Cancer cell lines for drug discovery and development

Establishment and characterization of NCC-PS2-C1: a novel cell line of high-grade pleomorphic spindle cell sarcoma, most consistent with myxofibrosarcoma

Pleomorphic sarcoma (PS) is a heterogeneous group of malignant mesenchymal tumors that lack specific histological differentiation. PS is characterized by genetic instability and diversity and unique histological features such as pronounced morphologic pleomorphism. PS is one of the most common soft tissue sarcomas. Complete surgical resection remains the only curative treatment and is often combined with neoadjuvant radiotherapy. Effective systemic chemotherapy is yet to be established, and PS frequently recurs locally and metastasizes to the lungs. Patient-derived cancer cell lines are invaluable tools for basic and preclinical research for developing novel chemotherapies. Herein, we report a high-grade pleomorphic spindle cell sarcoma, most consistent with myxofibrosarcoma cell line, NCC-PS2-C1, which was derived from a primary tumor specimen. NCC-PS2-C1 cells exhibited a range of copy number alterations. This cell line demonstrated consistent proliferation, spheroid formation, and invasive capabilities in vitro. Drug screening using NCC-PS2-C1 cells revealed that cobimetinib, crenolanib, and ixazomib were effective against PS. In conclusion, we established NCC-PS2-C1 cells from primary tumors of PS. This cell line is a valuable resource for developing novel chemotherapies.

Building A Unified Model for Drug Synergy Analysis Powered by Large Language Models

Drug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a novel unified Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models for the initial representation of drugs and cell lines. After demonstrating the relevance of these embeddings, we pre-train BAITSAO with a large-scale drug synergy database under a multi-task learning framework with rigorous selections of tasks. We demonstrate the superiority of the model architecture and the pre-trained strategies of BAITSAO over other methods through comprehensive benchmark analysis. Moreover, we investigate the sensitivity of BAITSAO and illustrate its unique functions including new drug discoveries, drug combinations-gene interaction, and multi-drug synergy predictions.

Unveiling racial disparities in prostate cancer using an integrative genomic and transcriptomic analysis

Prostate cancer exhibits significant racial disparities, with African American (AA) individuals showing ∼64% higher incidence and 2.3 times greater mortality rates compared to their Caucasian (CA) counterparts. Understanding the complex interplay of genetic, environmental, lifestyle, socioeconomic, and healthcare access factors is crucial for developing effective interventions to reduce this disproportionate burden. This study aims to uncover the genetic and transcriptomic differences driving these disparities through a comprehensive analysis using RNA sequencing (RNA-seq) and exome sequencing of prostate cancer tissues from both Black and White patients. Our transcriptomics analysis revealed enhanced activity in pathways linked to immune response and cellular interactions in AA prostate cancer samples, with notable regulation by histone-associated transcription factors (HIST1H1A, HIST1H1D, and HIST1H1B) suggests potential involvement of histone modification mechanisms. Additionally, pseudogenes and long non-coding RNAs (lncRNAs) among the regulated genes indicate non-coding elements' role in these disparities. Exome sequencing identified unique variants in AA patient samples within key genes, including TP73 (tumor suppression), XYLB (metabolism), ALDH4A1 (oxidative stress), PTPRB (cellular signaling), and HLA-DRB5 (immune response). These genetic variations likely contribute to disease progression and therapy response disparities. This study highlights the importance of considering genetic and epigenetic variations in developing tailored therapeutic approaches to improve treatment efficacy and reduce mortality rates across diverse populations.

Ex Vivo Drug Screening: An Emerging Paradigm in the Treatment of Childhood Cancer

Developing and providing the right therapy for the right patient (or personalized targeted treatments) is key to reducing side-effects and improving survival in childhood cancers. Most efforts aiming to personalize childhood cancer treatment use genomic analysis of malignancies to identify potentially targetable genetic events. But it is becoming clear that not all patients will have an actionable change, and in those that do there is no additional way to determine if treatments will be effective. Ex vivo drug screening is a laboratory technique used to test the effects of various drugs or compounds, on biological tissues or cells that have been removed from an organism. This information is then used to predict which cancer treatments will be most effective based on the therapeutic response in the tissue or cells removed from that individual. Its utility in personalizing treatments in childhood cancer is increasingly recognized. In this review we describe the different methods for ex vivo drug screening and the advantages and disadvantages of each technique. We also present recent evidence that ex vivo screening may have utility in a variety of childhood malignancies including an overview of current clinical trials appraising its use. Finally, we discuss the research questions and hurdles that must be overcome before ex vivo screening can be widely used in pediatric oncology.

Design and Synthesis of Topoisomerases-Histone Deacetylase Dual Targeted Quinoline-Bridged Hydroxamates as Anticancer Agents

The multifactorial nature of cancer requires treatment that involves simultaneous targeting of associated overexpressed proteins and cell signaling pathways, possibly leading to synergistic effects. Herein, we present a systematic study that involves the simultaneous inhibition of human topoisomerases (hTopos) and histone deacetylases (HDACs) by multitargeted quinoline-bridged hydroxamic acid derivatives. These compounds were rationally designed considering pharmacophoric features and catalytic sites of the cross-talk proteins, synthesized, and assessed for their anticancer potential. Our findings revealed that the compound 5c significantly produced anticancer effects in vitro and in vivo by reducing the tumor growth and its size in the A549 cell-induced lung cancer xenograft model through multiple mechanisms, primarily by multi-inhibition of hTopoI/II and HDACs, especially HDAC1 via atypical binding. The present paper discusses detailed mechanistic biological investigations, structure-activity effects supported by computational docking studies, and DMPK studies and provides future scope for lead optimization and modification.

Genome-Wide Screening in Haploid Stem Cells Reveals Synthetic Lethality Targeting MLH1 and TP53 Deficient Tumours

Synthetic lethality is defined as a type of genetic interaction where the combination of two genetic events results in cell death, whereas each of them separately does not. Synthetic lethality can be a useful tool in personalised oncology. MLH1 is a cancer-related gene that has a central role in DNA mismatch-repair and TP53 is the most frequently mutated gene in cancer. To identify genetic events that can lead to tumour death once either MLH1 or TP53 is mutated, a genome-wide genetic screening was performed. Thus, mutations in all protein-coding genes were introduced into haploid human embryonic stem cells (hESCs) with and without loss-of-function mutations in the MLH1 or TP53 genes. These experiments uncovered a list of putative hits with EXO1, NR5A2, and PLK2 genes for MLH1, and MYH10 gene for TP53 emerging as the most promising candidates. Synthetic lethal interactions of these genes were validated genetically or chemically using small molecules that inhibit these genes. The specific effects of SR1848, which inhibits NR5A2, ON1231320 or BI2536, which inhibits PLK2, and blebbistatin, which inhibits MYH10, were further validated in cancer cell lines. Finally, animal studies with CCL xenografts showed the selective effect of the small molecule BI2536 on MLH1-null tumours and of blebbistatin on TP53-mutated tumours. Thus, demonstrating their potential for personalised medicine, and the robustness of genetic screening in haploid hESCs in the context of cancer therapeutics.

Exploring Cervical Adenocarcinoma: Epidemiological Insights, Diagnostic and Therapeutic Challenges, and Pathogenetic Mechanisms

BACKGROUND

Cervical cancer poses a significant threat to women's health and encompasses various histological types, including squamous cell carcinoma (SCC), cervical adenocarcinoma (CA), and adenosquamous carcinoma. CA, in particular, presents a formidable challenge in clinical management due to its low early detection rate, pronounced aggressiveness, high recurrence rate, and mortality, compounded by the complexities associated with late-stage treatment. There is limited understanding of the similarities and differences in the pathogenesis mechanisms between CA and SCC, such as tumor heterogeneity and the tumor immune microenvironment (TME).

METHODS

A literature search was carried out in the PubMed, Web of Science, and Google Scholar databases using the following research terms: "gynecological oncology," "cervical cancer," "cervical adenocarcinoma," "epidemiology," "diagnosis and treatment of cervical adenocarcinoma," "Human papillomavirus," "World Health Organization," "tumor microenvironment," "single-cell RNA sequencing," "molecular mechanism," and "preclinical research model."

CONCLUSION

This review consolidates the epidemiological characteristics, diagnostic and therapeutic hurdles, and the latest advances in research on CA. It aims to highlight the significant heterogeneity of the TME characteristics exhibited by CA compared to SCC. Additionally, we also summarize the common preclinical models for CA and discuss the advantages and disadvantages of using various models in research. We aspire that the discussions presented herein will offer novel insights and directions for subsequent research, as well as clinical diagnosis and treatment strategies for CA.

Advancing precision therapy for colorectal cancer: Developing clinical indications for multi-target kinase inhibitor BPR1J481 using patient-derived xenograft models

The large and rapid increase in the incidence and mortality of colorectal cancer (CRC) demonstrates the urgent need for new drugs with higher efficacy to treat CRC. However, the lack of applicable and reliable preclinical models significantly hinders the progress of drug development. Patient-derived xenograft (PDX) models are currently considered reliable in vivo preclinical models for predicting drug efficacy in cancer patients. This study successfully uses the CRC PDX model to develop clinical indications for the new multi-target kinase inhibitor BPR1J481 and demonstrated the anti-cancer mechanism and competitive advantages of this drug candidate. The results demonstrate that BPR1J481 exhibits significant anticancer efficacy by inducing apoptosis in CRC PDX tumor tissues and corresponding PDX-derived CRC cells. Through kinase competitive binding and kinase activity assays, we discover that BPR1J481 effectively inhibits SRC kinase activity by directly binding to its active site. The reduction in SRC phosphorylation observed in CRC PDX tumor tissues and derived cells upon treatment with BPR1J481 further validates its inhibitory potential. Furthermore, the decrease in viable cells after SRC knockout and the poorer prognosis observed in patients with higher SRC expression, emphasizes the critical significance and clinical relevance of SRC in CRC. Additionally, BPR1J481 exhibits robust anti-angiogenic effects by suppressing VEGF- and PDGF-induced endothelial cell proliferation, migration, and capillary-like tube formation through inhibition of VEGFR2 and PDGFRβ phosphorylation. Remarkably, BPR1J481 appears to demonstrate greater efficacy against CRC compared to regorafenib. These findings highlight the therapeutic potential of BPR1J481 for patients with CRC.

Thermoresponsive biomaterial system of irinotecan and curcumin for the treatment of colorectal cancer: in-vitro and in-vivo investigations

This study aims to develop a thermoresponsive biomaterial system of irinotecan (IRT) and curcumin (CUR) nano-transferosomal gel (IRT-CUR-NTG) for targeting colorectal cancer (CRC). The IRT-CUR-NTs were statistically optimized and loaded into poloxamer-based thermosensitive gel. Transmission electron microscopy (TEM), Differential scanning calorimetry (DSC) and Fourier-transform infrared spectroscopy (FTIR) of the IRT-CUR-NTs were performed, whereas pH, gelation time, gelation temperature, gel and mucoadhesive strength of the IRT-CUR-NTG were investigated. In-vitro release and anticancer analyses were explored using HT29 cells. Additionally, in-vivo pharmacokinetics study was investigated followed by histopathological examination and in-vivo anticancer analysis. The PS, PDI, ZP, %EE of IRT and %EE of CUR were found to be 136.15 nm, 0.143, -15.5 mV, 95.05% and 85.12%, respectively. IRT-CUR-NTs exhibited spherical shape with no chemical interactions among the constituents. Similarly, IRT-CUR-NTG was homogenous gel suitable for rectal administration. IRT-CUR-NTG manifested prolonged release profiles of IRT and CUR. Moreover, a significantly enhanced (4-fold) bioavailability and no toxicity of IRT-CUR-NTG was observed when compared with conventional gel. IRT-CUR-NTs were found to be more effective against HT29 cell lines. In-vivo antitumor analysis demonstrated significantly reduced tumor volume and tumor mass after treatment with IRT-CUT-NTG, indicating improved antitumor effect. It can be concluded that IRT-CUR-NTG is suitable biomaterial system for colorectal cancer.

Establishing Patient-Derived Xenograft (PDX) Models of Lymphomas

Patient-derived xenograft (PDX) models of lymphoma typically involve the injection of human tumor cells into an immunocompromised murine host. PDXs have the advantage that the tumor cells grow in a 3D environment within the mouse, meaning the selection pressure of in vitro establishment is avoided and the tumor cells better maintain their genetic heterogeneity. Here, we outline a method for producing and maintaining a PDX model of lymphoma. We describe three different methods to isolate a single cell suspension of the primary patient tumor, followed by either subcutaneous or intraperitoneal injection into an immunocompromised mouse. We then detail how to monitor tumor growth and how to harvest, passage, and store the tumors once they have grown. We highlight and discuss important protocol considerations including technical hints as well as the advantages and disadvantages of the methods described.

Preclinical Models for Functional Precision Lung Cancer Research

Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody-drug conjugates, and emerging investigational treatments. While traditional human lung cancer cell lines offer a basic framework for cancer research, they often lack the tumor heterogeneity and intricate tumor-stromal interactions necessary to accurately predict patient-specific clinical outcomes. Patient-derived xenografts (PDXs), however, retain the original tumor's histopathology and genetic features, providing a more reliable model for predicting responses to systemic therapeutics, especially molecularly targeted therapies. For studying immunotherapies and antibody-drug conjugates, humanized PDX mouse models, syngeneic mouse models, and genetically engineered mouse models (GEMMs) are increasingly utilized. Despite their value, these in vivo models are costly, labor-intensive, and time-consuming. Recently, patient-derived lung cancer organoids (LCOs) have emerged as a promising in vitro tool for functional precision oncology studies. These LCOs demonstrate high success rates in growth and maintenance, accurately represent the histology and genomics of the original tumors and exhibit strong correlations with clinical treatment responses. Further supported by advancements in imaging, spatial and single-cell transcriptomics, proteomics, and artificial intelligence, these preclinical models are reshaping the landscape of drug development and functional precision lung cancer research. This integrated approach holds the potential to deliver increasingly accurate, personalized treatment strategies, ultimately enhancing patient outcomes in lung cancer.

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation

Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and research applications is essential. In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds' targets of newly discovered molecules. A Python programming protocol obtained 3431 cyanobacteria bioactive compounds, 373 unique protein targets, and 3027 molecular descriptors. PaDEL-descriptor, Mordred, and Drugtax software were used to calculate the chemical descriptors for each bioactive compound database record. The biochemical descriptors were then used to determine the most promising protein targets for human therapeutic approaches and environmental bioremediation using the best machine learning (ML) model. The creation of our database, coupled with the integration of computational docking protocols, represents an innovative approach to understanding the potential of cyanobacteria bioactive compounds. This resource, adhering to the findability, accessibility, interoperability, and reuse of digital assets (FAIR) principles, is an excellent tool for pharmaceutical and bioremediation researchers. Moreover, its capacity to facilitate the exploration of specific compounds' interactions with environmental pollutants is a significant advancement, aligning with the increasing reliance on data science and machine learning to address environmental challenges. This study is a notable step forward in leveraging cyanobacteria for both therapeutic and ecological sustainability.

A precision oncology-focused deep learning framework for personalized selection of cancer therapy

Precision oncology matches tumors to targeted therapies based on the presence of actionable molecular alterations. However, most tumors lack actionable alterations, restricting treatment options to cytotoxic chemotherapies for which few data-driven prioritization strategies currently exist. Here, we report an integrated computational/experimental treatment selection approach applicable for both chemotherapies and targeted agents irrespective of actionable alterations. We generated functional drug response data on a large collection of patient-derived tumor models and used it to train ScreenDL, a novel deep learning-based cancer drug response prediction model. ScreenDL leverages the combination of tumor omic and functional drug screening data to predict the most efficacious treatments. We show that ScreenDL accurately predicts response to drugs with diverse mechanisms, outperforming existing methods and approved biomarkers. In our preclinical study, this approach achieved superior clinical benefit and objective response rates in breast cancer patient-derived xenografts, suggesting that testing ScreenDL in clinical trials may be warranted.

Best holdout assessment is sufficient for cancer transcriptomic model selection

Guidelines in statistical modeling for genomics hold that simpler models have advantages over more complex ones. Potential advantages include cost, interpretability, and improved generalization across datasets or biological contexts. We directly tested the assumption that small gene signatures generalize better by examining the generalization of mutation status prediction models across datasets (from cell lines to human tumors and vice versa) and biological contexts (holding out entire cancer types from pan-cancer data). We compared model selection between solely cross-validation performance and combining cross-validation performance with regularization strength. We did not observe that more regularized signatures generalized better. This result held across both generalization problems and for both linear models (LASSO logistic regression) and non-linear ones (neural networks). When the goal of an analysis is to produce generalizable predictive models, we recommend choosing the ones that perform best on held-out data or in cross-validation instead of those that are smaller or more regularized.

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

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

Ex Vivo Intestinal Organoid Models: Current State-of-the-Art and Challenges in Disease Modelling and Therapeutic Testing for Colorectal Cancer

Despite improvements in participation in population-based screening programme, colorectal cancer remains a major cause of cancer-related mortality worldwide. Targeted interventions are desirable to reduce the health and economic burden of this disease. Two-dimensional monolayers of colorectal cancer cell lines represent the traditional in vitro models for disease and are often used for diverse purposes, including the delineation of molecular pathways associated with disease aetiology or the gauging of drug efficacy. The lack of complexity in such models, chiefly the limited epithelial cell diversity and differentiation, attenuated mucus production, lack of microbial interactions and mechanical stresses, has driven interest in the development of more holistic and physiologically relevant in vitro model systems. In particular, established ex vivo patient-derived explant and patient-derived tumour xenograft models have been supplemented by progress in organoid and microfluidic organ-on-a-chip cultures. Here, we discuss the applicability of advanced culturing technologies, such as organoid systems, as models for colorectal cancer and for testing chemotherapeutic drug sensitivity and efficacy. We highlight current challenges associated with organoid technologies and discuss their future for more accurate disease modelling and personalized medicine.

Adunctin E from Conamomum rubidum Induces Apoptosis in Lung Cancer via HSP90AA1 Modulation: A Network Pharmacology and In Vitro Study

Lung cancer stands out as a leading cause of death among various cancer types, highlighting the urgent need for effective anticancer drugs and the discovery of new compounds with potent therapeutic properties. Natural sources, such as the Conamomum genus, offer various bioactive compounds. Adunctin E (AE), a dihydrochalcone derived from Conamomum rubidum, exhibited several pharmacological activities, and its potential as an anticancer agent remains largely unexplored. Thus, this study aimed to elucidate its apoptotic-inducing effect and identify its molecular targets. The network pharmacology analysis led to the identification of 71 potential targets of AE against lung cancer. Subsequent gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway enrichment analyses revealed the involvement of these targets in cancer-associated signaling pathways. Notably, HSP90AA1, MAPK1, and PIK3CA emerged as key players in apoptosis. In silico molecular docking and dynamic simulations suggested a strong and stable interaction between AE and HSP90AA1. In vitro experiments further confirmed a significant apoptotic-inducing effect of AE on lung cancer cell lines A549 and H460. Furthermore, immunoblot analysis exhibited a substantial decrease in HSP90AA1 levels in response to AE treatment. These findings support the potential anticancer activity of AE through the HSP90AA1 mechanism, underscoring its promise as a novel compound worthy of further research and development for anti-lung cancer therapy.

Goblet cell differentiation subgroups in colorectal cancer

The poor prognosis of relatively undifferentiated cancers has long been recognized, suggesting that selection against differentiation and in favor of uncontrolled growth is one of the most powerful drivers of cancer progression. Goblet cells provide the mucous surface of the gut, and when present in colorectal cancers (CRC), the cancers are called mucinous. We have used the presence of MUC2, the main mucous product of goblet cells, and an associated gene product, TFF3, to classify a large panel of nearly 80 CRC-derived cell lines into five categories based on their levels of MUC2 and TFF3 expression. We have then shown that these five patterns of expression can be easily identified in the direct analysis of tumor specimens allowing a much finer characterization of CRCs with respect to the presence of goblet cell differentiation. In particular, about 30% of all CRCs fall into the category of expressing TFF3 but not MUC2, which has not previously been acknowledged. Using the cell line data, we suggest that there are up to 12 genes (MUC2, TFF3, ATOH1, SPDEF, CDX1, CDX2, GATA6, HES1, ETS2, OLFM4, TOX3, and LGR5) that may be involved in selection against goblet cell differentiation in CRC by changes in methylation rather than mutations. Of these, LGR5, which is particularly associated with lack of goblet cell features, may function in the control of differentiation rather than direct control of cell growth, as has so far mostly been assumed. These results emphasize the importance of methylation changes in driving cancer progression.

Oncofetal morphogenesis similar to embryonic gut formation by a subpopulation of DLD-1 human colon cancer cells

Cancer stem cells (CSC) are thought to be responsible for cancer phenotypes and cellular heterogeneity. Here we demonstrate that the human colon cancer cell line DLD1 contains two types of CSC-like cells that undergo distinct morphogenesis in the reconstituted basement membrane gel Matrigel. In our method with cancer cell spheroids, the parent cell line (DLD1-P) developed grape-like budding structures, whereas the other (DLD1-Wm) and its single-cell clones dynamically developed worm-like ones. Gene expression analysis suggested that the former mimicked intestinal crypt-villus morphogenesis, while the latter mimicked embryonic hindgut development. The organoids of DLD1-Wm cells rapidly extended in two opposite directions by expressing dipolar proteolytic activity. The invasive morphogenesis required the expression of MMP-2 and CD133 genes and ROCK activity. These cells also exhibited gastrula-like morphogenesis even in two-dimensional cultures without Matrigel. Moreover, the two DLD1 cell lines showed clear differences in cellular growth, tumor growth and susceptibility to paclitaxel. This study also provides a simple organoid culture method for human cancer cell lines. HT-29 and other cancer cell lines underwent characteristic morphogenesis in direct contact with normal fibroblasts. Such organoid cultures would be useful for investigating the nature of CSCs and for screening anti-cancer drugs. Our results lead to the hypothesis that CSC-like cells with both invasive activity and a fetal phenotype, i. e. oncofetal CSCs, are generated in some types of colon cancers.

The roles of patient-derived xenograft models and artificial intelligence toward precision medicine

Patient-derived xenografts (PDX) involve transplanting patient cells or tissues into immunodeficient mice, offering superior disease models compared with cell line xenografts and genetically engineered mice. In contrast to traditional cell-line xenografts and genetically engineered mice, PDX models harbor the molecular and biologic features from the original patient tumor and are generationally stable. This high fidelity makes PDX models particularly suitable for preclinical and coclinical drug testing, therefore better predicting therapeutic efficacy. Although PDX models are becoming more useful, the several factors influencing their reliability and predictive power are not well understood. Several existing studies have looked into the possibility that PDX models could be important in enhancing our knowledge with regard to tumor genetics, biomarker discovery, and personalized medicine; however, a number of problems still need to be addressed, such as the high cost and time-consuming processes involved, together with the variability in tumor take rates. This review addresses these gaps by detailing the methodologies to generate PDX models, their application in cancer research, and their advantages over other models. Further, it elaborates on how artificial intelligence and machine learning were incorporated into PDX studies to fast-track therapeutic evaluation. This review is an overview of the progress that has been done so far in using PDX models for cancer research and shows their potential to be further improved in improving our understanding of oncogenesis.

Cell Migration-Proliferation Dichotomy in Cancer: Biological Fact or Experimental Artefact?

The migration-proliferation dichotomy (MPD) has long been observed in cultured cancer cells. This phenomenon is not only relevant to tumour progression but may also have therapeutic significance in clinical cancer. However, MPD has rarely been investigated in primary cancer. This study aimed to either confirm or disprove the existence of MPD in primary cancer. Using primary gastric, colorectal and prostate cancer (GC, CRC and PCa) cohorts from the Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center, this study interrogated the MPD phenomenon by utilising RNA-Seq-based proliferation (CIN70 signature) and migration (epithelial-mesenchymal transition) indices, as well as gene set enrichment analyses (GSEA). Alternative hypothetical migration-proliferation models-The simultaneous migration-proliferation (SMP) and phenotype-refractory (PR) models-were compared to the MPD model by probing the migration-proliferation relationships within cancer stages and between early- and late-stage diseases using chi-square and independent T tests, z-score statistics and GSEA. The results revealed an inverse relationship between migration and proliferation signatures overall in the GC, CRC and PCa cohorts, as well as in early- and late-stage diseases. Additionally, a shift in proliferation- to migration dominance was observed from early- to late-stage diseases in the GC and CRC cohorts but not in the PCa cohorts, which showed enhanced proliferation dominance in metastatic tumours compared to primary cancers. The above features exhibited by the cancer cohorts are in keeping with the MPD model of the migration-proliferation relationship at the cellular level and exclude the SMP and PR migration-proliferation models.

Selection of the Most Suitable Culture Medium for Patient-Derived Lung Cancer Organoids

INTRODUCTION

Patient-derived organoids have emerged as a promising in vitro model for precision medicine, particularly in cancer, but also in noncancer-related diseases. However, the optimal culture medium for culturing patient-derived lung organoids has not yet been agreed upon. This study aimed to shed light on the optimal selection of a culture media for developing studies using patient-derived lung organoids.

METHODS

Tumor and normal paired tissue from 71 resected non-small cell lung cancer patients were processed for organoid culture. Lung cancer organoids (LCOs) were derived from tumor tissue and normal lung organoids (LNOs) from nonneoplastic lung tissue. Three different culture media were compared: permissive culture medium (PCM), limited culture medium (LCM), and minimum basal medium (MBM). We assessed their effectiveness in establishing organoid cultures, promoting organoid growth and viability, and compared their differential phenotypic characteristics.

RESULTS

While PCM was associated with the highest success rate and useful for long-term expansion, MBM was the best option to avoid normal organoid overgrowth in the organoid culture. The density, size, and viability of LNOs were reduced using LCM and severely affected with MBM. LNOs cultured in PCM tend to differentiate to bronchospheres, while alveolosphere differentiation can be observed in those cultured with LCM. The morphological phenotype of LCO was influenced by the culture media of election. Mesenchymal cell overgrowth was observed when LCM was used.

CONCLUSION

This work highlights the importance of considering the research objectives when selecting the most suitable culture medium for growing patient-derived lung organoids.

INTRODUCTION

Patient-derived organoids have emerged as a promising in vitro model for precision medicine, particularly in cancer, but also in noncancer-related diseases. However, the optimal culture medium for culturing patient-derived lung organoids has not yet been agreed upon. This study aimed to shed light on the optimal selection of a culture media for developing studies using patient-derived lung organoids.

METHODS

Tumor and normal paired tissue from 71 resected non-small cell lung cancer patients were processed for organoid culture. Lung cancer organoids (LCOs) were derived from tumor tissue and normal lung organoids (LNOs) from nonneoplastic lung tissue. Three different culture media were compared: permissive culture medium (PCM), limited culture medium (LCM), and minimum basal medium (MBM). We assessed their effectiveness in establishing organoid cultures, promoting organoid growth and viability, and compared their differential phenotypic characteristics.

RESULTS

While PCM was associated with the highest success rate and useful for long-term expansion, MBM was the best option to avoid normal organoid overgrowth in the organoid culture. The density, size, and viability of LNOs were reduced using LCM and severely affected with MBM. LNOs cultured in PCM tend to differentiate to bronchospheres, while alveolosphere differentiation can be observed in those cultured with LCM. The morphological phenotype of LCO was influenced by the culture media of election. Mesenchymal cell overgrowth was observed when LCM was used.

CONCLUSION

This work highlights the importance of considering the research objectives when selecting the most suitable culture medium for growing patient-derived lung organoids.

Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity

Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.

Exhaustive in vitro evaluation of the 9-drug cocktail CUSP9 for treatment of glioblastoma

The CUSP9 protocol is a polypharmaceutical strategy aiming at addressing the complexity of glioblastoma by targeting multiple pathways. Although the rationale for this 9-drug cocktail is well-supported by theoretical and in vitro data, its effectiveness compared to its 511 possible subsets has not been comprehensively evaluated. Such an analysis could reveal if fewer drugs could achieve similar or better outcomes. We conducted an exhaustive in vitro evaluation of the CUSP9 protocol using COMBImageDL, our specialized framework for testing higher-order drug combinations. This study assessed all 511 subsets of the CUSP9v3 protocol, in combination with temozolomide, on two clonal cultures of glioma-initiating cells derived from patient samples. The drugs were used at fixed, clinically relevant concentrations, and the experiment was performed in quadruplicate with endpoint cell viability and live-cell imaging readouts. Our results showed that several lower-order drug combinations produced effects equivalent to the full CUSP9 cocktail, indicating potential for simplified regimens in personalized therapy. Further validation through in vivo and precision medicine testing is required. Notably, a subset of four drugs (auranofin, disulfiram, itraconazole, sertraline) was particularly effective, reducing cell growth, altering cell morphology, increasing apoptotic-like cells within 4-28 h, and significantly decreasing cell viability after 68 h compared to untreated cells. This study underscores the importance and feasibility of comprehensive in vitro evaluations of complex drug combinations on patient-derived tumor cells, serving as a critical step toward (pre-)clinical development.

Innovating cancer drug discovery with refined phenotypic screens

Before molecular pathways in cancer were known to a depth that could predict targets, drug development relied on phenotypic screening, where the effectiveness of candidate chemicals is judged from functional readouts without considering the mechanisms of action. The unraveling of tumor-specific pathways has brought targets for molecularly driven drug discovery, but precedents in the field have shown that awareness of pathways does not necessarily predict therapeutic efficacy, and many cancers still lack druggable targets. Phenotypic screening therefore retains a niche in drug development where a targeted approach is not informative. We analyze the unique advantages of phenotypic screens, and how technological advances have improved their discovery power. Notable advances include the use of larger biological panels and refined protocols that address the disease-relevance and increase data content with imaging and omic approaches.

Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer

With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.

Ultrasmall Carbon Nanodots as Theranostic Nanoheaters for Precision Breast Cancer Phototherapy: Establishing the Translational Potential in Tumor-in-a-Dish Models

This study investigates the remarkable attributes of sulfur-doped carbon nanodots (CDs) synthesized in high yield and a narrow size distribution (4.8 nm). These CDs exhibit notable features, including potential bioelimination through renal clearance and efficient photothermal conversion in the near-infrared region with multicolor photoluminescence across the visible spectrum. Our research demonstrates high biocompatibility and effective near-infrared (NIR)-triggered photothermal toxicity when targeting mammospheres and patient-derived tumor organoids. Moreover, the study delves into the intricate cellular responses induced by CD-mediated hyperthermia. This involves efficient tumor mass death, activation of the p38-mitogen-activated protein kinase (MAPK) pathway, and upregulation of genes associated with apoptosis, hypoxia, and autophagy. The interaction of CDs with mammospheres reveals their ability to penetrate the complex microenvironment, impeded at 4 °C, indicating an energy-dependent endocytosis mechanism. This observation underscores the CDs' potential for targeted drug delivery, particularly in anticancer therapeutics. This investigation contributes to understanding the multifunctional properties of sulfur-doped CDs and highlights their promising applications in cancer therapeutics. Utilizing 3-D tumor-in-a-dish patients' organoids enhances translational potential, providing a clinically relevant platform for assessing therapeutic efficacy in a context mirroring the physiological conditions of cancerous tissues.

TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning

Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. Here, we evaluated commonly used machine learning methods and presented TransCell, a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Among these models, TransCell had the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and had comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAF resistance in melanoma cell lines. Together, we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal (http://apps.octad.org/transcell/) that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.

HPV, HBV, and HIV-1 Viral Integration Site Mapping: A Streamlined Workflow from NGS to Genomic Insights of Carcinogenesis

Viral integration within the host genome plays a pivotal role in carcinogenesis. Various disruptive mechanisms are involved, leading to genomic instability, mutations, and DNA damage. With next-generation sequencing (NGS), we can now precisely identify viral and host genomic breakpoints and chimeric sequences, which are useful for integration site analysis. In this study, we evaluated a commercial hybrid capture NGS panel specifically designed for detecting three key viruses: HPV, HBV, and HIV-1. We also tested workflows for Viral Hybrid Capture (VHC) and Viral Integration Site (VIS) analysis, leveraging customized viral databases in CLC Microbial Genomics. By analyzing sequenced data from virally infected cancer cell lines (including SiHa, HeLa, CaSki, C-33A, DoTc2, 2A3, SCC154 for HPV; 3B2, SNU-182 for HBV; and ACH-2 for HIV-1), we precisely pinpointed viral integration sites. The workflow also highlighted disrupted and neighboring human genes that may play a crucial role in tumor development. Our results included informative virus-host read mappings, genomic breakpoints, and integration circular plots. These visual representations enhance our understanding of the integration process. In conclusion, our seamless end-to-end workflow bridges the gap in understanding viral contributions to cancer development, paving the way for improved diagnostics and treatment strategies.

Transcriptome-wide gene expression outlier analysis pinpoints therapeutic vulnerabilities in colorectal cancer

Multiple strategies are continuously being explored to expand the drug target repertoire in solid tumors. We devised a novel computational workflow for transcriptome-wide gene expression outlier analysis that allows the systematic identification of both overexpression and underexpression events in cancer cells. Here, it was applied to expression values obtained through RNA sequencing in 226 colorectal cancer (CRC) cell lines that were also characterized by whole-exome sequencing and microarray-based DNA methylation profiling. We found cell models displaying an abnormally high or low expression level for 3533 and 965 genes, respectively. Gene expression abnormalities that have been previously associated with clinically relevant features of CRC cell lines were confirmed. Moreover, by integrating multi-omics data, we identified both genetic and epigenetic alternations underlying outlier expression values. Importantly, our atlas of CRC gene expression outliers can guide the discovery of novel drug targets and biomarkers. As a proof of concept, we found that CRC cell lines lacking expression of the MTAP gene are sensitive to treatment with a PRMT5-MTA inhibitor (MRTX1719). Finally, other tumor types may also benefit from this approach.

An Efficient Vector-Based CRISPR/Cas9 System in Zebrafish Cell Line

The clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system has been widely applied in animals as an efficient genome editing tool. However, the technique is difficult to implement in fish cell lines partially due to the lack of efficient promoters to drive the expression of both sgRNA and the Cas9 protein within a single vector. In this study, it was indicated that the zebrafish U6 RNA polymerase III (ZFU6) promoter could efficiently induce tyrosinase (tyr) gene editing and lead to loss of retinal pigments when co-injection with Cas9 mRNA in zebrafish embryo. Furthermore, an optimized all-in-one vector for expression of the CRISPR/Cas9 system in the zebrafish fibroblast cell line (PAC2) was constructed by replacing the human U6 promoter with ZFU6 promoter, basing on the lentiCRISPRV2 system that widely applied in mammal cells. This new vector could successfully target the cellular communication network factor 2a (ctgfa) gene and demonstrated its function in the PAC2 cell. Notably, the vector could also be used to edit the endogenous EMX1 gene in the mammal 293 T cell line, implying its wide application potential. In conclusion, we established a new gene editing tool for zebrafish cell line, which could be a useful in vitro platform for high-throughput analyzing gene function in fish.

TempEasy 3D Hydrogel Coculture System Provides Mechanistic Insights into Prostate Cancer Bone Metastasis

Patients diagnosed with advanced prostate cancer (PCa) often experience incurable bone metastases; however, a lack of relevant experimental models has hampered the study of disease mechanisms and the development of therapeutic strategies. In this study, we employed the recently established Temperature-based Easy-separable (TempEasy) 3D cell coculture system to investigate PCa bone metastasis. Through coculturing PCa and bone cells for 7 days, our results showed a reduction in PCa cell proliferation, an increase in neovascularization, and an enhanced metastasis potential when cocultured with bone cells. Additionally, we observed increased cell proliferation, higher stemness, and decreased bone matrix protein expression in bone cells when cocultured with PCa cells. Furthermore, we demonstrated that the stiffness of the extracellular matrix had a negligible impact on molecular responses in both primary (PCa cells) and distant malignant (bone cells) sites. The TempEasy 3D hydrogel coculture system is an easy-to-use and versatile coculture system that provides valuable insights into the mechanisms of cell-cell communication and interaction in cancer metastasis.

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.

Deciphering the role of FUS::DDIT3 expression and tumor microenvironment in myxoid liposarcoma development

BACKGROUND

Myxoid liposarcoma (MLS) displays a distinctive tumor microenvironment and is characterized by the FUS::DDIT3 fusion oncogene, however, the precise functional contributions of these two elements remain enigmatic in tumor development.

METHODS

To study the cell-free microenvironment in MLS, we developed an experimental model system based on decellularized patient-derived xenograft tumors. We characterized the cell-free scaffold using mass spectrometry. Subsequently, scaffolds were repopulated using sarcoma cells with or without FUS::DDIT3 expression that were analyzed with histology and RNA sequencing.

RESULTS

Characterization of cell-free MLS scaffolds revealed intact structure and a large variation of protein types remaining after decellularization. We demonstrated an optimal culture time of 3 weeks and showed that FUS::DDIT3 expression decreased cell proliferation and scaffold invasiveness. The cell-free MLS microenvironment and FUS::DDIT3 expression both induced biological processes related to cell-to-cell and cell-to-extracellular matrix interactions, as well as chromatin remodeling, immune response, and metabolism. Data indicated that FUS::DDIT3 expression more than the microenvironment determined the pre-adipocytic phenotype that is typical for MLS.

CONCLUSIONS

Our experimental approach opens new means to study the tumor microenvironment in detail and our findings suggest that FUS::DDIT3-expressing tumor cells can create their own extracellular niche.

Spheroids and organoids derived from colorectal cancer as tools for in vitro drug screening

Colorectal cancer (CRC) is a heterogeneous disease. Conventional two-dimensional (2D) culture employing cell lines was developed to study the molecular properties of CRC in vitro. Although these cell lines which are isolated from the tumor niche in which cancer develop, the translation to human model such as studying drug response is often hindered by the inability of cell lines to recapture original tumor features and the lack of heterogeneous clinical tumors represented by this 2D model, differed from in vivo condition. These limitations which may be overcome by utilizing three-dimensional (3D) culture consisting of spheroids and organoids. Over the past decade, great advancements have been made in optimizing culture method to establish spheroids and organoids of solid tumors including of CRC for multiple purposes including drug screening and establishing personalized medicine. These structures have been proven to be versatile and robust models to study CRC progression and deciphering its heterogeneity. This review will describe on advances in 3D culture technology and the application as well as the challenges of CRC-derived spheroids and organoids as a mode to screen for anticancer drugs.

Applied models and molecular characteristics of small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care.

Human patient derived organoids: an emerging precision medicine model for gastrointestinal cancer research

Gastrointestinal cancers account for approximately one-third of the total global cancer incidence and mortality with a poor prognosis. It is one of the leading causes of cancer-related deaths worldwide. Most of these diseases lack effective treatment, occurring as a result of inappropriate models to develop safe and potent therapies. As a novel preclinical model, tumor patient-derived organoids (PDOs), can be established from patients' tumor tissue and cultured in the laboratory in 3D architectures. This 3D model can not only highly simulate and preserve key biological characteristics of the source tumor tissue in vitro but also reproduce the in vivo tumor microenvironment through co-culture. Our review provided an overview of the different in vitro models in current tumor research, the derivation of cells in PDO models, and the application of PDO model technology in gastrointestinal cancers, particularly the applications in combination with CRISPR/Cas9 gene editing technology, tumor microenvironment simulation, drug screening, drug development, and personalized medicine. It also elucidates the ethical status quo of organoid research and the current challenges encountered in clinical research, and offers a forward-looking assessment of the potential paths for clinical organoid research advancement.

In-vivo screening implicates endoribonuclease Regnase-1 in modulating senescence-associated lysosomal changes

Accumulation of senescent cells accelerates aging and age-related diseases, whereas preventing this accumulation extends the lifespan in mice. A characteristic of senescent cells is increased staining with β-galactosidase (β-gal) ex vivo. Here, we describe a progressive accumulation of β-gal staining in the model organism C. elegans during aging. We show that distinct pharmacological and genetic interventions targeting the mitochondria and the mTORC1 to the nuclear core complex axis, the non-canonical apoptotic, and lysosomal-autophagy pathways slow the age-dependent accumulation of β-gal. We identify a novel gene, rege-1/Regnase-1/ZC3H12A/MCPIP1, modulating β-gal staining via the transcription factor ets-4/SPDEF. We demonstrate that knocking down Regnase-1 in human cell culture prevents senescence-associated β-gal accumulation. Our data provide a screening pipeline to identify genes and drugs modulating senescence-associated lysosomal phenotypes.

Phenotypic screen of sixty-eight colorectal cancer cell lines identifies CEACAM6 and CEACAM5 as markers of acid resistance

Elevated cancer metabolism releases lactic acid and CO2 into the under-perfused tumor microenvironment, resulting in extracellular acidosis. The surviving cancer cells must adapt to this selection pressure; thus, targeting tumor acidosis is a rational therapeutic strategy to manage tumor growth. However, none of the major approved treatments are based explicitly on disrupting acid handling, signaling, or adaptations, possibly because the distinction between acid-sensitive and acid-resistant phenotypes is not clear. Here, we report pH-related phenotypes of sixty-eight colorectal cancer (CRC) cell lines by measuring i) extracellular acidification as a readout of acid production by fermentative metabolism and ii) growth of cell biomass over a range of extracellular pH (pHe) levels as a measure of the acid sensitivity of proliferation. Based on these measurements, CRC cell lines were grouped along two dimensions as "acid-sensitive"/"acid-resistant" versus "low metabolic acid production"/"high metabolic acid production." Strikingly, acid resistance was associated with the expression of CEACAM6 and CEACAM5 genes coding for two related cell-adhesion molecules, and among pH-regulating genes, of CA12. CEACAM5/6 protein levels were strongly induced by acidity, with a further induction under hypoxia in a subset of CRC lines. Lack of CEACAM6 (but not of CEACAM5) reduced cell growth and their ability to differentiate. Finally, CEACAM6 levels were strongly increased in human colorectal cancers from stage II and III patients, compared to matched samples from adjacent normal tissues. Thus, CEACAM6 is a marker of acid-resistant clones in colorectal cancer and a potential motif for targeting therapies to acidic regions within the tumors.

Dynamic tissue model in vitro and its application for assessment of microplastics-induced toxicity to air-blood barrier (ABB)

The replication of the hominine physiological environment was identified as an effectual strategy to develop the physiological model in vitro to perform the intuitionistic assessment of toxicity of contaminations. Herein, we proposed a dynamic interface strategy that accurately mimicked the blood flow and shear stress in human capillaries to subtly evaluate the physiological damages. To proof the concept, the dynamic air-blood barrier (ABB) model in vitro was developed by the dynamic interface strategy and was utilized to assess the toxicity of polyethylene terephthalate microplastics (PET-MPs). The developed dynamic ABB model was compared with the static ABB model developed by the conventional Transwell® system and the animal model, then the performance of the dynamic ABB model in evaluation of the PET-MPs induced pulmonary damage via replicating the hominine ABB. The experimental data revealed that the developed dynamic ABB model in vitro effectively mimicked the physiological structure and barrier functions of human ABB, in which more sophisticated physiological microenvironment enabled the distinguishment of the toxicities of PET-MPs in different sizes and different concentrations comparing with the static ABB model constructed on Transwell® systems. Furthermore, the consistent physiological and biochemical characters adopted dynamic ABB model could be achieved in a quick manner referring with that of the mouse model in the evaluation of the microplastics-induced pulmonary damage. The proposed dynamic interface strategy supplied a general approach to develop the hominine physiological environment in vitro and exhibited a potential to develop the ABB model in vitro to evaluate the hazards of inhaled airborne pollutants.

Recent advances in the application of induced pluripotent stem cell technology to the study of myeloid malignancies

Acquired myeloid malignancies are a spectrum of clonal disorders known to be caused by sequential acquisition of genetic lesions in hematopoietic stem and progenitor cells, leading to their aberrant self-renewal and differentiation. The increasing use of induced pluripotent stem cell (iPSC) technology to study myeloid malignancies has helped usher a paradigm shift in approaches to disease modeling and drug discovery, especially when combined with gene-editing technology. The process of reprogramming allows for the capture of the diversity of genetic lesions and mutational burden found in primary patient samples into individual stable iPSC lines. Patient-derived iPSC lines, owing to their self-renewal and differentiation capacity, can thus be a homogenous source of disease relevant material that allow for the study of disease pathogenesis using various functional read-outs. Furthermore, genome editing technologies like CRISPR/Cas9 enable the study of the stepwise progression from normal to malignant hematopoiesis through the introduction of specific driver mutations, individually or in combination, to create isogenic lines for comparison. In this review, we survey the current use of iPSCs to model acquired myeloid malignancies including myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN), acute myeloid leukemia and MDS/MPN overlap syndromes. The use of iPSCs has enabled the interrogation of the underlying mechanism of initiation and progression driving these diseases. It has also made drug testing, repurposing, and the discovery of novel therapies for these diseases possible in a high throughput setting.

Drug Synergism of Anticancer Action in Combination with Favipiravir and Paclitaxel on Neuroblastoma Cells

Background and Objectives: Favipiravir (FPV) is an antiviral medication and has an inhibitory effect on Cytochrome P450 (CYP2C8) protein, which is mainly involved in drug metabolism in the liver, and the expression of this gene is known to be enhanced in neuronal cells. The metabolization of Paclitaxel (PTX), a chemotherapeutic drug used in cancer patients, was analyzed for the first time in the human SH-SY5Y neuroblastoma cell line for monitoring possible synergistic effects when administered with FPV. Materials and Methods: Further, in vitro cytotoxic and genotoxic evaluations of FPV and PTX were also performed using wide concentration ranges in a human fibroblast cell culture (HDFa). Nuclear abnormalities were examined under a fluorescent microscope using the Hoechst 33258 fluorescent staining technique. In addition, the synergistic effects of these two drugs on cultured SH-SY5Y cells were determined by MTT cell viability assay. In addition, the death mechanisms that can occur in SHSY-5Y were revealed by using the flow cytometry technique. Results: Cell viability analyses on the HDFa healthy cell culture showed that both FPV and PTX have inhibitory effects at higher concentrations. On the other hand, there were no significant differences in nuclear abnormality numbers when both of the compounds were applied together. Cell viability analyses showed that FPV and PTX applications have higher cytotoxicity, which indicated synergistic toxicity against the SHSY-5Y cell line. Also, PTX exhibited higher anticancer properties against the neuroblastoma cell line when applied with FPV, as shown in both cytotoxicity and flow cytometry analyses. Conclusions: In light of our findings, the anticancer properties of PTX can be enhanced when the drug application is coupled with FPV exposure. Moreover, these results put forth that the anticancer drug dosage should be evaluated carefully in cancer patients who take COVID-19 treatment with FPV.

Complex in vitro Model: A Transformative Model in Drug Development and Precision Medicine

In vitro and in vivo models play integral roles in preclinical drug research, evaluation, and precision medicine. In vitro models primarily involve research platforms based on cultured cells, typically in the form of two-dimensional (2D) cell models. However, notable disparities exist between 2D cultured cells and in vivo cells across various aspects, rendering the former inadequate for replicating the physiologically relevant functions of human or animal organs and tissues. Consequently, these models failed to accurately reflect real-life scenarios post-drug administration. Complex in vitro models (CIVMs) refer to in vitro models that integrate a multicellular environment and a three-dimensional (3D) structure using bio-polymer or tissue-derived matrices. These models seek to reconstruct the organ- or tissue-specific characteristics of the extracellular microenvironment. The utilization of CIVMs allows for enhanced physiological correlation of cultured cells, thereby better mimicking in vivo conditions without ethical concerns associated with animal experimentation. Consequently, CIVMs have gained prominence in disease research and drug development. This review aimed to comprehensively examine and analyze the various types, manufacturing techniques, and applications of CIVM in the domains of drug discovery, drug development, and precision medicine. The objective of this study was to provide a comprehensive understanding of the progress made in CIVMs and their potential future use in these fields.

Establishment and characterization of NCC-DFSP4-C1: a novel cell line from a patient with dermatofibrosarcoma protuberans having the fibrosarcomatous transformation

Dermatofibrosarcoma protuberans (DFSP) is a superficial low-grade sarcoma, genetically characterized by a fusion gene in collagen type I α (COL1A1) gene and platelet-derived growth factor subunit β (PDGFB). DFSP is locally aggressive and does not typically metastasize. However, DFSP with fibrosarcomatous transformation, which occurs in 7-16% of DFSP cases, demonstrates a poor prognosis than classic DFSP with a higher local recurrence rate and metastatic potential. Although imatinib, a PDGF receptor inhibitor, is a potent therapeutic agent for classic DFSP, it is less effective for DFSP with fibrosarcomatous transformation. The development of definitive chemotherapies for DFSP with fibrosarcomatous transformation is required. Patient-derived tumor cell lines are indispensable tools for preclinical research to discover novel therapeutic agents. However, only seven cell lines were derived from DFSP, out of which only two were established from DFSP with fibrosarcomatous transformation. Hence, in the present study, we established a novel DFSP cell line, NCC-DFSP4-C1, from a surgically resected DFSP tumor specimen with fibrosarcomatous transformation. NCC-DFSP4-C1 harbored an identical COL1A1-PDGFB fusion gene as its donor tumor. NCC-DFSP4-C1 cells retained the morphology of their donor tumor and demonstrated constant proliferation, spheroid formation, and invasion capability in vitro. By screening a drug library, we found that bortezomib and romidepsin demonstrated the strongest suppressive effects on the proliferation of NCC-DFSP4-C1 cells. In conclusion, we report a novel cell line of DFSP with fibrosarcomatous transformation, and demonstrate its utility in the development of novel therapeutic agents for DFSP.

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.

Introduction of Androgen Receptor Targeting shRNA Inhibits Tumor Growth in Patient-Derived Prostate Cancer Xenografts

Patient-derived xenograft (PDX) models have been established as important preclinical cancer models, overcoming some of the limitations associated with the use of cancer cell lines. The utility of prostate cancer PDX models has been limited by an inability to genetically manipulate them in vivo and difficulties sustaining PDX-derived cancer cells in culture. Viable, short-term propagation of PDX models would allow in vitro transfection with traceable reporters or manipulation of gene expression relevant to different studies within the prostate cancer field. Here, we report an organoid culture system that supports the growth of prostate cancer PDX cells in vitro and permits genetic manipulation, substantially increasing the scope to use PDXs to study the pathobiology of prostate cancer and define potential therapeutic targets. We have established a short-term PDX-derived in vitro cell culture system which enables genetic manipulation of prostate cancer PDXs LuCaP35 and BM18. Genetically manipulated cells could be re-established as viable xenografts when re-implanted subcutaneously in immunocompromised mice and were able to be serially passaged. Tumor growth of the androgen-dependent LuCaP35 PDX was significantly inhibited following depletion of the androgen receptor (AR) in vivo. Taken together, this system provides a method to generate novel preclinical models to assess the impact of controlled genetic perturbations and allows for targeting specific genes of interest in the complex biological setting of solid tumors.

Expression of polymeric immunoglobulin receptor (PIGR) and the effect of PIGR overexpression on breast cancer cells

Polymeric immunoglobulin receptor (PIGR) has a major role in mucosal immunity as a transporter of polymeric immunoglobulin across the epithelial cells. The aim of this study was to determine the effect of PIGR on cellular behaviours and chemo-sensitivity of MCF7 and MDA-MB468 breast cancer cell lines. Basal levels of PIGR mRNA and protein expression in MCF7 and MDA-MB468 cells were evaluated by real time quantitative polymerase chain reaction and Western blotting, respectively. MCF7/PIGR and MDA-MB468/PIGR stable cell lines, overexpressing the PIGR gene, were generated using a lentiviral vector with tetracycline dependent induction of expression. Cell viability, cell proliferation and chemo-sensitivity of PIGR transfected cells were evaluated and compared with un-transfected cells to determine the effect of PIGR overexpression on cell phenotype. The levels of PIGR mRNA and protein expression were significantly higher in MDA-MB468 cells than in MCF7 cells (380-fold, p < 0.0001). However, the differential expression of PIGR in these two cell lines did not lead to significant differences in chemosensitivity. Viral overexpression of PIGR was also not found to change any of the parameters measured in either cell line. PIGR per se did not affect cellular behaviours and chemosensitivity of these breast cancer cell lines.

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.

Few-Shot Drug Synergy Prediction With a Prior-Guided Hypernetwork Architecture

Predicting drug synergy is critical to tailoring feasible drug combination treatment regimens for cancer patients. However, most of the existing computational methods only focus on data-rich cell lines, and hardly work on data-poor cell lines. To this end, here we proposed a novel few-shot drug synergy prediction method (called HyperSynergy) for data-poor cell lines by designing a prior-guided Hypernetwork architecture, in which the meta-generative network based on the task embedding of each cell line generates cell line dependent parameters for the drug synergy prediction network. In HyperSynergy model, we designed a deep Bayesian variational inference model to infer the prior distribution over the task embedding to quickly update the task embedding with a few labeled drug synergy samples, and presented a three-stage learning strategy to train HyperSynergy for quickly updating the prior distribution by a few labeled drug synergy samples of each data-poor cell line. Moreover, we proved theoretically that HyperSynergy aims to maximize the lower bound of log-likelihood of the marginal distribution over each data-poor cell line. The experimental results show that our HyperSynergy outperforms other state-of-the-art methods not only on data-poor cell lines with a few samples (e.g., 10, 5, 0), but also on data-rich cell lines.

Portable optical spectroscopic assay for non-destructive measurement of key metabolic parameters on in vitro cancer cells and organotypic fresh tumor slices

To enable non-destructive metabolic characterizations on in vitro cancer cells and organotypic tumor models for therapeutic studies in an easy-to-access way, we report a highly portable optical spectroscopic assay for simultaneous measurement of glucose uptake and mitochondrial function on various cancer models with high sensitivity. Well-established breast cancer cell lines (MCF-7 and MDA-MB-231) were used to validate the optical spectroscopic assay for metabolic characterizations, while fresh tumor samples harvested from both animals and human cancer patients were used to test the feasibility of our optical metabolic assay for non-destructive measurement of key metabolic parameters on organotypic tumor slices. Our optical metabolic assay captured that MCF-7 cells had higher mitochondrial metabolism, but lower glucose uptake compared to the MDA-MB-231 cells, which is consistent with our microscopy imaging and flow cytometry data, as well as the published Seahorse Assay data. Moreover, we demonstrated that our optical assay could non-destructively measure both glucose uptake and mitochondrial metabolism on the same cancer cell samples at one time, which remains challenging by existing metabolic tools. Our pilot tests on thin fresh tumor slices showed that our optical assay captured increased metabolic activities in tumors compared to normal tissues. Our non-destructive optical metabolic assay provides a cost-effective way for future longitudinal therapeutic studies using patient-derived organotypic fresh tumor slices through the lens of tumor energetics, which will significantly advance translational cancer research.