Tumor characteristics associated with engraftment of patient-derived non-small cell lung cancer xenografts in immunocompromised mice

Consistency between Primary Uterine Corpus Malignancies and Their Corresponding Patient-Derived Xenograft Models

Patient-derived xenograft (PDX) models retain the characteristics of tumors and are useful tools for personalized therapy and translational research. In this study, we aimed to establish PDX models for uterine corpus malignancies (UC-PDX) and analyze their similarities. Tissue fragments obtained from 92 patients with uterine corpus malignancies were transplanted subcutaneously into immunodeficient mice. Histological and immunohistochemical analyses were performed to compare tumors of patients with PDX tumors. DNA and RNA sequencing were performed to validate the genetic profile. Furthermore, the RNA in extracellular vesicles (EVs) extracted from primary and PDX tumors was analyzed. Among the 92 cases, 52 UC-PDX models were established, with a success rate of 56.5%. The success rate depended on tumor histology and staging. The pathological and immunohistochemical features of primary and PDX tumors were similar. DNA sequencing revealed similarities in gene mutations between the primary and PDX tumors. RNA sequencing showed similarities in gene expressions between primary and PDX tumors. Furthermore, the RNA profiles of the EVs obtained from primary and PDX tumors were similar. As UC-PDX retained the pathological and immunohistochemical features and gene profiles of primary tumors, they may provide a platform for developing personalized medicine and translational research.

Patient Characteristics Associated with Growth of Patient-Derived Tumor Implants in Mice (Patient-Derived Xenografts)

Background: patient-derived xenografts (PDXs) have defined the field of translational cancer research in recent years, becoming one of the most-used tools in early drug development. The process of establishing cancer models in mice has turned out to be challenging, since little research focuses on evaluating which factors impact engraftment success. We sought to determine the clinical, pathological, or molecular factors which may predict better engraftment rates in PDXs. Methods: between March 2017 and January 2021, tumor samples obtained from patients with primary or metastatic cancer were implanted into athymic nude mice. A full comprehensive evaluation of baseline factors associated with the patients and patients' tumors was performed, with the goal of potentially identifying predictive markers of engraftment. We focused on clinical (patient factors) pathological (patients' tumor samples) and molecular (patients' tumor samples) characteristics, analyzed either by immunohistochemistry (IHC) or next-generation sequencing (NGS), which were associated with the likelihood of final engraftment, as well as with tumor growth rates in xenografts. Results: a total of 585 tumor samples were collected and implanted. Twenty-one failed to engraft, due to lack of malignant cells. Of 564 tumor-positive samples, 187 (33.2%) grew at time of analysis. The study was able to find correlation and predictive value for engraftment for the following: the use of systemic antibiotics by the patient within 2 weeks of sampling (38.1% (72/189) antibiotics- group vs. 30.7% (115/375) no-antibiotics) (p = 0.048), and the administration of systemic steroids to the patients within 2 weeks of sampling (41.5% (34/48) steroids vs. 31.7% (153/329), no-steroids) (p = 0.049). Regarding patient's baseline tests, we found certain markers could help predict final engraftment success: for lactate dehydrogenase (LDH) levels, 34.1% (140/411) of tumors derived from patients with baseline blood LDH levels above the upper limit of normality (ULN) achieved growth, against 30.7% (47/153) with normal LDH (p = 0.047). Histological tumor characteristics, such as grade of differentiation, were also correlated. Grade 1: 25.4% (47/187), grade 2: 34.8% (65/187) and grade 3: 40.1% (75/187) tumors achieved successful growth (p = 0.043), suggesting the higher the grade, the higher the likelihood of success. Similarly, higher ki67 levels were also correlated with better engraftment rates: low (Ki67 < 15%): 8.9% (9/45) achieved growth vs. high (Ki67 ≥ 15%): 31% (35/113) (p: 0.002). Other markers of aggressiveness such as the presence of lymphovascular invasion in tumor sample of origin was also predictive: 42.2% (97/230) with lymphovascular vs. 26.9% (90/334) of samples with no invasion (p = 0.0001). From the molecular standpoint, mismatch-repair-deficient (MMRd) tumors showed better engraftment rates: 62.1% (18/29) achieved growth vs. 40.8% (75/184) of proficient tumors (p = 0.026). A total of 84 PDX were breast models, among which 57.9% (11/19) ER-negative models grew, vs. 15.4% (10/65) of ER-positive models (p = 0.0001), also consonant with ER-negative tumors being more aggressive. BRAFmut cancers are more likely to achieve engraftment during the development of PDX models. Lastly, tumor growth rates during first passages can help establish a cutoff point for the decision-making process during PDX development, since the higher the tumor grades, the higher the likelihood of success. Conclusions: tumors with higher grade and Ki67 protein expression, lymphovascular and/or perineural invasion, with dMMR and are negative for ER expression have a higher probability of achieving growth in the process of PDX development. The use of steroids and/or antibiotics in the patient prior to sampling can also impact the likelihood of success in PDX development. Lastly, establishing a cutoff point for tumor growth rates could guide the decision-making process during PDX development.

Establishment of a high-fidelity patient-derived xenograft model for cervical cancer enables the evaluation of patient's response to conventional and novel therapies

BACKGROUND

Recurrent or metastatic cervical cancer (r/m CC) often has poor prognosis owing to its limited treatment options. The development of novel therapeutic strategies has been hindered by the lack of preclinical models that accurately reflect the biological and genomic heterogeneity of cervical cancer (CC). Herein, we aimed to establish a large patient-derived xenograft (PDX) biobank for CC, evaluate the consistency of the biologic indicators between PDX and primary tumor tissues of patients, and explore its utility for assessing patient's response to conventional and novel therapies.

METHODS

Sixty-nine fresh CC tumor tissues were implanted directly into immunodeficient mice to establish PDX models. The concordance of the PDX models with their corresponding primary tumors (PTs) was compared based on the clinical pathological features, protein biomarker levels, and genomic features through hematoxylin & eosin staining, immunohistochemistry, and whole exome sequencing, respectively. Moreover, the clinical information of CC patients, RNA transcriptome and immune phenotyping of primary tumors were integrated to identify the potential parameters that could affect the success of xenograft engraftment. Subsequently, PDX model was evaluated for its capacity to mirror patient's response to chemotherapy. Finally, PDX model and PDX-derived organoid (PDXO) were utilized to evaluate the therapeutic efficacy of neratinib and adoptive cell therapy (ACT) combination strategy for CC patients with human epidermal growth factor receptor 2 (HER2) mutation.

RESULTS

We established a PDX biobank for CC with a success rate of 63.8% (44/69). The primary features of established PDX tumors, including clinicopathological features, the expression levels of protein biomarkers including Ki67, α-smooth muscle actin, and p16, and genomics, were highly consistent with their PTs. Furthermore, xenograft engraftment was likely influenced by the primary tumor size, the presence of follicular helper T cells and the expression of cell adhesion-related genes in primary tumor tissue. The CC derived PDX models were capable of recapitulating the patient's response to chemotherapy. In a PDX model, a novel therapeutic strategy, the combination of ACT and neratinib, was shown to effectively inhibit the growth of PDX tumors derived from CC patients with HER2-mutation.

CONCLUSIONS

We established by far the largest PDX biobank with a high engraftment rate for CC that preserves the histopathological and genetic characteristics of patient's biopsy samples, recapitulates patient's response to conventional therapy, and is capable of evaluating the efficacy of novel therapeutic modalities for CC.

Generation, evolution, interfering factors, applications, and challenges of patient-derived xenograft models in immunodeficient mice

Establishing appropriate preclinical models is essential for cancer research. Evidence suggests that cancer is a highly heterogeneous disease. This follows the growing use of cancer models in cancer research to avoid these differences between xenograft tumor models and patient tumors. In recent years, a patient-derived xenograft (PDX) tumor model has been actively generated and applied, which preserves both cell-cell interactions and the microenvironment of tumors by directly transplanting cancer tissue from tumors into immunodeficient mice. In addition to this, the advent of alternative hosts, such as zebrafish hosts, or in vitro models (organoids and microfluidics), has also facilitated the advancement of cancer research. However, they still have a long way to go before they become reliable models. The development of immunodeficient mice has enabled PDX to become more mature and radiate new vitality. As one of the most reliable and standard preclinical models, the PDX model in immunodeficient mice (PDX-IM) exerts important effects in drug screening, biomarker development, personalized medicine, co-clinical trials, and immunotherapy. Here, we focus on the development procedures and application of PDX-IM in detail, summarize the implications that the evolution of immunodeficient mice has brought to PDX-IM, and cover the key issues in developing PDX-IM in preclinical studies.

Phenotyping of lymphoproliferative tumours generated in xenografts of non-small cell lung cancer

Background

Patient-derived xenograft (PDX) models involve the engraftment of tumour tissue in immunocompromised mice and represent an important pre-clinical oncology research method. A limitation of non-small cell lung cancer (NSCLC) PDX model derivation in NOD-scid IL2Rgammanull (NSG) mice is that a subset of initial engraftments are of lymphocytic, rather than tumour origin.

Methods

The immunophenotype of lymphoproliferations arising in the lung TRACERx PDX pipeline were characterised. To present the histology data herein, we developed a Python-based tool for generating patient-level pathology overview figures from whole-slide image files; PATHOverview is available on GitHub (https://github.com/EpiCENTR-Lab/PATHOverview).

Results

Lymphoproliferations occurred in 17.8% of lung adenocarcinoma and 10% of lung squamous cell carcinoma transplantations, despite none of these patients having a prior or subsequent clinical history of lymphoproliferative disease. Lymphoproliferations were predominantly human CD20+ B cells and had the immunophenotype expected for post-transplantation diffuse large B cell lymphoma with plasma cell features. All lymphoproliferations expressed Epstein-Barr-encoded RNAs (EBER). Analysis of immunoglobulin light chain gene rearrangements in three tumours where multiple tumour regions had resulted in lymphoproliferations suggested that each had independent clonal origins.

Discussion

Overall, these data suggest that B cell clones with lymphoproliferative potential are present within primary NSCLC tumours, and that these are under continuous immune surveillance. Since these cells can be expanded following transplantation into NSG mice, our data highlight the value of quality control measures to identify lymphoproliferations within xenograft pipelines and support the incorporation of strategies to minimise lymphoproliferations during the early stages of xenograft establishment pipelines.

Application status and future prospects of the PDX model in lung cancer

Lung cancer is one of the most prevalent, fatal, and highly heterogeneous diseases that, seriously threaten human health. Lung cancer is primarily caused by the aberrant expression of multiple genes in the cells. Lung cancer treatment options include surgery, radiation, chemotherapy, targeted therapy, and immunotherapy. In recent decades, significant progress has been made in developing therapeutic agents for lung cancer as well as a biomarker for its early diagnosis. Nonetheless, the alternative applications of traditional pre-clinical models (cell line models) for diagnosis and prognosis prediction are constrained by several factors, including the lack of microenvironment components necessary to affect cancer biology and drug response, and the differences between laboratory and clinical results. The leading reason is that substantial shifts accrued to cell biological behaviors, such as cell proliferative, metastatic, invasive, and gene expression capabilities of different cancer cells after decades of growing indefinitely in vitro. Moreover, the introduction of individualized treatment has prompted the development of appropriate experimental models. In recent years, preclinical research on lung cancer has primarily relied on the patient-derived tumor xenograft (PDX) model. The PDX provides stable models with recapitulate characteristics of the parental tumor such as the histopathology and genetic blueprint. Additionally, PDXs offer valuable models for efficacy screening of new cancer drugs, thus, advancing the understanding of tumor biology. Concurrently, with the heightened interest in the PDX models, potential shortcomings have gradually emerged. This review summarizes the significant advantages of PDXs over the previous models, their benefits, potential future uses and interrogating open issues.

LINC01089, suppressed by YY1, inhibits lung cancer progression by targeting miR-301b-3p/HPDG axis

PURPOSE

LINC01089 is a newly identified lncRNA and rarely reported in human cancers. Our study aimed to investigate its role in lung cancer.

METHODS

YY1, LINC01089, and miR-301b-3p levels in lung cancer tissues and cells were assessed using qRT-PCR. Bioinformatics analysis and luciferase reporter, ChIP, and RIP assays were carried out for determining the relationships among YY1, LINC01089, miR-301b-3p, and HPGD. Gain- and loss-of-function assays were carried out to confirm the impacts of LINC01089 and HPDG in lung cancer cells. CCK-8 assay was used to assess cell proliferation rate, and Transwell assay was applied to measure cell invasion and migration. An in vivo tumor model was applied for validating the role of LINC01089.

RESULTS

LINC01089 was decreased in lung cancer tissues and cells, and low LINC01089 level predicted a poor clinical outcome. YY1 directly bound to LINC01089 promoter region and inhibited its transcription. LINC01089 knockdown thwarted the proliferation, invasion, and migration capacity of H1299 and A549 cells and aggravated tumor growth. Specifically, LINC01089 functioned as a competing endogenous RNA of miR-301b-3p to modulate HPGD and thereby affected lung cancer progression.

CONCLUSION

Our data revealed that LINC01089, directly suppressed by YY1, inhibited lung cancer progression by targeting the miR-301b-3p/HPGD axis. Graphical abstract 1. LINC01089 expression was downregulated in lung cancer tisuues and cell lines, and low LINC01089 levels predicted a poor clinical outcome. 2. LINC01089 knockdown enhanced proliferation, invasion, and migration of H1299 and A549 cells in vitro and promoted lung cancer cell tumorigenesis and metastasis in vivo. 3. LINC01089, directly suppressed by YY1, functioned as a competing endogenous RNA against miR-301b-3p to increase HPGD expression.

Patient-derived xenograft models for gastrointestinal tumors: A single-center retrospective study

BACKGROUND

Patient-derived xenograft (PDX) models have shown a great efficiency in preclinical and translational applications. Gastrointestinal (GI) tumors have a strong heterogeneity, and the engraftment rate of PDX models remarkably vary. However, the clinicopathological and molecular characteristics affecting the engraftment rate still remain elusive.

METHODS

A total of 312 fresh tumor tissue samples from patients with GI cancer were implanted into immunodeficient mice. The median follow-up time of patients was 37 months. Patients' characteristics were compared in terms of PDX growth and overall survival. PDX models of 3-6 generations were used for drug evaluation.

RESULTS

In total, 171 (54.8%, 171/312) PDX models were established, including 85 PDX models of colorectal cancer, 21 PDX models of esophageal cancer, and 65 PDX models of gastric cancer. Other than tumor site, histology, differentiation degree, and serum alpha-fetoprotein (AFP) level, no significant differences were found between transplantation of xenografts and patients' characteristics. For patients who had undergone neoadjuvant therapy, the incidence of tumor formation was higher in those with progressive disease (PD) or stable disease (SD). In gastric cancer, the results showed a higher transplantation rate in deficient mismatch repair (dMMR) tumors, and Ki-67 could be an important factor affecting the engraftment rate. The gene mutation status of RAS and BRAF, two important molecular markers in colorectal cancer, showed a high degree of consistency between patients' tumors and PDXs. However, no significant effects of these two mutations on PDX engraftment rate were observed. More importantly, in this study although KRAS mutations were detected in two clinical cases, evident tumor inhibition was still observed after cetuximab treatment in both PDX models and patients.

CONCLUSION

A large-scale PDX model including 171 cases was successfully established for GI tumors in our center. The relationship between clinicopathological and molecular features and engraftment rates were clarified. Furthermore, this resource provides us with profound insights into tumor heterogeneity, making these models valuable for PDX-guided treatment decisions, and offering the PDX model as a great tool for personalized treatment and translation research.

A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.

SIGNIFICANCE

Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

Patient-Derived Pancreatic Cancer Cells Induce C2C12 Myotube Atrophy by Releasing Hsp70 and Hsp90

Pancreatic cancer (PC) patients are highly prone to cachexia, a lethal wasting syndrome featuring muscle wasting with an undefined etiology. Recent data indicate that certain murine cancer cells induce muscle wasting by releasing Hsp70 and Hsp90 through extracellular vesicles (EVs) to activate p38β MAPK-mediated catabolic pathways primarily through Toll-like receptor 4 (TLR4). However, whether human PC induces cachexia through releasing Hsp70 and Hsp90 is undetermined. Here, we investigated whether patient-derived PC cells induce muscle cell atrophy directly through this mechanism. We compared cancer cells isolated from patient-derived xenografts (PDX) from three PC patients who had cachexia (PCC) with those of three early-stage lung cancer patients without cachexia (LCC) and two renal cancer patients who were not prone to cachexia (RCC). We observed small increases of Hsp70 and Hsp90 released by LCC and RCC in comparison to non-cancer control cells (NCC). However, PCC released markedly higher levels of Hsp70 and Hsp90 (~ 6-fold on average) than LCC and RCC. In addition, PCC released similarly increased levels of Hsp70/90-containing EVs. In contrast to RCC and LCC, PCC-conditioned media induced a potent catabolic response in C2C12 myotubes including the activation of p38 MAPK and transcription factor C/EBPβ, upregulation of E3 ligases UBR2 and MAFbx, and increase of autophagy marker LC3-II, resulting in the loss of the myosin heavy chain (MHC ~50%) and myotube diameter (~60%). Importantly, the catabolic response was attenuated by Hsp70- and Hsp90-neutralizing antibodies in a dose-dependent manner. These data suggest that human PC cells release high levels of Hsp70 and Hsp90 that induce muscle atrophy through a direct action on muscle cells.

Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles

Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life-threatening side effects. Accurately anticipating doxorubicin-resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single-gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin-response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard-scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive (median MCC and AUC of 0.14 and 0.57, respectively). As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy.

CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts

Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.

The Patient-Derived Cancer Organoids: Promises and Challenges as Platforms for Cancer Discovery

The cancer burden is rapidly increasing in most countries, and thus, new anticancer drugs for effective cancer therapy must be developed. Cancer model systems that recapitulate the biological processes of human cancers are one of the cores of the drug development process. PDCO has emerged as a unique model that preserves the genetic, physiological, and histologic characteristics of original cancer, including inter- and intratumoral heterogeneities. Due to these advantages, the PCDO model is increasingly investigated for anticancer drug screening and efficacy testing, preclinical patient stratification, and precision medicine for selecting the most effective anticancer therapy for patients. Here, we review the prospects and limitations of PDCO compared to the conventional cancer models. With advances in culture success rates, co-culture systems with the tumor microenvironment, organoid-on-a-chip technology, and automation technology, PDCO will become the most promising model to develop anticancer drugs and precision medicine.

Unbiased peptoid cell screen identifies a peptoid targeting newly appeared cell surface vimentin on tumor transformed early lung cancer cells

To identify potential new reagents and biomarkers for early lung cancer detection we combined the use of a novel preclinical isogenic model of human lung epithelial cells comparing non-malignant cells with those transformed to full malignancy using defined oncogenic changes and our on-bead two color (red and green stained cells) (OBTC) peptoid combinatorial screening methodology. The preclinical model used normal parent lung epithelial cells (HBEC3-KT, labeled with green dye) and isogenic fully malignant transformed derivatives (labeled with a red dye) via the sequential introduction of key genetic alterations of p53 knockdown, oncogenic KRAS and overexpression of cMYC (HBEC3^{p53, KRAS, cMYC}). Using the unbiased OBTC screening approach, we tested 100,000 different peptoids and identified only one (named JM3A) that bound to the surface of the HBEC3^{p53, KRAS, cMYC} cells (red cells) but not HBEC3-KT cells (green cells). Using the JM3A peptoid and proteomics, we identified the protein bound as vimentin using multiple validation approaches. These all confirmed the cell surface expression of vimentin (CSV) on transformed (HBEC3^{p53, KRAS, cMYC}) but not on untransformed (HBEC3-KT) cells. JM3A coupled with fluorophores was able to detect and stain cell surface vimentin on very early stage lung cancers but not normal lung epithelial cells in a fashion comparable to that using anti-vimentin antibodies. We conclude: using a combined isogenic preclinical model of lung cancer and two color screening of a large peptoid library, we have identified differential expression of cell surface vimentin (CSV) after malignant transformation of lung epithelial cells, and developed a new peptoid reagent (JM3A) for detection of CSV which works well in staining of early stage NSCLCs. This new, highly specific, easy to prepare, CSV detecting JM3A peptoid provides an important new reagent for identifying cancer cells in early stage tumors as well as a resource for detection and isolating of CSV expressing circulating tumor cells.

Establishment and evaluation of retroperitoneal liposarcoma patient-derived xenograft models: an ideal model for preclinical study

Retroperitoneal liposarcoma (RLPS) is one of the most common subtypes of retroperitoneal soft tissue sarcomas. It is characterized by poor sensitivity to radiotherapy and chemotherapy and a low success rate of complete surgical resection. However, there are few reliable preclinical RLPS models for target discovery and therapy research. In this study, we aimed to establish RLPS patient-derived xenograft (PDX) models that are useful for biological research and preclinical drug trials. A total of 56 freshly resected RLPS tissues were subcutaneously transplanted into non-obese diabetic-severe combined immune deficient (NOD-SCID) mice, with subsequent xenotransplantation into second-generation mice. The tumor engraftment rate of first generation PDXs was 44.64%, and higher success rates were obtained from implantations of dedifferentiated, myxous, pleomorphic, high-grade liposarcomas and those with retroperitoneal organ infiltration. The first- and second- generation PDX models preserved the histopathological morphology, gene mutation profiles and MDM2 amplification of the primary tissues. PDX models can also provide the benefit of retaining original tumor biology and microenvironment characteristics, such as abnormal adipose differentiation, elevated Ki67 levels, high microvessel density, cancer-associated fibroblast presence, and tumor-associated macrophage infiltration. Overall survival (OS) and disease-free survival (DFS) of patients with successful first-generation PDX engraftment were significantly poorer than those with failed engraftment. Treatment with MDM2 inhibitor RG7112 significantly suppressed tumor growth of DDLPS PDX in mice. In conclusion, we successfully established RLPS PDX models that were histologically, genetically, and molecularly consistent with the original tissues. These models might provide opportunities for advancing RLPS tumor biology research, facilitating the development of novel drugs, particularly those targeting MDM2 amplification, adipose differentiation process, angiogenesis, cancer-associated fibroblasts, and so on.

Epstein Barr virus-positive B-cell lymphoma is highly vulnerable to MDM2 inhibitors in vivo

MDM2 inhibitors have potent in vivo activity against and could be a novel therapy for EBV-positive B-cell lymphoma.

EBV positivity or loss of BCL6 expression can be a potential predictive biomarker for response to MDM2 inhibitors in patients with lymphoma

Epstein-Barr virus–positive (EBV-positive) B-cell lymphomas are common in immunocompromised patients and remain an unmet medical need. Here we report that MDM2 inhibitors (MDM2is) navtemadlin and idasanutlin have potent in vivo activity in EBV-positive B-cell lymphoma established in immunocompromised mice. Tumor regression was observed in all 5 EBV-positive xenograft–associated B-cell lymphomas treated with navtemadlin or idasanutlin. Molecular characterization showed that treatment with MDM2is resulted in activation of p53 pathways and downregulation of cell cycle effectors in human lymphoma cell lines that were either EBV-positive or had undetectable expression of BCL6, a transcriptional inhibitor of the TP53 gene. Moreover, treatment with navtemadlin resulted in tumor regression and prevented systemic dissemination of EBV-positive lymphoma derived from 2 juvenile patients with posttransplant lymphoproliferative diseases, including 1 whose tumor was resistant to virus-specific T-cell therapy. These results provide proof-of-concept for targeted therapy of EBV-positive lymphoma with MDM2is and the feasibility of using EBV infection or loss of BCL6 expression to identify responders to MDM2is.

Structure-based classification predicts drug response in EGFR-mutant NSCLC

Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18–21 and are established driver mutations in non-small cell lung cancer (NSCLC)^1–3. Targeted therapies are approved for patients with ‘classical’ mutations and a small number of other mutations^4–6. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown^1,3,7–10. Here we characterize the mutational landscape in 16,715 patients with EGFR-mutant NSCLC, and establish the structure–function relationship of EGFR mutations on drug sensitivity. We found that EGFR mutations can be separated into four distinct subgroups on the basis of sensitivity and structural changes that retrospectively predict patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. Together, these data delineate a structure-based approach for defining functional groups of EGFR mutations that can effectively guide treatment and clinical trial choices for patients with EGFR-mutant NSCLC and suggest that a structure–function-based approach may improve the prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.

Structural classification of mutations in the epidermal growth factor receptor causing non-small cell lung cancer is a better predictor of patient outcomes following drug treatment than traditional exon-based classification.

Patient-Derived Xenograft Models in Cervical Cancer: A Systematic Review

BACKGROUND

Patient-derived xenograft (PDX) models have been a focus of attention because they closely resemble the tumor features of patients and retain the molecular and histological features of diseases. They are promising tools for translational research. In the current systematic review, we identify publications on PDX models of cervical cancer (CC-PDX) with descriptions of main methodological characteristics and outcomes to identify the most suitable method for CC-PDX.

METHODS

We searched on PubMed to identify articles reporting CC-PDX. Briefly, the main inclusion criterion for papers was description of PDX created with fragments obtained from human cervical cancer specimens, and the exclusion criterion was the creation of xenograft with established cell lines.

RESULTS

After the search process, 10 studies were found and included in the systematic review. Among 98 donor patients, 61 CC-PDX were established, and the overall success rate was 62.2%. The success rate in each article ranged from 0% to 75% and was higher when using severe immunodeficient mice such as severe combined immunodeficient (SCID), nonobese diabetic (NOD) SCID, and NOD SCID gamma (NSG) mice than nude mice. Subrenal capsule implantation led to a higher engraftment rate than orthotopic and subcutaneous implantation. Fragments with a size of 1-3 mm3 were suitable for CC-PDX. No relationship was found between the engraftment rate and characteristics of the tumor and donor patient, including histology, staging, and metastasis. The latency period varied from 10 days to 12 months. Most studies showed a strong similarity in pathological and immunohistochemical features between the original tumor and the PDX model.

CONCLUSION

Severe immunodeficient mice and subrenal capsule implantation led to a higher engraftment rate; however, orthotopic and subcutaneous implantation were alternatives. When using nude mice, subrenal implantation may be better. Fragments with a size of 1-3 mm3 were suitable for CC-PDX. Few reports have been published about CC-PDX; the results were not confirmed because of the small sample size.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

Mapping lung squamous cell carcinoma pathogenesis through in vitro and in vivo models

Lung cancer is the main cause of cancer death worldwide, with lung squamous cell carcinoma (LUSC) being the second most frequent subtype. Preclinical LUSC models recapitulating human disease pathogenesis are key for the development of early intervention approaches and improved therapies. Here, we review advances and challenges in the generation of LUSC models, from 2D and 3D cultures, to murine models. We discuss how molecular profiling of premalignant lesions and invasive LUSC has contributed to the refinement of in vitro and in vivo models, and in turn, how these systems have increased our understanding of LUSC biology and therapeutic vulnerabilities.

Prognostic value of patient-derived xenograft engraftment in pediatric sarcomas

The goals of this work were to identify factors favoring patient-derived xenograft (PDX) engraftment and study the association between PDX engraftment and prognosis in pediatric patients with Ewing sarcoma, osteosarcoma, and rhabdomyosarcoma. We used immunodeficient mice to establish 30 subcutaneous PDX from patient tumor biopsies, with a successful engraftment rate of 44%. Age greater than 12 years and relapsed disease were patient factors associated with higher engraftment rate. Tumor type and biopsy location did not associate with engraftment. PDX models retained histology markers and most chromosomal aberrations of patient samples during successive passages in mice. Model treatment with irinotecan resulted in significant activity in 20 of the PDXs and replicated the response of rhabdomyosarcoma patients. Successive generations of PDXs responded similarly to irinotecan, demonstrating functional stability of these models. Importantly, out of 68 tumor samples from 51 patients with a median follow-up of 21.2 months, PDX engraftment from newly diagnosed patients was a prognostic factor significantly associated with poor outcome (p = 0.040). This association was not significant for relapsed patients. In the subgroup of patients with newly diagnosed Ewing sarcoma classified as standard risk, we found higher risk of relapse or refractory disease associated with those samples that produced stable PDX models (p = 0.0357). Overall, our study shows that PDX engraftment predicts worse outcome in newly diagnosed pediatric sarcoma patients.

The Essential Factors of Establishing Patient-derived Tumor Model

Establishing an applicable preclinical model is vital for translational cancer research. Patient-derived xenograft has been important preclinical model systems and widely used for cancer research. Patient-derived xenograft models that represent the tumors of the patients are necessary to better translate research discoveries and to test potential therapeutic approaches. However, research in this field is hampered by the limited engraftment rate. In this review, we go over a large number of researches on patient-derived xenograft transplantation and firstly systematically summarize the main factors in methodology to successfully establish models. These results will be applied to the development of patient-derived xenograft leading to better preclinical research.

KRT-232 and navitoclax enhance trametinib's anti-Cancer activity in non-small cell lung cancer patient-derived xenografts with KRAS mutations

Activating mutations of the KRAS gene are one of the major genomic alterations associated with tumorigenesis of non-small cell lung cancer (NSCLC). Thus far, treatment of KRAS-mutant NSCLC remains an unmet medical need. We determined the in vivo treatment responses of 13 KRAS mutant and 14 KRAS wild type NSCLC patient-derived xenografts (PDXs) to agents that target known NSCLC vulnerabilities: the MEK inhibitor trametinib, the MDM2 inhibitor KRT-232, and the BCL-X_L/BCL-2 inhibitor navitoclax. The results showed that the tumor regression rate after single agent therapy with KRT-232, trametinib and navitoclax was 11%, 10% and 0%, respectively. Combination therapies of trametinib plus KRT-232 and trametinib plus navitoclax led to improved partial response rates over single-agent activity in a subset of PDX models. Tumor regression was observed in 23% and 50% of PDXs after treatment with trametinib plus KRT-232 and trametinib plus navitoclax, respectively. The disease control rates in KRAS-mutant PDXs tested were 90%-100% after treatment with trametinib plus KRT-232 or plus navitoclax. A correlation analysis of treatment responses and genomic and proteomic biomarkers revealed that sensitivity to KRT-232 was significantly associated with TP53 wild-type or STK11 mutant genotypes (P<0.05). The levels of several proteins, including GSK3b, Nrf2, LKB1/pS334, and SMYD3, were significantly associated with sensitivity to trametinib plus navitoclax. Thus, the combination of trametinib plus KRT-232 or navitoclax resulted in improved efficacy compared with the agents alone in a subgroup of NSCLC PDX model with KRAS mutations. Expanded clinical trials of these targeted drug combinations in NSCLC are warranted.

From clinical specimens to human cancer preclinical models-a journey the NCI-cell line database-25 years later

The intramural the National Cancer Institute (NCI) and more recently the University of Texas Southwestern Medical Center with many different collaborators comprised a complex, multi-disciplinary team that collaborated to generated large, comprehensively annotated, cell-line related research resources which includes associated clinical, and molecular characterization data. This material has been shared in an anonymized fashion to accelerate progress in overcoming lung cancer, the leading cause of cancer death across the world. However, this cell line collection also includes a range of other cancers derived from patient-donated specimens that have been remarkably valuable for other types of cancer and disease research. A comprehensive analysis conducted by the NCI Center for Research Strategy of the 278 cell lines reported in the original Journal of Cellular Biochemistry Supplement, documents that these cell lines and related products have since been used in more than 14 000 grants, and 33 207 published scientific reports. This has resulted in over 1.2 million citations using at least one cell line. Many publications involve the use of more than one cell line, to understand the value of the resource collectively rather than individually; this method has resulted in 2.9 million citations. In addition, these cell lines have been linked to 422 clinical trials and cited by 4700 patents through publications. For lung cancer alone, the cell lines have been used in the research cited in the development of over 70 National Comprehensive Cancer Network clinical guidelines. Finally, it must be underscored again, that patient altruism enabled the availability of this invaluable research resource.

The Application of Patient-Derived Xenograft Models in Gynecologic Cancers

Recently, due to the limitations of cell line models and animal models in the preclinical research with insufficient reflecting the physiological situation of humans, patient-derived xenograft (PDX) models of many cancers have been widely developed because of their better representation of the tumor heterogeneity and tumor microenvironment with retention of the cellular complexity, cytogenetics, and stromal architecture. PDX models now have been identified as a powerful tool for determining cancer characteristics, developing new treatment, and predicting drug efficacy. An increase in attempts to generate PDX models in gynecologic cancers has emerged in recent years to understand tumorigenesis. Hence, this review summarized the generation of PDX models and engraftment success of PDX models in gynecologic cancers. Furthermore, we illustrated the similarity between PDX model and original tumor, and described preclinical utilization of PDX models in gynecologic cancers. It would help supply better personalized therapy for gynecologic cancer patients.

Enhanced Chemotherapeutic Efficacy of PLGA-Encapsulated Epigallocatechin Gallate (EGCG) Against Human Lung Cancer

Purpose

Currently, the clinical benefits of tea polyphenols have contributed to the development of efficient systemic delivery systems with adequate bioavailability and stability. In this study, we aimed to establish a nanoparticle model to overcome the shortcomings of epigallocatechin gallate (EGCG) in the treatment of lung cancer.

Materials and Methods

Poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) loaded with EGCG were prepared by the oil-in-water emulsion solvent evaporation technique. The characteristics of NPs, entrapment efficiency, and in vitro release were systematically evaluated. The cellular uptake, cytotoxic activity, and the effect of the formulation on cellular apoptosis of free-from EGCG and the NPs were compared. The interaction between protein-NF-κB and EGCG was detected by bio-layer interferometry (BLI). NF-κB signaling was evaluated by Western blotting and q-RT-PCR. The efficacy of the optimized nanoformulation was evaluated using a patient-derived tumor xenograft (PDX) model.

Results

EGCG-loaded NPs (175.8±3.8 nm in size) demonstrated its optimal efficacy, with approximately 86.0% of encapsulation efficiency and 14.2% of loading efficiency. Additionally, EGCG-encapsulated PLGA-NPs offered a 3-4-fold dose advantage compared to free EGCG in terms of exerting antiproliferative effects and inducing apoptosis at lower doses (12.5, 25 μM). Molecular interaction assays demonstrated that EGCG binds to NF-κB with high affnity (KD=4.8×10⁻⁵ M). EGCG-NPs were more effective at inhibiting NF-κB activation and suppressing the expression of NF-κB-regulated genes than free EGCG. Furthermore, EGCG-NPs showed superior anticancer activity in the PDX model than free EGCG.

Conclusion

These findings indicated that the prepared EGCG-NPs were more effective than free EGCG in inhibiting lung cancer tumors in the PDX model.

Application of Highly Immunocompromised Mice for the Establishment of Patient-Derived Xenograft (PDX) Models

Patient-derived xenograft (PDX) models are created by engraftment of patient tumor tissues into immunocompetent mice. Since a PDX model retains the characteristics of the primary patient tumor including gene expression profiles and drug responses, it has become the most reliable in vivo human cancer model. The engraftment rate increases with the introduction of Non-obese diabetic Severe combined immunodeficiency (NOD/SCID)-based immunocompromised mice, especially the NK-deficient NOD strains NOD/SCID/interleukin-2 receptor gamma chain(IL2Rγ)null (NOG/NSG) and NOD/SCID/Jak3(Janus kinase 3)null (NOJ). Success rates differ with tumor origin: gastrointestinal tumors acquire a higher engraftment rate, while the rate is lower for breast cancers. Subcutaneous transplantation is the most popular method to establish PDX, but some tumors require specific environments, e.g., orthotropic or renal capsule transplantation. Human hormone treatment is necessary to establish hormone-dependent cancers such as prostate and breast cancers. PDX mice with human hematopoietic and immune systems (humanized PDX) are powerful tools for the analysis of tumor-immune system interaction and evaluation of immunotherapy response. A PDX biobank equipped with patients' clinical data, gene-expression patterns, mutational statuses, tumor tissue architects, and drug responsiveness will be an authoritative resource for developing specific tumor biomarkers for chemotherapeutic predictions, creating individualized therapy, and establishing precise cancer medicine.