Patient-derived ovarian cancer explants: preserved viability and histopathological features in long-term agitation-based cultures

Mechanoimmunology in the solid tumor microenvironment

The tumor microenvironment (TME) is a complex and dynamic ecosystem that adjoins the cancer cells within solid tumors and comprises distinct components such as extracellular matrix, stromal and immune cells, blood vessels, and an abundance of signaling molecules. In recent years, the mechanical properties of the TME have emerged as critical determinants of tumor progression and therapeutic response. Aberrant mechanical cues, including altered tissue architecture and stiffness, contribute to tumor progression, metastasis, and resistance to treatment. Moreover, burgeoning immunotherapies hold great promise for harnessing the immune system to target and eliminate solid malignancies; however, their success is hindered by the hostile mechanical landscape of the TME, which can impede immune cell infiltration, function, and persistence. Consequently, understanding TME mechanoimmunology - the interplay between mechanical forces and immune cell behavior - is essential for developing effective solid cancer therapies. Here, we review the role of TME mechanics in tumor immunology, focusing on recent therapeutic interventions aimed at modulating the mechanical properties of the TME to potentiate T cell immunotherapies, and innovative assays tailored to evaluate their clinical efficacy.

Development of a long term, ex vivo, patient-derived explant model of endometrial cancer

Incidence of endometrial cancer (EC) is rising in the developed world. The current standard of care, hysterectomy, is often infeasible for younger patients and those with high body mass index. There are limited non-surgical treatment options and a lack of biologically relevant research models to investigate novel alternatives to surgery for EC. The aim of the present study was to develop a long-term, patient-derived explant (PDE) model of early-stage EC and demonstrate its use for investigating predictive biomarkers for a current non-surgical treatment option, the levonorgestrel intra-uterine system (LNG-IUS). Fresh tumour specimens were obtained from patients with early-stage endometrioid EC. Tumours were cut into explants, cultured on media-soaked gelatin sponges for up to 21 days and treated with LNG. Formalin-fixed, paraffin embedded (FFPE) blocks were generated for each explant after 21 days in culture. Tumour architecture and integrity were assessed by haematoxylin and eosin (H&E) and immunohistochemistry (IHC). IHC was additionally performed for the expression of five candidate biomarkers of LNG resistance. The developed ex vivo PDE model is capable of culturing explants from early-stage EC tumours long-term (21 Days). This model can complement existing models and may serve as a tool to validate results obtained in higher-throughput in vitro studies. Our study provides the foundation to validate the extent to which EC PDEs reflect patient response in future research.

The high-grade serous ovarian cancer metastasis and chemoresistance in 3D models

High-grade serous ovarian cancer (HGSOC) is the most frequent and aggressive type of epithelial ovarian cancer, with high recurrence rate and chemoresistance being the main issues in its clinical management. HGSOC is specifically challenging due to the metastatic dissemination via spheroids in the ascitic fluid. The HGSOC spheroids represent the invasive and chemoresistant cellular fraction, which is impossible to investigate in conventional two-dimensional (2D) monolayer cell cultures lacking critical cell-to-cell and cell-extracellular matrix interactions. Three-dimensional (3D) HGSOC cultures, where cells aggregate and exhibit relevant interactions, offer a promising in vitro model of peritoneal metastasis and multicellular drug resistance. This review summarizes recent studies of HGSOC in 3D culture conditions and highlights the role of multicellular HGSOC spheroids and ascitic environment in HGSOC metastasis and chemoresistance.

Comparative analysis of syngeneic mouse models of high-grade serous ovarian cancer

Ovarian cancers exhibit high rates of recurrence and poor treatment response. Preclinical models that recapitulate human disease are critical to develop new therapeutic approaches. Syngeneic mouse models allow for the generation of tumours comprising the full repertoire of non-malignant cell types but have expanded in number, varying in the cell type of origin, method for transformation, and ultimately, the properties of the tumours they produce. Here we have performed a comparative analysis of high-grade serous ovarian cancer models based on transcriptomic profiling of 22 cell line models, and intrabursal and intraperitoneal tumours from 12. Among cell lines, we identify distinct signalling activity, such as elevated inflammatory signalling in STOSE and OVE16 models, and MAPK/ERK signalling in ID8 and OVE4 models; metabolic differences, such as reduced glycolysis-associated expression in several engineered ID8 subclones; and relevant functional properties, including differences in EMT activation, PD-L1 and MHC class I expression, and predicted chemosensitivity. Among tumour samples, we observe increased variability and stromal content among intrabursal tumours. Finally, we predict differences in the microenvironment of ID8 models engineered with clinically relevant mutations. We anticipate that this work will serve as a valuable resource, providing new insight to help select models for specific experimental objectives.

Use of patient-derived explants as a preclinical model for precision medicine in colorectal cancer: A scoping review

PURPOSE

Whilst the treatment paradigm for colorectal cancer has evolved significantly over time, there is still a lack of reliable biomarkers of treatment response. Treatment decisions are based on high-risk features such as advanced TNM stage and histology. The role of the tumour microenvironment, which can influence tumour progression and treatment response, has generated considerable interest. Patient-derived explant cultures allow preservation of native tissue architecture and tumour microenvironment. The aim of the scoping review is to evaluate the utility of patient-derived explant cultures as a preclinical model in colorectal cancer.

METHODS

A search was conducted using Ovid MEDLINE, EMBASE, Web of Science, and Cochrane databases from start of database records to September 1, 2022. We included all peer-reviewed human studies in English language which used patient-derived explants as a preclinical model in primary colorectal cancer. Eligible studies were grouped into the following categories: assessing model feasibility; exploring tumour microenvironment; assessing ex vivo drug responses; discovering and validating biomarkers.

RESULTS

A total of 60 studies were eligible. Fourteen studies demonstrated feasibility of using patient-derived explants as a preclinical model. Ten studies explored the tumour microenvironment. Thirty-eight studies assessed ex vivo drug responses of chemotherapy agents and targeted therapies. Twenty-four studies identified potential biomarkers of treatment response.

CONCLUSIONS

Given the preservation of tumour microenvironment and tumour heterogeneity, patient-derived explants has the potential to identify reliable biomarkers, treatment resistance mechanisms, and novel therapeutic agents. Further validation studies are required to characterise, refine and standardise this preclinical model before it can become a part of precision medicine in colorectal cancer.

Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction

BACKGROUND AND AIM

Chemotherapy is a crucial component of cancer therapy, albeit with significant side effects. Chemotherapy either damages or inhibits the immune system; therefore, its efficacy varies according to the patient's immune state. Currently, there is no efficient model that incorporates tumor-immune-drug (TID) interactions to guide clinical medication strategies. In this study, we compared five different types of existing TID models with the aim to integrate them into a single, comprehensive model; our goal was to accurately reflect the reality of TID interactions to guide personalized cancer therapy.

METHODS

We studied four different drug treatment profiles: direct function, normal distribution function, sine function, and trapezoid function. We developed a platform capable of plotting all combinations of parameter sets and their corresponding treatment efficiency scores. Subsequently, we generated 10,000 random parameter combinations for an individual case and plotted two polygon graphs using a seismic colormap to depict efficacy of treatment. Then, we developed a platform providing treatment suggestions for all stages of tumors and varying levels of self-immunity. We created polygons demonstrating successful treatments according to parameters related to tumor and immune status.

RESULTS

The trapezoid drug treatment function achieved the best inhibitory effect on the tumor cell density. The treatment can be optimized with a high score indicating that the drug delivery interval had exceeded a specific value. More efficient parameter combinations existed when the immunity was strong compared to when it was weak, thus indicating that increasing the patient's self-immunity can make treatment much more effective.

CONCLUSIONS

In summary, we created a comprehensive model that can provide quantitative recommendations for a gentle, yet efficient, treatment customized according to the individual's tumor and immune system characteristics.

Making In Vitro Tumor Models Whole Again

As a reductionist approach, patient-derived in vitro tumor models are inherently still too simplistic for personalized drug testing as they do not capture many characteristics of the tumor microenvironment (TME), such as tumor architecture and stromal heterogeneity. This is especially problematic for assessing stromal-targeting drugs such as immunotherapies in which the density and distribution of immune and other stromal cells determine drug efficacy. On the other end, in vivo models are typically costly, low-throughput, and time-consuming to establish. Ex vivo patient-derived tumor explant (PDE) cultures involve the culture of resected tumor fragments that potentially retain the intact  TME of the original tumor. Although developed decades ago, PDE cultures have not been widely adopted likely because of their low-throughput and poor long-term viability. However, with growing recognition of the importance of patient-specific TME in mediating drug response, especially in the field of immune-oncology, there is an urgent need to resurrect these holistic cultures. In this Review, the key limitations of patient-derived tumor explant cultures are outlined and technologies that have been developed or could be employed to address these limitations are discussed. Engineered holistic tumor explant cultures may truly realize the concept of personalized medicine for cancer patients.

Harnessing preclinical models for the interrogation of ovarian cancer

Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

Preclinical models of epithelial ovarian cancer: practical considerations and challenges for a meaningful application

Despite many improvements in ovarian cancer diagnosis and treatment, until now, conventional chemotherapy and new biological drugs have not been shown to cure the disease, and the overall prognosis remains poor. Over 90% of ovarian malignancies are categorized as epithelial ovarian cancers (EOC), a collection of different types of neoplasms with distinctive disease biology, response to chemotherapy, and outcome. Advances in our understanding of the histopathology and molecular features of EOC subtypes, as well as the cellular origins of these cancers, have given a boost to the development of clinically relevant experimental models. The overall goal of this review is to provide a comprehensive description of the available preclinical investigational approaches aimed at better characterizing disease development and progression and at identifying new therapeutic strategies. Systems discussed comprise monolayer (2D) and three-dimensional (3D) cultures of established and primary cancer cell lines, organoids and patient-derived explants, animal models, including carcinogen-induced, syngeneic, genetically engineered mouse, xenografts, patient-derived xenografts (PDX), humanized PDX, and the zebrafish and the laying hen models. Recent advances in tumour-on-a-chip platforms are also detailed. The critical analysis of strengths and weaknesses of each experimental model will aid in identifying opportunities to optimize their translational value.

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.

Pathophysiological and pharmacological considerations to improve the design and application of antibody-drug conjugates

Antibody-drug conjugates (ADC) have emerged as one of the pillars of clinical disease management in oncology. The biggest hurdle to widespread development and application of ADCs has been a narrow therapeutic index. Advances in antibody technologies and formats as well as novel linker and payload chemistries have begun to facilitate structural improvements to ADCs. However, the interplay of structural characteristics with physiologic and pharmacologic factors determining therapeutic success has garnered less attention. This review elaborates on the pharmacology of ADCs, the pathophysiology of cancerous tissues, and the reciprocal consequences on ADC properties and functions. While most currently approved ADCs utilize either microtubule inhibition or DNA damage as primary mechanisms of action, we present arguments to expand this repertoire and highlight the need for payload mechanisms that exploit disease-specific vulnerabilities. We promote the idea that the choice of antibody format, targeting antigen, linker properties, and payload of an ADC should be deliberately fit for purpose by taking the pathophysiology of disease and the specific pharmacology of the drug entity into account, thus allowing a higher probability of clinical success.

Three-Dimensional Modelling of Ovarian Cancer: From Cell Lines to Organoids for Discovery and Personalized Medicine

Ovarian cancer has the highest mortality of all of the gynecological malignancies. There are several distinct histotypes of this malignancy characterized by specific molecular events and clinical behavior. These histotypes have differing responses to platinum-based drugs that have been the mainstay of therapy for ovarian cancer for decades. For histotypes that initially respond to a chemotherapeutic regime of carboplatin and paclitaxel such as high-grade serous ovarian cancer, the development of chemoresistance is common and underpins incurable disease. Recent discoveries have led to the clinical use of PARP (poly ADP ribose polymerase) inhibitors for ovarian cancers defective in homologous recombination repair, as well as the anti-angiogenic bevacizumab. While predictive molecular testing involving identification of a genomic scar and/or the presence of germline or somatic BRCA1 or BRCA2 mutation are in clinical use to inform the likely success of a PARP inhibitor, no similar tests are available to identify women likely to respond to bevacizumab. Functional tests to predict patient response to any drug are, in fact, essentially absent from clinical care. New drugs are needed to treat ovarian cancer. In this review, we discuss applications to address the currently unmet need of developing physiologically relevant in vitro and ex vivo models of ovarian cancer for fundamental discovery science, and personalized medicine approaches. Traditional two-dimensional (2D) in vitro cell culture of ovarian cancer lacks critical cell-to-cell interactions afforded by culture in three-dimensions. Additionally, modelling interactions with the tumor microenvironment, including the surface of organs in the peritoneal cavity that support metastatic growth of ovarian cancer, will improve the power of these models. Being able to reliably grow primary tumoroid cultures of ovarian cancer will improve the ability to recapitulate tumor heterogeneity. Three-dimensional (3D) modelling systems, from cell lines to organoid or tumoroid cultures, represent enhanced starting points from which improved translational outcomes for women with ovarian cancer will emerge.

Patient-Derived Explants as a Precision Medicine Patient-Proximal Testing Platform Informing Cancer Management

Precision medicine approaches that inform clinical management of individuals with cancer are progressively advancing. Patient-derived explants (PDEs) provide a patient-proximal ex vivo platform that can be used to assess sensitivity to standard of care (SOC) therapies and novel agents. PDEs have several advantages as a patient-proximal model compared to current preclinical models, as they maintain the phenotype and microenvironment of the individual tumor. However, the longevity of PDEs is not compatible with the timeframe required to incorporate candidate therapeutic options identified by whole exome sequencing (WES) of the patient’s tumor. This review investigates how PDE longevity varies across tumor streams and how this is influenced by tissue preparation. Improving longevity of PDEs will enable individualized therapeutics testing, and thus contribute to improving outcomes for people with cancer.

Patient-Derived Explants of Colorectal Cancer: Histopathological and Molecular Analysis of Long-Term Cultures

Simple Summary

Colorectal cancer is the third most common cancer type among men and women. Prescription of medical treatments for cancer often relies on a process of trial and potential error, more recently guided by patient stratification based on biomarkers. Nonetheless, available biomarkers do not accurately predict patient response and there is a need for predictive and translational models to provide proper clinical information on treatment guidance. Herein, we developed an ex vivo model of colorectal cancer, using fresh tumour samples to establish explant cultures, taking advantage of agitation-based culture systems. We performed a thorough characterisation over one month in culture and observed preservation of original tumour genetic features and partial preservation of architecture and non-malignant cells that compose the tumour microenvironment. Our findings highlight the importance of detailed model characterisation and support the applicability of our model in pre- and co-clinical settings.

Abstract

Colorectal cancer (CRC) is one of the most common cancers worldwide. Although short-term cultures of tumour sections and xenotransplants have been used to determine drug efficacy, the results frequently fail to confer clinically useful information. Biomarker discovery has changed the paradigm for advanced CRC, though the presence of a biomarker does not necessarily translate into therapeutic success. To improve clinical outcomes, translational models predictive of drug response are needed. We describe a simple method for the fast establishment of CRC patient-derived explant (CRC-PDE) cultures from different carcinogenesis pathways, employing agitation-based platforms. A total of 26 CRC-PDE were established and a subset was evaluated for viability (n = 23), morphology and genetic key alterations (n = 21). CRC-PDE retained partial tumor glandular architecture and microenvironment features were partially lost over 4 weeks of culture. Key proteins (p53 and Mismatch repair) and oncogenic driver mutations of the original tumours were sustained throughout the culture. Drug challenge (n = 5) revealed differential drug response from distinct CRC-PDE cases. These findings suggest an adequate representation of the original tumour and highlight the importance of detailed model characterisation. The preservation of key aspects of the CRC microenvironment and genetics supports CRC-PDE potential applicability in pre- and co-clinical settings, as long as temporal dynamics are considered.

Application of LDH assay for therapeutic efficacy evaluation of ex vivo tumor models

The current standard preclinical oncology models are not able to fully recapitulate therapeutic targets and clinically relevant disease biology, evidenced by the 90% attrition rate of new therapies in clinical trials. Three-dimensional (3D) culture systems have the potential to enhance the relevance of preclinical models. However, the limitations of currently available cellular assays to accurately evaluate therapeutic efficacy in these models are hindering their widespread adoption. We assessed the compatibility of the lactate dehydrogenase (LDH) assay in 3D spheroid cultures against other commercially available readout methods. We developed a standardized protocol to apply the LDH assay to ex vivo cultures, considering the impact of culture growth dynamics. We show that accounting for growth rates and background release levels of LDH are sufficient to make the LDH assay a suitable methodology for longitudinal monitoring and endpoint assessment of therapeutic efficacy in both cell line-derived xenografts (xenospheres) and patient-derived explant cultures. This method has the added value of being non-destructive and not dependent on reagent penetration or manipulation of the parent material. The establishment of reliable readout methods for complex 3D culture systems will further the utility of these tumor models in preclinical and co-clinical drug development studies.

The Epithelial to Mesenchymal Transition Related Gene Calumenin Is an Adverse Prognostic Factor of Bladder Cancer Correlated With Tumor Microenvironment Remodeling, Gene Mutation, and Ferroptosis

The tumor microenvironment (TME) plays a critical regulatory role in bladder cancer (BLCA) progression and metastasis. Epithelial-mesenchymal transition (EMT) presents as an essential mechanism of tumor invasion and metastasis. Accumulating pieces of evidence indicated that several microenvironmental factors, including fibroblasts, endothelial, and immune cells, induced EMT in tumor cells. As a hallmark gene of the EMT process, calumenin (CALU) was previously reported to directly impact cancer metastasis. However, the functions and molecular mechanisms of CALU have been rarely reported in BLCA. By multi-omics bioinformatics analysis of 408 TCGA BLCA patients, we demonstrated that CALU was an independent risk factor for BLCA outcome. Subsequently, we verified the correlation of CALU with cancer-associated fibroblasts (CAFs) and tumor-infiltrating immune cells. The results suggested a positive correlation of CALU with CAFs, CD8+ T cells and macrophages. Also, CALU was significantly associated with multiple immune checkpoint-related genes, which ultimately influenced patients' responsiveness to immunotherapy. Further, we found that the impact of CALU on BLCA prognosis might also be correlated with gene mutations and ferroptosis. Finally, we validated the roles of CALU by single-cell RNA sequencing, PCR and immunohistochemistry. In conclusion, we found that CALU affected BLCA prognosis associated with multiple mechanisms, including TME remodeling, gene mutation and ferroptosis. Further studies on CALU may provide new targets for BLCA immunotherapy and precision medicine.

The Peripheral Immune Landscape of Breast Cancer: Clinical Findings and In Vitro Models for Biomarker Discovery

Breast cancer is the deadliest female malignancy worldwide and, while much is known about phenotype and function of infiltrating immune cells, the same attention has not been paid to the peripheral immune compartment of breast cancer patients. To obtain faster, cheaper, and more precise monitoring of patients' status, it is crucial to define and analyze circulating immune profiles. This review compiles and summarizes the disperse knowledge on the peripheral immune profile of breast cancer patients, how it departs from healthy individuals and how it changes with disease progression. We propose this data to be used as a starting point for validation of clinically relevant biomarkers of disease progression and therapy response, which warrants more thorough investigation in patient cohorts of specific breast cancer subtypes. Relevant clinical findings may also be explored experimentally using advanced 3D cellular models of human cancer-immune system interactions, which are under intensive development. We review the latest findings and discuss the strengths and limitations of such models, as well as the future perspectives. Together, the scientific advancement of peripheral biomarker discovery and cancer-immune crosstalk in breast cancer will be instrumental to uncover molecular mechanisms and putative biomarkers and drug targets in an all-human setting.