The clinical relevance of cancer cell lines

Targeting PLOD2 suppresses invasion and metastatic potential in radiorecurrent prostate cancer

Background

Metastatic relapse of prostate cancer after radiotherapy is a significant cause of prostate cancer-related morbidity and mortality. PLOD2 is a mediator of invasion and metastasis that we identified as being upregulated in our highly aggressive radiorecurrent prostate cancer cell line.

Methods

Patient dataset analysis was conducted using a variety of prostate cancer cohorts. Prostate cancer cell lines were treated with siRNA, or the drug PX-478 prior to in vitro invasion, migration, or in vivo chick embryo (CAM) extravasation assay. Protein levels were detected by western blot. For RNA analysis, RNA sequencing was conducted on PLOD2 knockdown cells and validated by qRT-PCR.

Results

PLOD2 is a negative prognostic factor associated with biochemical relapse, driving invasion, migration, and extravasation in radiorecurrent prostate cancer. Mechanistically, HIF1α upregulation drives PLOD2 expression in our radiorecurrent prostate cancer cells, which is effectively inhibited by HIF1α inhibitor PX-478 to reduce invasion, migration, and extravasation. Finally, the long non-coding RNA LNCSRLR acts as a promoter of invasion downstream of PLOD2.

Conclusions

Together, our results demonstrate for the first time the role of PLOD2 in radiorecurrent prostate cancer invasiveness, and point towards its potential as a therapeutic target to reduce metastasis and improve survival outcomes in prostate cancer patients.

Reduced EO771-induced tumour growth and increased overall-survival of mice ablated for immune cell-specific catalytic subunit Cβ2 of protein kinase A

Ablation of the immune-specific catalytic subunit Cβ2 of protein kinase A is associated with a proinflammatory phenotype and increased sensitivity to autoimmunity in mice. Here we show that tumour growth of the adenocarcinoma cell line EO771 in the breast and in the lung after injection into the mammary fat pad and tail vein, respectively, was significantly reduced in mice ablated for Cβ2 compared to wild-type mice. In both cases, the breast and lung tumours showed increased infiltration of immune cells in the mice lacking Cβ2 compared to wild-type mice. Despite this, it appeared that solid tissue- versus intravenously injected EO771 cells evoked different immune responses. This was reflected by significantly increased levels of splenic proinflammatory immune cells and circulating cytokines in Cβ2 ablated mice carrying breast- but not the lung tumours. Moreover, Cβ2 ablated mice injected with EO771 cells showed increased overall survival compared to wild-type mice. Taken together, our results suggest for a role for immune cell-specific Cβ2 in protecting against tumour growth induced by EO771 cells in mice that is reflected in improved overall survival.

Clinical and pathological predictors of engraftment for patient-derived xenografts in lung adenocarcinoma

Patient-derived xenografts (PDXs) are increasingly utilized in preclinical drug efficacy studies due to their ability to retain the molecular, histological, and drug response characteristics of patient tumors. This study aimed to investigate the factors influencing the successful engraftment of PDXs. Lung adenocarcinoma PDXs were established using freshly resected tumor tissues obtained through surgery. Radiological data of pulmonary nodules from this PDX cohort were analyzed, categorizing them into solid tumors and tumors with ground-glass opacity (GGO) based on preoperative CT images. Gene mutation status was obtained from next generation sequencing data and MassARRAY panel. A total of 254 resected primary lung adenocarcinomas were utilized for PDX establishment, with successful initial engraftment in 58 cases (22.8 %); stable engraftment defined as at least three serial passages was observed in 43 cases (16.9 %). The stable engraftment rates of PDXs from solid tumors and tumors with GGO were 22.1 % (42 of 190 cases) and 1.6 % (1 of 64 cases), respectively (P < 0.001). Adenocarcinomas with advanced stage, poor differentiation, solid histologic subtype, and KRAS or TP53 gene mutations were associated with stable PDX engraftment. Avoiding tumors with GGO features could enhance the cost-effectiveness of establishing PDX models from early-stage resected lung adenocarcinomas.

Evaluation of ex vivo drug combination optimization platform in recurrent high grade astrocytic glioma: An interventional, non-randomized, open-label trial protocol

INTRODUCTION

High grade astrocytic glioma (HGG) is a lethal solid malignancy with high recurrence rates and limited survival. While several cytotoxic agents have demonstrated efficacy against HGG, drug sensitivity testing platforms to aid in therapy selection are lacking. Patient-derived organoids (PDOs) have been shown to faithfully preserve the biological characteristics of several cancer types including HGG, and coupled with the experimental-analytical hybrid platform Quadratic Phenotypic Optimization Platform (QPOP) which evaluates therapeutic sensitivity at a patient-specific level, may aid as a tool for personalized medical decisions to improve treatment outcomes for HGG patients.

METHODS

This is an interventional, non-randomized, open-label study, which aims to enroll 10 patients who will receive QPOP-guided chemotherapy at the time of first HGG recurrence following progression on standard first-line therapy. At the initial presentation of HGG, tumor will be harvested for primary PDO generation during the first biopsy/surgery. At the point of tumor recurrence, patients will be enrolled onto the main study to receive systemic therapy as second-line treatment. Subjects who undergo surgery at the time of recurrence will have a second harvest of tissue for PDO generation. Established PDOs will be subject to QPOP analyses to determine their therapeutic sensitivities to specific panels of drugs. A QPOP-guided treatment selection algorithm will then be used to select the most appropriate drug combination. The primary endpoint of the study is six-month progression-free survival. The secondary endpoints include twelve-month overall survival, RANO criteria and toxicities. In our radiological biomarker sub-study, we plan to evaluate novel radiopharmaceutical-based neuroimaging in determining blood-brain barrier permeability and to assess in vivo drug effects on tumor vasculature over time.

TRIAL REGISTRATION

This trial was registered on 8th September 2022 with ClinicalTrials.gov Identifier: NCT05532397.

Establishment and characterization of novel high mucus-producing lung tumoroids derived from a patient with pulmonary solid adenocarcinoma

Among mucus-producing lung cancers, invasive mucinous adenocarcinoma of the lung is a rare and unique subtype of pulmonary adenocarcinoma. Notably, mucus production may also be observed in the five subtypes of adenocarcinoma grouped under the higher-level diagnosis of Invasive Non-mucinous Adenocarcinomas (NMA). Overlapping pathologic features in mucus-producing tumors can cause diagnostic confusion with significant clinical consequences. In this study, we established lung tumoroids, PDT-LUAD#99, from a patient with NMA and mucus production. The tumoroids were derived from the malignant pleural effusion of a patient with lung cancer and have been successfully developed for long-term culture (> 11 months). Karyotyping by fluorescence in situ hybridization using an alpha-satellite probe showed that tumoroids harbored aneuploid karyotypes. Subcutaneous inoculation of PDT-LUAD#99 lung tumoroids into immunodeficient mice resulted in tumor formation, suggesting that the tumoroids were derived from cancer. Xenografts from PDT-LUAD#99 lung tumoroids reproduced the solid adenocarcinoma with mucin production that was observed in the patient's metastatic lymph nodes. Immunoblot analysis showed MUC5AC secretion into the culture supernatant of PDT-LUAD#99 lung tumoroids, which in contradistinction was barely detected in the culture supernatants of NCI-A549 and NCI-H2122 pulmonary adenocarcinoma cells known for their mucin-producing abilities. Here, we established a novel high-mucus-producing lung tumoroids from a solid adenocarcinoma. This preclinical model may be useful for elucidating the pathogenesis of mucus-producing lung cancer.

Rat Models of Hormone Receptor-Positive Breast Cancer

Hormone receptor-positive (HR+) breast cancer (BC) is the most common type of breast cancer among women worldwide, accounting for 70-80% of all invasive cases. Patients with HR+ BC are commonly treated with endocrine therapy, but intrinsic or acquired resistance is a frequent problem, making HR+ BC a focal point of intense research. Despite this, the malignancy still lacks adequate in vitro and in vivo models for the study of its initiation and progression as well as response and resistance to endocrine therapy. No mouse models that fully mimic the human disease are available, however rat mammary tumor models pose a promising alternative to overcome this limitation. Compared to mice, rats are more similar to humans in terms of mammary gland architecture, ductal origin of neoplastic lesions and hormone dependency status. Moreover, rats can develop spontaneous or induced mammary tumors that resemble human HR+ BC. To date, six different types of rat models of HR+ BC have been established. These include the spontaneous, carcinogen-induced, transplantation, hormone-induced, radiation-induced and genetically engineered rat mammary tumor models. Each model has distinct advantages, disadvantages and utility for studying HR+ BC. This review provides a comprehensive overview of all published models to date.

IMMUNOTAR - Integrative prioritization of cell surface targets for cancer immunotherapy

Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of less toxic immunotherapies; however, identifying targets for immunotherapies remains a challenge in the field. To address this challenge, we developed IMMUNOTAR, a computational tool that systematically prioritizes and identifies candidate immunotherapeutic targets. IMMUNOTAR integrates user-provided RNA-sequencing or proteomics data with quantitative features extracted from publicly available databases based on predefined optimal immunotherapeutic target criteria and quantitatively prioritizes potential surface protein targets. We demonstrate the utility and flexibility of IMMUNOTAR using three distinct datasets, validating its effectiveness in identifying both known and new potential immunotherapeutic targets within the analyzed cancer phenotypes. Overall, IMMUNOTAR enables the compilation of data from multiple sources into a unified platform, allowing users to simultaneously evaluate surface proteins across diverse criteria. By streamlining target identification, IMMUNOTAR empowers researchers to efficiently allocate resources and accelerate immunotherapy development.

Methods for assessing and removing non-specific photoimmunotherapy damage in patient-derived tumor cell culture models

Tumor-targeted, activatable photoimmunotherapy (taPIT) has been shown to selectively destroy tumor in a metastatic mouse model. However, the photoimmunoconjugate (PIC) used for taPIT includes a small fraction of non-covalently associated (free) benzoporphyrin derivative (BPD), which leads to non-specific killing in vitro. Here, we report a new treatment protocol for patient-derived primary tumor cell cultures ultrasensitive to BPD photodynamic therapy (BPD-PDT). Based on free BPD efflux dynamics, the updated in vitro taPIT protocol precludes non-specific BPD-PDT by silencing the effect of free BPD. Following incubation with PIC, incubating cells with PIC-free medium allows time for expulsion of free BPD whereas BPD covalently bound to PIC fragments is retained. Administration of the light dose after the intracellular free BPD drops below the threshold for inducing cell death helps to mitigate non-specific damage. In this study, we tested two primary ovarian tumor cell lines that are intrinsically chemoresistant, yet ultrasensitive to BPD-PDT such that small amounts of free BPD (a few percent of the total BPD dose) lead to potent induction of cell death upon irradiation. The modifications in the protocol suggested here improve in vitro taPIT experiments that lack in vivo mechanisms of free BPD clearance (i.e., lymph and blood flow).

Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection

Colorectal cancer (CRC) poses significant challenges in chemotherapy response prediction due to its molecular heterogeneity. This study introduces an innovative methodology that leverages gene expression data generated from matched colorectal tumor and organoid samples to enhance prediction accuracy. By applying Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across multiple datasets, we identify critical gene modules and hub genes that correlate with patient responses, particularly to 5-fluorouracil (5-FU). This integrative approach advances precision medicine by refining chemotherapy regimen selection based on individual tumor profiles. Our predictive model demonstrates superior accuracy over traditional methods on independent datasets, illustrating significant potential in addressing the complexities of high-dimensional genomic data for cancer biomarker research.

Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications

The induced pluripotent stem cell (iPSC) technology has transformed in vitro research and holds great promise to advance regenerative medicine. iPSCs have the capacity for an almost unlimited expansion, are amenable to genetic engineering, and can be differentiated into most somatic cell types. iPSCs have been widely applied to model human development and diseases, perform drug screening, and develop cell therapies. In this review, we outline key developments in the iPSC field and highlight the immense versatility of the iPSC technology for in vitro modeling and therapeutic applications. We begin by discussing the pivotal discoveries that revealed the potential of a somatic cell nucleus for reprogramming and led to successful generation of iPSCs. We consider the molecular mechanisms and dynamics of somatic cell reprogramming as well as the numerous methods available to induce pluripotency. Subsequently, we discuss various iPSC-based cellular models, from mono-cultures of a single cell type to complex three-dimensional organoids, and how these models can be applied to elucidate the mechanisms of human development and diseases. We use examples of neurological disorders, coronavirus disease 2019 (COVID-19), and cancer to highlight the diversity of disease-specific phenotypes that can be modeled using iPSC-derived cells. We also consider how iPSC-derived cellular models can be used in high-throughput drug screening and drug toxicity studies. Finally, we discuss the process of developing autologous and allogeneic iPSC-based cell therapies and their potential to alleviate human diseases.

NMR Metabolomics of Primary Ovarian Cancer Cells in Comparison to Established Cisplatin-Resistant and -Sensitive Cell Lines

Cancer cell lines are frequently used in metabolomics, such as in vitro tumor models. In particular, A2780 cells are commonly used as a model for ovarian cancer to evaluate the effects of drug treatment. Here, we compare the NMR metabolomics profiles of A2780 and cisplatin-resistant A2780 cells with those of cells derived from 10 patients with high-grade serous ovarian carcinoma (collected during primary cytoreduction before any chemotherapeutic treatment). Our analysis reveals a substantial similarity among all primary cells but significant differences between them and both A2780 and cisplatin-resistant A2780 cells. Notably, the patient-derived cells are closer to the resistant A2780 cells when considering the exo-metabolome, whereas they are essentially equidistant from A2780 and A2780-resistant cells in terms of the endo-metabolome. This behavior results from dissimilarities in the levels of several metabolites attributable to the differential modulation of underlying biochemical pathways. The patient-derived cells are those with the most pronounced glycolytic phenotype, whereas A2780-resistant cells mainly diverge from the others due to alterations in a few specific metabolites already known as markers of resistance.

O-Glycome Profiling of Breast Cancer Cell Lines to Understand Breast Cancer Brain Metastasis

Breast cancer is the second leading cause of cancer-related death among women and a major source of brain metastases. Despite the increasing incidence of brain metastasis from breast cancer, the underlying mechanisms remain poorly understood. Altered glycosylation is known to play a role in various diseases including cancer metastasis. However, profiling studies of O-glycans and their isomers in breast cancer brain metastasis (BCBM) are scarce. This study analyzed the expression of O-glycans and their isomers in human breast cancer cell lines (MDA-MB-231, MDA-MB-361, HTB131, and HTB22), a brain cancer cell line (CRL-1620), and a brain metastatic breast cancer cell line (MDA-MB-231BR) using nanoLC-MS/MS, identifying 27 O-glycan compositions. We observed significant upregulation in the expression of HexNAc1Hex1NeuAc2 and HexNAc2Hex3, whereas the expression of HexNAc1Hex1NeuAc1 was downregulated in MDA-MB-231BR compared to other cell lines. In our isomeric analysis, we observed notable alterations in the isomeric forms of the O-glycan structure HexNAc1Hex1NeuAc1 in a comparison of different cell lines. Our analysis of O-glycans and their isomers in cancer cells demonstrated that changes in their distribution can be related to the metastatic process. We believe that our investigation will contribute to an enhanced comprehension of the significance of O-glycans and their isomers in BCBM.

Culture media and format alter cellular composition and barrier integrity of porcine colonoid-derived monolayers

Intestinal organoid technology has revolutionized our approach to in vitro cell culture due in part to their three-dimensional structures being more like the native tissue from which they were derived with respect to cellular composition and architecture. For this reason, organoids are becoming the new gold standard for undertaking intestinal epithelial cell research. Unfortunately, their otherwise advantageous three-dimensional geometry prevents easy access to the apical epithelium, which is a major limitation when studying interactions between dietary or microbial components and host tissues. To overcome this problem, we developed porcine colonoid-derived monolayers cultured on both permeable Transwell inserts and tissue culture treated polystyrene plates. We found that seeding density and culture format altered the expression of genes encoding markers of specific cell types (stem cells, colonocytes, goblets, and enteroendocrine cells), and barrier maturation (tight junctions). Additionally, we found that changes to the formulation of the culture medium altered the cellular composition of colonoids and of monolayers derived from them, resulting in cultures with an increasingly differentiated phenotype that was similar to that of their tissue of origin.

Biomimetic microfluidic chips for toxicity assessment of environmental pollutants

Various types of pollutants widely present in environmental media, including synthetic and natural chemicals, physical pollutants such as radioactive substances, ultraviolet rays, and noise, as well as biological organisms, pose a huge threat to public health. Therefore, it is crucial to accurately and effectively explore the human physiological responses and toxicity mechanisms of pollutants to prevent diseases caused by pollutants. The emerging toxicological testing method biomimetic microfluidic chips (BMCs) exhibit great potential in environmental pollutant toxicity assessment due to their superior biomimetic properties. The BMCs are divided into cell-on-chips and organ-on-chips based on the distinctions in bionic simulation levels. Herein, we first summarize the characteristics, emergence and development history, composition and structure, and application fields of BMCs. Then, with a focus on the toxicity mechanisms of pollutants, we review the applications and advances of the BMCs in the toxicity assessment of physical, chemical, and biological pollutants, respectively, highlighting its potential and development prospects in environmental toxicology testing. Finally, the opportunities and challenges for further use of BMCs are discussed.

Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction

MOTIVATION

Personalized cancer treatments require accurate drug response predictions. Existing deep learning methods show promise but higher accuracy is needed to serve the purpose of precision medicine. The prediction accuracy can be improved with not only topology but geometrical information of drugs.

RESULTS

A novel deep learning methodology for drug response prediction is presented, named Hi-GeoMVP. It synthesizes hierarchical drug representation with multi-omics data, leveraging graph neural networks and variational autoencoders for detailed drug and cell line representations. Multi-task learning is employed to make better prediction, while both 2D and 3D molecular representations capture comprehensive drug information. Testing on the GDSC dataset confirms Hi-GeoMVP's enhanced performance, surpassing prior state-of-the-art methods by improving the Pearson correlation coefficient from 0.934 to 0.941 and decreasing the root mean square error from 0.969 to 0.931. In the case of blind test, Hi-GeoMVP demonstrated robustness, outperforming the best previous models with a superior Pearson correlation coefficient in the drug-blind test. These results underscore Hi-GeoMVP's capabilities in drug response prediction, implying its potential for precision medicine.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/matcyr/Hi-GeoMVP.

The importance of preclinical models in cholangiocarcinoma

Cholangiocarcinoma (CCA) is an adenocarcinoma of the hepatobiliary system with a grim prognosis. Incidence is rising globally and surgery is currently the only curative treatment, but is only available for patients who are fit and diagnosed in an early-stage of disease progression. Great importance has been placed on developing preclinical models to help further our understanding of CCA and potential treatments to improve therapeutic outcomes. Preclinical models of varying complexity and cost have been established, ranging from more simplistic in vitro 2D CCA cell lines in culture, to more complex in vivo genetically engineered mouse models. Currently there is no single model that faithfully recaptures the complexities of human CCA and the in vivo tumour microenvironment. Instead a multi-model approach should be used when designing preclinical trials to study CCA and potential therapies.

Toward reproducible tumor organoid culture: focusing on primary liver cancer

Organoids present substantial potential for pushing forward preclinical research and personalized medicine by accurately recapitulating tissue and tumor heterogeneity in vitro. However, the lack of standardized protocols for cancer organoid culture has hindered reproducibility. This paper comprehensively reviews the current challenges associated with cancer organoid culture and highlights recent multidisciplinary advancements in the field with a specific focus on standardizing liver cancer organoid culture. We discuss the non-standardized aspects, including tissue sources, processing techniques, medium formulations, and matrix materials, that contribute to technical variability. Furthermore, we emphasize the need to establish reproducible platforms that accurately preserve the genetic, proteomic, morphological, and pharmacotypic features of the parent tumor. At the end of each section, our focus shifts to organoid culture standardization in primary liver cancer. By addressing these challenges, we can enhance the reproducibility and clinical translation of cancer organoid systems, enabling their potential applications in precision medicine, drug screening, and preclinical research.

Ex vivo discovery of synergistic drug combinations for hematologic malignancies

Combination therapies have improved outcomes for patients with acute myeloid leukemia (AML). However, these patients still have poor overall survival. Although many combination therapies are identified with high-throughput screening (HTS), these approaches are constrained to disease models that can be grown in large volumes (e.g., immortalized cell lines), which have limited translational utility. To identify more effective and personalized treatments, we need better strategies for screening and exploring potential combination therapies. Our objective was to develop an HTS platform for identifying effective combination therapies with highly translatable ex vivo disease models that use size-limited, primary samples from patients with leukemia (AML and myelodysplastic syndrome). We developed a system, ComboFlow, that comprises three main components: MiniFlow, ComboPooler, and AutoGater. MiniFlow conducts ex vivo drug screening with a miniaturized flow-cytometry assay that uses minimal amounts of patient sample to maximize throughput. ComboPooler incorporates computational methods to design efficient screens of pooled drug combinations. AutoGater is an automated gating classifier for flow cytometry that uses machine learning to rapidly analyze the large datasets generated by the assay. We used ComboFlow to efficiently screen more than 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. In this screen, ComboFlow identified the known synergistic combination of bortezomib and panobinostat. ComboFlow also identified a novel drug combination, dactinomycin and fludarabine, that synergistically killed leukemic cells in 35 % of AML samples. This combination also had limited effects in normal, hematopoietic progenitors. In conclusion, ComboFlow enables exploration of massive landscapes of drug combinations that were previously inaccessible in ex vivo models. We envision that ComboFlow can be used to discover more effective and personalized combination therapies for cancers amenable to ex vivo models.

Functional Homologous Recombination (HR) Screening Shows the Majority of BRCA1/2-Mutant Breast and Ovarian Cancer Cell Lines Are HR-Proficient

Tumors with a pathogenic BRCA1/2 mutation are homologous recombination (HR)-deficient (HRD) and consequently sensitive to platinum-based chemotherapy and Poly-[ADP-Ribose]-Polymerase inhibitors (PARPi). We hypothesized that functional HR status better reflects real-time HR status than BRCA1/2 mutation status. Therefore, we determined the functional HR status of 53 breast cancer (BC) and 38 ovarian cancer (OC) cell lines by measuring the formation of RAD51 foci after irradiation. Discrepancies between functional HR and BRCA1/2 mutation status were investigated using exome sequencing, methylation and gene expression data from 50 HR-related genes. A pathogenic BRCA1/2 mutation was found in 10/53 (18.9%) of BC and 7/38 (18.4%) of OC cell lines. Among BRCA1/2-mutant cell lines, 14/17 (82.4%) were HR-proficient (HRP), while 1/74 (1.4%) wild-type cell lines was HRD. For most (80%) cell lines, we explained the discrepancy between functional HR and BRCA1/2 mutation status. Importantly, 12/14 (85.7%) BRCA1/2-mutant HRP cell lines were explained by mechanisms directly acting on BRCA1/2. Finally, functional HR status was strongly associated with COSMIC single base substitution signature 3, but not BRCA1/2 mutation status. Thus, the majority of BRCA1/2-mutant cell lines do not represent a suitable model for HRD. Moreover, exclusively determining BRCA1/2 mutation status may not suffice for platinum-based chemotherapy or PARPi patient selection.

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.

Simultaneous inhibition of FAK and ROS1 synergistically repressed triple-negative breast cancer by upregulating p53 signalling

BACKGROUND

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype lacking effective targeted therapies, necessitating innovative treatment approaches. While targeting ROS proto-oncogene 1 (ROS1) with crizotinib has shown promise, resistance remains a limitation. Recent evidence links focal adhesion kinase (FAK) to drug resistance, prompting our study to assess the combined impact of FAK inhibitor IN10018 and crizotinib in TNBC and elucidate the underlying mechanisms.

METHODS

We employed the Timer database to analyze FAK and ROS1 mRNA levels in TNBC and adjacent normal tissues. Furthermore, we investigated the correlation between FAK, ROS1, and TNBC clinical prognosis using the GSE database. We conducted various in vitro assays, including cell viability, colony formation, flow cytometry, EdU assays, and western blotting. Additionally, TNBC xenograft and human TNBC organoid models were established to assess the combined therapy's efficacy. To comprehensively understand the synergistic anti-tumor mechanisms, we utilized multiple techniques, such as RNA sequencing, immunofluorescence, cell flow cytometry, C11-BODIPY staining, MDA assay, and GSH assay.

RESULTS

The Timer database revealed higher levels of FAK and ROS1 in TNBC tissues compared to normal tissues. Analysis of GEO databases indicated that patients with high FAK and ROS1 expression had the poorest prognosis. Western blotting confirmed increased p-FAK expression in crizotinib-resistant TNBC cells. In vitro experiments showed that the combination therapy down-regulated cyclin B1, p-Cdc2, and Bcl2 while up-regulating BAX, cleaved-Caspase-3, cleaved-Caspase-9, and cleaved PARP. In TNBC xenograft models, the tumor volume in the combination therapy group was 73% smaller compared to the control group (p < 0.0001). Additionally, the combination therapy resulted in a 70% reduction in cell viability in human TNBC organoid models (p < 0.0001). RNA sequencing analysis of TNBC cells and xenograft tumor tissues highlighted enrichment in oxidative stress, glutathione metabolism, and p53 pathways. The combined group displayed a fivefold rise in the reactive oxygen species level, a 69% decrease in the GSH/GSSG ratio, and a sixfold increase in the lipid peroxidation in comparison to the control group. Western blotting demonstrated p53 upregulation and SCL7A11 and GPX4 downregulation in the combination group. The addition of a p53 inhibitor reversed these effects.

CONCLUSION

Our study demonstrates that the combination of IN10018 and crizotinib shows synergistic antitumor effects in TNBC. Mechanistically, this combination inhibits cell proliferation, enhances apoptosis, and induces ferroptosis, which is associated with increased p53 levels.

PANCDR: precise medicine prediction using an adversarial network for cancer drug response

Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.

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.

Sex difference in liver diseases: How preclinical models help to dissect the sex-related mechanisms sustaining NAFLD and hepatocellular carcinoma

Only a few preclinical findings are confirmed in the clinic, posing a critical issue for clinical development. Therefore, identifying the best preclinical models can help to dissect molecular and mechanistic insights into liver disease pathogenesis while being clinically relevant. In this context, the sex relevance of most preclinical models has been only partially considered. This is particularly significant in NAFLD and HCC, which have a higher prevalence in men when compared to pre-menopause women but not to those in post-menopausal status, suggesting a role for sex hormones in the pathogenesis of the diseases. This review gathers the sex-relevant findings and the available preclinical models focusing on both in vitro and in vivo studies and discusses the potential implications and perspectives of introducing the sex effect in the selection of the best preclinical model. This is a critical aspect that would help to tailor personalized therapies based on sex.

Beyond the surface: Investigation of tumorsphere morphology using volume electron microscopy

The advent of volume electron microscopy (vEM) has provided unprecedented insights into cellular and subcellular organization, revolutionizing our understanding of cancer biology. This study presents a previously unexplored comparative analysis of the ultrastructural disparities between cancer cells cultured as monolayers and tumorspheres. By integrating a robust workflow that incorporates high-pressure freezing followed by freeze substitution (HPF/FS), serial block face scanning electron microscopy (SBF-SEM), manual and deep learning-based segmentation, and statistical analysis, we have successfully generated three-dimensional (3D) reconstructions of monolayer and tumorsphere cells, including their subcellular organelles. Our findings reveal a significant degree of variation in cellular morphology in tumorspheres. We observed the increased prevalence of nuclear envelope invaginations in tumorsphere cells compared to monolayers. Furthermore, we detected a diverse range of mitochondrial morphologies exclusively in tumorsphere cells, as well as intricate cellular interconnectivity within the tumorsphere architecture. These remarkable ultrastructural differences emphasize the use of tumorspheres as a superior model for cancer research due to their relevance to in vivo conditions. Our results strongly advocate for the utilization of tumorsphere cells in cancer research studies, enhancing the precision and relevance of experimental outcomes, and ultimately accelerating therapeutic advancements.

Organosulfur Compound Identified from Striga angustifolia (D. Don) C.J. Saldanha Inhibits Lung Cancer Growth and Induces Apoptosis via p53/mTOR Signaling Pathway

The Striga angustifolia (D. Don) C.J. Saldanha was used as an Ayurvedic and homeopathic medicine for cancer by the tribal peoples of the Maruthamalai Hills, Coimbatore, India. Hence, the traditional use that has been proven to be effective lacks convincing scientific references. This present study was conducted to investigate the presence of potentially bioactive compounds from S. angustifolia and provides a scientific basis for the ethnobotanical utility. The organosulfur compound 5,5'-dithiobis(1-phenyl-1H-tetrazole) (COMP1) was isolated from S. angustifolia extracts, and the structures of COMP1 were elucidated and characterized by using 13C and 1H nuclear magnetic resonance (NMR) and single crystal X-ray powder diffraction (XRD). Our findings showed that COMP1 significantly reduced cell proliferation of breast and lung cancer cells, but not that of non-malignant epithelial cells. Further analysis revealed that COMP1 promoted cell cycle arrest and apoptosis of lung cancer cells. Mechanistically, COMP1 facilitates p53 activity and inhibits mammalian target of rapamycin (mTOR) signaling, thereby inducing cell cycle arrest and apoptosis of lung cancer cells by inhibiting cell growth. Our findings suggest that COMP1 may serve as a potential drug for lung cancer through the regulation of p53/mTOR pathways.

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis

BACKGROUND

Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods.

METHODS

On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas.

RESULTS

Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.

CONCLUSION

Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.

Preclinical Models of Visceral Sarcomas

Visceral sarcomas are a rare malignant subgroup of soft tissue sarcomas (STSs). STSs, accounting for 1% of all adult tumors, are derived from mesenchymal tissues and exhibit a wide heterogeneity. Their rarity and the high number of histotypes hinder the understanding of tumor development mechanisms and negatively influence clinical outcomes and treatment approaches. Although some STSs (~20%) have identifiable genetic markers, as specific mutations or translocations, most are characterized by complex genomic profiles. Thus, identification of new therapeutic targets and development of personalized therapies are urgent clinical needs. Although cell lines are useful for preclinical investigations, more reliable preclinical models are required to develop and test new potential therapies. Here, we provide an overview of the available in vitro and in vivo models of visceral sarcomas, whose gene signatures are still not well characterized, to highlight current challenges and provide insights for future studies.

Breast cancer organoids and their applications for precision cancer immunotherapy

Immunotherapy is garnering increasing attention as a therapeutic strategy for breast cancer (BC); however, the application of precise immunotherapy in BC has not been fully studied. Further studies on BC immunotherapy have a growing demand for preclinical models that reliably recapitulate the composition and function of the tumor microenvironment (TME) of BC. However, the classic two-dimensional in vitro and animal in vivo models inadequately recapitulate the intricate TME of the original tumor. Organoid models which allow the regular culture of primitive human tumor tissue are increasingly reported that they can incorporate immune components. Therefore, organoid platforms can be used to replicate the BC-TME to achieve the immunotherapeutic reaction modeling and facilitate relevant preclinical trial. In this study, we have investigated different organoid culture methods for BC-TME modeling and their applications for precision immunotherapy in BC.

Polyurethane scaffold-based 3D lung cancer model recapitulates in vivo tumor biological behavior for nanoparticulate drug screening

Lung cancer is the leading cause of cancer mortality worldwide. Preclinical studies in lung cancer hold the promise of screening for effective antitumor agents, but mechanistic studies and drug discovery based on 2D cell models have a high failure rate in getting to the clinic. Thus, there is an urgent need to explore more reliable and effective in vitro lung cancer models. Here, we prepared a series of three-dimensional (3D) waterborne biodegradable polyurethane (WBPU) scaffolds as substrates to establish biomimetic tumor models in vitro. These 3D WBPU scaffolds were porous and could absorb large amounts of free water, facilitating the exchange of substances (nutrients and metabolic waste) and cell growth. The scaffolds at wet state could simulate the mechanics (elastic modulus ∼1.9 kPa) and morphology (porous structures) of lung tissue and exhibit good biocompatibility. A549 lung cancer cells showed adherent growth pattern and rapidly formed 3D spheroids on WBPU scaffolds. Our results showed that the scaffold-based 3D lung cancer model promoted the expression of anti-apoptotic and epithelial-mesenchymal transition-related genes, giving it a more moderate growth and adhesion pattern compared to 2D cells. In addition, WBPU scaffold-established 3D lung cancer model revealed a closer expression of proteins to in vivo tumor, including tumor stem cell markers, cell proliferation, apoptosis, invasion and tumor resistance proteins. Based on these features, we further demonstrated that the 3D lung cancer model established by the WBPU scaffold was very similar to the in vivo tumor in terms of both resistance and tolerance to nanoparticulate drugs. Taken together, WBPU scaffold-based lung cancer model could better mimic the growth, microenvironment and drug response of tumor in vivo. This emerging 3D culture system holds promise to shorten the formulation cycle of individualized treatments and reduce the use of animals while providing valid research data for clinical trials.

A human pilot study on positive electrostatic charge effects in solid tumors of the late-stage metastatic patients

Background

Correlative interactions between electrical charges and cancer cells involve important unknown factors in cancer diagnosis and treatment. We previously reported the intrinsic suppressive effects of pure positive electrostatic charges (PEC) on the proliferation and metabolism of invasive cancer cells without any effect on normal cells in cell lines and animal models. The proposed mechanism was the suppression of pro-caspases 3 and 9 with an increase in Bax/Bcl2 ratio in exposed malignant cells and perturbation induced in the KRAS pathway of malignant cells by electrostatic charges due to the phosphate molecule electrostatic charge as the trigger of the pathway. This study aimed to examine PECs as a complementary treatment for patients with different types of solid metastatic tumors, who showed resistance to chemotherapy and radiotherapy.

Methods

In this study, solid metastatic tumors of the end-stage patients (n = 41) with various types of cancers were locally exposed to PEC for at least one course of 12 days. The patient's signs and symptoms, the changes in their tumor size, and serum markers were followed up from 30 days before positive electrostatic charge treating (PECT) until 6 months after the study.

Results

Entirely, 36 patients completed the related follow-ups. Significant reduction in tumor sizes and cancer-associated enzymes as well as improvement in cancer-related signs and symptoms and patients' lifestyles, without any side effects on other tissues or metabolisms of the body, were observed in more than 80% of the candidates.

Conclusion

PECT induced significant cancer remission in combination with other therapies. Therefore, this non-ionizing radiation would be a beneficial complementary therapy, with no observable side effects of ionizing radiotherapy, such as post-radiation inflammation.

Genomic, transcriptomic and epigenomic sequencing data of the B-cell leukemia cell line REH

OBJECTIVES

The aim of this data paper is to describe a collection of 33 genomic, transcriptomic and epigenomic sequencing datasets of the B-cell acute lymphoblastic leukemia (ALL) cell line REH. REH is one of the most frequently used cell lines for functional studies of pediatric ALL, and these data provide a multi-faceted characterization of its molecular features. The datasets described herein, generated with short- and long-read sequencing technologies, can both provide insights into the complex aberrant karyotype of REH, and be used as reference datasets for sequencing data quality assessment or for methods development.

DATA DESCRIPTION

This paper describes 33 datasets corresponding to 867 gigabases of raw sequencing data generated from the REH cell line. These datasets include five different approaches for whole genome sequencing (WGS) on four sequencing platforms, two RNA sequencing (RNA-seq) techniques on two different sequencing platforms, DNA methylation sequencing, and single-cell ATAC-sequencing.

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression

The pursuit of precision oncology heavily relies on large-scale genomic and pharmacological data garnered from preclinical cancer model systems such as cell lines. While cell lines are instrumental in understanding the interplay between genomic programs and drug response, it well-established that they are not fully representative of patient tumors. Development of integrative methods that can systematically assess the commonalities between patient tumors and cell-lines can help bridge this gap. To this end, we introduce the Integrative Principal Component Regression (iPCR) model which uncovers both joint and model-specific structured variations in the genomic data of cell lines and patient tumors through matrix decompositions. The extracted joint variation is then used to predict patient drug responses based on the pharmacological data from preclinical models. Moreover, the interpretability of our model allows for the identification of key driver genes and pathways associated with the treatment-specific response in patients across multiple cancers. We demonstrate that the outputs of the iPCR model can assist in inferring both model-specific and shared co-expression networks between cell lines and patients. We show that iPCR performs favorably compared to competing approaches in predicting patient drug responses, in both simulation studies and real-world applications, in addition to identifying key genomic drivers of cancer drug responses.

CCLHunter: An efficient toolkit for cancer cell line authentication

Cancer cell lines are essential in cancer research, yet accurate authentication of these cell lines can be challenging, particularly for consanguineous cell lines with close genetic similarities. We introduce a new Cancer Cell Line Hunter (CCLHunter) method to tackle this challenge. This approach utilizes the information of single nucleotide polymorphisms, expression profiles, and kindred topology to authenticate 1389 human cancer cell lines accurately. CCLHunter can precisely and efficiently authenticate cell lines from consanguineous lineages and those derived from other tissues of the same individual. Our evaluation results indicate that CCLHunter has a complete accuracy rate of 93.27%, with an accuracy of 89.28% even for consanguineous cell lines, outperforming existing methods. Additionally, we provide convenient access to CCLHunter through standalone software and a web server at https://ngdc.cncb.ac.cn/cclhunter.

WCRC-25: A novel luminal Invasive Lobular Carcinoma cell line model

Breast cancer is categorized by the molecular and histologic presentation of the tumor, with the major histologic subtypes being No Special Type (NST) and Invasive Lobular Carcinoma (ILC). ILC are characterized by growth in a single file discohesive manner with stromal infiltration attributed to their hallmark pathognomonic loss of E-cadherin ( CDH1 ). Few ILC cell line models are available to researchers. Here we report the successful establishment and characterization of a novel ILC cell line, WCRC-25, from a metastatic pleural effusion from a postmenopausal Caucasian woman with metastatic ILC. WCRC-25 is an ER-negative luminal epithelial ILC cell line with both luminal and Her2-like features. It exhibits anchorage independent growth and haptotactic migration towards Collagen I. Sequencing revealed a CDH1 Q706* truncating mutation, together with mutations in FOXA1, CTCF, BRCA2 and TP53 , which were also seen in a series of metastatic lesions from the patient. Copy number analyses revealed amplification and deletion of genes frequently altered in ILC while optical genome mapping revealed novel structural rearrangements. RNA-seq analysis comparing the primary tumor, metastases and the cell line revealed signatures for cell cycle progression and receptor tyrosine kinase signaling. To assess targetability, we treated WCRC-25 with AZD5363 and Alpelisib confirming WCRC-25 as susceptible to PI3K/AKT signaling inhibition as predicted by our RNA sequencing analysis. In conclusion, we report WCRC-25 as a novel ILC cell line with promise as a valuable research tool to advance our understanding of ILC and its therapeutic vulnerabilities.

Financial support

The work was in part supported by a Susan G Komen Leadership Grant to SO (SAC160073) and NCI R01 CA252378 (SO/AVL). AVL and SO are Komen Scholars, Hillman Foundation Fellows and supported by BCRF. This project used the UPMC Hillman Cancer Center and Tissue and Research Pathology/Pitt Biospecimen Core shared resource which is supported in part by award P30CA047904. This research was also supported in part by the University of Pittsburgh Center for Research Computing, RRID:SCR_022735, through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483. Finally, partial support was provided by the Magee-Womens Research Institute and Foundation, The Shear Family Foundation, and The Metastatic Breast Cancer Network.

Critical evaluation of cell lysis methods for metallodrug studies in cancer cells

Intracellular accumulation studies are a key step in metallodrug development but often variable results are obtained. Therefore, we aimed here to investigate different protocols for efficient and reproducible lysis of cancer cells in terms of protein content in lysates and in cell uptake studies of the Ru anticancer complex [chlorido(8-oxyquinolinato)(η6-p-cymene)ruthenium(II)] ([Ru(cym)(HQ)Cl]). The physical lysis methods osmosis and sonication were chosen for comparison with chemical lysis with the radioimmunoprecipitation assay (RIPA) buffer. Based on the protein content and the total Ru accumulated in the lysates, the latter determined using inductively coupled plasma-mass spectrometry, RIPA buffer was the most efficient lysis method. Measurements of plastic adsorption blanks revealed that the higher Ru content determined in the RIPA buffer lysis samples may be due a higher amount of Ru extracted from the plastic incubation plates compared with osmosis and sonication. Overall, we found that the choice of lysis method needs to be matched to the information sought and we suggest the least disruptive osmosis method might be the best choice for labile drug-biomolecule adducts. Minimal differences were found for experiments aimed at measuring the overall cell uptake of the Ru complex.

An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures

Drug resistance currently poses the greatest barrier to cancer treatments. To overcome drug resistance, drug combination therapy has been proposed as a promising treatment strategy. Herein, we present Re-Sensitizing Drug Prediction (RSDP), a novel computational strategy, for predicting the personalized cancer drug combination A + B by reversing the resistance signature of drug A. The process integrates multiple biological features using a robust rank aggregation algorithm, including Connectivity Map, synthetic lethality, synthetic rescue, pathway, and drug target. Bioinformatics assessments revealed that RSDP achieved a relatively accurate prediction performance for identifying personalized combinational re-sensitizing drug B against cell line-specific intrinsic resistance, cell line-specific acquired resistance, and patient-specific intrinsic resistance to drug A. In addition, we developed the largest resource of cell line-specific cancer drug resistance signatures, including intrinsic and acquired resistance, as a byproduct of the proposed strategy. The findings indicate that personalized drug resistance signature reversal is a promising strategy for identifying personalized drug combinations, which may guide future clinical decisions regarding personalized medicine.

Preclinical tumor mouse models for studying esophageal cancer

Preclinical models are extensively employed in cancer research because they can be manipulated in terms of their environment, genome, molecular biology, organ systems, and physical activity to mimic human behavior and conditions. The progress made in in vivo cancer research has resulted in significant advancements, enabling the creation of spontaneous, metastatic, and humanized mouse models. Most recently, the remarkable and extensive developments in genetic engineering, particularly the utilization of CRISPR/Cas9, transposable elements, epigenome modifications, and liquid biopsies, have further facilitated the design and development of numerous mouse models for studying cancer. In this review, we have elucidated the production and usage of current mouse models, such as xenografts, chemical-induced models, and genetically engineered mouse models (GEMMs), for studying esophageal cancer. Additionally, we have briefly discussed various gene-editing tools that could potentially be employed in the future to create mouse models specifically for esophageal cancer research.

Comparison of Tumour-Specific Phenotypes in Human Primary and Expandable Pancreatic Cancer Cell Lines

There is an ongoing need for patient-specific chemotherapy for pancreatic cancer. Tumour cells isolated from human tissues can be used to predict patients' response to chemotherapy. However, the isolation and maintenance of pancreatic cancer cells is challenging because these cells become highly vulnerable after losing the tumour microenvironment. Therefore, we investigated whether the cells retained their original characteristics after lentiviral transfection and expansion. Three human primary pancreatic cancer cell lines were lentivirally transduced to create expandable (Ex) cells which were then compared with primary (Pri) cells. No obvious differences in the morphology or epithelial-mesenchymal transition (EMT) were observed between the primary and expandable cell lines. The two expandable cell lines showed higher proliferation rates in the 2D and 3D models. All three expandable cell lines showed attenuated migratory ability. Differences in gene expression between primary and expandable cell lines were then compared using RNA-Seq data. Potential target drugs were predicted by differentially expressed genes (DEGs), and differentially expressed pathways (DEPs) related to tumour-specific characteristics such as proliferation, migration, EMT, drug resistance, and reactive oxygen species (ROS) were investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We found that the two expandable cell lines expressed similar chemosensitivity and redox-regulatory capability to gemcitabine and oxaliplatin in the 2D model as compared to their counterparts. In conclusion, we successfully generated expandable primary pancreatic cancer cell lines using lentiviral transduction. These expandable cells not only retain some tumour-specific biological traits of primary cells but also show an ongoing proliferative capacity, thereby yielding sufficient material for drug response assays, which may provide a patient-specific platform for chemotherapy drug screening.

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.

Humanized Mouse Models for Immuno-oncology Drug Discovery

Breakthroughs in cancer treatment with immunotherapeutics have provided long-term patient benefits for many different types of cancer. However, complete response is not achieved in many patients and tumor types, and the mechanisms underlying this lack of response are poorly understood. Despite this, numerous new targets, therapeutics, and drug combinations are being developed and tested in clinical trials. Preclinical models that recapitulate the complex human tumor microenvironment and the interplay between tumor and immune cells within the cancer-immunity cycle are needed to improve our understanding and screen new therapeutics for efficacy and safety/toxicity. Humanized mice, encompassing human tumors and human immune cells engrafted on immunodeficient mice, have been widely used for many years in immuno-oncology, with developments to improve both the humanization and the translational value central to the next generation of models. In this overview, we discuss recent advances in humanized models relevant to immuno-oncology drug discovery, the advantages and limitations of such models, the application of humanized models for efficacy and safety assessments of immunotherapeutics, and the potential opportunities. © 2023 Crown Bioscience. Current Protocols published by Wiley Periodicals LLC.

Nanoparticles-induced potential toxicity on human health: Applications, toxicity mechanisms, and evaluation models

Nanoparticles (NPs) have become one of the most popular objects of scientific study during the past decades. However, despite wealth of study reports, still there is a gap, particularly in health toxicology studies, underlying mechanisms, and related evaluation models to deeply understanding the NPs risk effects. In this review, we first present a comprehensive landscape of the applications of NPs on health, especially addressing the role of NPs in medical diagnosis, therapy. Then, the toxicity of NPs on health systems is introduced. We describe in detail the effects of NPs on various systems, including respiratory, nervous, endocrine, immune, and reproductive systems, and the carcinogenicity of NPs. Furthermore, we unravels the underlying mechanisms of NPs including ROS accumulation, mitochondrial damage, inflammatory reaction, apoptosis, DNA damage, cell cycle, and epigenetic regulation. In addition, the classical study models such as cell lines and mice and the emerging models such as 3D organoids used for evaluating the toxicity or scientific study are both introduced. Overall, this review presents a critical summary and evaluation of the state of understanding of NPs, giving readers more better understanding of the NPs toxicology to remedy key gaps in knowledge and techniques.

Feature selection translates drug response predictors from cell lines to patients

Targeted therapies and chemotherapies are prevalent in cancer treatment. Identification of predictive markers to stratify cancer patients who will respond to these therapies remains challenging because patient drug response data are limited. As large amounts of drug response data have been generated by cell lines, methods to efficiently translate cell-line-trained predictors to human tumors will be useful in clinical practice. Here, we propose versatile feature selection procedures that can be combined with any classifier. For demonstration, we combined the feature selection procedures with a (linear) logit model and a (non-linear) K-nearest neighbor and trained these on cell lines to result in LogitDA and KNNDA, respectively. We show that LogitDA/KNNDA significantly outperforms existing methods, e.g., a logistic model and a deep learning method trained by thousands of genes, in prediction AUC (0.70-1.00 for seven of the ten drugs tested) and is interpretable. This may be due to the fact that sample sizes are often limited in the area of drug response prediction. We further derive a novel adjustment on the prediction cutoff for LogitDA to yield a prediction accuracy of 0.70-0.93 for seven drugs, including erlotinib and cetuximab, whose pathways relevant to anti-cancer therapies are also uncovered. These results indicate that our methods can efficiently translate cell-line-trained predictors into tumors.

The Growing Importance of Three-Dimensional Models and Microphysiological Systems in the Assessment of Mycotoxin Toxicity

Current investigations in the field of toxicology mostly rely on 2D cell cultures and animal models. Although well-accepted, the traditional 2D cell-culture approach has evident drawbacks and is distant from the in vivo microenvironment. To overcome these limitations, increasing efforts have been made in the development of alternative models that can better recapitulate the in vivo architecture of tissues and organs. Even though the use of 3D cultures is gaining popularity, there are still open questions on their robustness and standardization. In this review, we discuss the current spheroid culture and organ-on-a-chip techniques as well as the main conceptual and technical considerations for the correct establishment of such models. For each system, the toxicological functional assays are then discussed, highlighting their major advantages, disadvantages, and limitations. Finally, a focus on the applications of 3D cell culture for mycotoxin toxicity assessments is provided. Given the known difficulties in defining the safety ranges of exposure for regulatory agency policies, we are confident that the application of alternative methods may greatly improve the overall risk assessment.

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.

Considerations for modelling diffuse high-grade gliomas and developing clinically relevant therapies

Diffuse high-grade gliomas contain some of the most dangerous human cancers that lack curative treatment options. The recent molecular stratification of gliomas by the World Health Organisation in 2021 is expected to improve outcomes for patients in neuro-oncology through the development of treatments targeted to specific tumour types. Despite this promise, research is hindered by the lack of preclinical modelling platforms capable of recapitulating the heterogeneity and cellular phenotypes of tumours residing in their native human brain microenvironment. The microenvironment provides cues to subsets of glioma cells that influence proliferation, survival, and gene expression, thus altering susceptibility to therapeutic intervention. As such, conventional in vitro cellular models poorly reflect the varied responses to chemotherapy and radiotherapy seen in these diverse cellular states that differ in transcriptional profile and differentiation status. In an effort to improve the relevance of traditional modelling platforms, recent attention has focused on human pluripotent stem cell-based and tissue engineering techniques, such as three-dimensional (3D) bioprinting and microfluidic devices. The proper application of these exciting new technologies with consideration of tumour heterogeneity and microenvironmental interactions holds potential to develop more applicable models and clinically relevant therapies. In doing so, we will have a better chance of translating preclinical research findings to patient populations, thereby addressing the current derisory oncology clinical trial success rate.

Citrinin as a potential anti-cancer therapy: A comprehensive review

Citrinin (CIT) is a polyketide-derived mycotoxin, which is produced by many fungal strains belonging to the gerena Monascus, Aspergillus, and Penicillium. It has been postulated that mycotoxins have several toxic mechanisms and are potentially used as antineoplastic agents. Therefore, the present study carried out a systematic review, including articles from 1978 to 2022, by collecting evidence in experimental studies of CIT antiplorifactive activity in cancer. The Data indicate that CIT intervenes in important mediators and cell signaling pathways, including MAPKs, ERK1/2, JNK, Bcl-2, BAX, caspases 3,6,7 and 9, p53, p21, PARP cleavage, MDA, reactive oxygen species (ROS) and antioxidant defenses (SOD, CAT, GST and GPX). These factors demonstrate the potential antitumor drug CIT in inducing cell death, reducing DNA repair capacity and inducing cytotoxic and genotoxic effects in cancer cells.

Genomic and Transcriptomic Analyses of Malignant Pleural Mesothelioma (MPM) Samples Reveal Crucial Insights for Preclinical Testing

Cell lines are extensively used to study cancer biology. However, the use of highly passaged commercial cell lines has to be questioned, as they do not closely resemble the originating tumor. To understand the reliability of preclinical models for Malignant pleural mesothelioma (MPM) studies, we have performed whole transcriptome and whole exome analyses of fresh frozen MPM tumors and compared them to cell lines generated from these tumors, as well as commercial cell lines and a preclinical MPM mouse model. Patient-derived cell lines were generated from digested fresh tumors and whole exome sequencing was performed on DNA isolated from formalin-fixed, paraffin-embedded (FFPE) tumor samples, corresponding patient-derived cell lines, and normal tissue. RNA sequencing libraries were prepared from 10 fresh frozen tumor samples, the 10 corresponding patient-derived cell lines, and 7 commercial cell lines. Our results identified alterations in tumor suppressor genes such as FBXW7, CDKN2A, CDKN2B, and MTAP, all known to drive MPM tumorigenesis. Patient-derived cell lines correlate to a high degree with their originating tumor. Gene expressions involved in multiple pathways such as EMT, apoptosis, myogenesis, and angiogenesis are upregulated in tumor samples when compared to patient-derived cell lines; however, they are downregulated in commercial cell lines compared to patient-derived cell lines, indicating significant differences between the two model systems. Our results show that the genome and transcriptome of tumors correlate to a higher degree with patient-derived cell lines rather than commercial cell lines. These results are of major relevance for the scientific community in regard to using cell lines as an appropriate model, resembling the pathway of interest to avoid misleading results for clinical applications.

Preclinical models to study patient-derived circulating tumor cells and metastasis

Circulating tumor cells (CTCs) that are detached from the tumor can be precursors of metastasis. The majority of studies focus on enumeration of CTCs from patient blood to predict recurrence and therapy outcomes. Very few studies have managed to expand CTCs to investigate their functional dynamics with respect to genetic changes, tumorigenic potential, and response to drug treatment. A growing amount of evidence based on successful CTC expansion has revealed novel therapeutic targets that are associated with the process of metastasis. In this review, we summarize the successes, challenges, and limitations that collectively contribute to the better understanding of metastasis using patient-derived CTCs as blood-borne seeds of metastasis. The roadblocks and future avenues to move CTC-based scientific discoveries forward are also discussed.

Experimental in vitro, ex vivo and in vivo models in prostate cancer research

Androgen deprivation therapy has a central role in the treatment of advanced prostate cancer, often causing initial tumour remission before increasing independence from signal transduction mechanisms of the androgen receptor and then eventual disease progression. Novel treatment approaches are urgently needed, but only a fraction of promising drug candidates from the laboratory will eventually reach clinical approval, highlighting the demand for critical assessment of current preclinical models. Such models include standard, genetically modified and patient-derived cell lines, spheroid and organoid culture models, scaffold and hydrogel cultures, tissue slices, tumour xenograft models, patient-derived xenograft and circulating tumour cell eXplant models as well as transgenic and knockout mouse models. These models need to account for inter-patient and intra-patient heterogeneity, the acquisition of primary or secondary resistance, the interaction of tumour cells with their microenvironment, which make crucial contributions to tumour progression and resistance, as well as the effects of the 3D tissue network on drug penetration, bioavailability and efficacy.