Evaluating the transcriptional fidelity of cancer models

Clinical relevance and therapeutic predictive ability of hypoxia biomarkers in head and neck cancer tumour models

Tumour hypoxia promotes poor patient outcomes, with particularly strong evidence for head and neck squamous cell carcinoma (HNSCC). To effectively target hypoxia, therapies require selection biomarkers and preclinical models that can accurately model tumour hypoxia. We established 20 patient-derived xenograft (PDX) and cell line-derived xenograft (CDX) models of HNSCC that we characterised for their fidelity to represent clinical HNSCC in gene expression, hypoxia status and proliferation and that were evaluated for their sensitivity to hypoxia-activated prodrugs (HAPs). PDX models showed greater fidelity in gene expression to clinical HNSCC than cell lines, as did CDX models relative to their paired cell lines. PDX models were significantly more hypoxic than CDX models, as assessed by hypoxia gene signatures and pimonidazole immunohistochemistry, and showed similar hypoxia gene expression to clinical HNSCC tumours. Hypoxia or proliferation status alone could not determine HAP sensitivity across our 20 HNSCC and two non-HNSCC tumour models by either tumour growth inhibition or killing of hypoxia cells in an ex vivo clonogenic assay. In summary, our tumour models provide clinically relevant HNSCC models that are suitable for evaluating hypoxia-targeting therapies; however, additional biomarkers to hypoxia are required to accurately predict drug sensitivity.

Genomic profiling of a multi-lineage and multi-passage patient-derived xenograft biobank reflects heterogeneity of ovarian cancer

Ovarian cancer (OC) manifests as a complex disease characterized by inter- and intra-patient heterogeneity. Despite enhanced biological and genetic insights, OC remains a recalcitrant malignancy with minimal survival improvement. Based on multi-site sampling and a multi-lineage patient-derived xenograft (PDX) establishment strategy, we present herein the establishment of a comprehensive PDX biobank from histologically and molecularly heterogeneous OC patients. Comprehensive profiling of matched PDX and patient samples demonstrates that PDXs closely recapitulate parental tumors. By leveraging multi-lineage models, we reveal that the previously reported genomic disparities of PDX could be mainly attributed to intra-patient spatial heterogeneity instead of substantial model-independent genomic evolution. Moreover, DNA damage response pathway inhibitor (DDRi) screening uncovers heterogeneous responses across models. Prolonged iterative drug exposure recapitulates acquired drug resistance in initially sensitive models. Meanwhile, interrogation of induced drug-resistant (IDR) models reveals that suppressed interferon (IFN) response and activated Wnt/β-catenin signaling contribute to acquired DDRi drug resistance.

Research progress on the application of organoids in gynecological tumors

Organoids are in vitro 3D models that maintain their own tissue structure and function. They largely overcome the limitations of traditional tumor models and have become a powerful research tool in the field of oncology in recent years. Gynecological malignancies are major diseases that seriously threaten the life and health of women and urgently require the establishment of models with a high degree of similarity to human tumors for clinical studies to formulate individualized treatments. Currently, organoids are widely studied in exploring the mechanisms of gynecological tumor development as a means of drug screening and individualized medicine. Ovarian, endometrial, and cervical cancers as common gynecological malignancies have high morbidity and mortality rates among other gynecological tumors. Therefore, this study reviews the application of modelling, drug efficacy assessment, and drug response prediction for ovarian, endometrial, and cervical cancers, thereby clarifying the mechanisms of tumorigenesis and development, and providing precise treatment options for gynecological oncology patients.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Evaluating cell culture reliability in pediatric brain tumor primary cells through DNA methylation profiling

In vitro models of pediatric brain tumors (pBT) are instrumental for better understanding the mechanisms contributing to oncogenesis and testing new therapies; thus, ideally, they should recapitulate the original tumor. We applied DNA methylation (DNAm) and copy number variation (CNV) profiling to characterize 241 pBT samples, including 155 tumors and 86 pBT-derived cell cultures, considering serum vs serum-free conditions, late vs early passages, and dimensionality (2D vs 3D cultures). We performed a t-SNE classification and identified differentially methylated regions in tumors compared to cell models. Early cell cultures recapitulate the original tumor, but serum media and 2D culturing were demonstrated to significantly contribute to the divergence of DNAm profiles from the parental ones. All divergent cells clustered together acquiring a common deregulated epigenetic signature suggesting a shared selective pressure. We identified a set of hypomethylated genes shared among unfaithful cells converging on response to growth factors and migration pathways, such as signaling cascade activation, tissue organization, and cellular migration. In conclusion, DNAm and CNV are informative tools that should be used to assess the recapitulation of pBT-cells from parental tumors.

Progress and perspective of organoid technology in breast cancer research

ABSTRACT

Breast cancer, a malignant tumor with a high incidence in women, lacks in vitro research models that can represent the biological functions of breast tumors in vivo. As a new biological tool, the organoid model has unique advantages over traditional methods, such as cell culture and patient-derived xenografts. Combining organoids with other emerging technologies, such as gene engineering and microfluidic chip technology, provides an effective method to compensate for the deficiencies in organoid models of breast cancer in vivo. The emergence of breast cancer organoids has provided new tools and research directions in precision medicine, personality therapy, and drug research. In this review, we summarized the merits and demerits of organoids compared to traditional biological models, explored the latest developments in the combination of new technologies and organoid models, and discussed the construction methods and application prospects of different breast organoid models.

Systems approach for congruence and selection of cancer models towards precision medicine

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation

Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.

Platform-agnostic CellNet enables cross-study analysis of cell fate engineering protocols

Optimization of cell engineering protocols requires standard, comprehensive quality metrics. We previously developed CellNet, a computational tool to quantitatively assess the transcriptional fidelity of engineered cells compared with their natural counterparts, based on bulk-derived expression profiles. However, this platform and others were limited in their ability to compare data from different sources, and no current tool makes it easy to compare new protocols with existing state-of-the-art protocols in a standardized manner. Here, we utilized our prior application of the top-scoring pair transformation to build a computational platform, platform-agnostic CellNet (PACNet), to address both shortcomings. To demonstrate the utility of PACNet, we applied it to thousands of samples from over 100 studies that describe dozens of protocols designed to produce seven distinct cell types. We performed an in-depth examination of hepatocyte and cardiomyocyte protocols to identify the best-performing methods, characterize the extent of intra-protocol and inter-lab variation, and identify common off-target signatures, including a surprising neural/neuroendocrine signature in primary liver-derived organoids. We have made PACNet available as an easy-to-use web application, allowing users to assess their protocols relative to our database of reference engineered samples, and as open-source, extensible code.

Tumor organoid model of colorectal cancer (Review)

The establishment of self-organizing 'mini-gut' organoid models has brought about a significant breakthrough in biomedical research. Patient-derived tumor organoids have emerged as valuable tools for preclinical studies, offering the retention of genetic and phenotypic characteristics of the original tumor. These organoids have applications in various research areas, including in vitro modelling, drug discovery and personalized medicine. The present review provided an overview of intestinal organoids, focusing on their unique characteristics and current understanding. The progress made in colorectal cancer (CRC) organoid models was then delved into, discussing their role in drug development and personalized medicine. For instance, it has been indicated that patient-derived tumor organoids are able to predict response to irinotecan-based neoadjuvant chemoradiotherapy. Furthermore, the limitations and challenges associated with current CRC organoid models were addressed, along with proposed strategies for enhancing their utility in future basic and translational research.

Patient-derived organoids in translational oncology and drug screening

Patient-derived organoids (PDO) are a new biomedical research model that can reconstruct phenotypic and genetic characteristics of the original tissue and are useful for research on pathogenesis and drug screening. To introduce the progression in this field, we review the key factors of constructing organoids derived from epithelial tissues and cancers, covering culture medium and matrix, morphological characteristics, genetic profiles, high-throughput drug screening, and application potential. We also discuss the co-culture system of cancer organoids with tumor microenvironment (TME) associated cells. The co-culture system is widely used in evaluating crosstalk of cancer cells with TME components, such as fibroblasts, endothelial cells, immune cells, and microorganisms. The article provides a prospective for standardized cultivation mode, automatic morphological evaluation, and drug sensitivity screening using high-throughput methods.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy

Advances in cancer immunotherapy, particularly immune checkpoint inhibitors, have dramatically improved the prognosis for patients with metastatic melanoma and other previously incurable cancers. However, patients with pancreatic ductal adenocarcinoma (PDAC) generally do not respond to these therapies. PDAC is exceptionally difficult to treat because of its often late stage at diagnosis, modest mutation burden, and notoriously complex and immunosuppressive tumor microenvironment. Simultaneously interrogating features of cancer, immune, and other cellular components of the PDAC tumor microenvironment is therefore crucial for identifying biomarkers of immunotherapeutic resistance and response. Notably, single-cell and multiomics technologies, along with the analytical tools for interpreting corresponding data, are facilitating discoveries of the systems-level cellular and molecular interactions contributing to the overall resistance of PDAC to immunotherapy. Thus, in this review, we will explore how multiomics and single-cell analyses provide the unprecedented opportunity to identify biomarkers of resistance and response to successfully sensitize PDAC to immunotherapy.

m6A-related long noncoding RNAs predict prognosis and indicate therapeutic response in endometrial carcinoma

BACKGROUND

N6-methyladenosine (m6A) has been identified as the most common, abundant, and conserved internal transcriptional modification. Long noncoding RNAs (lncRNAs) are noncoding RNAs consisting of more than 200 nucleotides, and the expression of various lncRNAs may affect cancer prognosis. The impact of m6A-associated lncRNAs on uterine corpus endometrial carcinoma (UCEC) prognosis is unknown.

METHODS

In this study, UCEC prognosis-related m6A lncRNAs were screened, bioinformatics analysis was performed, and experimental validation was conducted. Endometrial carcinoma (EC) and normal tissue samples were obtained from The Cancer Genome Atlas. The prognosis-related m6A lncRNAs screened by the least absolute shrinkage and selection operator method were used for multivariate Cox proportional risk regression modeling. Principal component analysis and Gene Ontology, immune function difference, and drug sensitivity analyses of the prognostic models were performed. Prognostic analysis was conducted for m6A-associated lncRNAs. The immune infiltration relationship of m6A-associated lncRNAs in EC was identified using the ssGSEA immune infiltration algorithm. A competing endogenouse RNA network was constructed using the LncACTdb database. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) assays were used to validate the differences in m6A-related lncRNA expression in normal and EC cells.

RESULTS

CDKN2B-AS1 and MIR924HG were found to be risk factors for EC. RAB11B-AS1 was a protective factor in EC patients. MIR924HG expression was upregulated in KLE and RL95-2 endometrial cancer cell lines. Prognostic models involved RAB11B-AS1, LINC01812, HM13-IT1, TPM1-AS, SLC16A1-AS1, LINC01936, and CDKN2B-AS1. The high-risk group was more sensitive to five compounds (ABT.263, ABT.888, AP.24534, ATRA, and AZD.0530) than the low-risk group.

CONCLUSION

These findings contribute to understanding of the function of m6A-related lncRNAs in UCEC and provide promising therapeutic strategies for UCEC.

Advances in the Lung Cancer Immunotherapy Approaches

Despite the progress in the comprehension of LC progression, risk, immunologic control, and treatment choices, it is still the primary cause of cancer-related death. LC cells possess a very low and heterogeneous antigenicity, which allows them to passively evade the anticancer defense of the immune system by educating cytotoxic lymphocytes (CTLs), tumor-infiltrating lymphocytes (TILs), regulatory T cells (Treg), immune checkpoint inhibitors (ICIs), and myeloid-derived suppressor cells (MDSCs). Though ICIs are an important candidate in first-line therapy, consolidation therapy, adjuvant therapy, and other combination therapies involving traditional therapies, the need for new predictive immunotherapy biomarkers remains. Furthermore, ICI-induced resistance after an initial response makes it vital to seek and exploit new targets to benefit greatly from immunotherapy. As ICIs, tumor mutation burden (TMB), and microsatellite instability (MSI) are not ideal LC predictive markers, a multi-parameter analysis of the immune system considering tumor, stroma, and beyond can be the future-oriented predictive marker. The optimal patient selection with a proper adjuvant agent in immunotherapy approaches needs to be still revised. Here, we summarize advances in LC immunotherapy approaches with their clinical and preclinical trials considering cancer models and vaccines and the potential of employing immunology to predict immunotherapy effectiveness in cancer patients and address the viewpoints on future directions. We conclude that the field of lung cancer therapeutics can benefit from the use of combination strategies but with comprehension of their limitations and improvements.

Modelling aggressive prostate cancers of young men in immune-competent mice, driven by isogenic Trp53 alterations and Pten loss

Understanding prostate cancer onset and progression in order to rationally treat this disease has been critically limited by a dire lack of relevant pre-clinical animal models. We have generated a set of genetically engineered mice that mimic human prostate cancer, initiated from the gland epithelia. We chose driver gene mutations that are specifically relevant to cancers of young men, where aggressive disease poses accentuated survival risks. An outstanding advantage of our models are their intact repertoires of immune cells. These mice provide invaluable insight into the importance of immune responses in prostate cancer and offer scope for studying treatments, including immunotherapies. Our prostate cancer models strongly support the role of tumour suppressor p53 in functioning to critically restrain the emergence of cancer pathways that drive cell cycle progression; alter metabolism and vasculature to fuel tumour growth; and mediate epithelial to mesenchymal-transition, as vital to invasion. Importantly, we also discovered that the type of p53 alteration dictates the specific immune cell profiles most significantly disrupted, in a temporal manner, with ramifications for disease progression. These new orthotopic mouse models demonstrate that each of the isogenic hotspot p53 amino acid mutations studied (R172H and R245W, the mouse equivalents of human R175H and R248W respectively), drive unique cellular changes affecting pathways of proliferation and immunity. Our findings support the hypothesis that individual p53 mutations confer their own particular oncogenic gain of function in prostate cancer.

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

Molecularly targeted treatments and immunotherapy are cornerstones in oncology, with demonstrated efficacy across different tumor types. Nevertheless, the overwhelming majority metastatic disease is incurable due to the onset of drug resistance. Preclinical models including genetically engineered mouse models, patient-derived xenografts and two- and three-dimensional cell cultures have emerged as a useful resource to study mechanisms of cancer progression and predict efficacy of anticancer drugs. However, variables including tumor heterogeneity and the complexities of the microenvironment can impair the faithfulness of these platforms. Here, we will discuss advantages and limitations of these preclinical models, their applicability for drug testing and in co-clinical trials and potential strategies to increase their reliability in predicting responsiveness to anticancer medications.

Patient-Derived Lung Tumoroids—An Emerging Technology in Drug Development and Precision Medicine

Synthetic 3D multicellular systems derived from patient tumors, or tumoroids, have been developed to complete the cancer research arsenal and overcome the limits of current preclinical models. They aim to represent the molecular and structural heterogeneity of the tumor micro-environment, and its complex network of interactions, with greater accuracy. They are more predictive of clinical outcomes, of adverse events, and of resistance mechanisms. Thus, they increase the success rate of drug development, and help clinicians in their decision-making process. Lung cancer remains amongst the deadliest of diseases, and still requires intensive research. In this review, we analyze the merits and drawbacks of the current preclinical models used in lung cancer research, and the position of tumoroids. The introduction of immune cells and healthy regulatory cells in autologous tumoroid models has enabled their application to most recent therapeutic concepts. The possibility of deriving tumoroids from primary tumors within reasonable time has opened a direct approach to patient-specific features, supporting their future role in precision medicine.

Computational estimation of quality and clinical relevance of cancer cell lines

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.

CNpare: matching DNA copy number profiles

SUMMARY

Selecting the optimal cancer cell line for an experiment can be challenging given the diversity of lines available. Here, we present CNpare, which identifies similar cell line models based on genome-wide DNA copy number.

AVAILABILITY

CNpare is available as an R package at https://github.com/macintyrelab/CNpare. All analysis performed in the manuscript can be reproduced via the code found at https://github.com/macintyrelab/CNpare_analyses.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DNA-based copy number analysis confirms genomic evolution of PDX models

Genomic evolution of patient-derived xenografts (PDXs) may lead to their gradual divergence away of their tumors of origin. We previously reported the genomic evolution of the copy number (CN) landscapes of PDXs during their engraftment and passaging¹. However, whether PDX models are highly stable throughout passaging², or can evolve CNAs rapidly^1,3, remains controversial. Here, we reassess the genomic evolution of PDXs using DNA-based CN profiles. We find strong evidence for genomic evolution in the DNA-based PDX data: a median of ~10% of the genome is differentially altered between matched primary tumors (PTs) and PDXs across cohorts (range, 0% to 73% across all models). In 24% of the matched PT-PDX samples, over a quarter of the genome is differentially affected by CN alterations. Moreover, in matched analyses of PTs and their derived PDXs at multiple passages, later-passage PDXs are significantly less similar to their parental PTs than earlier-passage PDXs, indicative of genomic divergence. We conclude that PDX models indeed evolve throughout their derivation and propagation, and that the phenotypic consequences of this evolution ought to be assessed in order to determine its relevance to the proper application of these valuable cancer models.

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.

Temozolomide sensitivity of malignant glioma cell lines - a systematic review assessing consistencies between in vitro studies

BACKGROUND

Malignant glioma cell line models are integral to pre-clinical testing of novel potential therapies. Accurate prediction of likely efficacy in the clinic requires that these models are reliable and consistent. We assessed this by examining the reporting of experimental conditions and sensitivity to temozolomide in glioma cells lines.

METHODS

We searched Medline and Embase (Jan 1994-Jan 2021) for studies evaluating the effect of temozolomide monotherapy on cell viability of at least one malignant glioma cell line. Key data items included type of cell lines, temozolomide exposure duration in hours (hr), and cell viability measure (IC50).

RESULTS

We included 212 studies from 2789 non-duplicate records that reported 248 distinct cell lines. The commonest cell line was U87 (60.4%). Only 10.4% studies used a patient-derived cell line. The proportion of studies not reporting each experimental condition ranged from 8.0-27.4%, including base medium (8.0%), serum supplementation (9.9%) and number of replicates (27.4%). In studies reporting IC50, the median value for U87 at 24 h, 48 h and 72 h was 123.9 μM (IQR 75.3-277.7 μM), 223.1 μM (IQR 92.0-590.1 μM) and 230.0 μM (IQR 34.1-650.0 μM), respectively. The median IC50 at 72 h for patient-derived cell lines was 220 μM (IQR 81.1-800.0 μM).

CONCLUSION

Temozolomide sensitivity reported in comparable studies was not consistent between or within malignant glioma cell lines. Drug discovery science performed on these models cannot reliably inform clinical translation. A consensus model of reporting can maximise reproducibility and consistency among in vitro studies.

Three-dimensional organoid culture unveils resistance to clinical therapies in adult and pediatric glioblastoma

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Glioblastoma organoid cultures preserve diversity of proliferative cell phenotypes.

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Heterogeneous 3D cultures recapitulate resistance to clinical GBM therapeutics.

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Patient specimens show different behavior depending on 2D vs 3D growth.

Background

Glioblastoma (GBM) is the most common primary brain tumor with a dismal prognosis. The inherent cellular diversity and interactions within tumor microenvironments represent significant challenges to effective treatment. Traditional culture methods such as adherent or sphere cultures may mask such complexities whereas three-dimensional (3D) organoid culture systems derived from patient cancer stem cells (CSCs) can preserve cellular complexity and microenvironments. The objective of this study was to determine if GBM organoids may offer a platform, complimentary to traditional sphere culture methods, to recapitulate patterns of clinical drug resistance arising from 3D growth.

Methods

Adult and pediatric surgical specimens were collected and established as organoids. We created organoid microarrays and visualized bulk and spatial differences in cell proliferation using immunohistochemistry (IHC) staining, and cell cycle analysis by flow cytometry paired with 3D regional labeling. We tested the response of CSCs grown in each culture method to temozolomide, ibrutinib, lomustine, ruxolitinib, and radiotherapy.

Results

GBM organoids showed diverse and spatially distinct proliferative cell niches and include heterogeneous populations of CSCs/non-CSCs (marked by SOX2) and cycling/senescent cells. Organoid cultures display a comparatively blunted response to current standard-of-care therapy (combination temozolomide and radiotherapy) that reflects what is seen in practice. Treatment of organoids with clinically relevant drugs showed general therapeutic resistance with drug- and patient-specific antiproliferative, apoptotic, and senescent effects, differing from those of matched sphere cultures.

Conclusions

Therapeutic resistance in organoids appears to be driven by altered biological mechanisms rather than physical limitations of therapeutic access. GBM organoids may therefore offer a key technological approach to discover and understand resistance mechanisms of human cancer cells.

From bench to bedside: Single-cell analysis for cancer immunotherapy

Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.

Developing and validating model systems for immuno-oncology

Owing to clinical success of immune-checkpoint blockade, immunotherapy is becoming a cornerstone of modern oncology, and immuno-oncology is at the forefront of basic cancer research. This commentary outlines future opportunities for immuno-oncology modeling.