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Cancer is a heterogeneous disease. Each individual tumor is unique and characterized by structural, cellular, genetic and molecular features. Therefore, patient-derived cancer models are indispensable tools in cancer research and have been actively introduced into the healthcare system. For instance, patient-derived models provide a good reproducibility of susceptibility and resistance of cancer cells against drugs, allowing personalized therapy for patients. In this article, we review the advantages and disadvantages of the following patient-derived models of cancer: (1) PDC—patient-derived cell culture, (2) PDS—patient-derived spheroids and PDO—patient-derived organoids, (3) PDTSC—patient-derived tissue slice cultures, (4) PDX—patient-derived xenografts, humanized PDX, as well as PDXC—PDX-derived cell cultures and PDXO—PDX-derived organoids. We also provide an overview of current clinical investigations and new developments in the area of patient-derived cancer models. Moreover, attention is paid to databases of patient-derived cancer models, which are collected in specialized repositories. We believe that the widespread use of patient-derived cancer models will improve our knowledge in cancer cell biology and contribute to the development of more effective personalized cancer treatment strategies.

Cancer is as unique as the person fighting it. With the exception of a few biomarker-driven therapies, patients go through rounds of trial-and-error approaches to find the best treatment. Using patient-derived cell lines, we show that zebrafish larvae xenotransplants constitute a fast and highly sensitive in vivo model for differential therapy response, with resolution to reveal intratumor functional cancer heterogeneity. We screened international colorectal cancer therapeutic guidelines and determined distinct functional tumor behaviors (proliferation, metastasis, and angiogenesis) and differential sensitivities to standard therapy. We observed a general higher sensitivity to FOLFIRI [5-fluorouracil(FU)+irinotecan+folinic acid] than to FOLFOX (5-FU+oxaliplatin+folinic acid), not only between isogenic tumors but also within the same tumor. We directly compared zebrafish xenografts with mouse xenografts and show that relative sensitivities obtained in zebrafish are maintained in the rodent model. Our data also illustrate how KRAS mutations can provide proliferation advantages in relation to KRASWT and how chemotherapy can unbalance this advantage, selecting for a minor clone resistant to chemotherapy. Zebrafish xenografts provide remarkable resolution to measure Cetuximab sensitivity. Finally, we demonstrate the feasibility of using primary patient samples to generate zebrafish patient-derived xenografts (zPDX) and provide proof-of-concept experiments that compare response to chemotherapy and biological therapies between patients and zPDX. Altogether, our results suggest that zebrafish larvae xenografts constitute a promising fast assay for precision medicine, bridging the gap between genotype and phenotype in an in vivo setting.

Neuroblastoma (NB) is the most common extracranial solid malignancy in children. Despite current aggressive treatment regimens, the prognosis for high‐risk NB patients remains poor, with the survival of less than 40%. Amplification/stabilization of MYCN oncogene, in NB is associated with a high risk of recurrence. Thus, there is an urgent need for novel therapeutics. The deregulated expression of microRNA (miR) is reported in NB; nonetheless, its effect on MYCN regulation is poorly understood. First, we identified that miR‐15a‐5p, miR‐15b‐5p, and miR‐16‐5p (hereafter miR‐15a, miR‐15b or miR‐16) were down‐regulated in patient‐derived xenografts (PDX) with high MYCN expression. MiR targeting sequences on MYCN mRNA were predicted using online databases such as TargetScan and miR database. The R2 database, containing 105 NB patients, showed an inverse correlation between MYCN mRNA and deleted in lymphocytic leukemia (DLEU) 2, a host gene of miR‐15. Moreover, overexpression of miR‐15a, miR‐15b or miR‐16 significantly reduced the levels of MYCN mRNA and N‐Myc protein. Conversely, inhibiting miR dramatically enhanced MYCN mRNA and N‐Myc protein levels, as well as increasing mRNA half‐life in NB cells. By performing immunoprecipitation assays of argonaute‐2 (Ago2), a core component of the RNA‐induced silencing complex, we showed that miR‐15a, miR‐15b and miR‐16 interact with MYCN mRNA. Luciferase reporter assays showed that miR‐15a, miR‐15b and miR‐16 bind with 3’UTR of MYCN mRNA, resulting in MYCN suppression. Moreover, induced expression of miR‐15a, miR‐15b and miR‐16 significantly reduced the proliferation, migration, and invasion of NB cells. Finally, transplanting miR‐15a‐, miR‐15b‐ and miR‐16‐expressing NB cells into NSG mice repressed tumor formation and MYCN expression. These data suggest that miR‐15a, miR‐15b and miR‐16 exert a tumor‐suppressive function in NB by targeting MYCN. Therefore, these miRs could be considered as potential targets for NB treatment. A model summarizing how miR‐15a, miR‐15b, and miR‐16 suppress tumor progression in neuroblastoma by targeting MYCN. When miRs are overexpressed, argonaute‐2‐mediated interaction of miR with MYCN mRNA increases, followed by degradation of MYCN, leading to neuroblastoma regression.

Immunotherapy has revolutionized cancer treatment, however, not all tumor types and patients are completely responsive to this approach. Establishing predictive pre‐clinical models would allow for more accurate and practical immunotherapeutic drug development. Mouse models are extensively used as in vivo system for biomedical research. However, due to the significant differences between rodents and human, it is impossible to translate most of the findings from mouse models to human. Pharmacological development and advancing personalized medicine using patient‐derived xenografts relies on producing mouse models in which murine cells and genes are substituted with their human equivalent. Humanized mice (HM) provide a suitable platform to evaluate xenograft growth in the context of a human immune system. In this review, we discussed recent advances in the generation and application of HM models. We also reviewed new insights into the basic mechanisms, pre‐clinical evaluation of onco‐immunotherapies, current limitations in the application of these models as well as available improvement strategies. Finally, we pointed out some issues for future studies. Establishing predictive pre‐clinical models leads toward more accurate and practical immunotherapeutic development. Humanized mice (HM) provide a suitable platform to discern human‐specific disease pathogenesis and evaluate an array of novel therapeutics. This review discusses recent progresses in the production and deployment of HM in the study of cancer immunotherapy.

Patient-derived tumor xenografts (PDXs), in which tumor fragments surgically dissected from cancer patients are directly transplanted into immunodeficient mice, have emerged as a useful model for translational research aimed at facilitating precision medicine. PDX susceptibility to anti-cancer drugs is closely correlated with clinical data in patients, from whom PDX models have been derived. Accumulating evidence suggests that PDX models are highly effective in predicting the efficacy of both conventional and novel anti-cancer therapeutics. This also allows “co-clinical trials,” in which pre-clinical investigations in vivo and clinical trials could be performed in parallel or sequentially to assess drug efficacy in patients and PDXs. However, tumor heterogeneity present in PDX models and in the original tumor samples constitutes an obstacle for application of PDX models. Moreover, human stromal cells originally present in tumors dissected from patients are gradually replaced by host stromal cells as the xenograft grows. This replacement by murine stroma could preclude analysis of human tumor-stroma interactions, as some mouse stromal cytokines might not affect human carcinoma cells in PDX models. The present review highlights the biological and clinical significance of PDX models and three-dimensional patient-derived tumor organoid cultures of several kinds of solid tumors, such as those of the colon, pancreas, brain, breast, lung, skin, and ovary.

Gold nanorods (AuNRs)-assisted plasmonic photothermal therapy (AuNRs-PPTT) is a promising strategy for combating cancer in which AuNRs absorb near-infrared light and convert it into heat, causing cell death mainly by apoptosis and/or necrosis. Developing a valid PPTT that induces cancer cell apoptosis and avoids necrosis in vivo and exploring its molecular mechanism of action is of great importance. Furthermore, assessment of the long-term fate of the AuNRs after treatment is critical for clinical use. We first optimized the size, surface modification [rifampicin (RF) conjugation], and concentration (2.5 nM) of AuNRs and the PPTT laser power (2 W/cm²) to achieve maximal induction of apoptosis. Second, we studied the potential mechanism of action of AuNRs-PPTT using quantitative proteomic analysis in mouse tumor tissues. Several death pathways were identified, mainly involving apoptosis and cell death by releasing neutrophil extracellular traps (NETs) (NETosis), which were more obvious upon PPTT using RF-conjugated AuNRs (AuNRs@RF) than with polyethylene glycol thiol-conjugated AuNRs. Cytochrome c and p53-related apoptosis mechanisms were identified as contributing to the enhanced effect of PPTT with AuNRs@RF. Furthermore, Pin1 and IL18-related signaling contributed to the observed perturbation of the NETosis pathway by PPTT with AuNRs@RF. Third, we report a 15-month toxicity study that showed no long-term toxicity of AuNRs in vivo. Together, these data demonstrate that our AuNRs-PPTT platform is effective and safe for cancer therapy in mouse models. These findings provide a strong framework for the translation of PPTT to the clinic.

Biomarkers which better match anticancer drugs with cancer driver genes hold the promise of improved clinical responses and cure rates. We developed a precision medicine platform of rapid high‐throughput drug screening (HTS) and patient‐derived xenografting (PDX) of primary tumor tissue, and evaluated its potential for treatment identification among 56 consecutively enrolled high‐risk pediatric cancer patients, compared with conventional molecular genomics and transcriptomics. Drug hits were seen in the majority of HTS and PDX screens, which identified therapeutic options for 10 patients for whom no targetable molecular lesions could be found. Screens also provided orthogonal proof of drug efficacy suggested by molecular analyses and negative results for some molecular findings. We identified treatment options across the whole testing platform for 70% of patients. Only molecular therapeutic recommendations were provided to treating oncologists and led to a change in therapy in 53% of patients, of whom 29% had clinical benefit. These data indicate that in vitro and in vivo drug screening of tumor cells could increase therapeutic options and improve clinical outcomes for high‐risk pediatric cancer patients. Synopsis A precision diagnostic platform integrating genomics and transcriptomics with drug testing of patient's primary tumor cells in high throughput drug screening (HTS) and patient‐derived xenograft (PDX) was established to improve identification of therapies in high‐risk pediatric cancer patients. Treatment options could be identified for 70% of patients across the four‐part platform. HTS provided orthogonal proof of drug efficacy suggested by molecular analyses and identified many new drug responses without prior molecular hallmarks. Effective treatments were observed in more than half of PDX models. There was a strong correlation between HTS and PDX results, and the clinical responses in patients. Graphical Abstract A precision diagnostic platform integrating genomics and transcriptomics with drug testing of patient's primary tumor cells in high throughput drug screening (HTS) and patient‐derived xenograft (PDX) was established to improve identification of therapies in high‐risk pediatric cancer patients.

Stimuli‐responsive nanomedicines have become a recent research focus as a candidate for cancer treatment because of their effectiveness, sensibility, and minimal invasiveness. In this work, a novel nanosystem is developed based on Cu2−xS@MnS core–shell nanoparticles (CSNPs) in which the Cu2−xS core serves as a photosensitizer to generate hyperthermia and reactive oxygen species (ROS), and the MnS shell is used in H2O2‐responsive O2 production. Cu2−xS@MnS CSNPs with an independent core and shell ratio are synthesized by a controllable hot‐injection method, resulting in an optimal photothermal (PT) effect with a PT conversion efficiency of up to 47.9%. An enhanced photodynamic (PD) effect also occurs in an H2O2 environment. More significantly, in vivo experiments demonstrate that Cu2−xS@MnS CSNPs can mediate tumor shrinkage in both HeLa tumor cell line‐derived xenograft (CDX) and head and neck squamous cell carcinoma (HNSCC) patient‐derived xenograft (PDX) models, with the capability of being used as a T1‐enhanced magnetic resonance (MR) contrast agent. These results suggest the great potential of as‐prepared Cu2−xS@MnS CSNPs as photo/H2O2‐responsive therapeutic‐agents against tumors, even in a complicated and heterogeneous environment, thus promoting the clinical translation of nanomedicine. Cu2−xS@MnS core–shell nanoparticles are developed for cancer theranostics. The Cu2−xS core serves as a photosensitizer to generate hyperthermia and reactive oxygen species, and the MnS shell is used in H2O2‐responsive O2 production, therefore mediating tumor shrinkage in both HeLa tumor cell line‐derived xenograft and head and neck squamous cell carcinoma patient‐derived xenograft models, with the capability of being used as a T1‐enhanced magnetic resonance contrast agent.

Background Mouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. However, currently available algorithms are limited and improvements in accuracy and ease of use are necessary. Results We developed the R-package XenofilteR, which separates mouse from human sequence reads based on the edit-distance between a sequence read and reference genome. To assess the accuracy of XenofilteR, we generated sequence data by in silico mixing of mouse and human DNA sequence data. These analyses revealed that XenofilteR removes > 99.9% of sequence reads of mouse origin while retaining human sequences. This allowed for mutation analysis of xenograft samples with accurate variant allele frequencies, and retrieved all non-synonymous somatic tumor mutations. Conclusions XenofilteR accurately dissects RNA and DNA sequences from mouse and human origin, thereby outperforming currently available tools. XenofilteR is open source and available at https://github.com/PeeperLab/XenofilteR .

Background In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. Methods In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. Results Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. Conclusion We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.