidative stress.

Despite advances in diagnosis and treatment, gastric cancer remains the third most common cause of cancer-related death in humans. The establishment of relevant animal models of gastric cancer is critical for further research. Due to the complexity of the tumor microenvironment and the genetic heterogeneity of gastric cancer, the commonly used preclinical animal models fail to adequately represent clinically relevant models of gastric cancer. However, patient-derived models are able to replicate as much of the original inter-tumoral and intra-tumoral heterogeneity of gastric cancer as possible, reflecting the cellular interactions of the tumor microenvironment. In addition to implanting patient tissues or primary cells into immunodeficient mouse hosts for culture, the advent of alternative hosts such as humanized mouse hosts, zebrafish hosts, and in vitro culture modalities has also facilitated the advancement of gastric cancer research. This review highlights the current status, characteristics, interfering factors, and applications of patient-derived models that have emerged as more valuable preclinical tools for studying the progression and metastasis of gastric cancer.