Patient-derived cancer models: Valuable platforms for anticancer drug testing

A novel approach to engineering three-dimensional bladder tumor models for drug testing

Bladder cancer (BCa) poses a significant health challenge, particularly affecting men with higher incidence and mortality rates. Addressing the need for improved predictive models in BCa treatment, this study introduces an innovative 3D in vitro patient-derived bladder cancer tumor model, utilizing decellularized pig bladders as scaffolds. Traditional 2D cell cultures, insufficient in replicating tumor microenvironments, have driven the development of sophisticated 3D models. The study successfully achieved pig bladder decellularization through multiple cycles of immersion in salt solutions, resulting in notable macroscopic and histological changes. This process confirmed the removal of cellular components while preserving the native extracellular matrix (ECM). Quantitative analysis demonstrated the efficacy of decellularization, with a remarkable reduction in DNA concentration, signifying the removal of over 95% of cellular material. In the development of the in vitro bladder cancer model, muscle invasive bladder cancer patients' cells were cultured within decellularized pig bladders, yielding a three-dimensional cancer model. Optimal results were attained using an air-liquid interface technique, with cells injected directly into the scaffold at three distinct time points. Histological evaluations showcased characteristics resembling in vivo tumors derived from bladder cancer patients' cells. To demonstrate the 3D cancer model's effectiveness as a drug screening platform, the study treated it with Cisplatin (Cis), Gemcitabine (Gem), and a combination of both drugs. Comprehensive cell viability assays and histological analyses illustrated changes in cell survival and proliferation. The model exhibited promising correlations with clinical outcomes, boasting an 83.3% reliability rate in predicting treatment responses. Comparison with traditional 2D cultures and spheroids underscored the 3D model's superiority in reliability, with an 83.3% predictive capacity compared to 50% for spheroids and 33.3% for 2D culture. Acknowledging limitations, such as the absence of immune and stromal components, the study suggests avenues for future improvements. In conclusion, this innovative 3D bladder cancer model, combining decellularization and patient-derived cells, marks a significant advancement in preclinical drug testing. Its potential for predicting treatment outcomes and capturing patient-specific responses opens new avenues for personalized medicine in bladder cancer therapeutics. Future refinements and validations with larger patient cohorts hold promise for revolutionizing BCa research and treatment strategies.

Antibody-Drug Conjugates to Promote Immune Surveillance: Lessons Learned from Breast Cancer

Antibody-drug conjugates (ADCs) represent an effective class of agents for the treatment of several tumor types, including breast cancer (BC), featuring approved molecules such as trastuzumab-emtansine, trastuzumab-deruxtecan, and sacituzumab-govitecan. Immune-checkpoint inhibitors (ICIs) also showed activity in selected BC subtypes, and two agents, pembrolizumab and atezolizumab, are currently approved for the treatment of triple-negative BC patients. The potential synergy between ADCs and immunotherapy in BC remains an area of active investigation. Preclinical studies suggest that ADCs promote immune surveillance, modulating tumor microenvironment, inducing immunogenic cell death, and enhancing antitumor immunity. Translational evidence has shown potential predictive biomarkers for ADCs alone or in combination with immunotherapy, including expression of target antigen, oncogenic pathways, tumor-infiltrating lymphocytes, and neutrophil-to-lymphocyte ratio. Given this background, several clinical trials evaluated ADC-ICI combinations in BC patients, demonstrating promising outcomes with an overall manageable toxicity profile, and many studies are currently ongoing to confirm the efficacy and feasibility of this therapeutic approach. In the present review, we summarized the available evidence about the integration of ADCs and immunotherapy for the management of BC, emphasizing the need for further translational and clinical investigations to optimize this treatment strategy and elucidate predictive biomarkers, eventually improving patient outcomes.

TOWARDS Study: Patient-Derived Xenograft Engraftment Predicts Poor Survival in Patients With Newly Diagnosed Triple-Negative Breast Cancer

PURPOSE

Assessing risk of recurrence for nonmetastatic triple-negative breast cancer (TNBC) is a key determinant of therapeutic strategy. The best predictor of recurrence risk is failure to achieve a pathologic complete response after preoperative chemotherapy, but it imperfectly correlates with the definitive end points of relapse-free and overall survival (OS). The inability to accurately predict recurrence has led to increasingly toxic treatment regimens for patients with early-stage TNBC. Better assays for recurrence risk are needed to tailor aggressive therapy for patients who need it and avoid overtreatment and unnecessary toxicity for those at low risk. The purpose of this study was to determine if patient-derived xenograft (PDX) engraftment of newly diagnosed breast tumors can serve as an accurate predictor of recurrence and death from breast cancer.

METHODS

This study was a blinded noninterventional trial comprising 80 patients with newly diagnosed, nonmetastatic, estrogen receptor (ER)-negative or ER-low breast cancer.

RESULTS

PDX engraftment was strongly associated with relapse in 1 year: 8 of 18 (44.4%) patients whose tumors engrafted relapsed versus 1 of 62 (1.6%) patients whose tumors did not engraft (P < .0001). Patients whose tumors engrafted had a hazard ratio (HR) for relapse of 17.5. HRs for OS and breast cancer-specific survival in PDX+ patients were 21.1 and 39.5, respectively.

CONCLUSION

We report that the ability of a tumor to engraft as a PDX predicts early recurrence by serving as a functional readout of aggressiveness and prospectively identifies the most devastating tumors. This provides new opportunity to develop surrogate assays, such as biomarkers of engraftment, which will extend the clinical feasibility of this finding.

Alginate-Chitosan Biodegradable and Biocompatible Based Hydrogel for Breast Cancer Immunotherapy and Diagnosis: A Comprehensive Review

Breast cancer is the most common type of cancer and the second leading cause of cancer-related mortality in females. There are many side effects due to chemotherapy and traditional surgery, like fatigue, loss of appetite, skin irritation, and drug resistance to cancer cells. Immunotherapy has become a hopeful approach toward cancer treatment, generating long-lasting immune responses in malignant tumor patients. Recently, hydrogel has received more attention toward cancer therapy due to its specific characteristics, such as decreased toxicity, fewer side effects, and better biocompatibility drug delivery to the particular tumor location. Researchers globally reported various investigations on hydrogel research for tumor diagnosis. The hydrogel-based multilayer platform with controlled nanostructure has received more attention for its antitumor effect. Chitosan and alginate play a leading role in the formation of the cross-link in a hydrogel. Also, they help in the stability of the hydrogel. This review discusses the properties, preparation, biocompatibility, and bioavailability of various research and clinical approaches of the multipolymer hydrogel made of alginate and chitosan for breast cancer treatment. With a focus on cases of breast cancer and the recovery rate, there is a need to find out the role of hydrogel in drug delivery for breast cancer treatment.

Targeting PI3K/AKT/mTOR signaling to overcome drug resistance in cancer

This review comprehensively explores the challenge of drug resistance in cancer by focusing on the pivotal PI3K/AKT/mTOR pathway, elucidating its role in oncogenesis and resistance mechanisms across various cancer types. It meticulously examines the diverse mechanisms underlying resistance, including genetic mutations, feedback loops, and microenvironmental factors, while also discussing the associated resistance patterns. Evaluating current therapeutic strategies targeting this pathway, the article highlights the hurdles encountered in drug development and clinical trials. Innovative approaches to overcome resistance, such as combination therapies and precision medicine, are critically analyzed, alongside discussions on emerging therapies like immunotherapy and molecularly targeted agents. Overall, this comprehensive review not only sheds light on the complexities of resistance in cancer but also provides a roadmap for advancing cancer treatment.

Advancement in Cancer Vasculogenesis Modeling through 3D Bioprinting Technology

Cancer vasculogenesis is a pivotal focus of cancer research and treatment given its critical role in tumor development, metastasis, and the formation of vasculogenic microenvironments. Traditional approaches to investigating cancer vasculogenesis face significant challenges in accurately modeling intricate microenvironments. Recent advancements in three-dimensional (3D) bioprinting technology present promising solutions to these challenges. This review provides an overview of cancer vasculogenesis and underscores the importance of precise modeling. It juxtaposes traditional techniques with 3D bioprinting technologies, elucidating the advantages of the latter in developing cancer vasculogenesis models. Furthermore, it explores applications in pathological investigations, preclinical medication screening for personalized treatment and cancer diagnostics, and envisages future prospects for 3D bioprinted cancer vasculogenesis models. Despite notable advancements, current 3D bioprinting techniques for cancer vasculogenesis modeling have several limitations. Nonetheless, by overcoming these challenges and with technological advances, 3D bioprinting exhibits immense potential for revolutionizing the understanding of cancer vasculogenesis and augmenting treatment modalities.

Extracellular Vesicles in Breast Cancer: From Intercellular Communication to Therapeutic Opportunities

Breast cancer, a multifaceted and heterogeneous disease, poses significant challenges in terms of understanding its intricate resistance mechanisms and devising effective therapeutic strategies. This review provides a comprehensive overview of the intricate landscape of extracellular vesicles (EVs) in the context of breast cancer, highlighting their diverse subtypes, biogenesis, and roles in intercellular communication within the tumour microenvironment (TME). The discussion spans various aspects, from EVs and stromal cells in breast cancer to their influence on angiogenesis, immune response, and chemoresistance. The impact of EV production in different culture systems, including two dimensional (2D), three dimensional (3D), and organoid models, is explored. Furthermore, this review delves into the therapeutic potential of EVs in breast cancer, presenting emerging strategies such as engineered EVs for gene delivery, nanoplatforms for targeted chemotherapy, and disrupting tumour derived EVs as a treatment approach. Understanding these complex interactions of EV within the breast cancer milieu is crucial for identifying resistance mechanisms and developing new therapeutic targets.

Promising preclinical models for lung cancer research-lung cancer organoids: a narrative review

Background and Objective

Traditional cell line models are the commonly used preclinical models for lung cancer research. However, cell lines cannot recapitulate the complex tumor heterogeneity and cannot mimic the microenvironment of human cancer. Recently, 3D multicellular in vitro self-assembled models called "organoids" have been developed at a fast pace in the field of research, which can mimic the actual primary tumor. At present, several studies have reported on protocols of lung cancer organoids (LCOs) generation, and using LCOs can provide novel insight into the basic and translational research of lung cancer. However, the establishment of the LCO models remains challenging due to the complexity of lung cancer and the immaturity of organoid technology, so it is necessary to understand the influences of different methodologies on LCO generation and review the applications and limitations of LCO models.

Methods

In this review, we searched the literature in the recent ten years in the field of LCOs.

Key Content and Findings

We summarized the methodology, the problems, and the solutions in the LCOs generation, its application and limitations, as well as proposing future challenges and perspectives.

Conclusions

Currently, LCOs are successfully generated via exploring the methodology by the researchers. Though there are still challenges in clinical application, LCOs are applied in some cancer studies including investigation of anti-cancer treatment response in vitro, modeling tumor immune microenvironment, and construction of organ chips, which are forging a promising path towards precision medicine.

Establishment and Thorough Characterization of Xenograft (PDX) Models Derived from Patients with Pancreatic Cancer for Molecular Analyses and Chemosensitivity Testing

Patient-derived xenograft (PDX) tumor models are essential for identifying new biomarkers, signaling pathways and novel targets, to better define key factors of therapy response and resistance mechanisms. Therefore, this study aimed at establishing pancreas carcinoma (PC) PDX models with thorough molecular characterization, and the identification of signatures defining responsiveness toward drug treatment. In total, 45 PC-PDXs were generated from 120 patient tumor specimens and the identity of PDX and corresponding patient tumors was validated. The majority of engrafted PDX models represent ductal adenocarcinomas (PDAC). The PDX growth characteristics were assessed, with great variations in doubling times (4 to 32 days). The mutational analyses revealed an individual mutational profile of the PDXs, predominantly showing alterations in the genes encoding KRAS, TP53, FAT1, KMT2D, MUC4, RNF213, ATR, MUC16, GNAS, RANBP2 and CDKN2A. Sensitivity of PDX toward standard of care (SoC) drugs gemcitabine, 5-fluorouracil, oxaliplatin and abraxane, and combinations thereof, revealed PDX models with sensitivity and resistance toward these treatments. We performed correlation analyses of drug sensitivity of these PDX models and their molecular profile to identify signatures for response and resistance. This study strongly supports the importance and value of PDX models for improvement in therapies of PC.

High-throughput screening as a drug repurposing strategy for poor outcome subgroups of pediatric B-cell precursor Acute Lymphoblastic Leukemia

Although a great cure rate has been achieved for pediatric BCP-ALL, approximately 15% of patients do not respond to conventional chemotherapy and experience disease relapse. A major effort to improve the cure rates by treatment intensification would result in an undesirable increase in treatment-related toxicity and mortality, raising the need to identify novel therapeutic approaches. High-throughput (HTP) drug screening enables the profiling of patients' responses in vitro and allows the repurposing of compounds currently used for other diseases, which can be immediately available for clinical application. The aim of this study was to apply HTP drug screening to identify potentially effective compounds for the treatment of pediatric BCP-ALL patients with poor prognosis, such as patients with Down Syndrome (DS) or carrying rearrangements involving PAX5 or KMT2A/MLL genes. Patient-derived Xenografts (PDX) samples from 34 BCP-ALL patients (9 DS CRLF2r, 15 PAX5r, 10 MLLr), 7 human BCP-ALL cell lines and 14 hematopoietic healthy donor samples were screened on a semi-automated HTP drug screening platform using a 174 compound library (FDA/EMA-approved or in preclinical studies). We identified 9 compounds active against BCP-ALL (ABT-199/venetoclax, AUY922/luminespib, dexamethasone, EC144, JQ1, NVP-HSP990, paclitaxel, PF-04929113 and vincristine), but sparing normal cells. Ex vivo validations confirmed that the BCL2 inhibitor venetoclax exerts an anti-leukemic effect against all three ALL subgroups at nanomolar concentrations. Overall, this study points out the benefit of HTP screening application for drug repurposing to allow the identification of effective and clinically translatable therapeutic agents for difficult-to-treat childhood BCP-ALL subgroups.

Patient-derived tumor models in cancer research: Evaluation of the oncostatic effects of melatonin

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

Cotargeting Phosphoinositide 3-Kinase and Focal Adhesion Kinase Pathways Inhibits Proliferation of NF2 Schwannoma Cells

Neurofibromatosis Type 2 (NF2) is a tumor predisposition syndrome caused by germline inactivating mutations in the NF2 gene encoding the merlin tumor suppressor. Patients develop multiple benign tumor types in the nervous system including bilateral vestibular schwannomas (VS). Standard treatments include surgery and radiation therapy, which may lead to loss of hearing, impaired facial nerve function, and other complications. Kinase inhibitor monotherapies have been evaluated clinically for NF2 patients with limited success, and more effective nonsurgical therapies are urgently needed. Schwannoma model cells treated with PI3K inhibitors upregulate activity of the focal adhesion kinase (FAK) family as a compensatory survival pathway. We screened combinations of 13 clinically relevant PI3K and FAK inhibitors using human isogenic normal and merlin-deficient Schwann cell lines. The most efficacious combination was PI3K/mTOR inhibitor omipalisib with SRC/FAK inhibitor dasatinib. Sub-GI50 doses of the single drugs blocked phosphorylation of their major target proteins. The combination was superior to either single agent in promoting a G1 cell-cycle arrest and produced a 44% decrease in tumor growth over a 2-week period in a pilot orthotopic allograft model. Evaluation of single and combination drugs in six human primary VS cell models revealed the combination was superior to the monotherapies in 3 of 6 VS samples, highlighting inter-tumor variability between patients consistent with observations from clinical trials with other molecular targeted agents. Dasatinib alone performed as well as the combination in the remaining three samples. Preclinically validated combination therapies hold promise for NF2 patients and warrants further study in clinical trials.

The challenge of making the right choice: patient avatars in the era of cancer immunotherapies

Immunotherapies are a key therapeutic strategy to fight cancer. Diverse approaches are used to activate tumor-directed immunity and to overcome tumor immune escape. The dynamic interplay between tumor cells and their tumor(immune)microenvironment (T(I)ME) poses a major challenge to create appropriate model systems. However, those model systems are needed to gain novel insights into tumor (immune) biology and a prerequisite to accurately develop and test immunotherapeutic approaches which can be successfully translated into clinical application. Several model systems have been established and advanced into so-called patient avatars to mimic the patient´s tumor biology. All models have their advantages but also disadvantages underscoring the necessity to pay attention in defining the rationale and requirements for which the patient avatar will be used. Here, we briefly outline the current state of tumor model systems used for tumor (immune)biological analysis as well as evaluation of immunotherapeutic agents. Finally, we provide a recommendation for further development to make patient avatars a complementary tool for testing and predicting immunotherapeutic strategies for personalization of tumor therapies.

Modeling the Tumor Microenvironment and Cancer Immunotherapy in Next-Generation Humanized Mice

Cancer immunotherapy has brought significant clinical benefits to numerous patients with malignant disease. However, only a fraction of patients experiences complete and durable responses to currently available immunotherapies. This highlights the need for more effective immunotherapies, combination treatments and predictive biomarkers. The molecular properties of a tumor, intratumor heterogeneity and the tumor immune microenvironment decisively shape tumor evolution, metastasis and therapy resistance and are therefore key targets for precision cancer medicine. Humanized mice that support the engraftment of patient-derived tumors and recapitulate the human tumor immune microenvironment of patients represent a promising preclinical model to address fundamental questions in precision immuno-oncology and cancer immunotherapy. In this review, we provide an overview of next-generation humanized mouse models suitable for the establishment and study of patient-derived tumors. Furthermore, we discuss the opportunities and challenges of modeling the tumor immune microenvironment and testing a variety of immunotherapeutic approaches using human immune system mouse models.

Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.