Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies. Although numerous prospective drugs against cancer have been developed and shown promising therapeutic efficacy in vitro, only a few of them have been proven safe and effective in the context of complex in vivo experiments.4 These problems are largely due to the lack of research tools to reveal the genuine status of cancer in real-world patients.In the realm of cancer treatment, the advent of targeted therapies and immunotherapies has greatly enriched the arsenal against cancer and provided patients with better therapeutic outcomes and milder side effects. Nonetheless, many problems have restrained improvement in the prognosis of cancer patients. First, only a small proportion of cancer patients benefit from the drugs. Therefore, robust biomarkers are necessary to accurately select the patients who will respond to or resist these therapies. However, current biomarkers and signatures are insufficient to reflect the genuine cancer status, let alone classify the patients accurately.5 Moreover, intratumor spatial and temporal heterogeneity added complexity to cancer, which refers to the diverse cancer cell phenotypes and different states of cancer cells within a single patient caused by cancer evolution and anti-cancer treatment selection.6 Besides, cancer spatial architecture is shaped by the interaction among different cell components and tissue structures, such as vascular distribution. Grasping these fundamental characteristics and recapitulating such evolutionary characteristics of human cancer is the critical prerequisite for cancer models to accurately reflect the response cancer and tackle the cancer treatment challenges.Oncogenesis is a dynamic process that results from many intertwined factors. During cancer progression, genetic and epigenetic aberrations lead to distinct genomic landscapes among patients, and different treatments further fuel genomic evolution and remodeling.7 Genetically engineered mice (GEM) models and GEM-derived allografts are suitable for studying the role of specific genes in cancer initiation and development. However, such models cannot reflect diverse driver mutations and extensive genomic alterations observed in human cancer. Chemical carcinogen-induced mouse models are time-honored models that serve as a tool to study cancer etiology and cancer biology. However, these models induce unpredictable cancer landscapes that are hard to repeat, let alone the effects of different dosing protocols and animal strains. Besides, in the animal-derived cancer models mentioned above, their interspecies inconsistency with human cancer leads to underlying distinct protein functions and oncogenesis mechanisms. Therefore, human cancer originated models are more reliable for studying therapies that rely on complex intracellular pathways and intercellular interactions. Human cancer cell lines and cell line-derived xenografts have been widely used because of their consistency, availability, and cost-effectiveness. Besides, the 3D culture of in vitro cancer cell lines partly mimics the in vivo structure of cancer, such as the different morphological and biological features of different cell layers. However, they only represent the cancers with specific gene aberration under a settled genomic background. Besides, these models lack tumor heterogeneity and fall short of recapitulating tumor architecture. Consequently, the drug response of these models seldom represents the authentic response of patients, and their consistency with clinical trials in drug response is limited.8Accordingly, animal models such as genome-edited mouse models, patient-derived organoids, and patient-derived xenografts (PDX) have been used to study cancer biology and capture the cancer landscape.9 Although tumor-on-chip can emulate more and more components in the cancer microenvironment, such as microvascular structure,10 their consistency with cancer patients has not undergone rigorous examination. Besides, limited by technology support, tumor-on-chip has a long way to go before it gets popularized in cancer research centers worldwide. Comparatively, models that utilize samples derived from cancer patients remain the most plausible and accessible choice in reflecting genuine cancer structure. Patient-derived organoids (PDO), which are cultured from cancer cells from patients, serve as a cost-effective tool to retain cancer information. Thanks to technical support such as 3D co-culture assays, researchers seek to recapitulate cancer cell-stromal interactions in PDO models.11 However, in vitro models are not ideal for novel therapy testing because they cannot reflect many in vivo properties, such as pharmacokinetics performance, when it comes to preclinical drug testing. The PDX models are established by transplanting fresh tumor tissue resected from human cancer into mice.12 Comparatively, the PDX model excels in reflecting the characteristics of cancer and simulates tumor progression and evolution in human patients. The PDX model produces the most convincing preclinical results and is considered one of the most promising models to handle the conundrum troubling clinicians, such as identifying prognosis biomarkers, exploring the effect of intratumor heterogeneity on tumor progression, and evaluating new drugs.13To faithfully reflect the landscape of human cancer, advanced preclinical models have been exploited. Microfluidic models facilitate the development of tumor-on-chip, a type of in vitro platform that accurately emulates various properties in the cancer microenvironment, such as the changes of glucose or oxygen availability at different positions in tumor structure. Such platforms enable researchers to manipulate many factors that may affect cancer growth and explore the effect of these factors on cancer.In this review, we delineated the establishment process of PDX models and the advantages and inadequacies of PDX models. Furthermore, we summarized how the current technologies boost the application of PDX models Fig. . Finally14 Later, researchers proved that when treated with chemotherapies, PDX models showed comparable responses as their counterpart patients.15 Even though discussion about the techniques of PDX model construction persisted, the unsatisfying transplantation rate of PDX models limited their application. Moreover, because researchers lacked anti-cancer drug choices back then, the function of the PDX model was confined to predicting the drug efficacy for the patient it was derived from. In contrast, more and more cancer types had in vitro cultured human cancer cell lines. Because of their consistency, cost-effectiveness, and accessibility, cell line-derived xenograft models became and have remained the workhorse of evaluating novel anti-cancer compounds.PDX models have gone through a history of “rediscovery”. The first reported patient-derived xenograft that accorded to the definition of PDX models could be traced back to 1969 when Rygaard and Povlsen removed colon adenocarcinoma from a patient and planted the tumor fragments into nude mice.8 The high attrition rate in pharmaceutical industries has raised dissatisfaction.16 In the meantime, researchers reported similar responses to chemotherapy in corresponding patients and PDX models.17 Moreover, researchers started to use histopathology and PCR-based technologies to validate the conformity of patients and PDX models. In 2006, Manuel Hidalgo’s group established a pancreatic PDX platform for drug screening and biomarker discovery, which was one of the pioneers.18 In the 2010s, the optimization of PDX establishment technologies and the popularization of sequencing technologies have boosted the resurgence of PDX models. A colorectal cancer PDX platform was utilized to identify HER-2 inhibitors to treat cetuximab-resistant patients, which is the paradigm to show the role of PDX models in targeted therapy.19 In 2015, Clohessy et al. developed the concept of “mouse hospital”, which refers to in vivo drug testing in models that recapitulate different cancer subtypes before heading into clinical trials.20 Although they mainly referred to GEM models when bringing up the concept, PDX models gradually gained wide acceptance in drug testing henceforth. In 2016, Gao established about 1000 PDX models and tested drug responses on them following clinical trial design, which is a paradigm of the patient derived clinical trial (PCT).13 The wide application of sequencing further validated the genomic consistency between patients and PDX models and facilitated preclinical studies of targeted therapies in PDX models. From then on, more and more platforms validated that PDX models recapitulated the cancer landscape faithfully. Therefore, in the era of immunotherapy and targeted therapy, PDX models have been widely used in preclinical drug tests. Instead of merely serving as the “avatar” of their corresponding donors, they can guide clinical decisions by predicting the molecule signatures that signify sensitivity to the drugs. A timeline highlighting the recent progress of PDX models is summarized in Fig. Nonetheless, researchers gradually realized the inadequacy of cancer cell lines in studying anti-cancer drug efficacy. In 2001, Johnson et al. reported that the consistency of drug response between cell-lined derived models and clinical trials was far from satisfying.PDX is currently the most effective preclinical model for phenocopying cancer intratumor heterogeneity, preserving intrinsic tumor architectures, and studying drug response and resistance. Current PDX models have been applied to a wide variety of cancer types Table .Table 1P21 , it could be cut off, segmented, and reimplanted for passage. Moreover, the time of PDX establishment gradually stabilizes at 40–50 days with passages.22 To avoid tumor engraftment rejection in mouse models, conventional PDX models are typically created using immunocompromised mice, such as athymic nude mice, severe combined immunodeficiency (SCID) mice, non-obese diabetic-severe combined immunodeficiency (NOD-SCID) mice, NOD-SCID-/IL2λ-receptor null (NSG) mice, BALB/cRag2 null/ IL2λ-receptor null (BRG) mice and Rag-2 null/Jak3 null (BRJ) mice.23 Different mouse strains have various degrees of immunosuppression, and they are thus endowed with different engraftment rates, which are higher in more immunocompromised mice (BRG/BRJ > NSG > NOD/SCID > SCID > nude).24To establish PDX models, primary or metastatic tumors are cut into pieces and transplanted with maintained tissue structures. The tumor pieces can be implanted subcutaneously, orthotopically, or heterotopically into the intracapsular fat pad, the anterior compartment of the eye, or under the renal capsule21 Fig. . Differe25  and patient-derived tumor fragments are named humanized PDX.43In addition to the intrinsic components of the tumor microenvironment (TME), tumor-immune interaction is also implicated in cancer patient survival and tumor behaviors, which cannot be restored in general PDX. To this end, PDX with functional human immune systems could be a powerful tool for in vivo tumor immunology and immunotherapy research. Human peripheral blood mononuclear cells (PBMCs), hematopoietic stem and progenitor cells (HSPCs), or fetal hematopoietic tissues are resources for engraftmentAn ideal preclinical model should fully capture and maintain all the characteristics of the parental tumor and reconstruct the real tumor-immune interaction to enable in-depth insight into tumor evolution and be a silver bullet for precise drug research. Existing PDX models have shown satisfactory performance but still need to improve in several respects.46 Orthotopic transplantation of glioblastoma (GBM) could retain the key phenotypes, molecular characteristics, and the similar morphology and invasion pattern.47 Mutation frequencies of commonly mutated genes in cancer, including KRAS, BRAF, TP53, PIK3CA, CTNNB1, and EGFR, were consistent with clinical data in PDX models.48 Zhao et al. treated patients and PDX models separately with a novel KRAS G12C inhibitor to assess the emergent genetic alteration after treatment, which was similar in both groups.49 PDX models can also recognize cancer subtypes based on molecular characteristics. In the context of breast cancer PDX models, Georgopoulou et al. identified 13 cellular phenotypes of breast cancer by single-cell mass cytometry. The phenotypes determined in treatment-naïve models can predict the response to anti-cancer therapies such as chemotherapies and targeted kinase inhibitors. Moreover, mass cytometry uncovered that in a single PDX model, cells with different oncogene signaling showed different responses to targeted therapies.50 Besides, the well-acknowledged consensus molecular subtypes (CMS) of colorectal cancer (CRC) have been recapitulated in PDX models.51 PDX could maintain high genomic stability through at least the first 10 passages, ensuring a long enough experiment window52  rather than peripheral T cells.60 Cancer cells acquired lymphocytes’ membrane immune regulatory proteins such as CTLA-4 via trogocytosis.61 Meanwhile, changes in tumor gene expression occur, which are reflected by upregulated immunosuppressive molecules and decreased production of human pro-inflammatory cytokines.62 This simulation of tumor-immune interaction in the human body allows researchers to understand better the mechanisms of tumor evolution, which was previously confined by the lack of clinical biopsies from various stages before time. One difficulty, however, is that human immune cells are not permanent in PDX because of the lack of genes encoding the cytokines necessary for human immune cells. To tackle this problem, Duy Tri Le, et al. transplanted a fresh, undisrupted piece of solid tumor into mice and obtained TIL-derived PDX.63 This unique PDX based on TIL in tumors does not require in vitro cell expansion and cytokine maintenance to achieve long-term immune system reconstitution. Graft versus host disease (GvHD) is another challenge that recognizes and attacks murine tissue as a foreign body, shortening the experimental window and impeding immunotherapy research. Correspondingly, many studies have generated specific PDX models with deficient MHC to eliminate GvHD.66In addition, PDX models present an unprecedented chance to study the cancer-immune interaction in animal models. The vascular structure and antigen presentation machinery in PDX models were conserved to ensure T cell homing to the tumor and effective antigen recognition.60  and radiotherapy had a concordant therapeutic effect in CRC patients and PDX.69 Several large-scale PDX clinical trials investigated drug responses and resistance mechanisms, and their findings confirmed the reproducibility and clinical translatability of PDX,70 indicating that PDX could retain therapeutic accuracy and is a functional clinically relevant model.44 Clinical trials aiming to determine the reliability of PDX for precision treatment, explore the mechanisms of resistance, or achieve other research objectives are summarized in Table In addition to maintaining genomic heterogeneity, translational PDX models are supposed to reproduce the drug response observed in patients. A combination immunotherapy of nivolumab and ipilimumab significantly increased IFN-γ and IL-6 production and decreased the CD4/CD8 ratio in the PDX model, resulting in an immunoreactive TME as expected and clinically observed71 TME provides structural support and has a massive impact on tumor development by mediating signal transduction and cell migration.72 Preserving TME from the donor tumor is the premise for studying tumor behavior in vivo. Studies have shown that PDX models retain principal peculiarities such as tissue structure, subtle microscopic details, and biological behaviors.52 However, after two to five passages, tumor stroma is almost entirely replaced by murine-derived ECM and stromal cells, among which the most predominant member, CAFs, take the fastest replacement efficiency. Even so, Arnaud Blomme et al. conducted a comparative metabolic analysis between parental tumors and corresponding PDXs. They found that metabolic profiles of both tumor cells and stromal cells remained stable for at least four passages, while the replacement occurred at the second passage.73 This study indicated that replacing human stroma was an acceptable drawback at the early stage of PDX research. Nevertheless, it causes trouble for the study time window and brings uncertainty for various kinds of research. Considering the critical role of CAFs in the modulation of TME, co-implantation of matched human CAFs and tumor fragments along with PDX passage may help alleviate this problem. Besides, due to species specificity, murine-derived cytokines and chemokines fail to maintain the functions of immune cells in humanized PDX. Murine IL-2 has a low activation effect on human T cells, as does murine IL-15 on NK cells.75 It hinders immune cell activation and tumor-immune interaction without human cytokine and chemokine secretion76 , interleukin-3 (IL-3), granulocyte-macrophage colony-stimulating factor (GM-CSF), and thrombopoietin (TPO) to their respective mouse loci to support the development of human innate immune cells, including monocytes/macrophages and NK cells, which resembled the infiltration patterns in human tumors.78 Meraz et al. developed humanized PDX from fresh cord blood CD34 + stem cells, which reconstituted an human innate and adaptive immune system with less time and replicated human response to anti-PD-1.41 An updated study introduced human thymus engineering derived from inducible pluripotent stem cells to generate diverse human T cell populations in humanized mice.79 This novel technology may leapfrog the troubles in the scarce resources of transplanted tissues and the experimental ethics. Looking at stroma replacement another way, the composition and proportion of stromal cells could make a difference to the subtype classification of tumors, especially in CRC, thereby influencing the treatment choice. CMS4, the molecular subtype with the worst prognosis in CRC, has long been thought to have the property of epithelial-to-mesenchymal transition, which was now found to attribute mostly to stromal cells rather than tumor cells themselves.81 It reminds us that deciphering tumor cell-specific peculiarities with less stroma influence may reduce the heterogeneity of detection results to a certain extent and bring some new ideas to tumor subtype classification in clinical practice. Claudio Isella and colleagues assessed tumor intrinsic transcriptional features through human-specific expression profiling of CRC PDX and proposed CRC intrinsic gene signatures (CRIS) that exclude stroma-derived genes. Compared to CMS, CRIS more accurately reflects intrinsic tumor characteristics to stratify CRC populations and provides a better spatial and temporal classification of CRC. In addition, there is little overlap between CRIS and CMS, providing a better insight into CRC heterogeneity when used together.82The tumor microenvironment represents the complexity of the tumor and its surrounding components, including the extracellular matrix (ECM), stromal cells , pro-inflammatory cells), immune cells, and secreted factors.83 This may be due to the sampling bias caused by spatial heterogeneity, different capacity to engraft and proliferate once injected, or tumor evolution and selection during PDX passages. In GBM PDX, although common molecular drivers were captured at frequencies comparable with the primary tumor, some alterations were gained or lost as if it were a process of clonal selection.47 Moreover, as GBM PDX passages increased, accelerated cell growth and increased malignancy were observed.84 Later-passage PDX models showed reduced similarity with primary tumors in DNA-based copy number profiles.85 These observations indicate that utilizing PDX with fewer passages could do better for fidelity. As for presenting the subtype heterogeneity of CRC patients, the four CMS subtypes displayed various engraftment rates and passage rates in PDX, among which CMS1 and CMS4 showed significant advantages. Given the potential subtype-specific drug sensitivity, it may skew the results of drug screening research.87 In addition, microsatellite instable (MSI) tumors carrying germline mutations easily retained their histological features compared to those with MLH1 promoter hypermethylation.88 Regarding intratumor heterogeneity, subclones may have different chances to keep going in PDX passages. Mixed or spindle cell uveal melanoma was characterized as epithelioid uveal melanoma in PDX, which indicates that these tumor cells were more likely to survive and grow in PDX.89 By comparing the gene signature of cancer cell subtypes between the biopsy from patients and the PDX models of small-cell lung cancer (SCLC), Lissa et al. uncovered that SCLC PDX models had a significantly higher proportion of neuroendocrine cells compared to that of the biopsy.90 This ill-recapitulation of intratumor heterogeneity and tumor differentiation may result from clonal selection under experimental processing or tumor evolution due to TME instability. These studies remind us that tumor heterogeneity might not be fully reflected during the establishment and passage of PDX  using glioblastoma cell cultures derived from patients and evaluated their growth longitudinally employing neural network analysis. They discovered that compared to the corresponding mouse PDX models, zebrafish models showed similar growth, invasion, and survival tendencies.104 In non-small cell lung cancer, Ali et al. implanted PDX tissues into zebrafish embryos and generated drug responses like mouse PDX models and the patients. Moreover, ZTX models recapitulated the invasive characteristics of the tissues and can be used to predict lymph node metastasis.105 In another study, Pizon et al. used chick chorioallantoic membrane to generate breast cancer PDX models, which showed a positive correlation with the primary tumor in terms of aggressiveness and proliferation.106Because of the relatively long cycle and high cost of establishing mouse PDX models, researchers started looking for alternative species to serve as more efficient preclinical platforms. Zebrafish xenograft models have become a great alternative, as they support short-cycle, large-scale ex vivo assays at a low cost.107 and EurOPDX.108 A major challenge of multi-center PDX collection is to guarantee consistency among the PDXs from different centers. The PDXNet treated pre-validated PDX models from different centers with temozolomide, and they discovered that the PDXs from each center had the predicted response to the drug.109 To facilitate the management of PDX models among different centers and guarantee the quality and accessibility of the PDX models, Meehan et al. presented PDX-MI, which stands for “PDX models minimal information standard”. PDX-MI includes information regarding four aspects: clinical features, model creation, model quality assurance, and study of the model.110 Besides the consortium-led PDX biobanks, many organizations provide PDX model platforms with genomic data.111 When testing the function of novel drugs, researchers can pick out PDX models from the biobanks according to cancer types or molecular features  can assist clinicians in identifying the genomic and transcriptomic features of metastatic cancer cells and guiding the treatment against late-stage cancer.118 Faugeroux et al. established a CDX model in the context of prostate cancer and identified that a subclone with TP53 loss triggers cancer metastasis, facilitating the drug screening for patients.119PDX cannot fully copy the cancer microenvironment in the human body because of many innate factors, such as the surrounding murine-derived stromal cells and the lack of a fully equipped immune system. Besides, each PDX model can only reflect the status of cancer at one single stage. When a patient undergoes several lines of treatment, real-time detection is necessary to reveal the change in tumor characteristics. The detection of circulating tumor DNA (ctDNA) and circulating tumor cells (CTC) derived from liquid biopsies has been proven as an effective non-invasive method to capture the properties of cancer. Yaegashi et al. validated that if choosing the mutation targets correctly, ctDNA monitoring is sufficient to reflect the tumor burden.121 Using CRISPR-Cas screening technologies, Lin et al. identified several targets against acute myeloid leukemia in PDX models.122 Hulton et al. developed a Cas9 lentiviral vector that can directly target PDX cancer cells in vivo, which significantly facilitates the gene programming of PDX models and the in vivo detection of potential druggable candidates.123 Similarly, Wirth et al. developed a chemotherapy-resistant acute lymphoblastic leukemia PDX model and conducted in vivo CRISPR/Cas9 dropout screens to determine the genes that cancer cells depend on. They identified BCL2 and successfully recovered the tumor sensitivity toward chemotherapy.124 Interestingly, Carlet et al. used Cre-ERT2 inducible RNAi to mimic anti-cancer therapy and silence genes in PDX models, which combined the properties of the Cre-loxP system and RNAi techniques.125 A DNA barcode sequence is a type of unique sequence which can track the exact cells or components that carry it.126 Researchers have loaded DNA barcodes and drugs in nanoparticles and injected them into tumor-bearing mice, which helped identify the exact drugs that most efficiently kill cancer cells.127Functional genomics approaches such as the short hairpin RNA (shRNA) library and the CRISPR screen have enabled researchers to identify novel drug targets in cancer, which has been extensively applied in vitro. However, due to the lack of interaction with other cells, such experiments cannot accurately reflect cancer cell status. Therefore, when combined with PDX models, functional genomics approaches can fully uncover cancer cell vulnerabilities in the context of other cell components in the cancer microenvironment.128 Moreover, the transcriptome can reflect the interaction among different components of cancer, such as the signaling transduction process between cancer cells and stromal cells.129 Mirhadi et al. conducted proteome analysis on a cohort of 137 NSCLC PDX models and identified different proteome subtypes with distinct outcomes and candidate targets.130 A study conducted metabolomic profiling in the context of PDX models of PDAC. It established a metabolic signature that significantly correlated with the prognosis of PDAC patients and aligned with the PDAC transcriptomic phenotypes.131 As demonstrated above, getting hold of the genome, transcriptome, proteome, and metabolome from the PDX cohort would provide numerous information that can guide personalized medicine. Moreover, the omics study’s ‘resolution ratio’ has progressed from bulk to single-cell analysis.132 Dimitrov-Markov et al. created PDX models of metastatic PDAC and sequenced single-cell RNA from circulating tumor cells. They found that the CTCs are highly metastatic and different from the matched primary and metastatic tumors.133 In another study, Mori et al. established PDX models of breast cancer that initially resided in the same breast tumors but had distinct responses to estrogen.134 Grosselin et al. conducted ChIP-seq at single-cell resolution to observe the chromatin landscapes of breast cancer xenografts and revealed intratumor heterogeneity at the chromatin level. They discovered that loss of chromatin marks H3K27me3, a transcriptional repressor against genes that contribute to drug resistance, was detected not only in resistant cells but also in a group of cells resident in drug-sensitive tumors.135 Still, researchers are developing updates on these technologies to solve new problems. The multiregional sequencing approach (MRA) collects DNA samples from multiple regions in one tumor and conducts NGS analysis to study intratumor heterogeneity. Sato et al. integrated MRA and PDX and detected the dynamic change of the subclonal architecture of CRC.136 Species-specific RNA sequencing has been applied to PDX models to distinguish the transcriptional signature originating from murine stromal cells and explore the crosstalk between cancer cells and stromal cells.81 Shared peptide allocation (SPA), a novel protein quantification method based on MS data, is designed to distinguish the unique and mutual peptides in the sample mixed with human cancer tissues and mouse tissues.137 The epigenome landscape reflects the epigenetic alterations of genes, which occur more frequently and diversely than genetic mutations and also reflect gene functions. DNA methylation is another common epigenetic modification form, and Tomar et al. analyzed the DNA methylome in ovarian cancer PDX models and determined the relationship between gene methylation and prognosis.138 The assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) identifies open chromatin regions that participate in cellular activity. ATAC-seq profile coupled with whole exome sequencing probes the active chromatin site as a potential druggable target.139 Interestingly, to study drug resistance in preclinical models, Tedesco et al. developed an integrated technology named scGET-seq that probes genomic and epigenomic sequences concomitantly at the single-cell level.140 Moreover, based on the unspliced and spliced mRNA abundance data from single-cell RNA sequencing, RNA velocity can be calculated to show the dynamic changes of the transcriptome and reflect how cancer cells go through different states.141 Based on mass spectrometry, the development of cytometry with time-of-flight (CyTOF) analysis supports high-throughput analysis of cell phenotypes.50 Meanwhile, phosphoproteomics can capture phosphoproteins, one of the most important post-translational modifications, and help to expose details about the protein status.142Recent studies have emphasized the role of epigenetic changes in cancer drug resistance, which makes it necessary to detect gene modification events alongside gene expression. Multi-omics studies consist of genomics, epigenomics, transcriptomics, proteomics, and metabolomics, a thriving field providing researchers with much more data than ever before. Combining PDXs with technologies such as next-generation sequencing, transcriptome sequencing, and mass spectrometry (MS) can track the change in cancer cell status from different levels as they receive different treatments. Roche et al. compared the transcriptomics of primary tumor samples and PDX samples, and they showed consistency in the gene changes connected to cancer growth and proliferation.143Despite the large amount of data from the multi-omics study, it is still difficult to link the behavior of specific cancer cells to cancer progression. As mentioned above, DNA barcodes can track cellular behaviors at the single-cell level. Therefore, barcoded cancer cells can uncover the proliferation and metastasis tendency at a single-cell level, thus connecting cellular behavior with single-cell mRNA sequencing and revealing the cellular basis of intratumor heterogeneity.144 Specially designed for PDX platforms, DRAP is a data analysis software designed that processes data separately according to different PDX preclinical trial designs.145 As mentioned above, one of the most important missions of PDX models is to identify reliable biomarkers of drug response based on pharmacogenomic datasets, that is, the pharmacologic and high-throughput sequencing profiles of PDX. Several computational platforms have been constructed to analyze preclinical pharmacogenomic data and identify robust biomarkers that predict patient drug response and prognosis.147 Machine learning is a revolutionary technology that has been widely used in the field of translational medicine. The neural network of machine learning can achieve diversified tasks based on massive data. PDX drug discovery datasets and those from cell lines and patients provide a platform with adequate data, and researchers have accomplished various tasks using this platform. Based on the multi-omics dataset in PDX studies, artificial intelligence is widely exploited to predict a patient’s response to treatment. Jiang et al. proposed that drug molecular and cellular targets, drug responses, and adverse reactions are closely intertwined. Hence, they developed DrugOrchestra, a deep learning framework that integrates the abovementioned tasks and predicts the potency of novel compounds according to their molecular structure. Besides, machine learning strategies such as transfer learning149 and few-shot learning150 can also address the challenge of translating the preclinical data into clinical contexts and transferring the predictors from preclinical models to human cancer applications.Robust data analysis tools facilitate the application of the PDX platform. CancerCellNet uses machine learning algorithms to assess the transcriptional fidelity of PDX models to natural tumors.151 In the context of sarcoma PDX models, Jardim-Perassi et al. integrated the data of multiparametric MRI and histology into deep learning models to predict hypoxia and the response to hypoxia-activated prodrugs.152 Roy et al. extracted 48 features such as noise, resolution, and tumor volume from the MRI results of triple-negative breast cancer PDX models and discussed how the factors affect the radiomic analysis.153 Following this study, they further identified 64 robust features and used machine learning to select several strong biomarkers that constituted signatures that can be used to predict prognosis.154Many studies sought to test the commonalities between xenografts and tumors in the human body from the perspective of radiomics. Magnetic resonance imaging (MRI) helps to extract diverse information from PDX models. Tiwari et al. optimized MRS in a determined sequence to compare the production of 2-hydroxyglutarate between the PDX models and patients, which proved the similar IDH metabolism between PDX models and parental tumors.155 In another study, Russel et al. used a radiotracer named 18F-FAC, which has a similar structure to gemcitabine, to track gemcitabine uptake in pancreatic cancer PDX models. Such a surrogate helps to determine drug uptake in patients conveniently.156 Interestingly, Almstedt et al. tagged the patient-derived GBM cells with green fluorescent protein (GFP), implanted them in zebrafish embryos, and observed their growth by light-sheet imaging. In this manner, they realized real-time observation of cancer initiation and development.104Besides, integrating imaging technologies and PDX models contributes to the study of medicine uptake and distribution in a non-invasive manner. Using micro-computed tomography (µCT) imaging on PDX models, Moss et al. clarified that the density of vessels supporting pericytes is critical to liposome accumulation and distribution.157; while binding an anti-CD24 antibody with Alexa Fluor 680 enabled monitoring the therapeutic efficacy of carboplatin-paclitaxel against ovarian cancer in the xenografts.158 Fonnes et al. established an orthotopic PDX model of endometrial carcinoma in another study. They utilized fluorescence-binding antibodies against epithelial cell adhesion molecules to track cancer’s development and anti-cancer treatment efficacy successfully.159Similarly, other practical techniques, such as in vivo imaging systems, need thorough validation before they are used on patients, and the PDX model is an ideal preclinical model. In the context of ovarian carcinoma, CD24 is highly expressed. Kleinmanns et al. conjugated an anti-CD24 antibody with Alexa Fluor 750 and used the antibody to provide real-time feedback on surgeries of metastatic ovarian carcinoma PDX models160 therapeutic effects, and drug resistance mechanisms of current treatments against cancer.161 This part delineates the application of PDX in the field of cancer treatment, including chemotherapy, targeted therapy, immunotherapy, and other novel therapies.Because of the PDX model’s fidelity to replicate patient diversity and the PDX model’s diversity to reflect diversified patients and real-world scenarios, the PDX model has been widely employed in exploring the pharmacokinetics and pharmacodynamics,Chemotherapy is the most classical antitumor drug and clinically verified repeatedly in various cancers. However, chemotherapy resistance is the leading cause of tumor-related death worldwide. Moreover, there is a lack of appropriate methods to evaluate chemosensitivity to guide first-line chemotherapy. The PDX model has much to do with the selection and improvement of chemotherapy, including detecting sensitivity, exploring resistance-associated characteristics, and providing a suitable platform to test novel drugs and delivery methods.162 Moreover, for tumors responsive to polychemotherapy, such as triple-negative breast cancer (TNBC), using PDX to test the antitumor activity of a single drug and combination therapy is of great significance to guide individualized treatment with minimum toxicity.Clinicians have established a series of standards for chemotherapy selections. However, when faced with several drugs to choose from and inter-tumor heterogeneity among patients, inappropriate selection may not maximize patient benefit. With the help of PDX, researchers could test drug sensitivity at an individual level beforehand. Studies revealed that PDX-guided chemotherapy significantly upregulated overall survival (OS) and disease-free survival (DFS) compared with standard treatment of gemcitabine and oxaliplatin in gallbladder cancer patients.163 Nonetheless, they also found RAS-mediated resistance to SYK inhibition, which indicates the complexity of the whole genome picture and emphasizes the importance of personalized detection and treatment. Li et al. established chemo-resistant and chemo-sensitive ovarian cancer PDX models with paclitaxel and carboplatin.164 Through whole exome and RNA sequencing, they screened out the most resistance-related gene HLADPA1 and validated its association with resistance to initial chemotherapy in TCGA datasets. This research method was closely related to clinical practice and provided an entry point for in-depth research.PDX is also a practical tool to excavate targets for drug resistance and test combined drugs afterward. Researchers detected constitutive phosphorylation of spleen tyrosine kinase (SYK) in infant acute lymphoblastic leukemia PDX, the combination of SYK inhibitor and chemotherapy could significantly enhance therapeutic efficacy.Conventional chemotherapy usually acts through DNA damage, which could be evaded by tumor cell-induced cellular dormancy. Meanwhile, other factors, such as the tumor extracellular matrix, drug bioavailability, and the effect of targeting, all affect the efficacy of chemotherapy. It is an effective and convincing way to modify and detect these influencing factors by the PDX model.165 This inhibitor significantly suppressed tumor growth in BRAFV600E- and NRASQ61R-driven melanoma PDX, indicating a novel way to combat chemotherapy resistance and proposing a new idea of targeted chemotherapy. Some tumors, such as appendiceal mucinous carcinoma peritonei (MCP), exhibited an inadequate response to chemotherapy due to the protective effect of abundant extracellular mucus, which impeded drug delivery. Researchers developed a combination of bromelain and N-acetylcysteine to achieve mucolysis and significantly enhanced the chemotherapeutic effect in MCP PDX.166 Moreover, boosting the bioavailability of drugs by chemical modification is essential to improve the efficacy of chemotherapy. Modified gemcitabine, termed 4-N-stearoylGem, strongly inhibited tumor growth in pancreatic cancer PDX and surprisingly showed an antiangiogenic effect by reducing vascular endothelial growth factor receptors expression.167 Combining some novel delivery systems and technologies also showed a promising future for chemotherapy. In bladder cancer PDX, the nano-sized drug delivery system poly (OEGMA)-PTX@Ce6 (NPs@Ce6) combined with chemo-photodynamic therapy markedly improved tumor targeting and suppressed tumor growth up to 98%. In addition, this combined therapy was versatile and went beyond the functions of conventional chemotherapy, with the ability to upregulate oxidative phosphorylation and reactive oxygen species generation, and downregulate tumor growth, invasion, and metastasis signals.168Researchers invented a small molecule inhibitor called phendione that induced a DNA damage response without causing DNA breaks or allowing cellular dormancy.169 Moreover, intratumor heterogeneity restrains the therapeutic effect of these therapies, and cancer cells become liable to mutate under the pressure of targeted therapy, which further intensifies cancer heterogeneity.170 Therefore, the ensuing targeted strategies may have distinct effects on different cancer cell subclones, facilitating drug-resistant subclones outgrowth and an overall poor response.171 The PDX model presents the molecular characteristics and the complexity of the cancer landscape, which helps to give authentic feedback when treated with targeted therapies. The combination of next-generation sequencing, immunohistochemistry, and in situ hybridization captures the histopathological and molecular signatures and guides target therapy for the patients represented by PDX models.172 PDX models have aided in developing numerous targeted therapies for various cancer types , but the mTOR inhibitor everolimus is not universally effective in treating gastric cancer. Fukamachi et al. derived cancer cell lines from diffuse-type GC PDX models and discovered they are sensitive to everolimus. Further, they found that the GC cells from PDX belong to cluster II of diffuse-type GC in clinical practice, which is characterized by chromosomally unstable (CIN) or MSI.176 Similarly, PDX trials can identify patients with specific mutations that lead them to resist paticular target therapy. Kemper et al. established a PDX trial from melanoma metastases and identified the BRAFV600E kinase domain as a resistance mechanism. Then they validated the efficacy of the pan-RAF dimerization inhibitor to eliminate this subclone.177 In high-grade serous ovarian carcinoma (HGSC), suppression of RAD51C leads to defective homologous recombination and marks sensitivity to PARP inhibitors. Nesic et al. established RAD51C promoter methylation PDX models and discovered that while homozygous RAD51C methylation indicates PARP inhibitor sensitivity, a single copy of unmethylated RAD51C causes drug resistance.178Numerous PDX biobanks worldwide collect cancer across the diverse genomic and transcriptomic background. Therefore, when a novel therapy is developed, these platforms are an ideal tool for performing preclinical screening to identify signatures and biomarkers that serve as criteria to recognize patients who can benefit from the therapy. The Novartis Institutes for BioMedical Research established a PCT that followed the “one animal per model per treatment’ design and tested the response to 62 treatment strategies.179 Afatinib, a TKI targeting HER2 and EGFR, outstood the drug sensitivity test in a PDX model derived from a CRC patient resistant to multiple lines of treatment; the patient used afatinib subsequently and achieved progression-free survival for three months.180 Coussy et al. used PDX models to study the therapeutic choices for patients with luminal androgen receptor triple-negative breast cancer resistant to enzalutamide and discovered significant enrichment in PIK3CA and AKT1 mutations. Consequently, mTOR and PI3K inhibitors are proven effective for these patients.181 Metastatic colorectal cancer patients with KRAS and BRAF mutations have poor prognoses. Therefore, Area et al. established PDX models for them and examined the vulnerability to the PARP inhibitor olaparib and the chemotherapy drug oxaliplatin. They discovered that a subset of PDX models is deficient in HR, and they are sensitive to olaparib, and such response is positively correlated with oxaliplatin vulnerability. As oxaliplatin is a common choice for such patients, this study provides a rationale for sequential treatment with oxaliplatin and olaparib.182 With the aid of whole exome sequencing and transcriptome sequencing, PDX cohorts would facilitate the identification of biomarkers that can predict the drug sensitivity of patients.Moreover, establishing PDX models for patient subtypes resistant to current treatment options helps to investigate new therapies. Many researchers have exploited the drugs selected in PDX models on patients and tested their efficacy.183 Silic-Benussi et al. revealed that, in the context of T-cell acute lymphoblastic leukemia (T-ALL), mTOR pathway activation led to drug resistance to glucocorticoid because of an insufficient level of ROS. When treating with the mTOR inhibitor everolimus in the T-ALL PDX model, they observed an increased level of ROS accompanied by a decreased capacity of ROS scavenging and significant therapeutic effects.184 Zhang et al. treated a KRAS G13D mutant CRC PDX model with cetuximab and detected the expression level changes of different genes, thereby determining potential genes contributing to acquired resistance.185 Another study investigated the mechanism of EGFR inhibitor resistance and discovered that the remaining tumor cells after EGFR treatment have high HER-2 and HER-3 activity. Subsequently, pan-EGFR antibodies significantly reduced the residual disease.186 To test the effect and mechanisms of EGFR-TKI on esophageal squamous cell carcinoma (ESCC), Liu et al. treated ESCC PDX models with afatinib and examined the underlying mechanism. They discovered that afatinib exerts its function by suppressing EGFR downstream pathways and inducing cell apoptosis. Besides, aberrant phosphorylation of Src family kinases (SFKs) leads to resistance to afatinib, and the combination of EGFR and SFK inhibitors can overcome the afatinib resistance.187 Li et al. established three PDX models to explore the drug resistance mechanism of a pediatric BRAFV600E-mutant brain tumor against MEK inhibitor trametinib. They found that the decoupling of TORC1 signaling, originally a downstream pathway of MEK, led to resistance to trametinib; moreover, the combination of a TORC1 inhibitor and trametinib postponed the development of trametinib resistance.188Compared to normal mouse xenograft models, PDX models can be generated with tumor cells from patients who undergo resistance to certain therapies, which makes them better models for studying the mechanisms of cancer drug resistance to existing therapies and facilitating the development of novel therapeutic targets. Utilizing establishing PDX models from drug-resistant patients, researchers identified mutation sites such as MET that led to cetuximab resistance.48 Zhao et al. established the HER2 + PDX model and confirmed the efficacy of neratinib, a pan-HER tyrosine kinase inhibitor, in combination with CDK4/6, mTOR, or MEK inhibitors in the context of HER2 + cancer.189 DNA-dependent protein kinase (DNA-PK) plays essential roles in DNA damage response and repair. Fok et al. combined a DNA-PK inhibitor, AZD7648, with chemotherapy or the PARP inhibitor olaparib to test its role as a potential sensitizer of other drugs targeting DNA repair. The synergistic effect on cell growth inhibition was validated, and the combination of AZD7648 and olaparib effectively increased the genomic instability.190 The IDH mutation has been an important target for acute myeloid leukemia (AML), which blocks AML cells’ differentiation. To increase the therapeutic effect of the IDH inhibitor, Liu et al. screened the genes that promote response to the IDH1 inhibitor, and they identified the critical role of STAT5 in stemness regulation. Correspondingly, combining an IDH1 inhibitor and a STAT5 inhibitor achieved a synergistic effect in IDH1 inhibitor-mutated AML PDX models.191When utilizing targeted therapies to suppress a pathway that fuels cancer, a combination of drugs that aim at different “nodes” of the pathway would prevent bypass activation and promote cancer elimination. These combinations need preclinical validation to examine the efficacy and potential toxicity. As KRAS and BRAF mutations are identified as the drug-resistant mutations of cetuximab, Yao et al. established a PDX trial and validated that concurrent inhibition of RAS and EGFR had a synergistic effect on the treatment of BRAF and KRAS mutation cancer models.192 In gastrointestinal stromal tumors (GIST), tyrosine-protein kinase KIT has been a key target, while secondary KIT mutation is the central drug-resistance mechanism against tyrosine kinase inhibitors (TKI). Correspondingly, Gebreyohannes et al. tested avapritinib, a highly selective inhibitor of KIT, on GIST PDX models with different KIT mutations, which had a superior or equal therapeutic effect compared to standard TKI.193 Combining BRAF and MEK inhibitors has been an effective strategy for BRAF-mutant melanoma, and mTOR signaling is a pathway suppressed under this treatment. Therefore, when melanoma is resistant to this combination, one possible mechanism is an alternative activation of mTORC signaling. Therefore, Wang et al. established BRAF-mutant melanoma PDX models resistant to BRAF and MEK inhibitors and reduced their growth with mTOR inhibitors.194 Aberrant activation of STAT3 inhibitor has been a target for NSCLC because it contributes to the secondary resistance against EGFR-TKI. To deal with such resistance, Zheng et al. screened out a new STAT3 inhibitor, W2014-S, and proved its efficacy in NSCLC PDX models with abnormal STAT3 activation.195 Therefore, PDX models provide solid preclinical evidence of drug efficacy.When cancer develops resistance to existing target therapies, the resistance mechanisms are distinct, and it is hard to find a one-size-fits-all compound that combats cancer. Furthermore, because of intratumor heterogeneity, the proportion of cells that develops a particular resistance mechanism is uncertain. In this scenario, PDX models satisfy the need to test the efficacy of new drug choices under corresponding genomic landscapes. HER-2 activating mutations led to resistance to cetuximab in colorectal cell lines. Kavuri et al. sequenced 48 colorectal cancer PDX models derived from patients resistant to cetuximab and discovered HER2 mutations in four models. The combination of two HER-2 targeted therapies produced tumor regression in the models.13 Aberrant activation of STAT3 inhibitor has been a target for NSCLC because it contributes to the secondary resistance against EGFR-TKI. To deal with such resistance, Zheng et al. screened out a new STAT3 inhibitor, W2014-S, and proved its efficacy in NSCLC PDX models with abnormal STAT3 activation. Moreover, W2014-S had a synergistic effect with the EGFR-TKI gefitinib on NSCLC PDX models that have acquired resistance to EGFR-TKIs.195 The IDH mutation has been an essential target for AML, which blocks AML cells’ differentiation. To increase the therapeutic effect of the IDH inhibitor, Liu et al. screened the genes that promoted response to the IDH1 inhibitor and identified STAT5 as a key node in stemness regulation. In IDH1 inhibitor-mutated AML PDX models, combining an IDH1 inhibitor and a STAT5 inhibitor achieved a synergistic effect.191Moreover, regarding drugs designed for novel targets that have yet to be widely applied, preclinical results from PDX models provide solid evidence for further clinical trials. The Novartis Institutes for BioMedical Research established a PCT that followed the “one animal per model per treatment” design to simulate intertumor heterogeneity. In total, they tested the response to 62 treatment strategies.196 and only a limited cancer patient population benefits from immunotherapy. For instance, among CRC patients, only those with MSI status may benefit from immunotherapies, accounting only for 10% of all CRC patients.197 Cell-mediated immunity is essential for tumor immunity. Tumor cells present their antigens via the MHCI-antigen complex, and CD8 + T cells are activated when the CD8 molecule recognizes the tumor neoantigen by interacting directly with MHC-I.198 MSI results from the dysfunction of the mismatch repair system, which increases cancer neoantigen presentation, and MSI status thus indicates the patient’s response to immunotherapy.199 For the majority of tumor patients who are microsatellite stable (MSS), the cancer genome is less prone to mutate, and MSS cells are less likely to produce tumor antigens, which means that the immune response against cancer cells is not activated.201 Consequently, the MSS tumor immune microenvironment is “cold.” Researchers are exploring regimens to activate the immune response against cancer. Combining immunotherapy with targeted therapy, chemotherapy, radiotherapy, or alternative immunotherapies has improved the therapeutic effects. However, the immune status of patients cannot be fully captured, which makes it hard to keep track of the effects of the regimens. For example, it is impossible to take biopsies from patients after every stage of treatment. Therefore, models that simulate the immune landscape of cancer become necessary to improve existing therapies.Immunotherapy stands for a bright future for a wide variety of tumors. Its combination with other therapeutic regimens, including targeted therapy and many other forefront treatments yet to come, is a research hotspot that deserves full attention. However, the treatment efficacy lags,On the other hand, cytotoxic T cells are activated more easily for MSI patients, but intratumor heterogeneity still hinders the efficacy of immunotherapy. Mutations in different genetic loci in distinct cancer subclones may produce diversified tumor neoantigens, which are expressed dispersedly in relatively low levels. Consequently, these neoantigens cannot generate enough T-cell clones. Moreover, other factors, such as depletion of previous tumor antigens and dysfunction of MHC presentation, deprive the tumor-infiltrating lymphocytes of the original cytotoxic functions. However, traditional detection methods cannot fully capture the heterogeneity within a single patient. Similarly, an ‘avatar’ of the tumor in the human body is needed to dissect the overall situation of immune stimulation and improve the treatment against tumors.67 These encouraging findings raise the question of what causes the immune barrier in MSS tumors and encourage us to dig deeper. Dysfunction and exhaustion of infiltrating immune cells in the TME are also hurdles to immunotherapy. A subset of CD8 + T cells was found to progressively expand in the TME, with the highest levels of proliferation and activation but the loss of IFN-γ production and the highest apoptotic tendency.202 This subset differed from the classic exhausted phenotype and was named the burned-out CD8 + T cell subset (Ebo). The abundance of Ebo was associated with immunotherapy resistance and could be minimized using avelumab (anti-PD-L1) in humanized PDX, indicating that the PD-1/PD-L1 pathway was crucial for its expansion, which was contradictory with prior studies revealing PD-1/PD-L1 as proliferation suppressors.204 The heterogeneity of cell subsets could be ubiquitous, and its impact on immunotherapy efficacy awaits further exploration.Many unsolved problems in clinical practice seriously hamper the effectiveness of cancer treatment. PDX model as a feasible and reliable tool could and did help a lot in investigating the mechanisms of drug resistance and optimized combination therapies. In CRC PDX models, anti-PD-1 therapy strongly inhibited tumor growth in MSI models. In MSS models, it worked for the first ten days and was followed by a rapid tumor progression, which was consistent with clinical observation. The difference between the two groups was due to the disparate levels of infiltration and activation of immune cells in TME.205 The immune response can be further activated by targeting CD105, specifically expressed on immunosuppressive cells such as mesenchymal stromal cells, tumor-associated macrophages, and cancer-associated fibroblasts. The relief of immunosuppression in the TME helped inhibit tumor growth more effectively in vivo.206 Studies found that thymocyte selection-associated high mobility group box protein (TOX) could bind to PD-1 in the cytoplasm and facilitate the endocytic recycling of PD1 in infiltrating CD8 + T cells, resulting in T cell exhaustion. TOX knockdown significantly alleviated exhaustion, increased TILs with upregulated IFN-γ and TNF-α, and maximized anti-PD-1 effectiveness in HCC PDX, proposing a new possibility of combination therapy.207 More interestingly, relieving tumor hypoxia by delivering exogenous H2O2 and catalase loaded within liposomes significantly reversed immunosuppressive TME and enhanced immunotherapy.208 Such a relatively simple and safe treatment opens a new direction for tumor therapy and is promising in translational medicine. Besides, many studies discovered a close relationship between the immune system and metabolism. Simvastatin, targeting cholesterol biosynthesis, inhibited lncRNA SNHG29-mediated YAP activation, thereby repressing PD-L1 expression at the transcriptional level. Knockdown of lncRNA SNHG29 promoted cytotoxic T lymphocyte (CTL) infiltration and killing in CRC PDX, suggesting a new mechanism for a classic drug.209 Other metabolism-related targets correlating with immune response, such as chondroitin-6-sulfate in CRC,210 could also be further validated in humanized PDX models.PDX is also an excellent tool for testing the efficacy of potential combined therapies. Treatments that could turn the TME from immunosuppressive status into an immunoreactive status in a specific way would make the most of immunotherapy. IL-15 could promote NK cell maturation and function, with which the anti-disialoganglioside (GD2) antibody (mediating antibody-dependent cell-mediated cytotoxicity (ADCC)) exhibited an improved tumor-killing effect in neuroblastoma PDX.6 Many savvy techniques have been applied in PDX models to enhance the performance of immune cells. As a classical target, HER-2 is highly expressed in tumors and lowly but extensively in normal tissues; hence, conventional HER-2 CART is potentially toxic to patients. Researchers designed an antibody-based switchable CAR T cell that bounded a specific peptide genetically engrafted onto a tumor-binding Fab molecule, the activity of which could be monitored in vivo based on dosage.211 As the target cells and immune cells connected, the “switch” took command of antigen specificity and T cell activation and induced complete tumor regression in PDX. Hunting for antigens with the highest tumor specificity is another way to optimize CART therapy. B7-H3 is a type I transmembrane protein highly expressed in human tumors and restrictedly expressed in normal tissues. It mediates the immunosuppressive process by reducing type I interferon released by T cells and cytotoxic activity by NK cells,213 making it an ideal immunotherapy target. With its antibody and antibody-drug conjugates being verified in PDX models,214 B7-H3.CAR-T was also tested in PDX and effectively controlled tumor growth without toxicity.213How to specifically recognize tumor cells without off-tumor killing, augment infiltration of immune cells into tumor sites, and avert immune cell exhaustion induced by the immunosuppressive effect of the tumor microenvironment has long been a conundrum.216 Based on this, researchers constructed CAR T cells that simultaneously produced IL-17 and CCL19. These specific T cells led to an increase in many other tumor-infiltrating immune cells and the downregulation of immunosuppressive molecules such as PD-1 and TIGIT, exhibiting significant inhibition of tumor growth in PDX.217 Another design to treat GBM was a synthetic Notch receptor. It recognized either a tumor-specific but heterogeneous neoantigen (EGFRvIII) or a normal central nervous system-specific antigen (myelin oligodendrocyte glycoprotein). Subsequently, it activated a transcriptional output of a CAR that was designed against the more homogeneous GBM antigens, EphA2 and IL13Rα2.218 Such an approach complemented the antigens, increased the tumor-specificity, and enhanced CAR T cell persistence, providing ideas for improving CART treatment for other tumor types.219 In addition to PD-1 and PD-L1, which have been studied extensively in recent years, other immunoregulatory molecules with potent functions are being dug up, such as CD137, a costimulatory receptor on antigen-primed T cells, which could ameliorate immune cell exhaustion by reducing the expression of exhaustion-related genes.220 CARs designed to target CD137 significantly increased T cell proliferation and survival and upregulated IL-2, TNF-α, and IFN-γ in melanoma and GBM PDXs.223 Combining CD137 with known immunotherapy targets or tumor-specific antigens may open a new chapter in cancer treatment. In addition to longevity, the functioning of CART therapy is also plagued by alloreactivity caused by HLA-mismatched donated effector cells, which may lead to GvHD. Knockout of the endogenous TCRβ chain of CART cells significantly ablated alloreactivity and showed the same ability to control tumor burden in PDX models.224 However, it brought about low T-cell persistence compared to the co-expression of endogenous TCR plus CAR. Future studies may orchestrate these findings, and it is promising that we can have our cake and eat it too.Mining ways to extend the functional status of adoptive cells is also an important research direction. For instance, the combination of interleukin 17 (IL-17) and chemokine (C–C motif) ligand 19 (CCL19) could facilitate the formation and maintenance of T cell zones in the secondary lymphoid organs.225 An uncapped 5′-triphosphate moiety (ppp-RNA) of virus RNA could be recognized by the cytoplasmic immune receptor retinoic acid-inducible gene-I (RIG-I) and promote the production of type I interferon and pro-inflammatory cytokines.226 Based on this mechanism, ppp-RNA therapy was tested in PDX and demonstrated reduced tumor burden accompanied by CD3 + T cell expansion.227 CD47, which is expressed on both tumor and healthy cells, binds to its receptor signal-regulatory protein alpha (SIRPα) on myeloid cells to deliver a “do not eat me” signal.228 Preclinically, CAR T therapy targeting highly expressed CD33 in acute myeloid leukemia is effective with no obvious toxicity in both in vitro and in vivo PDX models, allowing it to enter the first-ever human phase 1 clinical trial, completing the leap from bench to bed.229 CD47 blockade could fire innate immunity by triggering phagocytosis. In PDX models, it reduces liver micrometastasis and prolongs survival,231 offering a solid complement for clinical immunotherapy practice. Ex vivo expansion of NK cells from cancer patients possessed excellent tumor-killing ability in PDX under the administration of IL-2 injection to maintain cell viability, providing a promising path to reconstitute the patients’ exhausted immune function compatible with existing immunotherapies.232Existing immunotherapies are big steps forward in cancer treatment, but the beneficiaries are only a slight fraction, and the effective targets are still limited. It is thus imperative to devise new targets to complement the now available ones and develop new strategies to benefit patients more extensively. As a human body substitute, the PDX model has significant great contributions. The six-transmembrane epithelial antigen of the prostate (STEAP) is upregulated in multiple tumors and restricted in the normal tissue with T cell-engaging bispecific antibodies (T-BsAbs). Targeting STEAP in PDX effectively redirected T cells into tumor sites, thus significantly repressing tumor growth.233 Another anti-HER3 antibody-drug conjugate, EV20/MMAF, demonstrated potent anti-tumoral properties in several primary and secondary resistance models to commonly available anti-HER2 therapies.234Antibody-drug conjugates (ADCs) are a novel therapy that utilizes antibodies to target specific oncogenes and delivers the drugs to kill the cancer cells specifically. PDX models have been extensively used in the preclinical experiments of ADCs. A critical mechanism for patients resistant to EFGR inhibitors is the overexpression of HER3, which forms dimers with EGFR and HER2 as an alternative pathway. Haikala et al. treated EGFR-resistant PDX models effectively with HER3-Dxd, an ADC conjugated with the HER3 antibody. Moreover, when pretreated with an EGFR-TKI osimertinib, the membrane expression of HER3 was increased, and the efficacy of HER3-Dxd improved.235 PDX models have been extensively used to assess the efficacy of new potential drugs or carriers, such as microRNA,236 non-coding RNA,237 small-molecule inhibitors,238 and novel drug carriers such as functionalized hyaluronic acid239 and quantum dot.240 Moreover, PDX models can be used in designing radionuclide therapy, as they can recapitulate the biodistribution of radiotracers.241 The potential drugs that were validated in PDX models have been summarized in Table Besides, PDX is considered a persuasive tool to validate the efficacy of a new drug in the preclinical stage.Biomarker-based therapies, including targeted therapy and immunotherapy, have been the research focus in recent years to extend patients’ survival times, and encouragingly, they have shown promising therapeutic efficacy for a certain number of patients. However, how to recognize and stratify patients with different response potencies accurately using current and untapped biomarkers is still a conundrum. Current biomarkers are insufficient to mirror the genuine genetic status due to the ubiquitous inter- and intra-tumor heterogeneity, which is regarded as the “villain” in drug resistance. Even if novel biomarkers or signatures accurately reflect the cancer landscape, the consequent problem is the complexity of treatment selection. There are uncertain interactions between these biomarkers and inconclusive therapeutic effects on different patients. It is hence vital to perform expanded validation of treatment-related decision-making in reliable preclinical models.PDX is currently the best model to retain the original features of the tumor. More importantly, it became possible to mimic the interactions between the tumor and the immune system in vivo by establishing humanized PDX. It is known that there is a mutual influence between the tumor and the immune system, and they both adapt and make changes during their interactions. The emergence of humanized PDX makes for an optimized model and a more suitable platform for studying immunotherapy. Because PDX could preserve the genetic landscape of the parental tumor and parallel the clonal heterogeneity to a large extent, therapeutic effect testing and drug resistance exploration in PDX could reflect the real situation of cancer to the utmost. Intricate associations between classical biomarkers such as HER2, KRAS, and BRAF and drug response have been broadly investigated in PDX to find more clues to improve drug efficiency. Combination therapy and newly developed drugs could also be screened and validated in PDX to get reliable preclinical data for translational research. In addition, the availability of PDX allows for longitudinal observation of tumor heterogeneity and some characteristics, such as clonal metastasis tendency. PDX could be used to achieve personalized patient medication management, which is the ultimate translational goal.In addition to the advantages mentioned above, PDX still has some deficiencies to be solved. To perfect PDX models, efforts like improving transplantation methods and genetic modification have been made, coming at the cost of time and money. Moreover, novel support technologies such as multi-omics, massive data, and functional genomics facilitate the analysis of the PDX model’s properties. All these considerations highlight the promising futures offered by PDX to light the way for translational cancer research. However, they also put forward the inherent challenges to this method and the need to mine new ways to maximize the potential of PDX.


Escherichia coli largely differs from the response in Firmicutes (Bacillota), wherein synthesis and degradation of the messengers (p)ppGpp are orchestrated by the bifunctional Rel enzyme with synthetase and hydrolase activity and the two synthetases SasA/RelP and SasB/RelQ. Here we will summarize recent studies supporting the role of (p)ppGpp in the development of antibiotic resistance and tolerance as well as survival under adverse environmental conditions in Firmicutes. We will also discuss the impact of elevated (p)ppGpp levels on the development of persister cells and the establishment of persistent infections. (p)ppGpp levels are usually tightly controlled to allow optimal growth under non-stressed conditions. Upon the onset of certain ‘stringent conditions’ the sudden increase in (p)ppGpp levels limits growth while exerting protective effects. In Firmicutes, the (p)ppGpp-mediated restriction of GTP accumulation is one major mechanism of protection and survival under stresses such as antibiotic exposure.The stringent response and its signalling nucleotides, pppGpp and ppGpp, have been the subject of intense research since the discovery of (p)ppGpp in 1969. Recent studies have revealed that the downstream events that follow (p)ppGpp accumulation vary among species. Consequently, the stringent response as initially characterized in  An overview of the contribution of the stringent response and (p)ppGpp to the development of antibiotic resistance, tolerance, and persistence, as well as to survival under adverse environmental conditions in Firmicutes. Escherichia coli and the response in Firmicutes. Differences can be seen not only in the phenotypic consequences of (p)ppGpp accumulation but also in the main players involved in the synthesis and degradation of the messengers. The role of (p)ppGpp in bacterial survival, antibiotic resistance, and tolerance as well as persister cell formation has mostly been analysed in Proteobacteria. Here we will discuss whether and to what extent this holds true for Firmicutes.The stringent response or stringent control is a bacterial response to different forms of stress and is characterized by the synthesis of the messenger molecules pppGpp and ppGpp (hereafter referred to as (p)ppGpp).  or by the TDtest, a modified disk-diffusion assay that enables the semi-quantitative evaluation of tolerance level pGpp is synthesized by RelA-SpoT-homologs (RSH), which catalyse the transfer of pyrophosphate originating from ATP to the 3´ OH group of GTP, GDP, or GMP. Long RSH enzymes, characterized by an N-terminal enzymatic domain bearing distinct motifs for (p)ppGpp synthetase or hydrolase activity and a C-terminal regulatory domain, are present in nearly all bacteria and display both (p)ppGpp hydrolytic and biosynthetic activity  , SasA/RelP and SasB/RelQ. The (p)ppGpp synthesis by SAS enzymes is induced through transcriptional activation of the encoding genes and/or post-translational modifications. For example, RelP and RelQ from S. aureus makes the enzyme essential for the detoxification of (p)ppGpp produced by RelP or RelQ ppGpp overproduction ppGpp to produce pGpp, thereby modulating alarmone composition and function ppGpp in Firmicutes and thus important to reset the system. Under non-inducing conditions, Rel is primarily in a hydrolase-on/synthetase-off conformation ppGpp in Firmicutes does not interfere with RNA polymerase activity  is of particular concern. In the last few years, several groups have observed connections between methicillin resistance in S. aureus and the stringent response ppGpp produced by either Rel or RelQ can result in enhanced oxacillin resistance. In strain N315 relQ deletion alone did not affect the level of β-lactam resistance ppGpp synthesis as well as other interactions in purine metabolism lead to reduced β-lactam and glycopeptide sensitivity manifested by increased relative growth  strain of E. faecalis showed enhanced sensitivity not only towards the cell-wall targeting antibiotic vancomycin ppGpp also influences the sensitivity of bacteria to other antibiotics Table . A (p)ppB. subtilis, (p)ppGpp-mediated signalling was shown to enhance the expression of the ABCF ATPase VmIR—a resistance factor conferring resistance to the translation inhibitors pleuromutilin, linocsamide, and type A streptogramin—during challenge with VmIR-cognate antibiotics ppGpp synthesis pathway caused sensitivity to the pleuromutilin tiamulin, lincosamides such as the natural product lincomycin, as well as the more potent semi-synthetic antibiotic clindamycin, the recently developed fully synthetic iboxamycin, and the type A streptogramin virginiamycin M1 ppGpp-defective bacteria, but there have also been several studies showing that antibiotics that exhibit bacteriostatic activity in wild-type cells become bactericidal in (p)ppGpp-deficient cells Table  Kazmier. For examecA ppGpp concentrations. (p)ppGpp-mediated resistance might be conferred by upregulation of resistance genes, e.g. Antibiotic tolerance is an important contributor to antibiotic treatment failure, but it also acts as a precursor to antibiotic resistance ppGpp synthesis and again increased vancomycin tolerance ppGpp levels due to missing hydrolase activity ppGpp synthesis and thereby increased tolerance to vancomycin ppGpp0  strain is more sensitive to killing by norfloxacin and ampicillin , mutations of rel that likely lead to higher (p)ppGpp accumulation and an overall stringent-response-like transcription pattern have been linked to ciprofloxacin tolerance ppGpp synthetases . A de novo-generated strain carrying the sole mutation L61F in Rel was less susceptible to daptomycin in time-kill assays ppGpp analog relacin or transcriptional repression by antisense rel RNA reduced tolerance to clindamycin and metronidazole ppGpp in response to chloramphenicol treatment through rel, which protected the bacteria from antibiotic killing ppGpp on tolerance towards other antibiotics is less clear. In S. aureus, we could show that vancomycin-induced relPQ expression contributes to biofilm formation and biofilm-related antibiotic tolerance. Thus, RelP and RelQ play a major role in biofilm formation and maintenance under cell wall stress conditions  isolate harboured a rel mutation, resulting in elevated (p)ppGpp levels ppGpp, either through mutations of the Rel hydrolase or via activation of synthetases, can contribute to antibiotic tolerance without necessarily conferring resistance. However, (p)ppGpp is likely not the only or even the main trigger for bacterial tolerance.The molecular mechanisms underlying persister cell formation are highly debated, and the role of (p)ppGpp in this process remains unclear ppGpp contributes to persistence under these specific conditions. However, in many species, (p)ppGpp mutants are often severely impaired in many processes and often gain compensatory mutations. Thus, these studies were questioned for their ability to conclusively determine the involvement of (p)ppGpp in persister cell formation ppGpp and toxin/antitoxin systems were discussed as major triggers for dormancy/persistence, at least in Proteobacteria ppGpp stimulates the expression of hibernation factors to dimerize 70S ribosomes into 100S ribosomes ppGpp. However, (p)ppGpp did not affect the resuscitation of single-cell persisters ppGpp-dependent dimerization is a general mechanism leading to persistence ppGpp since a (p)ppGpp0 mutant shows a similar rate of persister formation ppGpp ppGpp synthetase-encoding gene in a pppGpp0 strain resulted in 100 S ribosome formation ppGpp synthesis. Cultures experiencing nutrient limitation contain distinct subpopulations of cells exhibiting either comparatively high or low protein synthesis activity. This heterogeneity is determined by (p)pGpp levels ppGpp-mediated persistence ppGpp contributes to antibiotic tolerance ppGpp in persister cell formation is still controversial, there is evidence that (p)ppGpp contributes to establish long-term (persistent) infections in diverse host niches. Of note, the term ‘persistent infection’ has to be clearly distinguished from ‘antibiotic persistence’ described above. Persistent infection is generally used to describe infections in the host that are not cleared by the host immune system, whereas antibiotic persistence describes a bacterial population that is refractory to antibiotic treatments ppGpp levels due to mutations of rel (F128Y) was isolated ppGpp levels compared to MRSA isolates from resolving bacteremia ppGpp levels has been linked to more chronic persistent infections. The mutations might have been selected due to their immune evasion properties or their higher antibiotic tolerance phenotype.Collectively, in E. coli ppGpp-independent mechanisms also account for growth-rate control when nutrients become limited. (p)ppGpp seems to have different roles depending on its abundance in the cell ppGpp restricts the upper limit of GTP to ensure optimal growth and survival.(p)ppGpp is a major regulator of growth-rate control in B. subtilis was also found to be lethal only in the absence of (p)ppGpp synthesis ppGpp0 mutant is diminished in the stationary phase, which has also been linked to uncontrolled GTP accumulation (our unpublished data). In the stationary phase, S. aureus acquires tolerance towards oxidative stress by maintenance of redox and iron homeostasis, which is mediated by (p)ppGpp ppGpp not only confers tolerance to antibiotics but also to ROS ppGpp synthesis.In certain media, the survival of the in vitro and during persistent infections in vivo. However, (p)ppGpp overproduction comes with a fitness disadvantage. Thus, (p)ppGpp levels are usually controlled to allow optimal growth under non-stressed conditions  but also to protect from sudden insults (stringent conditions). In Firmicutes, the (p)ppGpp-mediated restriction of GTP accumulation is at least one, if not the only, mechanism for protection. How uncontrolled GTP levels in pppGpp0 strains lead to cell death remains to be elucidated.A tight control of (p)ppGpp level is crucial for bacterial growth and survival. This balance is achieved by the activity of three (p)ppGpp synthetases  and the strong hydrolase activity of Rel. Rel and RelQ-dependent (p)ppGpp synthesis is usually restricted and activated under certain conditions, including amino acid limitation and allosteric activation via (p)ppGpp. Under ‘stringent conditions’, (p)ppGpp contributes to active adaptation to stressful conditions mainly via up-regulation of certain genes, e.g. amino acid synthesis genes, oxidative stress response genes, and virulence genes. (p)ppGpp acts mainly as an inhibitory molecule  influences the stringent phenotype. The affinity of these molecules towards different targets can differ considerably ppGpp is an important molecule that controls bacterial survival and virulence. Of note, there might be major differences also between the different Firmicute species with respect to (p)ppGpp activation pathways as well as down-stream effects, e.g. GTP dysregulation. However, comparative studies have so far been rare, and species-specific properties remain to be further elucidated. It seems likely that the machineries to synthesize and sense (p)ppGpp have evolved in the different species to optimize adaptation to the unique environmental niches.Common to all species is that high (p)ppGpp levels are growth limiting and thereby contributing to antibiotic tolerance. To what extent (p)ppGpp synthesis facilitates persister formation is still questionable. It was argued that the (p)ppGpp signalling pathway could be a prime target to simultaneously combat bacterial virulence and resistance. Although clinical validation of (p)ppGpp as a new target for antimicrobials is still missing, several approaches towards this end have been devised that show promise (for reviews, see Pulschen et al.


The purpose of this study was to determine differences in total (TCF), medial compartment (MCF), and lateral compartment (LCF) tibiofemoral joint compressive forces and related muscle forces between replaced and non-replaced limbs during level and uphill walking at an incline of 10°. A musculoskeletal modeling and simulation approach using static optimization was used to determine the muscle forces and TCF, MCF, and LCF for 25 patients with primary TKA. A statistical parametric mapping repeated-measures ANOVA was conducted on knee compressive forces and muscle forces using statistical parametric mapping. Greater TCF, MCF, and LCF values were observed throughout the loading response, mid-stance, and late stance during uphill walking. During level walking, knee extensor muscle forces were greater throughout the first 50% of the stance during level walking, yet greater during uphill walking during the last 50% of the stance. Conversely, knee flexor muscle forces were greater through the loading response and push-off phases of the stance. No between-limb differences were observed for compressive or muscle forces, suggesting that uphill walking may promote a more balanced loading of replaced and non-replaced limbs. Additionally, patients with TKA appear to rely on the hamstrings muscle group during the late stance for knee joint control, thus supporting uphill walking as an effective exercise modality to improve posterior chain muscle strength. It is projected that over the next several decades, the incidence of total knee arthroplasty (TKA) will grow over 400% . The priAlthough it has been incorporated in exercise and rehabilitative routines, one daily task those with TKA may encounter is uphill walking . This maObtaining true tibiofemoral compressive forces in vivo requires the use of specialized instrumented prostheses, which can be very costly and not practical for large-scale use. Furthermore, these instrumented prostheses only report forces in the replaced limb, and not in the contralateral, non-replaced limb, making between-limb comparisons impossible. Utilizing musculoskeletal modeling and simulation techniques, we recently demonstrated that peak tibiofemoral compressive forces between limbs (replaced vs. non-replaced) of patients with TKA may be more symmetrical between the replaced and non-replaced limbs than other joint kinetic variables have typically reported, and that different strategies in movement patterns may allow for this more balanced loading to occur . Though With an eye toward postoperative care and the return to more “normal” life in mind, we aimed to examine differences in compartment-specific tibiofemoral compressive forces , medial compartment compressive force (MCF), and lateral compartment compressive force (LCF)) and related knee-joint-spanning muscle forces between different limbs  and different slopes (0° (level), and 10° (uphill)) throughout the stance phase. Based on our previous work , we hypon = 10, Journey II, Smith & Nephew, Memphis, TN, USA), bi-cruciate retaining , or posterior stabilized implants . Inclusion/exclusion criteria and full data collection methods have been previously reported [2; or any neurological diseases. Prior to participation in the study, all participants provided informed consent approved by the University’s Institutional Review Board.Twenty-five patients with TKA  were recruited from a local orthopedic clinic to attend one laboratory session. Of our sample of twenty-five patients, all had received a primary TKA from the same orthopedic surgeon within 5 years of participation. Participants received either cruciate retaining  and 10° inclines on a customized adjustable ramp system that was instrumented with two force platforms. A trial was deemed successful if contact was made with only the force plate during the ramp ascent or level walking. To minimize the duration of the data collection session, ramp incline conditions were performed first, followed by the level walking conditions. Three-dimensional (3D) kinematics  and the ground reaction force  were simultaneously recorded. To define each anatomic segment, passive reflective markers were placed bilaterally on each participant’s acromion process, iliac crest, greater trochanter, medial and lateral femoral epicondyles, medial and lateral malleoli, the distal phalanx of the second toe, as well as the heads of the first and fifth metatarsals. Rigid-body marker clusters were used to track the motion of each segment during walking trials and were placed on the trunk, pelvis, thighs, shanks, and feet.A 16-channel surface electromyography (EMG) system  was used to record muscle EMG activations bilaterally on the following muscles: vastus lateralis, vastus medialis, medial head of the gastrocnemius, semitendinosus, and biceps femoris. The site of electrode attachment was cleaned and shaven prior to the application of electrodes. The placement of the EMG electrodes on the selected muscles was based on the recommendations of SENIAM . KinematAn open-source musculoskeletal model , 92 muscle–tendon actuators) was used to perform the simulations . The kneGeneralized joint coordinates derived from inverse kinematics calculations were exported from Visual3D  and imported into OpenSim for simulations . The generalized joint coordinates were applied to each subject-specific scaled musculoskeletal model. Inverse dynamics calculations were performed in OpenSim to compute lower-extremity joint moments. Next, muscle activations and forces were calculated using static optimization ,13. The Raw EMG signals were band-pass filtered at cutoff frequencies of 10 Hz and 450 Hz and then full-wave rectified. A moving root-mean-square (RMS) filter was used to filter the rectified EMG signals using a 60-millisecond moving window. The maximum value of the RMS EMG signals of three functional test trials was used to normalize the filtered EMG signals of the testing movement trials. Primary variables of interest included TCF, MCF, and LCF. Muscle forces of the knee extensors and flexors were also included as secondary variables. The knee extensors group includes the rectus femoris, vastus lateralis, vastus intermedius, and vastus medialis. The flexors include the biceps femoris long and short heads, semimembranosus, semitendinosus, sartorius, gracilis, and both medial and lateral heads of the gastrocnemius. All variables of interest were analyzed during the stance phase of the gait cycle, defined as the interval between the vertical GRF crossing a threshold of 10 N (heel-strike) and subsequently falling below the threshold (toe-off), as measured by in-ground force platforms. To examine differences in tibiofemoral compressive and muscle forces between limbs and slopes, one-dimensional statistical parametric mapping (SPM) using Random Field Theory to correct for Type I error inflation ,17 was iCompressive and muscle forces were compared using a 2 × 2  × slope ) SPM repeated-measures ANOVA. Limb and slope main effects were deemed significant when the SPM trajectory crossed the critical threshold . If a siThe TCF demonstrated a significant main effect of slope, indicating different TCFs between uphill and level walking during five separate stance intervals . During During the first 26% of the stance phase, the MCF was greater during uphill walking with a mean difference of 0.25 BW . During Both SPM tests for the knee-joint-spanning muscle forces revealed a significant main effect of slope. The knee extensor muscle forces were significantly lower during uphill walking during the loading response, by an average mean difference of 0.53 BW, yet greater by 0.24 BW during push-off when walking uphill . Knee flThe frontal-plane lower limb alignment was determined by calculating the mechanical axis angle using motion capture data ,22,23. TWe sought to investigate differences in tibiofemoral joint compressive forces  and knee-joint-spanning muscle forces between different limbs  and different slopes (0° and 10°) through the entirety of the stance phase. Our hypothesis was supported, in that no between-limb differences were observed at either slope and that the slope significantly impacted both compressive and muscle forces.Results for the TCF showed no differences between the replaced and non-replaced limbs. Inverse-dynamics-based studies have frequently used the internal knee extension to indicate overall loading at the knee joint ,27,28,29Similar trends in statistical significance throughout the stance phase were observed for the MCF and LCF, indicating a shift in compressive forces from the medial compartment to the lateral compartment when walking uphill. The change in magnitude of compressive force between level and uphill walking was substantially higher in the LCF than in the MCF. Both loading response and push-off TCF were larger in uphill walking, and the magnitude of the TCF seems to be in part influenced by the shift in compressive-force-off of the medial compartment and onto the lateral compartment of the knee. Given the nature of increased medial compartment joint loading  that was likely a contributing factor to knee osteoarthritis (OA) preceding TKA, these findings may be encouraging for uphill walking as a means to redistribute joint loads off of the medial compartment, and onto the lateral compartment. Though not statistically significant, a small decrease in loading response MCF was observed in the replaced limb F. QualitSignificant differences in knee extensor muscle force were present during both the loading response and push-off between slopes J. An incA secondary finding of this study is that changes to tibiofemoral joint compressive forces between slopes occur specifically during the loading response. Uphill walking increased the TCF, MCF, and LCF on average by 0.68, 0.25, and 0.41 BW, respectively, during the loading response phase. The knee extensors produce eccentric force to absorb loading to the knee joint and maintain posture, but quickly transition to concentric force production to extend the knee joint. In uphill walking, the TCF increased with slope during the loading response ; however, the knee extensor muscle force was lower than during level walking. Given the increase in loading response TCF during uphill walking that is not accompanied with a significant increase in knee extensor muscle forces, it is likely that dynamic control of the knee joint may be coupled through other joints such as the hip or ankle. Though knee-joint-spanning muscles cannot solely explain the behavior of the TCF, uphill walking may promote an environment that especially engages the quadriceps muscles during push-off without consequential increases in joint loads compared to level walking K, offeriWen et al. recommended against the use of 10° and 15° uphill walking during TKA rehabilitation due to an increased internal knee extension moment experienced by the replaced limb, and the association between knee extension moment, increased TCF, and damage to the knee prosthesis ,34,35. DThere are certain limitations to this work that need to be acknowledged. SPM analysis between groups or conditions mandates temporal synchrony for comparisons over time to be made. In order to meet these requirements, time-normalization (to 101 data points) was performed on compressive and muscle force waveforms. With such reductions in resolution, it is possible that true peak values may be reduced (or smoothed out) as a result of the time normalization, which may mute observable differences of compressive or muscle forces between limbs. However, we feel that the consequences of this time normalization were minimal given our objective of comparing between conditions within our sample.Lerner et al. reported contact force estimations using three variations of this model . The fulJoint loading appears to be similar between replaced and nonreplaced limbs during uphill and downhill walking. Due to the increased difficulty of the uphill walking task, the TCF and MCF were shown to increase with slope, while the loading response knee extensor muscle force and push-off knee flexor muscle forces were greater during level walking, suggesting the adoption of alternative gait strategies to reduce muscular demand at the knee following TKA. Uphill walking may be an effective exercise for high-intensity early and long-term rehabilitation programs, promoting increased muscular demand and quadriceps strengthening while encouraging the reacquisition of normal gait patterns following TKA.