Drug Response Research Articles

Generation of an induced pluripotent stem cell line (BIORTCi001-A) from a healthy adult indigenous Nigerian participant

Genetic backgrounds influence cellular phenotypes, drug responses, and health outcomes, yet most human iPSC lines are derived from individuals of European descent, with lines from indigenous Africans particularly scarce. Addressing this gap, we generated iPSCs from dermal fibroblasts of a healthy 60-year-old indigenous Nigerian male of the Babur ethnic group using Sendai virus. The iPSC line displayed a normal karyotype, was characterized for pluripotency markers and differentiated into neural progenitor cells and astrocytes. To enhance African representation in research, this iPSC line will be available to the scientific community, with ongoing efforts focused on creating an open-access African iPSC biobank.Resource Table:

An integrated pan-cancer assessment of prognosis, immune infiltration, and immunotherapy response for B7 family using multi-omics data

AimsB7 molecules (B7s) are crucial synergistic signals for effective immune surveillance against tumor cells. While previous studies have explored the association between the B7 family and cancer, most have been limited to specific genes or cancer subtypes. Main methodsOur study utilized multi-omics data to investigate potential correlations between B7s expression (B7s exp.) and prognosis, clinicopathological features, somatic mutations (SMs), copy number variations (CNVs), immune characteristics, tumor microenvironment (TME), microsatellite instability, tumor mutation burden, immune checkpoint gene (ICG), and drug responsiveness in TCGA tumors. Furthermore, the connection between B7s exp. and immunotherapy (IT) performance assessed in various validated datasets. Following this, immune infiltration analysis (IIA) was conducted based on B7s exp., CNVs, or SMs in bladder cancer (BLCA), complemented by real-time PCR (RT-PCR) and protein confirmation of B7-H3. Key findingsAcross most cancer types, B7s exp. was related to prognosis, clinicopathological characteristics, mutations, CNVs, ICG, TMB, TME. The examination of sensitivity to anticancer drugs unveiled correlations between B7 molecules and different drug sensitivities. Specific B7s exp. patterns were linked to the clinical effectiveness of IT. Using GSEA, several enriched immune-related functions and pathways were identified. Particularly in BLCA, IIA revealed significant connections between B7 CNVs, mutation status, and various immune cell infiltrates. RT-PCR confirmed elevated B7-H3 gene levels in BLCA tumor tissues. SignificanceThis study confirmed the significance of B7s exp. and genomic changes in predicting outcomes and treatment across different cancer types. Moreover, they indicate a critical function of B7s in BLCA and their potential as IT biomarkers.

Acquired Temozolomide Resistance Instructs Patterns of Glioblastoma Behavior in Gelatin Hydrogels.

Acquired drug resistance in glioblastoma (GBM) presents a major clinical challenge and is a key factor contributing to abysmal prognosis, with less than 15 months median overall survival. Aggressive chemotherapy with the frontline therapeutic, temozolomide (TMZ), ultimately fails to kill residual highly invasive tumor cells after surgical resection and radiotherapy. Here, a 3D engineered model of acquired TMZ resistance is reported using two isogenically matched sets of GBM cell lines encapsulated in gelatin methacrylol hydrogels. Response of TMZ-resistant versus TMZ-sensitive GBM cell lines within the gelatin-based extracellular matrix platform is benchmarked and drug response at physiologically relevant TMZ concentrations is further validated. The changes in drug sensitivity, cell invasion, and matrix-remodeling cytokine production are shown as the result of acquired TMZ resistance. This platform lays the foundation for future investigations targeting key elements of the GBM tumor microenvironment to combat GBM's devastating impact by advancing the understanding of GBM progression and treatment response to guide the development of novel treatment strategies.

Identification of Quiescent Cells in a Zebrafish T-Cell Acute Lymphoblastic Leukemia Model Using Cell Proliferation Staining.

Cellular quiescence is a state of growth arrest or slowed proliferation that is described in normal and cancer stem cells (CSCs). Quiescence may protect CSCs from antiproliferative chemotherapy drugs. In T-cell acute lymphoblastic leukemia (T-ALL) patient-derived xenograft (PDX) mouse models, quiescent cells are associated with treatment resistance and stemness. Cell proliferation dyes are popular tools for the tracking of cell division. The fluorescent dye is covalently anchored into amine groups on the membrane and macromolecules inside the cell. This allows for the tracking of labeled cells for up to 10 divisions, which can be resolved by flow cytometry. Ultimately, cells with the highest proliferation rates will have low dye retention, as it will be diluted with each cell division, while dormant, slower-dividing cells will have the highest retention. The use of cell proliferation dyes to isolate dormant cells has been optimized and described in T-ALL mouse models. Complementary to the existing mouse models, the rag2:Myc-derived zebrafish T-ALL model provides an excellent venue to interrogate self-renewal in T-ALL due to the high frequency of leukemic stem cells (LSCs) and the convenience of zebrafish for large-scale transplant experiments. Here, we describe the workflow for the staining of zebrafish T-ALL cells with a cell proliferation dye, optimizing the concentration of the dye for zebrafish cells, passaging successfully stained cells in vivo, and the collection of cells with varying levels of dye retention by live cell sorting from transplanted animals. Given the absence of well-established cell surface makers for LSCs in T-ALL, this approach provides a functional means to interrogate quiescent cells in vivo. For representative results, we describe the engraftment efficiency and the LSC frequency of high and low dye-retaining cells. This method can help investigate additional properties of quiescent cells, including drug response, transcriptional profiles, and morphology.

1H and 31P Magnetic Resonance Spectroscopic Metabolomic Imaging: Assessing Mitogen-Activated Protein Kinase Inhibition in Melanoma.

The MAPK signaling pathway with BRAF mutations has been shown to drive the pathogenesis of 40-60% of melanomas. Inhibitors of this pathway's BRAF and MEK components are currently used to treat these malignancies. However, responses to these treatments are not always successful. Therefore, identifying noninvasive biomarkers to predict treatment responses is essential for personalized medicine in melanoma. Using noninvasive 1H magnetic resonance spectroscopy (1H MRS), we previously showed that BRAF inhibition reduces lactate and alanine tumor levels in the early stages of effective therapy and could be considered as metabolic imaging biomarkers for drug response. The present work demonstrates that these metabolic changes observed by 1H MRS and those assessed by 31P MRS are also found in preclinical human melanoma models treated with MEK inhibitors. Apart from 1H and 31P MRS, additional supporting in vitro biochemical analyses are described. Our results indicate significant early metabolic correlations with response levels to MEK inhibition in the melanoma models and are consistent with our previous study of BRAF inhibition. Given these results, our study supports the potential clinical utility of noninvasive MRS to objectively image metabolic biomarkers for the early prediction of melanoma's response to MEK inhibition.

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Targeting AKR1B10 by Drug Repurposing with Epalrestat Overcomes Chemoresistance in Non-Small Cell Lung Cancer Patient-Derived Tumor Organoids.

Systemic treatments given to patients with non-small cell lung cancer (NSCLC) are often ineffective due to drug resistance. In the present study, we investigated patient-derived tumor organoids (PDTO) and matched tumor tissues from surgically treated patients with NSCLC to identify drug repurposing targets to overcome resistance toward standard-of-care platinum-based doublet chemotherapy. PDTOs were established from 10 prospectively enrolled patients with non-metastatic NSCLC from resected tumors. PDTOs were compared with matched tumor tissues by histopathology/immunohistochemistry, whole exome sequencing, and transcriptome sequencing. PDTO growths and drug responses were determined by measuring 3D tumoroid volumes, cell viability, and proliferation/apoptosis. Differential gene expression analysis identified drug-repurposing targets. Validations were performed with internal/external data sets of patients with NSCLC. NSCLC cell lines were used for aldo-keto reductase 1B10 (AKR1B10) knockdown studies and xenograft models to determine the intratumoral bioavailability of epalrestat. PDTOs retained histomorphology and pathological biomarker expression, mutational/transcriptomic signatures, and cellular heterogeneity of the matched tumor tissues. Five (50%) PDTOs were chemoresistant toward carboplatin/paclitaxel. Chemoresistant PDTOs and matched tumor tissues demonstrated overexpression of AKR1B10. Epalrestat, an orally available AKR1B10 inhibitor in clinical use for diabetic polyneuropathy, was repurposed to overcome chemoresistance of PDTOs. In vivo efficacy of epalrestat to overcome drug resistance corresponded to intratumoral epalrestat levels. PDTOs are efficient preclinical models recapitulating the tumor characteristics and are suitable for drug testing. AKR1B10 can be targeted by repurposing epalrestat to overcome chemoresistance in NSCLC. Epalrestat has the potential to advance to clinical trials in patients with drug-resistant NSCLC due to favorable toxicity, pharmacological profile, and bioavailability.

The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease.

The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments. Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management. The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries. As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced.

Photoactive metabolite mediated photodynamic therapy of Rhabdomyosarcoma cell lines using medicinal plants and Doxorubicin co-treatments

BackgroundMedicinal plant-mediated combinational therapies have gained importance globally due to minimal side effects and enhanced treatment outcomes compared to single-drug modalities. We aimed to analyze the cytotoxic potential of each conventional treatment i.e., photodynamic therapy (PDT), chemotherapy (doxorubicin hydrochloride; Dox-HCl) with or without various concentrations of medicinal plant extracts (PE) on soft tissue cancer Rhabdomyosarcoma (RD) cell line.MethodsThe Rhabdomyosarcoma (RD) cell line was cultured and treated with Photosensitizer (Photosense (AlPc4)), Chemo (Dox-HCl), and their combinations with different concentrations of each plant extract i.e., Thuja occidentalis, Moringa oleifera, Solanum surattense. For the source of illumination, a Diode laser (λ = 630 nm ± 1 nm, Pmax = 1.5 mW) was used. Photosensitizer uptake time (∼ 45 min) was optimized through spectrophotometric measurements (absorption spectroscopy). Drug response of each treatment arm was assessed post 24 h of administration using 3-(4, 5-dimethyl-2-thiazolyl)-2, 5- 5-diphenyl-2 H- tetrazolium bromide (MTT) assay.ResultsPE-mediated Chemo-Photodynamic therapy (PDT) exhibited synergistic effects (CI < 1). Moreover, Rhabdomyosarcoma culture pretreated with various plant extracts for 24 h exhibited significant inhibition of cell viability however most effective outcomes were shown by low and high doses of Moringa oleifera compared to other plant extracts. Post low doses treated culture with all plant extracts followed by PDT came up with more effectiveness when compared to all di-therapy treatments.ConclusionThe general outcome of this work shows that the ethanolic plant extracts (higher doses) promote the death of cancerous cells in a dose-dependent way and combining Dox-HCl and photo-mediated photodynamic therapy can yield better therapeutic outcomes.

Building a translational cancer dependency map for The Cancer Genome Atlas

Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of ‘maps’ detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities.

The Long Noncoding RNA PANDAR as a Prognostic and Diagnostic Biomarker in Panceratic Cancer ‏‏.

Pancreatic cancer (PC) is a lethal complication in the world, affecting around half a million individuals each year. The treatment of PC is relatively difficult due to the difficulty in making an early diagnosis. Most PC patients are confronted with locally metastatic or advanced diseases in the asymptomatic phase, and about 80% have late diagnosis with metastasis. Recently, long noncoding RNAs (lncRNAs) have drawn attention as a novel biological regulation layer. They take part in the regulation of mRNA and can be used as a prognostic factor or drug target. Based on their functions as regulators of PC initiation and progress, the lncRNAs can be categorized as tumor suppressors or oncogenic. They can be considered as a target for finding new biomarkers for prognosis, diagnosis, monitoring, and treating drug response in PC. Therefore, the present study summarizes the lncRNAs role in PC and the probable strategies to deal with their expression and controlling tumorigenesis and detection of the prognosis of PC.

A Transformative Technology Linking Patient’s mRNA Expression Profile to Anticancer Drug Efficacy

As precision medicine such as targeted therapy and immunotherapy often have limited accessibility, low response rate, and evolved resistance, it is urgent to develop simple, low-cost, and quick-turnaround personalized diagnostic technologies for drug response prediction with high sensitivity, speed, and accuracy. The major challenges of drug response prediction strategies employing digital database modeling are the scarcity of labeled clinical data, applicability only to a few classes of drugs, and losing the resolution at the individual patient level. Although these challenges have been partially addressed by large-scale cancer cell line datasets and more patient-relevant cell-based systems, the integration of different data types and data translation from pre-clinical to clinical utilities are still far-fetched. To overcome the current limitations of precision medicine with a clinically proven drug response prediction assay, we have developed an innovative and proprietary technology based on in vitro patient testing and in silico data analytics. First, a patient-derived gene expression signature was established via the transcriptomic profiling of cell-free mRNA (cfmRNA) from the patient’s blood. Second, a gene-to-drug data fusion and overlaying mechanism to transfer data were performed. Finally, a semi-supervised method was used for the database searching, matching, annotation, and ranking of drug efficacies from a pool of ~700 approved, investigational, or clinical trial drug candidates. A personalized drug response report can be delivered to inform clinical decisions within a week. The PGA (patient-derived gene expression-informed anticancer drug efficacy) test has significantly improved patient outcomes when compared to the treatment plans without PGA support. The implementation of PGA, which combines patient-unique cfmRNA fingerprints with drug mapping power, has the potential to identify treatment options when patients are no longer responding to therapy and when standard-of-care is exhausted.

Enhanced hepatotoxicity assessment through encapsulated HepG2 spheroids in gelatin hydrogel matrices: Bridging the gap from 2D to 3D culture

Conventional 2D drug screening often fails to accurately predict clinical outcomes. We present an innovative approach to improve hepatotoxicity assessment by encapsulating HepG2 spheroids in gelatin hydrogel matrices with different mechanical properties. Encapsulated spheroids exhibit sustained liver-specific functionality, enhanced expression of drug-metabolizing enzymes, and increased drug sensitivity compared to 2D cultures. The platform detects critical variations in drug response, with significant differences in IC50 values between 2D and spheroid cultures ranging from 1.3-fold to > 13-fold, particularly for acetaminophen. Furthermore, drug-metabolizing enzyme expression varies across hydrogel concentrations, suggesting a role for matrix mechanical properties in modulating hepatocyte function. This novel spheroid-hydrogel platform offers a transformative approach to hepatotoxicity assessment, providing increased sensitivity, improved prediction, and a more physiologically relevant environment. The use of such advanced in vitro models can accelerate drug development, reduce animal testing, and contribute to improved patient safety and clinical outcomes.

Pharmacogenomics: A Genetic Approach to Drug Development and Therapy.

The majority of the well-known pharmacogenomics research used in the medical sciences contributes to our understanding of medication interactions. It has a significant impact on treatment and drug development. The broad use of pharmacogenomics is required for the progress of therapy. The main focus is on how genes and an intricate gene system affect the body's reaction to medications. Novel biomarkers that help identify a patient group that is more or less likely to respond to a certain medication have been discovered as a result of recent developments in the field of clinical therapeutics. It aims to improve customized therapy by giving the appropriate drug at the right dose at the right time and making sure that the right prescriptions are issued. A combination of genetic, environmental, and patient variables that impact the pharmacokinetics and/or pharmacodynamics of medications results in interindividual variance in drug response. Drug development, illness susceptibility, and treatment efficacy are all impacted by pharmacogenomics. The purpose of this work is to give a review that might serve as a foundation for the creation of new pharmacogenomics applications, techniques, or strategies.

Isolation methods and characterization of primary rat neurovascular cells

BackgroundThere is significant interest in isolating cells of the blood-brain barrier (BBB) for use in in vitro screening of therapeutics and analyzing cell specific roles in neurovascular pathology. Primary brain cells play an advantageous role in BBB models; however, isolation procedures often do not produce cells at high enough yields for experiments. In addition, although numerous reports provide primary cell isolation methods, the field is lacking in documentation and detail of expected morphological changes that occur throughout culturing and there are minimal troubleshooting resources. Here, we present simplified, robust, and reproducible methodology for isolating astrocytes, pericytes, and endothelial cells, and demonstrate several morphological benchmarks for each cell type throughout the process and culture timeframe. We also analyze common considerations for developing neurovascular cell isolation procedures and recommend solutions for troubleshooting.ResultsThe presented methodology isolated astrocytes, pericytes, and endothelial cells and enabled cell attachment, maturation, and cell viability. We characterized milestones in cell maturation over 12 days in culture, a common timeline for applications of these cell types in BBB models. Phase contrast microscopy was used to show initial cell plating, attachment, and daily growth of isolated cells. Confocal microscopy images were analyzed to determine the identity of cell types and changes to cell morphology. Nuclear staining was also used to show the viability and proliferation of glial cells at four time points. Astrocyte branches became numerous and complex with increased culture time. Microglia, oligodendrocytes, and neurons were present in mixed glial cultures for 12 days, though the percentage of microglia and neurons expectedly decreased after passaging, with microglia demonstrating a less branched morphology.ConclusionsNeurovascular cells can be isolated through our optimized protocols that minimize cell loss and encourage the adhesion and proliferation of isolated cells. By identifying timepoints of viable glia and neurons within an astrocyte-dominant mixed culture, these cells can be used to evaluate drug targeting, uptake studies, and response to pathological stimulus in the neurovascular unit.

A clinically compatible in vitro drug-screening platform identifies therapeutic vulnerabilities in primary cultures of brain metastases

PurposeBrain metastases represent the most common intracranial tumors in adults and are associated with a poor prognosis. We used a personalized in vitro drug screening approach to characterize individual therapeutic vulnerabilities in brain metastases.MethodsShort-term cultures of cancer cells isolated from brain metastasis patients were molecularly characterized using next-generation sequencing and functionally evaluated using high-throughput in vitro drug screening to characterize pharmacological treatment sensitivities.ResultsNext-generation sequencing identified matched genetic alterations in brain metastasis tissue samples and corresponding short-term cultures, suggesting that short-term cultures of brain metastases are suitable models for recapitulating the genetic profile of brain metastases that may determine their sensitivity to anti-cancer drugs. Employing a high-throughput in vitro drug screening platform, we successfully screened the cultures of five brain metastases for response to 267 anticancer compounds and related drug response to genetic data. Among others, we found that targeted treatment with JAK3, HER2, or FGFR3 inhibitors showed anti-cancer effects in individual brain metastasis cultures.ConclusionOur preclinical study provides a proof-of-concept for combining molecular profiling with in vitro drug screening for predictive evaluation of therapeutic vulnerabilities in brain metastasis patients. This approach could advance the use of patient-derived cancer cells in clinical practice and might eventually facilitate decision-making for personalized drug treatment.

A pan-cancer analysis unveiling the function of NR4A family genes in tumor immune microenvironment, prognosis, and drug response.

NR4A family genes play crucial roles in cancers. However, the role of NR4A family genes in cancers remains paradoxical as they promote or suppress tumorigenesis. We aimed to conduct comprehensive analyses of the association between the expression of NR4A family genes and tumor microenvironment (TME) based on bioinformatics methods. We collected RNA-seq data from 33 cancer types and 20 normal tissue sites from the TCGA and GTEx databases. Expression patterns of NR4A family genes and their associations with DNA methylation, miRNA, overall survival, drug responses, and tumor microenvironment were investigated. Significant downregulation of all NR4A family genes was observed in 15 cancer types. DNA promoter methylation and expression of NR4A family genes were negatively correlated in five cancers. The expression of 10 miRNAs targeting NR4A family genes was negatively correlated with the expression of NR4A family genes. High expression of all NR4A family genes was associated with poor prognosis in stomach adenocarcinoma and increased expressions of NR4A2 and NR4A3 were associated with poor prognosis in adrenocortical carcinoma. In addition, we found an elevated expression of NR4A2, which enhances the response to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Interestingly, in breast cancer, NR4A3 was significantly associated with C2 (IFN-γ dominant), C3 (inflammatory), and C6 (TGF-β dominant) immune subtypes and infiltrated immune cell types, implying both oncogenic and tumor-suppressive functions of NR4A3 in breast cancer. The NR4A family genes have the potential to serve as a diagnostic, prognostic, and immunological marker of human cancers.

An immune biomarker associated with EMT serves as a predictor for prognosis and drug response in bladder cancer.

Bladder cancer (BLCA), which develops from the upper endometrial of the bladder, is the sixth most prevalent cancer across the globe. WDHD1 (WD repeat and HMG-box DNA binding protein 1 gene) directly affects signaling, the cell cycle, and the development of the cell skeleton. Uncertainty surrounds WDHD1's function in BLCA immunity and prognosis, though. Using weighed gene co-expression network analysis (WGCNA), initially, we first identified 32 risk factors in genes with differential expression for this investigation. Then, using a variety of bioinformatic techniques and experimental validation, we examined the connections between WDHD1 and BLCA expression, clinical pathological traits, WDHD1-related proteins, upper-skin-intermediate conversion (EMT), immune cell immersion, convergence factors, immune markers, and drug sensitivity. The findings demonstrated that we constructed a 32-gene risk-predicting model where WDHD1 was elevated as a representative gene expression in BLCA and related to a range of clinical traits. Furthermore, high WDHD1 expression was a standalone predictor associated with a worse survival rate. The most commonly recruited cells and their evolutionary patterns were highlighted to better comprehend WDHD1's function in cancer. High WDHD1 expression was associated with many aspects of immunology. Finally, the study found that individuals with high expression of WDHD1 were drug-sensitive to four different broad-spectrum anti-cancer drugs. These results describe dynamic changes in the tumor microenvironment in BLCA and provide evidence for the hypothesis that WDHD1 is a novel biomarker of tumor development. WDHD1 may therefore be a useful target for the detection and management of BLCA.

A 3D Pancreatic Cancer Model with Integrated Optical Sensors for Noninvasive Metabolism Monitoring and Drug Screening.

A distinct feature of pancreatic ductal adenocarcinoma (PDAC) is a prominent tumor microenvironment (TME) with remarkable cellular and spatial heterogeneity that meaningfully impacts disease biology and treatment resistance. The dynamic crosstalk between cancer cells and the dense stromal compartment leads to spatially and temporally heterogeneous metabolic alterations, such as acidic pH that contributes to drug resistance in PDAC. Thus, monitoring the extracellular pH metabolic fluctuations within the TME is crucial to predict and to quantify anticancer drug efficacy. Here,a simple and reliable alginate-based 3D PDAC model embedding ratiometric optical pH sensors and cocultures of tumor (AsPC-1) and stromal cells for simultaneously monitoring metabolic pH variations and quantify drug response is presented. By means of time-lapse confocal laser scanning microscopy (CLSM) coupled with a fully automated computational analysis, the extracellular pH metabolic variations are monitored and quantified over time during drug testing with gemcitabine, folfirinox, and paclitaxel, commonly used in PDAC therapy. In particular, the extracellular acidification is more pronounced after drugs treatment, resulting in increased antitumor effect correlated with apoptotic cell death. These findings highlight the importance of studying the influence of cellular metabolic mechanisms on tumor response to therapy in 3D tumor models, this being crucial for the development of personalized medicine approaches.

RYR2 deficient human model identifies calcium handling and metabolic dysfunction impacting pharmacological responses.

Creation of disease models utilizing hiPSCs in combination with CRISPR/Cas9 gene editing enable mechanistic insights into differential pharmacological responses. This allows translation of efficacy and safety findings from a healthy to a diseased state and provides a means to predict clinical outcome sooner during drug discovery. Calcium handling disturbances including reduced expression levels of the type 2 ryanodine receptor (RYR2) are linked to cardiac dysfunction; here we have created a RYR2 deficient human cardiomyocyte model that mimics some aspects of heart failure. RYR2 deficient cardiomyocytes show differential pharmacological responses to L-type channel calcium inhibitors. Phenotypic and proteomic characterization reveal novel molecular insights with altered expression of structural proteins including CSRP3, SLMAP, and metabolic changes including upregulation of the pentose phosphate pathway and increased sensitivity to redox alterations. This genetically engineered in vitro cardiovascular model of RYR2 deficiency supports the study of pharmacological responses in the context of calcium handling and metabolic dysfunction enabling translation of drug responses from healthy to perturbed cellular states.