Advances in patient-derived tumor xenografts: from target identification to predicting clinical response rates in oncology

Can current preclinical strategies for radiopharmaceutical development meet the needs of targeted alpha therapy?

Preclinical studies are essential for effectively evaluating TAT radiopharmaceuticals. Given the current suboptimal supply chain of these radionuclides, animal studies must be refined to produce the most translatable TAT agents with the greatest clinical potential. Vector design is pivotal, emphasizing harmonious physical and biological characteristics among the vector, target, and radionuclide. The scarcity of alpha-emitting radionuclides remains a significant consideration. Actinium-225 and lead-212 appear as the most readily available radionuclides at this stage. Available animal models for researchers encompass xenografts, allografts, and PDX (patient-derived xenograft) models. Emerging strategies for imaging alpha-emitters are also briefly explored. Ultimately, preclinical research must address two critical aspects: (1) offering valuable insights into balancing safety and efficacy, and (2) providing guidance on the optimal dosing of the TAT agent.

Orthotopic and metastatic tumour models in preclinical cancer research

Mouse models of disease play a pivotal role at all stages of cancer drug development. Cell-line derived subcutaneous tumour models are predominant in early drug discovery, but there is growing recognition of the importance of the more complex orthotopic and metastatic tumour models for understanding both target biology in the correct tissue context, and the impact of the tumour microenvironment and the immune system in responses to treatment. The aim of this review is to highlight the value that orthotopic and metastatic models bring to the study of tumour biology and drug development while pointing out those models that are most likely to be encountered in the literature. Important developments in orthotopic models, such as the increasing use of early passage patient material (PDXs, organoids) and humanised mouse models are discussed, as these approaches have the potential to increase the predictive value of preclinical studies, and ultimately improve the success rate of anticancer drugs in clinical trials.

Factors associated with engraftment success of patient-derived xenografts of breast cancer

BACKGROUND

Patient-derived xenograft (PDX) models serve as a valuable tool for the preclinical evaluation of novel therapies. They closely replicate the genetic, phenotypic, and histopathological characteristics of primary breast tumors. Despite their promise, the rate of successful PDX engraftment is various in the literature. This study aimed to identify the key factors associated with successful PDX engraftment of primary breast cancer.

METHODS

We integrated clinicopathological data with morphological attributes quantified using a trained artificial intelligence (AI) model to identify the principal factors affecting PDX engraftment.

RESULTS

Multivariate logistic regression analyses demonstrated that several factors, including a high Ki-67 labeling index (Ki-67LI) (p < 0.001), younger age at diagnosis (p = 0.032), post neoadjuvant chemotherapy (NAC) (p = 0.006), higher histologic grade (p = 0.039), larger tumor size (p = 0.029), and AI-assessed higher intratumoral necrosis (p = 0.027) and intratumoral invasive carcinoma (p = 0.040) proportions, were significant factors for successful PDX engraftment (area under the curve [AUC] 0.905). In the NAC group, a higher Ki-67LI (p < 0.001), lower Miller-Payne grade (p < 0.001), and reduced proportion of intratumoral normal breast glands as assessed by AI (p = 0.06) collectively provided excellent prediction accuracy for successful PDX engraftment (AUC 0.89).

CONCLUSIONS

We found that high Ki-67LI, younger age, post-NAC status, higher histologic grade, larger tumor size, and specific morphological attributes were significant factors for predicting successful PDX engraftment of primary breast cancer.

Contemporary preclinical mouse models for pediatric rhabdomyosarcoma: from bedside to bench to bedside

Background

Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue malignancy, characterized by high clinicalopathological and molecular heterogeneity. Preclinical in vivo models are essential for advancing our understanding of RMS oncobiology and developing novel treatment strategies. However, the diversity of scholarly data on preclinical RMS studies may challenge scientists and clinicians. Hence, we performed a systematic literature survey of contemporary RMS mouse models to characterize their phenotypes and assess their translational relevance.

Methods

We identified papers published between 01/07/2018 and 01/07/2023 by searching PubMed and Web of Science databases.

Results

Out of 713 records screened, 118 studies (26.9%) were included in the qualitative synthesis. Cell line-derived xenografts (CDX) were the most commonly utilized (n = 75, 63.6%), followed by patient-derived xenografts (PDX) and syngeneic models, each accounting for 11.9% (n = 14), and genetically engineered mouse models (GEMM) (n = 7, 5.9%). Combinations of different model categories were reported in 5.9% (n = 7) of studies. One study employed a virus-induced RMS model. Overall, 40.0% (n = 30) of the studies utilizing CDX models established alveolar RMS (aRMS), while 38.7% (n = 29) were embryonal phenotypes (eRMS). There were 20.0% (n = 15) of studies that involved a combination of both aRMS and eRMS subtypes. In one study (1.3%), the RMS phenotype was spindle cell/sclerosing. Subcutaneous xenografts (n = 66, 55.9%) were more frequently used compared to orthotopic models (n = 29, 24.6%). Notably, none of the employed cell lines were derived from primary untreated tumors. Only a minority of studies investigated disseminated RMS phenotypes (n = 16, 13.6%). The utilization areas of RMS models included testing drugs (n = 64, 54.2%), studying tumorigenesis (n = 56, 47.5%), tumor modeling (n = 19, 16.1%), imaging (n = 9, 7.6%), radiotherapy (n = 6, 5.1%), long-term effects related to radiotherapy (n = 3, 2.5%), and investigating biomarkers (n = 1, 0.8%). Notably, no preclinical studies focused on surgery.

Conclusions

This up-to-date review highlights the need for mouse models with dissemination phenotypes and cell lines from primary untreated tumors. Furthermore, efforts should be directed towards underexplored areas such as surgery, radiotherapy, and biomarkers.

Advancements in Preclinical Models of Pancreatic Cancer

ABSTRACT

Pancreatic cancer remains one of the deadliest of all cancer types with a 5-year overall survival rate of just 12%. Preclinical models available for understanding the disease pathophysiology have evolved significantly in recent years. Traditionally, commercially available 2-dimensional cell lines were developed to investigate mechanisms underlying tumorigenesis, metastasis, and drug resistance. However, these cells grow as monolayer cultures that lack heterogeneity and do not effectively represent tumor biology. Developing patient-derived xenografts and genetically engineered mouse models led to increased cellular heterogeneity, molecular diversity, and tissues that histologically represent the original patient tumors. However, these models are relatively expensive and very timing consuming. More recently, the advancement of fast and inexpensive in vitro models that better mimic disease conditions in vivo are on the rise. Three-dimensional cultures like organoids and spheroids have gained popularity and are considered to recapitulate complex disease characteristics. In addition, computational genomics, transcriptomics, and metabolomic models are being developed to simulate pancreatic cancer progression and predict better treatment strategies. Herein, we review the challenges associated with pancreatic cancer research and available analytical models. We suggest that an integrated approach toward using these models may allow for developing new strategies for pancreatic cancer precision medicine.

Urokinase-Type Plasminogen Activator Receptor (uPAR) Expression and [64Cu]Cu-DOTA-AE105 uPAR-PET/CT in Patient-Derived Xenograft Models of Oral Squamous Cell Carcinoma

PURPOSE

[64Cu]Cu-DOTA-AE105 urokinase-type plasminogen activator receptor (uPAR)-PET/CT is a novel and promising imaging modality for cancer visualization, although it has not been tested in head and neck cancer patients nor in preclinical models that closely resemble these heterogenous tumors, i.e., patient-derived xenograft (PDX) models. The aim of the present study was to establish and validate oral squamous cell carcinoma (OSCC) PDX models and to evaluate [64Cu]Cu-uPAR-PET/CT for tumor imaging in these models.

PROCEDURES

PDX flank tumor models were established by engrafting tumor tissue from three patients with locally advanced OSCC into immunodeficient mice. [64Cu]Cu-DOTA-AE105 was injected in passage 2 (P2) mice, and [64Cu]Cu-uPAR-PET/CT was performed 1 h and 24 h after injection. After the last PET scan, all animals were euthanized, and tumors dissected for autoradiography and immunohistochemical (IHC) staining.

RESULTS

Three PDX models were established, and all of them showed histological stability and unchanged heterogenicity, uPAR expression, and Ki67 expression through passages. A significant correlation between uPAR expression and tumor growth was found. All tumors of all models (n=29) showed tumor uptake of [64Cu]Cu-DOTA-AE105. There was a clear visual concordance between the distribution of uPAR expression (IHC) and [64Cu]Cu-DOTA-AE105 uptake pattern in tumor tissue (autoradiography). No significant correlation was found between IHC (H-score) and PET-signal (SUVmax) (r=0.34; p=0.07).

CONCLUSIONS

OSCC PDX models in early passages histologically mimic donor tumors and could serve as a valuable platform for the development of uPAR-targeted imaging and therapeutic modalities. Furthermore, [64Cu]Cu-uPAR-PET/CT showed target- and tumor-specific uptake in OSCC PDX models demonstrating the diagnostic potential of this modality for OSCC patients.

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.

Jean DJ, Tieu T, Ladd B, Hilbert B, Wang W, Alltucker JT, Manimala S, Kryukov GV, Brooijmans N, Dowdell G, Jonsson P, Huff M, Guzman-Perez A, Jackson EL, Goncalves MD, Stuart DD. STX-478, a Mutant-Selective, Allosteric PI3Kα Inhibitor Spares Metabolic Dysfunction and Improves Therapeutic Response in PI3Kα-Mutant Xenografts

Phosphoinositide 3-kinase α (PIK3CA) is one of the most mutated genes across cancers, especially breast, gynecologic, and head and neck squamous cell carcinoma tumors. Mutations occur throughout the gene, but hotspot mutations in the helical and kinase domains predominate. The therapeutic benefit of isoform-selective PI3Kα inhibition was established with alpelisib, which displays equipotent activity against the wild-type and mutant enzyme. Inhibition of wild-type PI3Kα is associated with severe hyperglycemia and rash, which limits alpelisib use and suggests that selectively targeting mutant PI3Kα could reduce toxicity and improve efficacy. Here we describe STX-478, an allosteric PI3Kα inhibitor that selectively targets prevalent PI3Kα helical- and kinase-domain mutant tumors. STX-478 demonstrated robust efficacy in human tumor xenografts without causing the metabolic dysfunction observed with alpelisib. Combining STX-478 with fulvestrant and/or cyclin-dependent kinase 4/6 inhibitors was well tolerated and provided robust and durable tumor regression in ER+HER2- xenograft tumor models.

SIGNIFICANCE

These preclinical data demonstrate that the mutant-selective, allosteric PI3Kα inhibitor STX-478 provides robust efficacy while avoiding the metabolic dysfunction associated with the nonselective inhibitor alpelisib. Our results support the ongoing clinical evaluation of STX-478 in PI3Kα-mutated cancers, which is expected to expand the therapeutic window and mitigate counterregulatory insulin release. See related commentary by Kearney and Vasan, p. 2313. This article is featured in Selected Articles from This Issue, p. 2293.

Pan-Cancer Pharmacogenomic Analysis of Patient-Derived Tumor Cells Using Clinically Relevant Drug Exposures

As a result of tumor heterogeneity and solid cancers harboring multiple molecular defects, precision medicine platforms in oncology are most effective when both genetic and pharmacologic determinants of a tumor are evaluated. Expandable patient-derived xenograft (PDX) mouse tumor and corresponding PDX culture (PDXC) models recapitulate many of the biological and genetic characteristics of the original patient tumor, allowing for a comprehensive pharmacogenomic analysis. Here, the somatic mutations of 23 matched patient tumor and PDX samples encompassing four cancers were first evaluated using next-generation sequencing (NGS). 19 antitumor agents were evaluated across 78 patient-derived tumor cultures using clinically relevant drug exposures. A binarization threshold sensitivity classification determined in culture (PDXC) was used to identify tumors that best respond to drug in vivo (PDX). Using this sensitivity classification, logic models of DNA mutations were developed for 19 antitumor agents to predict drug response. We determined that the concordance of somatic mutations across patient and corresponding PDX samples increased as variant allele frequency increased. Notable individual PDXC responses to specific drugs, as well as lineage-specific drug responses were identified. Robust responses identified in PDXC were recapitulated in vivo in PDX-bearing mice and logic modeling determined somatic gene mutation(s) defining response to specific antitumor agents. In conclusion, combining NGS of primary patient tumors, high-throughput drug screen using clinically relevant doses, and logic modeling, can provide a platform for understanding response to therapeutic drugs targeting cancer.

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.

Preclinical Models of Adrenocortical Cancer

Adrenocortical cancer is an aggressive endocrine malignancy with an incidence of 0.72 to 1.02 per million people/year, and a very poor prognosis with a five-year survival rate of 22%. As an orphan disease, clinical data are scarce, meaning that drug development and mechanistic research depend especially on preclinical models. While a single human ACC cell line was available for the last three decades, over the last five years, many new in vitro and in vivo preclinical models have been generated. Herein, we review both in vitro (cell lines, spheroids, and organoids) and in vivo (xenograft and genetically engineered mouse) models. Striking leaps have been made in terms of the preclinical models of ACC, and there are now several modern models available publicly and in repositories for research in this area.

OncoVee™-MiniPDX-guided anticancer treatment for HER2-negative intermediate-advanced gastric cancer patients: a single-arm, open-label phase I clinical study

BACKGROUND

Chemotherapy is the main treatment strategy for patients with advanced HER2-negative gastric cancer (GC); yet, many patients do not respond well to treatment. This study evaluated the sensitivity of a mini patient-derived xenograft (MiniPDX) animal model in patients with HER2-negative intermediate-advanced GC.

METHODS

In this single-arm, open-label clinical study, we consecutively recruited patients with HER2-negative advanced or recurrent GC from September 2018 to July 2021. Tumor tissues were subjected to MiniPDX drug sensitivity tests for screening individualized anti-tumor drugs; appropriate drug types or combinations were selected based on drug screening results. The primary endpoints were progression-free survival (PFS) and safety, and the secondary endpoints were overall survival (OS) and objective response rate (ORR).

RESULTS

A total of 17 patients were screened, and 14 eligible patients were included.The median follow-up time was 9 (2-34) months. The median PFS time was 14.1 (2-34) months, the median OS time was 16.9 (2-34) months, ORR was 42.9% (6/14), and DCR was 92.9% (13/14). The most common treatment-related adverse events (TRAE) were fatigue (14 (100%)), anorexia (13 (93%)) and insomnia (12 (86%)), and the most common grade 3 or worse TRAE was fatigue (6 (43%)), and anorexia (6 (43%)). The occurrence rate of myelosuppression, nausea and vomiting, abnormal liver enzymes, and other grade 3-4 chemotherapy adverse reactions were relatively low, and no grade 5 treatment-related adverse events occurred.

CONCLUSION

Screening HER2-negative medium-advanced GC/GJC chemotherapy regimens and targeted drugs based on MiniPDX animal models showed good tumor activity and safety.

Patient-derived models: Promising tools for accelerating the clinical translation of breast cancer research findings

Breast cancer has become the highest incidence of cancer in women. It was extensively and deeply studied by biologists and medical workers worldwide. However, the meaningful results in lab researches cannot be realized in clinical, and a part of new drugs in clinical experiments do not obtain as good results as the preclinical researches. It is urgently that promote a kind of breast cancer research models that can get study results closer to the physiological condition of the human body. Patient-derived models (PDMs) originating from clinical tumor, contain primary elements of tumor and maintain key clinical features of tumor. So they are promising research models to facilitate laboratory researches translate to clinical application, and predict the treatment outcome of patients. In this review, we summarize the establishment of PDMs of breast cancer, reviewed the application of PDMs in clinical translational researches and personalized precision medicine with breast cancer as an example, to improve the understanding of PDMs among researchers and clinician, facilitate them to use PDMs on a large scale of breast cancer researches and promote the clinical translation of laboratory research and new drug development.

Serum thrombospondin-1 serves as a novel biomarker and agonist of gemcitabine-based chemotherapy in intrahepatic cholangiocarcinoma

BACKGROUND

At present, the first-line treatment for advanced intrahepatic cholangiocarcinoma (ICC) is gemcitabine combined with cisplatin, but a considerable portion of ICC patients exhibit resistance to gemcitabine. Therefore, finding sensitisers for gemcitabine chemotherapy in ICC patients and predicting molecular markers for chemotherapy efficacy have become urgent needs.

METHODS

In this study, PDX models were established to conduct gemcitabine susceptibility tests. The selected PDX tissues of the chemotherapy-sensitive group and drug-resistant group were subjected to transcriptome sequencing and protein chip technology to identify the key genes. Sixty-one ICC patients treated with gemcitabine chemotherapy were recruited for clinical follow-up validation.

RESULTS

We found that thrombospondin-1 (TSP1) can predict gemcitabine chemosensitivity in ICC patients. The expression level of TSP1 could reflect the sensitivity of ICC patients to gemcitabine chemotherapy. Functional experiments further confirmed that TSP1 can increase the efficacy of gemcitabine chemotherapy for ICC. A mechanism study showed that TSP1 may affect the intake of oleic acid by binding to the CD36 receptor.

CONCLUSIONS

In summary, we found a key molecule-TSP1-that can predict and improve the sensitivity of ICC patients to gemcitabine chemotherapy, which is of great significance for the treatment of advanced cholangiocarcinoma.

Patient-derived xenograft model in colorectal cancer basic and translational research

Colorectal cancer (CRC) is one of the most popular malignancies globally, with 930 000 deaths in 2020. The evaluation of CRC-related pathogenesis and the discovery of potential therapeutic targets will be meaningful and helpful for improving CRC treatment. With huge efforts made in past decades, the systematic treatment regimens have been applied to improve the prognosis of CRC patients. However, the sensitivity of CRC to chemotherapy and targeted therapy is different from person to person, which is an important cause of treatment failure. The emergence of patient-derived xenograft (PDX) models shows great potential to alleviate the straits. PDX models possess similar genetic and pathological characteristics as the features of primary tumors. Moreover, PDX has the ability to mimic the tumor microenvironment of the original tumor. Thus, the PDX model is an important tool to screen precise drugs for individualized treatment, seek predictive biomarkers for prognosis supervision, and evaluate the unknown mechanism in basic research. This paper reviews the recent advances in constructed methods and applications of the CRC PDX model, aiming to provide new knowledge for CRC basic research and therapeutics.

Zebrafish Models of Paediatric Brain Tumours

Paediatric brain cancer is the second most common childhood cancer and is the leading cause of cancer-related deaths in children. Despite significant advancements in the treatment modalities and improvements in the 5-year survival rate, it leaves long-term therapy-associated side effects in paediatric patients. Addressing these impairments demands further understanding of the molecularity and heterogeneity of these brain tumours, which can be demonstrated using different animal models of paediatric brain cancer. Here we review the use of zebrafish as potential in vivo models for paediatric brain tumour modelling, as well as catalogue the currently available zebrafish models used to study paediatric brain cancer pathophysiology, and discuss key findings, the unique attributes that these models add, current challenges and therapeutic significance.

Patient-derived models of brain metastases recapitulate human disseminated disease

Dissemination of cancer cells from primary tumors to the brain occurs in many cancer patients, increasing morbidity and death. There is an unmet medical need to develop translational platforms to evaluate therapeutic responses. Toward this goal, we established a library of 23 patient-derived xenografts (PDXs) of brain metastases (BMs) from eight distinct primary tumors. In vivo tumor formation correlates with patients’ poor survival. Mouse subcutaneous xenografts develop spontaneous metastases and intracardiac PDXs increase dissemination to the CNS, both models mimicking the dissemination pattern of the donor patient. We test the FDA-approved drugs buparlisib (pan-PI3K inhibitor) and everolimus (mTOR inhibitor) and show their efficacy in treating our models. Finally, we show by RNA sequencing that human BMs and their matched PDXs have similar transcriptional profiles. Overall, these models of BMs recapitulate the biology of human metastatic disease and can be valuable translational platforms for precision medicine.

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Established PDXs of brain metastasis from multiple cancers

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PDXs recapitulate the dissemination pattern of patient tumors

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Patient-derived models of brain metastases are valuable to test anticancer drugs

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Human brain metastases and their PDXs retain similar transcriptional profiles

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.

Novel Ex Vivo Models of Epithelial Ovarian Cancer: The Future of Biomarker and Therapeutic Research

Epithelial ovarian cancer (EOC) is a heterogenous disease associated with variations in presentation, pathology and prognosis. Advanced EOC is typified by frequent relapse and a historical 5-year survival of less than 30% despite improvements in surgical and systemic treatment. The advent of next generation sequencing has led to notable advances in the field of personalised medicine for many cancer types. Success in achieving cure in advanced EOC has however been limited, although significant prolongation of survival has been demonstrated. Development of novel research platforms is therefore necessary to address the rapidly advancing field of early diagnostics and therapeutics, whilst also acknowledging the significant tumour heterogeneity associated with EOC. Within available tumour models, patient-derived organoids (PDO) and explant tumour slices have demonstrated particular promise as novel ex vivo systems to model different cancer types including ovarian cancer. PDOs are organ specific 3D tumour cultures that can accurately represent the histology and genomics of their native tumour, as well as offer the possibility as models for pharmaceutical drug testing platforms, offering timing advantages and potential use as prospective personalised models to guide clinical decision-making. Such applications could maximise the benefit of drug treatments to patients on an individual level whilst minimising use of less effective, yet toxic, therapies. PDOs are likely to play a greater role in both academic research and drug development in the future and have the potential to revolutionise future patient treatment and clinical trial pathways. Similarly, ex vivo tumour slices or explants have also shown recent renewed promise in their ability to provide a fast, specific, platform for drug testing that accurately represents in vivo tumour response. Tumour explants retain tissue architecture, and thus incorporate the majority of tumour microenvironment making them an attractive method to re-capitulate in vivo conditions, again with significant timing and personalisation of treatment advantages for patients. This review will discuss the current treatment landscape and research models for EOC, their development and new advances towards the discovery of novel biomarkers or combinational therapeutic strategies to increase treatment options for women with ovarian cancer.

Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models

Patient-derived xenografts (PDXs) are important in vivo models for the development of precision medicine. However, challenges exist regarding genetic alterations and relapse after primary treatment. Thus, PDX models are required as a new approach for preclinical and clinical studies. We established PDX models of gynecologic cancers and analyzed their clinical information. We subcutaneously transplanted 207 tumor tissues from patients with gynecologic cancer into nude mice from 2014 to 2019. The successful engraftment rate of ovarian, cervical, and uterine cancer was 47%, 64%, and 56%, respectively. The subsequent passages (P2 and P3) showed higher success and faster growth rates than the first passage (P1). Using gynecologic cancer PDX models, the tumor grade is a common clinical factor affecting PDX establishment. We found that the PDX success rate correlated with the patient’s prognosis, and also that ovarian cancer patients with a poor prognosis had a faster PDX growth rate (p < 0.0001). Next, the gene sets associated with inflammation and immune responses were shown in high-ranking successful PDX engraftment through gene set enrichment analysis and RNA sequencing. Up-regulated genes in successful engraftment were found to correlate with ovarian clear cell cancer patient outcomes via Gene Expression Omnibus dataset analysis.

Patient-derived xenograft models in hepatopancreatobiliary cancer

Animal models are crucial tools for evaluating the biological progress of human cancers and for the preclinical investigation of anticancer drugs and cancer prevention. Various animals are widely used in hepatopancreatobiliary cancer research, and mouse models are the most popular. Generally, genetic tools, graft transplantation, and chemical and physical measures are adopted to generate sundry mouse models of hepatopancreatobiliary cancer. Graft transplantation is commonly used to study tumour progression. Over the past few decades, subcutaneous or orthotopic cell-derived tumour xenograft models (CDX models) have been developed to simulate distinct tumours in patients. However, two major limitations exist in CDX models. One model poorly simulates the microenvironment of tumours in humans, such as the vascular, lymphatic and immune environments. The other model loses genetic heterogeneity compared with the corresponding primary tumour. Increased efforts have focused on developing better models for hepatopancreatobiliary cancer research. Hepatopancreatobiliary cancer is considered a tumour with high molecular heterogeneity, making precision medicine challenging in cancer treatment. Developing a new animal model that can better mimic tumour tissue and more accurately predict the efficacy of anticancer treatments is urgent. For the past several years, the patient-derived xenograft model (PDX model) has emerged as a promising tool for translational research. It can retain the genetic and histological stability of their originating tumour at limited passages and shed light on precision cancer medicine. In this review, we summarize the methodology, advantages/disadvantages and applications of PDX models in hepatopancreatobiliary cancer research.

Patient-derived xenograft models of neuroendocrine prostate cancer

In recent years, patient-derived xenografts (PDXs) have attracted much attention as clinically relevant models for basic and translational cancer research. PDXs retain the principal histopathological and molecular heterogeneity of their donor tumors and remain stable across passages. These characteristics allow PDXs to offer a reliable platform for better understanding cancer biology, discovering biomarkers and therapeutic targets, and developing novel therapies. A growing interest in generating neuroendocrine prostate cancer (NEPC) PDX models has been demonstrated, and such models have proven useful in several areas. This review provides a comprehensive summary of currently available NEPC PDX collections, encompassing 1) primary or secondary sites where patient samples were collected, 2) donor patients' treatment histories, 3) morphological features (i.e., small cell and large cell), and 4) genomic alterations. We also highlight suitable models for various research purposes, including identifying therapeutic targets and evaluating drug responses in models with specific genomic backgrounds. Finally, we provide perspectives on the current knowledge gaps and shed light on future applications and improvements of NEPC PDXs.

Personalized Treatment of Advanced Gastric Cancer Guided by the MiniPDX Model

Background

The morbidity and mortality of gastric cancer are high in China. There are challenges to develop precise and individualized drug regimens for patients with gastric cancer after a standard treatment. Choosing the most appropriate anticancer drug after a patient developing drug resistance is very important to improve the patient's prognosis. MiniPDX has been widely used as a new and reliable preclinical research model to predict the sensitivity of anticancer drugs.

Methods

The OncoVee® MiniPDX system developed by Shanghai LIDE Biotech Co., Ltd. was used to establish the MiniPDX models using specimens of patients with gastric cancer. The cancer tissues were biopsied under endoscopy, and then, the tumor cell suspension was prepared for a drug sensitivity test by subcutaneously implanting into Balb/c-nude mice. The selected optimal regimen obtained from the MiniPDX assay was used to treat patients with drug-resistant gastric cancer.

Results

We successfully established an individualized and sensitive drug screening system for four patients from January 2021 to July 2021. MiniPDX models identified potentially effective drugs for these four patients, with partial remission in two of the patients after treatment and disease progression in the remaining of two patients. Severe side effects from chemotherapy or targeted therapy were not observed in all patients.

Conclusion

Establishing a personalized drug screening system for patients with drug-resistant gastric cancer can guide the selection of clinical drugs, improve the clinical benefit of patients, and avoid ineffective treatments. It can be an effective supplement for treatment options.

An overview of machine learning methods for monotherapy drug response prediction

For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.

Therapeutic targeting of SLC6A8 creatine transporter suppresses colon cancer progression and modulates human creatine levels

Colorectal cancer (CRC) is a leading cause of cancer mortality. Creatine metabolism was previously shown to critically regulate colon cancer progression. We report that RGX-202, an oral small-molecule SLC6A8 transporter inhibitor, robustly inhibits creatine import in vitro and in vivo, reduces intracellular phosphocreatine and ATP levels, and induces tumor apoptosis. RGX-202 suppressed CRC growth across KRAS wild-type and KRAS mutant xenograft, syngeneic, and patient-derived xenograft (PDX) tumors. Antitumor efficacy correlated with tumoral expression of creatine kinase B. Combining RGX-202 with 5-fluorouracil or the DHODH inhibitor leflunomide caused regressions of multiple colorectal xenograft and PDX tumors of distinct mutational backgrounds. RGX-202 also perturbed creatine metabolism in patients with metastatic CRC in a phase 1 trial, mirroring pharmacodynamic effects on creatine metabolism observed in mice. This is, to our knowledge, the first demonstration of preclinical and human pharmacodynamic activity for creatine metabolism targeting in oncology, thus revealing a critical therapeutic target.

New horizons in early drugs development in solid cancers

PURPOSE OF REVIEW

Drug development is the process of bringing new anticancer agents into clinical practice. From the basic research to clinical research each step is essential and intimately linked. The aim of this review is to describe emerging preclinical models and to provide an overview of selected drugs recently developed in oncology.

RECENT FINDINGS

Preclinical models reproducing human immune-tumor interactions, 3D cell cultures and microfluidic platforms are of great interest for the development of immunotherapies and combination therapies and offer the opportunity to better understand the interplay between cancer and stromal cells.Following a better biological understanding of cancer and advances in precision oncology, new exciting drugs (e.g. antibodies-drugs conjugates [ADCs], immunotherapeutic strategies, molecular-targeted therapies) have entered the field of clinical research and even clinical practice.

SUMMARY

Recent improvements in preclinical models will allow an accurate selection of drug candidates for clinical research. Innovative drugs are currently being developed from early to later phases of development. An important remaining challenge in drug development is to set up a new model of patient-centered clinical research to facilitate quick access to innovation and target-oriented trials.

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.

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.

Future Match Making: When Pediatric Oncology Meets Organoid Technology

Unlike adult cancers that frequently result from the accumulation in time of mutational “hits” often linked to lifestyle, childhood cancers are emerging as diseases of dysregulated development through massive epigenetic alterations. The ability to reconstruct these differences in cancer models is therefore crucial for better understanding the uniqueness of pediatric cancer biology. Cancer organoids (i.e., tumoroids) represent a promising approach for creating patient-derived in vitro cancer models that closely recapitulate the overall pathophysiological features of natural tumorigenesis, including intra-tumoral heterogeneity and plasticity. Though largely applied to adult cancers, this technology is scarcely used for childhood cancers, with a notable delay in technological transfer. However, tumoroids could provide an unprecedented tool to unravel the biology of pediatric cancers and improve their therapeutic management. We herein present the current state-of-the-art of a long awaited and much needed matchmaking.

Cancer stem cell-targeted chimeric antigen receptor (CAR)-T cell therapy: Challenges and prospects

Cancer stem cells (CSCs) with their self-renewal ability are accepted as cells which initiate tumors. CSCs are regarded as interesting targets for novel anticancer therapeutic agents because of their association with tumor recurrence and resistance to conventional therapies, including radiotherapy and chemotherapy. Chimeric antigen receptor (CAR)-T cells are engineered T cells which express an artificial receptor specific for tumor associated antigens (TAAs) by which they accurately target and kill cancer cells. In recent years, CAR-T cell therapy has shown more efficiency in cancer treatment, particularly regarding blood cancers. The expression of specific markers such as TAAs on CSCs in varied cancer types makes them as potent tools for CAR-T cell therapy. Here we review the CSC markers that have been previously targeted with CAR-T cells, as well as the CSC markers that may be used as possible targets for CAR-T cell therapy in the future. Furthermore, we will detail the most important obstacles against CAR-T cell therapy and suggest solutions.

Translating complexity and heterogeneity of pancreatic tumor: 3D in vitro to in vivo models

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive type of cancer with an overall survival rate of less than 7-8%, emphasizing the need for novel effective therapeutics against PDAC. However only a fraction of therapeutics which seemed promising in the laboratory environment will eventually reach the clinic. One of the main reasons behind this low success rate is the complex tumor microenvironment (TME) of PDAC, a highly fibrotic and dense stroma surrounding tumor cells, which supports tumor progression as well as increases the resistance against the treatment. In particular, the growing understanding of the PDAC TME points out a different challenge in the development of efficient therapeutics - a lack of biologically relevant in vitro and in vivo models that resemble the complexity and heterogeneity of PDAC observed in patients. The purpose and scope of this review is to provide an overview of the recent developments in different in vitro and in vivo models, which aim to recapitulate the complexity of PDAC in a laboratory environment, as well to describe how 3D in vitro models can be integrated into drug development pipelines that are already including sophisticated in vivo models. Hereby a special focus will be given on the complexity of in vivo models and the challenges in vitro models face to reach the same levels of complexity in a controllable manner. First, a brief introduction of novel developments in two dimensional (2D) models and ex vivo models is provided. Next, recent developments in three dimensional (3D) in vitro models are described ranging from spheroids, organoids, scaffold models, bioprinted models to organ-on-chip models including a discussion on advantages and limitations for each model. Furthermore, we will provide a detailed overview on the current PDAC in vivo models including chemically-induced models, syngeneic and xenogeneic models, highlighting hetero- and orthotopic, patient-derived tissues (PDX) models, and genetically engineered mouse models. Finally, we will provide a discussion on overall limitations of both, in vitro and in vivo models, and discuss necessary steps to overcome these limitations to reach an efficient drug development pipeline, as well as discuss possibilities to include novel in silico models in the process.

A novel screening approach comparing kinase activity of small molecule inhibitors with similar molecular structures and distinct biologic effects in triple-negative breast cancer to identify targetable signaling pathways

Breast cancer affects women globally; the majority of breast cancer-related mortalities are due to metastasis. Acquisition of a mesenchymal phenotype has been implicated in the progression of breast cancer cells to an invasive, metastatic state. Triple-negative breast cancer (TNBC) subtypes have high rates of metastases, recurrence, and have poorer prognoses compared to other breast cancer types, partially due to lack of commonly targeted receptors. Kinases have diverse and pivotal functions in metastasis in TNBC, and discovery of new kinase targets for TNBC is warranted. We previously used a screening approach to identify intermediate-synthesis nonpotent, nonselective small-molecule inhibitors from the Published Kinase Inhibitor Set that reversed the mesenchymal phenotype in TNBC cells. Two of these inhibitors (GSK346294A and GSK448459A) are structurally similar, but have unique kinase activity profiles and exhibited differential biologic effects on TNBC cells, specifically on epithelial-to-mesenchymal transition (EMT). Here, we further interrogate these effects and compare activity of these inhibitors on transwell migration, gene (qRT-PCR) and protein (western blot) expressions, and cancer stem cell-like behavior. We incorporated translational patient-derived xenograft models in these studies, and we focused on the lead inhibitor hit, GSK346294A, to demonstrate the utility of our comparative analysis as a screening modality to identify novel kinase targets and signaling pathways to pursue in TNBC. This study introduces a new method for discovering novel kinase targets that reverse the EMT phenotype; this screening approach can be applied to all cancer types and is not limited to breast cancer.

Modeling human tumor-immune environments in vivo for the preclinical assessment of immunotherapies

Despite the significant contributions of immunocompetent mouse models to the development and assessment of cancer immunotherapies, they inadequately represent the genetic and biological complexity of corresponding human cancers. Immunocompromised mice reconstituted with a human immune system (HIS) and engrafted with patient-derived tumor xenografts are a promising novel preclinical model for the study of human tumor-immune interactions. Whilst overcoming limitations of immunocompetent models, HIS-tumor models often rely on reconstitution with allogeneic immune cells, making it difficult to distinguish between anti-tumor and alloantigen responses. Models that comprise of autologous human tumor and human immune cells provide a platform that is more representative of the patient immune-tumor interaction. However, limited access to autologous tissues, short experimental windows, and poor retention of tumor microenvironment and tumor infiltrating lymphocyte components are major challenges affecting the establishment and application of autologous models. This review outlines existing preclinical murine models for the study of immuno-oncology, and highlights innovations that can be applied to improve the feasibility and efficacy of autologous models.

Construction of in vitro patient-derived tumor models to evaluate anticancer agents and cancer immunotherapy

An in vitro assay system using patient-derived tumor models represents a promising preclinical cancer model that replicates the disease better than traditional cell culture models. Patient-derived tumor organoid (PDO) and patient-derived tumor xenograft (PDX) models have been previously established from different types of human tumors to recapitulate accurately and efficiently their tissue architecture and function. However, these models have low throughput and are challenging to construct. Thus, the present study aimed to establish a simple in vitro high-throughput assay system using PDO and PDX models. Furthermore, the current study aimed to evaluate different classes of anticancer drugs, including chemotherapeutic, molecular targeted and antibody drugs, using PDO and PDX models. First, an in vitro high-throughput assay system was constructed using PDO and PDX established from solid and hematopoietic tumors cultured in 384-well plates to evaluate anticancer agents. In addition, an in vitro evaluation system of the immune response was developed using PDO and PDX. Novel cancer immunotherapeutic agents with marked efficacy have been used against various types of tumor. Thus, there is an urgent need for in vitro functional potency assays that can simulate the complex interaction of immune cells with tumor cells and can rapidly test the efficacy of different immunotherapies or antibody drugs. An evaluation system for the antibody-dependent cellular cytotoxic activity of anti-epidermal growth factor receptor antibody and the cytotoxic activity of activated lymphocytes, such as cytotoxic T lymphocytes and natural killer cells, was constructed. Moreover, immune response assay systems with bispecific T-cell engagers were developed using effector cells. The present results demonstrated that in vitro assay systems using PDO and PDX may be suitable for evaluating anticancer agents and immunotherapy potency with high reproducibility and simplicity.

Benefits of Zebrafish Xenograft Models in Cancer Research

As a promising in vivo tool for cancer research, zebrafish have been widely applied in various tumor studies. The zebrafish xenograft model is a low-cost, high-throughput tool for cancer research that can be established quickly and requires only a small sample size, which makes it favorite among researchers. Zebrafish patient-derived xenograft (zPDX) models provide promising evidence for short-term clinical treatment. In this review, we discuss the characteristics and advantages of zebrafish, such as their transparent and translucent features, the use of vascular fluorescence imaging, the establishment of metastatic and intracranial orthotopic models, individual pharmacokinetics measurements, and tumor microenvironment. Furthermore, we introduce how these characteristics and advantages are applied other in tumor studies. Finally, we discuss the future direction of the use of zebrafish in tumor studies and provide new ideas for the application of it.

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.

Proteomic Resistance Biomarkers for PI3K Inhibitor in Triple Negative Breast Cancer Patient-Derived Xenograft Models

Simple Summary

The objective of this study is to identify potential proteomic biomarkers in triple negative breast cancer (TNBC) that associate with response to PI3K inhibitors which are in clinical trials. We tested a panel of TNBC patient-derived xenograft (PDX) models for their tumor growth response to a pan-PI3K inhibitor, BKM120. Proteomic analyses by reverse phase protein array (RPPA) of 182 markers were performed on baseline and post short-term treatment PDX samples, to correlate with tumor growth response. We identified several baseline and treatment induced proteomic biomarkers in association with resistance. These results provide important insights for the development of PI3K inhibitors in TNBC.

Abstract

PI3K pathway activation is frequently observed in triple negative breast cancer (TNBC). However, single agent PI3K inhibitors have shown limited anti-tumor activity. To investigate biomarkers of response and resistance mechanisms, we tested 17 TNBC patient-derived xenograft (PDX) models representing diverse genomic backgrounds and varying degrees of PI3K pathway signaling activities for their tumor growth response to the pan-PI3K inhibitor, BKM120. Baseline and post-treatment PDX tumors were subjected to reverse phase protein array (RPPA) to identify protein markers associated with tumor growth response. While BKM120 consistently reduced PI3K pathway activity, as demonstrated by reduced levels of phosphorylated AKT, percentage tumor growth inhibition (%TGI) ranged from 35% in the least sensitive to 84% in the most sensitive model. Several biomarkers showed significant association with resistance, including elevated baseline levels of growth factor receptors (EGFR, pHER3 Y1197), PI3Kp85 regulatory subunit, anti-apoptotic protein BclXL, EMT (Vimentin, MMP9, IntegrinaV), NFKB pathway (IkappaB, RANKL), and intracellular signaling molecules including Caveolin, CBP, and KLF4, as well as treatment-induced increases in the levels of phosphorylated forms of Aurora kinases. Interestingly, increased AKT phosphorylation or PTEN loss at baseline were not significantly correlated to %TGI. These results provide important insights into biomarker development for PI3K inhibitors in TNBC.

The Establishment of a Fast and Safe Orthotopic Colon Cancer Model Using a Tissue Adhesive Technique

Purpose

We aimed to develop a novel method for orthotopic colon cancer model, using tissue adhesive in place of conventional surgical method.

Materials and Methods

RFP HCT 116 cell line were used to establish the colon cancer model. Fresh tumor tissue harvested from a subcutaneous injection was grafted into twenty nude mice, divided into group A (suture method) and group B (tissue adhesive method). For the group A, we fixed the tissue on the serosa layer of proximal colon by 8-0 surgical suture. For the group B, tissue adhesive (10 μL) was used to fix the tumor. The mortality, tumor implantation success, tumor metastasis, primary tumor size, and operation time were compared between the two groups. Dissected tumor tissue was analyzed for the histology and immunohistochemistry. Also, we performed tumor marker analysis.

Results

We observed 30% increase in graft success and 20% decrease in mortality, by using tissue adhesive method, respectively. The median colon tumor size was significantly increased by 4 mm and operation time was shortened by 6.5 minutes. The H&E showed similar tumor structure between the two groups. The immunohistochemistry staining for cancer antigen 19-9, carcinoembryonic antigen, cytokeratin 20, and Ki-67 showed comparable intensities in both groups. Real-time quantitative reverse transcription analysis showed eight out of nine tumor markers are unchanged in the tissue adhesive group. Western blot indicated the tissue adhesive group expressed less p-JNK (apototic marker) and more p-MEK/p-p38 (proliferation marker) levels.

Conclusion

We concluded the tissue adhesive method is a quick and safe way to generate orthotopic, colon cancer model.

High-Dimensional Precision Medicine From Patient-Derived Xenografts

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

TAM family receptors in conjunction with MAPK signalling are involved in acquired resistance to PI3Kα inhibition in head and neck squamous cell carcinoma

Background

Aberrant activation of the phosphatidylinositol 3-kinase (PI3K) pathway is common in many malignancies, including head and neck squamous cell carcinoma (HNSCC). Despite pre-clinical and clinical studies, outcomes from targeting the PI3K pathway have been underwhelming and the development of drug resistance poses a significant barrier to patient treatment. In the present study, we examined mechanisms of acquired resistance to the PI3Kα inhibitor alpelisib (formerly BYL719) in HNSCC cell lines and patient-derived xenografts (PDXs).

Methods

Five unique PDX mouse models and three HNSCC cell lines were used. All cell lines and xenografts underwent genomic characterization prior to study. Serial drug treatment was conducted in vitro and in vivo to develop multiple, clinically-significant models of resistance to alpelisib. We then used reverse phase protein arrays (RPPAs) to profile the expression of proteins in parental and drug-resistant models. Top hits were validated by immunoblotting and immunohistochemistry. Flow cytometric analysis and RNA interference studies were then used to interrogate the molecular mechanisms underlying acquired drug resistance.

Results

Prolonged treatment with alpelisib led to upregulation of TAM family receptor tyrosine kinases TYRO3 and AXL. Importantly, a significant shift in expression of both TYRO3 and AXL to the cell surface was detected in drug-resistant cells. Targeted knockdown of TYRO3 and AXL effectively re-sensitized resistant cells to PI3Kα inhibition. In vivo, resistance to alpelisib emerged following 20–35 days of treatment in all five PDX models. Elevated TYRO3 expression was detected in drug-resistant PDX tissues. Downstream of TYRO3 and AXL, we identified activation of intracellular MAPK signalling. Inhibition of MAPK signalling also re-sensitized drug-resistant cells to alpelisib.

Conclusions

We have identified TYRO3 and AXL receptors to be key mediators of resistance to alpelisib, both in vitro and in vivo. Our findings suggest that pan-TAM inhibition is a promising avenue for combinatorial or second-line therapy alongside PI3Kα inhibition. These findings advance our understanding of the role TAM receptors play in modulating the response of HNSCC to PI3Kα inhibition and suggest a means to prevent, or at least delay, resistance to PI3Kα inhibition in order to improve outcomes for HNSCC patients.

A Biobank of Colorectal Cancer Patient-Derived Xenografts

Colorectal cancer (CRC) is a challenging disease, with a high mortality rate and limited effective treatment options, particularly for late-stage disease. Patient-derived xenografts (PDXs) have emerged as an informative, renewable experimental resource to model CRC architecture and biology. Here, we describe the generation of a biobank of CRC PDXs from stage I to stage IV patients. We demonstrate that PDXs within our biobank recapitulate the histopathological and mutation features of the original patient tumor. In addition, we demonstrate the utility of this resource in pre-clinical chemotherapy and targeted treatment studies, highlighting the translational potential of PDX models in the identification of new therapies that will improve the overall survival of CRC patients.

Modeling neoplastic disease with spheroids and organoids

Cancer is a complex disease in which both genetic defects and microenvironmental components contribute to the development, progression, and metastasization of disease, representing major hurdles in the identification of more effective and safer treatment regimens for patients. Three-dimensional (3D) models are changing the paradigm of preclinical cancer research as they more closely resemble the complex tissue environment and architecture found in clinical tumors than in bidimensional (2D) cell cultures. Among 3D models, spheroids and organoids represent the most versatile and promising models in that they are capable of recapitulating the heterogeneity and pathophysiology of human cancers and of filling the gap between conventional 2D in vitro testing and animal models. Such 3D systems represent a powerful tool for studying cancer biology, enabling us to model the dynamic evolution of neoplastic disease from the early stages to metastatic dissemination and the interactions with the microenvironment. Spheroids and organoids have recently been used in the field of drug discovery and personalized medicine. The combined use of 3D models could potentially improve the robustness and reliability of preclinical research data, reducing the need for animal testing and favoring their transition to clinical practice. In this review, we summarize the recent advances in the use of these 3D systems for cancer modeling, focusing on their innovative translational applications, looking at future challenges, and comparing them with most widely used animal models.

Developing oncolytic viruses for clinical use: A consortium approach

The use of oncolytic viruses forms an appealing approach for cancer treatment. On the one hand the viruses replicate in, and kill, tumor cells, leading to their intra-tumoral amplification. On the other hand the viral infection will activate virus-directed immune responses, and may trigger immune responses directed against tumor cells and tumor antigens. To date, a wide variety of oncolytic viruses is being developed for use in cancer treatment. While the development of oncolytic viruses has often been initiated by researchers in academia and other public institutions, a large majority of the final product development and the testing of these products in clinical trials is industry led. As a consequence relatively few pre-clinical and clinical studies evaluated different oncolytic viruses in competitive side-by-side preclinical or clinical studies. In this review we will summarize the steps and considerations essential in the development and characterization of oncolytic viruses, and describe our multidisciplinary academic consortium, which involves a dozen departments in three different Dutch universities, collaborating in the development of oncolytic viruses. This consortium has the ambition to develop a small series of oncolytic viruses and to evaluate these in various cancers.

Inhibition of PI3K by copanlisib exerts potent antitumor effects on Merkel cell carcinoma cell lines and mouse xenografts

Merkel cell carcinoma (MCC) is a highly aggressive neuroendocrine skin cancer with steadily increasing incidence and poor prognosis. Despite recent success with immunotherapy, 50% of patients still succumb to their diseases. To date, there is no Food and Drug Administration-approved targeted therapy for advanced MCC. Aberrant activation of phosphatidylinositide-3-kinase (PI3K)/AKT/mTOR pathway is frequently detected in MCC, making it an attractive therapeutic target. We previously found PI3K pathway activation in human MCC cell lines and tumors and demonstrated complete clinical response in a Stage IV MCC patient treated with PI3K inhibitor idelalisib. Here, we found that both PI3K-α and -δ isoforms are abundantly expressed in our MCC cell lines and clinical samples; we therefore examined antitumor efficacy across a panel of five PI3K inhibitors with distinctive isoform-specificities, including idelalisib (PI3K-δ), copanlisib (PI3K-α/δ), duvelisib (PI3K-γ/δ), alpelisib (PI3K-α), and AZD8186 (PI3K-β/δ). Of these, copanlisib exerts the most potent antitumor effects, markedly inhibiting cell proliferation, survival, and tumor growth by suppressing PI3K/mTOR/Akt activities in mouse models generated from MCC cell xenografts and patient-derived tumor xenografts. These results provide compelling preclinical evidence for application of copanlisib in advanced MCC with aberrant PI3K activation for which immunotherapy is insufficient, or patients who are unsuitable for immunotherapy.

Distinct cachexia profiles in response to human pancreatic tumours in mouse limb and respiratory muscle

BACKGROUND

Cancer cachexia is a life-threatening metabolic syndrome that causes significant loss of skeletal muscle mass and significantly increases mortality in cancer patients. Currently, there is an urgent need for better understanding of the molecular pathophysiology of this disease so that effective therapies can be developed. The majority of pre-clinical studies evaluating skeletal muscle's response to cancer have focused on one or two pre-clinical models, and almost all have focused specifically on limb muscles. In the current study, we reveal key differences in the histology and transcriptomic signatures of a limb muscle and a respiratory muscle in orthotopic pancreatic cancer patient-derived xenograft (PDX) mice.

METHODS

To create four cohorts of PDX mice evaluated in this study, tumours resected from four pancreatic ductal adenocarcinoma patients were portioned and attached to the pancreas of immunodeficient NSG mice.

RESULTS

Body weight, muscle mass, and fat mass were significantly decreased in each PDX line. Histological assessment of cryosections taken from the tibialis anterior (TA) and diaphragm (DIA) revealed differential effects of tumour burden on their morphology. Subsequent genome-wide microarray analysis on TA and DIA also revealed key differences between their transcriptomes in response to cancer. Genes up-regulated in the DIA were enriched for extracellular matrix protein-encoding genes and genes related to the inflammatory response, while down-regulated genes were enriched for mitochondria related protein-encoding genes. Conversely, the TA showed up-regulation of canonical atrophy-associated pathways such as ubiquitin-mediated protein degradation and apoptosis, and down-regulation of genes encoding extracellular matrix proteins.

CONCLUSIONS

These data suggest that distinct biological processes may account for wasting in different skeletal muscles in response to the same tumour burden. Further investigation into these differences will be critical for the future development of effective clinical strategies to counter cancer cachexia.

Patient-Derived Xenograft Models of Pancreatic Cancer: Overview and Comparison with Other Types of Models

Pancreatic cancer (PC) is anticipated to be second only to lung cancer as the leading cause of cancer-related deaths in the United States by 2030. Surgery remains the only potentially curative treatment for patients with pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. Multiple recent preclinical studies focus on identifying effective treatments for PDAC, but the models available for these studies often fail to reproduce the heterogeneity of this tumor type. Data generated with such models are of unknown clinical relevance. Patient-derived xenograft (PDX) models offer several advantages over human cell line-based in vitro and in vivo models and models of non-human origin. PDX models retain genetic characteristics of the human tumor specimens from which they were derived, have intact stromal components, and are more predictive of patient response than traditional models. This review briefly describes the advantages and disadvantages of 2D cultures, organoids and genetically engineered mouse (GEM) models of PDAC, and focuses on the applications, characteristics, advantages, limitations, and the future potential of PDX models for improving the management of PDAC.

Patient-derived xenografts as compatible models for precision oncology

Cancer is a very heterogeneous disease, displaying heterogeneity between patients (inter-tumoral heterogeneity) and heterogeneity within a patient (intra-tumoral heterogeneity). Precision oncology is a diagnostic and therapeutic approach for cancers based on the stratification of patients using genomic and molecular profiling of tumors. To develop diagnostic and therapeutic tools for the application of precision oncology, appropriate preclinical mouse models that reflect tumor heterogeneity are required. Patient-derived xenograft (PDX) models are generated by the engraftment of patient tumors into immunodeficient mice that retain several aspects of the patient’s tumor characteristics, including inter-tumoral heterogeneity and intra-tumoral heterogeneity. Therefore, PDX models can be applied in various developmental steps of cancer diagnostics and therapeutics, such as biomarker development, companion diagnostics, drug efficacy testing, overcoming drug resistance, and co-clinical trials. This review summarizes the diverse aspects of PDX models, addressing the factors considered for PDX generation, application of PDX models for cancer research, and future directions of PDX models.

Effects of anti-PD-1 immunotherapy on tumor regression: insights from a patient-derived xenograft model

Immunotherapies, such as checkpoint blockade of programmed cell death protein-1 (PD-1), have resulted in unprecedented improvements in survival for patients with lung cancer. Nonetheless, not all patients benefit equally and many issues remain unresolved, including the mechanisms of action and the possible effector function of immune cells from non-lymphoid lineages. The purpose of this study was to investigate whether anti-PD-1 immunotherapy acts on malignant tumor cells through mechanisms beyond those related to T lymphocyte involvement. We used a murine patient-derived xenograft (PDX) model of early-stage non–small cell lung carcinoma (NSCLC) devoid of host lymphoid cells, and studied the tumor and immune non-lymphoid responses to immunotherapy with anti-PD-1 alone or in combination with standard chemotherapy (cisplatin). An antitumor effect was observed in animals that received anti-PD-1 treatment, alone or in combination with cisplatin, likely due to a mechanism independent of T lymphocytes. Indeed, anti-PD-1 treatment induced myeloid cell mobilization to the tumor concomitant with the production of exudates compatible with an acute inflammatory reaction mediated by murine polymorphonuclear leukocytes, specifically neutrophils. Thus, while keeping in mind that more research is needed to corroborate our findings, we report preliminary evidence for a previously undescribed immunotherapy mechanism in this model, suggesting a potential cytotoxic action of neutrophils as PD-1 inhibitor effector cells responsible for tumor regression by necrotic extension.

Targeting Cancer Stem Cells by Genetically Engineered Chimeric Antigen Receptor T Cells

The term cancer stem cell (CSC) starts 25 years ago with the evidence that CSC is a subpopulation of tumor cells that have renewal ability and can differentiate into several distinct linages. Therefore, CSCs play crucial role in the initiation and the maintenance of cancer. Moreover, it has been proposed throughout several studies that CSCs are behind the failure of the conventional chemo-/radiotherapy as well as cancer recurrence due to their ability to resist the therapy and their ability to re-regenerate. Thus, the need for targeted therapy to eliminate CSCs is crucial; for that reason, chimeric antigen receptor (CAR) T cells has currently been in use with high rate of success in leukemia and, to some degree, in patients with solid tumors. This review outlines the most common CSC populations and their common markers, in particular CD133, CD90, EpCAM, CD44, ALDH, and EGFRVIII, the interaction between CSCs and the immune system, CAR T cell genetic engineering and signaling, CAR T cells in targeting CSCs, and the barriers in using CAR T cells as immunotherapy to treat solid cancers.