Relevant Model Research Articles

Designer cellular spheroids with DNA origami for drug screening.

Current in vitro models struggle to balance the complexity of human diseases with suitability for large-scale drug tests. While 3D cultures simulate human tissues, they lack cellular intricacy, and integrating these models with high-throughput drug screening remains a challenge. Here, we introduce a method that uses self-assembling nucleic acid nanostructures decorated living cells, termed NACs, to create spheroids with a customizable 3D layout. To demonstrate its uniqueness, our method effectively creates designer 3D spheroids by combining parenchymal cells, stromal cells, and immune cells, leading to heightened physiological relevance and detailed modeling of complex chronic diseases and immune-stromal interactions. Our approach achieves a high level of biological fidelity while being standardized and straightforward to construct with the potential for large-scale drug discovery applications. By merging the precision of DNA nanotechnology with advanced cell culture techniques, we are streamlining human-centric models, striking a balance between complexity and standardization, to boost drug screening efficiency.

Intention to use fintech services: An investigation into the moderation effects of quality of internet access and digital skills

This paper aims to investigate the moderating influence of the quality of access to internet and digital skills on the factors that influence the intention to use fintech services among the young working population in India. We use the Theory of Planned Behavior to examine the intention to adopt financial technology in a rapidly technologically transformative Indian landscape. We conducted an empirical investigation on 324 young workers in India using the survey method. The TPB model's relevance in an Indian context is validated. Attitude, perceived behavioral control, and subjective norms together accounted for 48.7% of the variation in the intention to use fintech services. The quality of internet access significantly moderated the positive effect of young workers' attitudes on their intention to use fintech. Digital skills significantly moderated the positive effects of attitude and perceived behavioral control on intentions to use fintech services. India is considered a very fast adopter of digital technology. In India, the use of electronic channels in financial service delivery is on the rise. With the wide geographic dispersion and huge population, the quality of internet access and digital skills can influence the intention to use fintech services. There can be vast differences in the behavioral mindset of people in a developing country like India compared to that of a developed one regarding the use and adoption of digital platforms for accessing financial services. Developers and regulators must adopt approaches and policies that consider these behavioral factors. This paper examines the Theory of Planned Behaviour in the context of a rapidly transforming behavioural context in India with the adoption of technology-based financial services. The importance of quality internet access and digital skills as factors moderating the adoption of technology is examined in this paper, unlike many previous studies.

Inverse-Free DZNN Models for Solving Time-Dependent Linear System via High-Precision Linear Six-Step Method.

Time-dependent linear system (TDLS) is usually encountered in scientific research, which is the mathematical formulation of many practical applications. Different from conventional inverse-need models, by utilizing zeroing neural network (ZNN) method twice, an inverse-free continuous ZNN (CZNN) model is developed for solving TDLS. For conveniently practical use, a discrete model is naturally desired. Superior to conventional discretization methods, a general linear six-step (LSS) method with the seventh-order precision and five variable parameters is proposed for the first time. Constraints about five variable parameters are theoretically analyzed to guarantee the efficacy of the general LSS method. Within constraints, 12 specific LSS methods are further developed. Aided with the general LSS method, an inverse-free discrete ZNN (DZNN) is proposed and termed DZNN-LSS model, and its precision is greatly improved compared with conventional discrete models. For comparison, three conventional discretization methods are also utilized to generate DZNN models. Detailed theoretical analyses are provided to prove the efficacy of relevant models. In addition, a specific TDLS example is considered to show the effectiveness and superiority of the DZNN-LSS model. More than that, applications to manipulator control and sound source localization are conducted to illustrate the applicability of the DZNN-LSS model.

A bioprinted sea-and-island multicellular model for dissecting human pancreatic tumor-stroma reciprocity and adaptive metabolism

Pancreatic ductal adenocarcinoma (PDAC) presents a formidable clinical challenge due to its intricate microenvironment characterized by desmoplasia and complex tumor-stroma interactions. Conventional models hinder studying cellular crosstalk for therapeutic development. To recapitulate key features of PDAC masses, this study creates a novel sea-and-island PDAC tumor construct (s&i PTC). The s&i PTC consists of 3D-printed islands of human PDAC cells positioned within an interstitial extracellular matrix (ECM) populated by human cancer-associated fibroblasts (CAFs). This design closely mimics the in vivo desmoplastic architecture and nutrient-poor conditions. The model enables studying dynamic tumor-stroma crosstalk and signaling reciprocity, revealing both known and yet-to-be-discovered multicellular metabolic adaptations. Using the model, we discovered the orchestrated dynamic alterations of CAFs under nutrient stress, resembling critical in vivo human tumor niches, such as the secretion of pro-tumoral inflammatory factors. Additionally, nutrient scarcity induces dynamic alterations in the ECM composition and exacerbates poor cancer cell differentiation—features well-established in PDAC progression. Proteomic analysis unveiled the enrichment of proteins associated with aggressive tumor behavior and ECM remodeling in response to poor nutritional conditions, mimicking the metabolic stresses experienced by avascular pancreatic tumor cores. Importantly, the model's relevance to patient outcomes is evident through an inverse correlation between biomarker expression patterns in the s&i PTCs and PDAC patient survival rates. Key findings include upregulated MMPs and key ECM proteins (such as collagen 11 and TGFβ) under nutrient-avid conditions, known to be regulated by CAFs, alongside the concomitant reduction in E-cadherin expression associated with a poorly differentiated PDAC state under nutrient deprivation. Furthermore, elevated levels of hyaluronic acid (HA) and integrins in response to nutrient deprivation underscore the model's fidelity to the PDAC microenvironment. We also observed increased IL-6 and reduced α-SMA expression under poor nutritional conditions, suggesting a transition of CAFs from myofibroblastic to inflammatory phenotypes under a nutrient stress akin to in vivo niches. In conclusion, the s&i PTC represents a significant advancement in engineering clinically relevant 3D models of PDAC masses. It offers a promising platform for elucidating tumor-stroma interactions and guiding future therapeutic strategies to improve patient outcomes.

Prediction of TNFRSF9 expression and molecular pathological features in thyroid cancer using machine learning to construct Pathomics models.

The TNFRSF9 molecule is pivotal in thyroid carcinoma (THCA) development. This study utilizes Pathomics techniques to predict TNFRSF9 expression in THCA tissue and explore its molecular mechanisms. Transcriptome data, pathology images, and clinical information from the cancer genome atlas (TCGA) were analyzed. Image segmentation and feature extraction were performed using the OTSU's algorithm and pyradiomics package. The dataset was split for training and validation. Features were selected using maximum relevance minimum redundancy recursive feature elimination (mRMR_RFE) and modeling conducted with the gradient boosting machine (GBM) algorithm. Model evaluation included receiver operating characteristic curve (ROC) analysis. The Pathomics model output a probabilistic pathomics score (PS) for gene expression prediction, with its prognostic value assessed in TNFRSF9 expression groups. Subsequent analysis involved gene set variation analysis (GSVA), immune gene expression, cell abundance, immunotherapy susceptibility, and gene mutation analysis. High TNFRSF9 expression correlated with worsened progression-free interval (PFI) and acted as an independent risk factor [hazard ratio (HR) = 2.178, 95% confidence interval (CI) 1.045-4.538, P = 0.038]. Nine pathohistological features were identified. The GBM Pathomics model demonstrated good prediction efficacy [area under the curve (AUC) 0.819 and 0.769] and clinical benefits. High PS was a PFI risk factor (HR = 2.156, 95% CI 1.047-4.440, P = 0.037). Patients with high PS potentially exhibited enriched pathways, increased TIGIT gene expression, Tregs infiltration (P < 0.0001), and higher rates of gene mutations (BRAF, TTN, TG). The GBM Pathomics model constructed based on the pathohistological features of H&E-stained sections well predicted the expression level of TNFRSF9 molecules in THCA.

Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures

In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model’s utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

Logistic curve modelling of sea surface temperature and latent heat flux variability in the Tropical Indian Ocean

The Tropical Indian Ocean (TIO) is one of the most vulnerable regions to climate change due to its unique ocean-atmosphere interactions. The region is characterized by warm temperatures, high evaporation rates, and strong convection, making it particularly vulnerable to greenhouse gases. This susceptibility leads to an overall trend of warming across the Earth's surface and atmosphere, intensifying sea surface temperatures (SST) and increasing evaporation rates, consequently influencing Surface Latent Heat Flux (SLHF). The strong influence of both SST and SLHF on atmospheric circulation and precipitation patterns makes them critical factors in determining the region's climate, particularly in monsoon-dominated regions. Analysis reveals a consistent upward trend in both SST and SLHF in the central region of TIO. To model this, standard Logistic Curve Model (LCM) has been applied to these parameters averaged over the central region of the TIO. The model is run for a period of 20 years from 2001 to 2020, grouped into four lustrums. The LCM-derived SST and SLHF are in good consistent with observed datasets during the above periods with, correlation coefficients ranging from 0.92 to 0.96 for SST and 0.86–0.90 for SLHF. Extending the model spatially across the entire TIO region explains the ability to project fluctuations in SST and SLHF values across different seasons. These findings highlight the model's relevance for capturing short-term, long-term, and seasonal variability of the parameters, providing important insights into regional climate dynamics.

Predicting Clinical Pharmacokinetics/Pharmacodynamics and Impact of Organ Impairment on siRNA-Based Therapeutics Using a Mechanistic Physiologically-Based Pharmacokinetic-Pharmacodynamic Model.

Approved and emerging siRNA therapeutics are primarily designed for targeted delivery to liver where the therapeutic gene silencing effects occurs. Impairment of hepatic/renal function and its impact on siRNA pharmacokinetics/pharmacodynamics (PKs/PDs) are yet to be mechanistically evaluated to describe the unanticipated clinical observations for this novel modality. We developed pathophysiologically relevant models for organ impairment within a physiologically-based PK-PD (PBPK-PD) modeling framework focusing on modality-specific mechanistic factors to evaluate impact on siRNA PKs and PDs. PBPK-PD models for two US Food and Drug Administration (FDA) approved siRNAs inclisiran and vutrisiran were developed as case studies leveraging available tissue-specific data and translated to humans. Key determinants of the clinical PK and PD of N-acetylgalactosamine conjugated siRNAs (GalNAc-siRNAs) with varying sequences were also identified to inform effective clinical translation strategies for emerging GalNAc-siRNA candidates. A 30-70% reduction in hepatic asialoglycoprotein receptors concentrations still allowed for sufficient amount of free cytoplasmic siRNA for RISC-loading to produce PD effects comparable in extent and duration to normal liver function. This included severe hepatic impairment for which no clinical data are available. Inclusion of other modality agnostic physiological changes relevant to organ impairment did not alter the findings. Changes in renal physiologies, including changes in GFR across various degrees of impairment, well predicted minimal changes in PD for inclisiran and vutrisiran. This work provides a quantitative mechanistic framework and insights on modality-specific factors that drive clinical translation and patient/disease-related factors that impact specific dosing considerations and clinical outcomes to help accelerate the optimal development of siRNA therapeutics.

Developing Clinically Relevant Decision Tree Models Using Machine-Learning to Predict Psychotherapy Success in Late Life Depression

Developing Clinically Relevant Decision Tree Models Using Machine-Learning to Predict Psychotherapy Success in Late Life Depression

Image dermoscopy skin lesion classification using deep learning method: systematic literature review

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.

Raman spectroscopy and one-dimensional convolutional neural network modeling as a real-time monitoring tool for in vitro transaminase-catalyzed synthesis of a pharmaceutically relevant amine precursor.

Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a one-dimensional convolutional neural network was developed to translate raw data augmented Raman spectra directly into substrate concentrations, with which the conversion from ketone to amine by ω-transaminase could be determined over time. The model showed very good predictive capabilities, with R2 values higher than 0.99 for the spectra included in the modeling and 0.964 for an independent dataset. However, the model could not extrapolate outside the concentrations specified by the model. The presented work shows the potential of Raman spectroscopy as a real-time monitoring tool for biocatalytic reactions.

AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction

Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data sparsity problem. Especially in industrial recommendation systems, the widely applied negative sample down-sampling technique due to resource limitation worsens the problem, resulting in a decline in performance. In this paper, we propose Auxiliary Match Tasks for enhancing Click-Through Rate (AT4CTR) prediction accuracy by alleviating the data sparsity problem. Specifically, we design two match tasks inspired by collaborative filtering to enhance the relevance modeling between user and item. As the "click" action is a strong signal which indicates the user's preference towards the item directly, we make the first match task aim at pulling closer the representation between the user and the item regarding the positive samples. Since the user's past click behaviors can also be treated as the user him/herself, we apply the next item prediction as the second match task. For both the match tasks, we choose the InfoNCE as their loss function. The two match tasks can provide meaningful training signals to speed up the model's convergence and alleviate the data sparsity. We conduct extensive experiments on one public dataset and one large-scale industrial recommendation dataset. The result demonstrates the effectiveness of the proposed auxiliary match tasks. AT4CTR has been deployed in the real industrial advertising system and has gained remarkable revenue.

Abstract 1514: A novel workflow to assess the T-cell and patient-derived organoid interaction

Abstract Immunotherapy is increasingly popular as a type of cancer treatment. These therapies include the use of Chimeric Antigen Receptor-engineered T-cells (CAR T-cells), tumor-infiltrating lymphocytes (TIL), and other genetically modified T-cells to specifically target the cancer cells. Although much success has been achieved with immunotherapy for treatment of blood cancers, its efficacy remains limited in solid tumors. One of the reasons for the low success rate is attributed to the solid tumor microenvironment (TME) where suppressive cytokines limit the tumor killing ability of T-cells. Thus, understanding the role the TME plays in T-cell responses is essential for the development of effective cancer therapies. The benefits of using 3-dimensional (3D) PDOs lie in the physical and chemical cues present within the TME that cannot be mimicked in traditional 2D monolayer cultures. Studies show that PDOs show similar responses to drugs as original tumors, suggesting the value of using PDOs to improve therapeutic outcomes. Thus, PDOs can provide more relevant physiological and pathological cancer models that recapitulate the basic features of primary tumors and is more suited for assessing the effectiveness of T-cell killing than 2D cell models. Despite the benefits associated with the use of PDOs, there are significant barriers to widespread adoption of PDOs in drug discovery. Organoid production is a costly and highly labor-intensive process. Moreover, organoid culture is a skilled manual process, and thus there can be significant variability between operators. To address the challenges associated with the use of PDOs in large scale applications, a semi-automated bioprocess has been developed for the large-scale expansion of assay ready organoids. Here, we developed a method to assess the effectiveness of T-cell invasion in solid tumors using patient derive organoids (PDOs). Using bioreactor expanded patient-derived colorectal cancer organoids (CRCs), activated PBMCs (human peripheral blood mononuclear cells) stained with CellTracker were added to CRCs (stained with MitoTracker) in a 96well microtiter plate and monitored on every 4 hours for 3 days using high content imager. To quantify T-cell invasion, we developed an image analysis method to measure the distance of each T-cell to the nearest organoid (interaction distance). We find that stimulated T-cell resulted in smaller interaction distance than non-stimulated T-cells. The results demonstrate the utility of the bioreactor-expanded organoids in large scale T-cell based screens. Citation Format: Zhisong Tong, Angeline Lim, Oksana Sirenko. A novel workflow to assess the T-cell and patient-derived organoid interaction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1514.

Abstract 1813: Discovery, biomarker validation, and pre-clinical profiling of a novel PKMYT1 inhibitor

Abstract Protein kinase membrane-associated tyrosine/threonine 1 (PKMYT1) is a negative regulator of CDK1 activity and has been identified as a putative synthetic lethal target in cancers which upregulate cyclin E (CCNE1). We used high-throughput virtual screening to identify inhibitors of PKMYT1 with high lipophilic ligand efficiency (LLE), which were rapidly optimized to potent leads with exceptional selectivity over a key off target, WEE1. Machine learning-driven optimization rapidly reduced mouse hepatocyte clearance to acceptable levels, delivering a compound with best-in-class potency, selectivity, and pharmacokinetic profiles in relevant animal models. Tool compounds from this series were used to validate a differentiated biomarker for sensitivity to PKMYT1 inhibition in cellular models, which could significantly expand the potential patient population. In vivo proof-of-concept studies are ongoing in a variety of cell line- and patient-derived xenograft models which represent the novel biomarker previously identified, to further validate our therapeutic strategy. Citation Format: Alfie Brennan, Jan Lanz, Daniel S. Miller, Oliver Vipond, Noah Harrison, James Aaronson, Anna Hercot. Discovery, biomarker validation, and pre-clinical profiling of a novel PKMYT1 inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1813.

Abstract 2839: The mouse models of human cancer database (MMHCdb)

Abstract The laboratory mouse plays a pivotal role as an experimental system for investigations into the genetic and genomic basis of cancer susceptibility, initiation, and progression and for preclinical research on novel treatment strategies for cancer. Advances in mouse models include humanized mice, transplant compliant mouse strains, and panels of genetically diverse mice (e.g., Diversity Outbred and Collaborative Cross). The distributed and heterogenous nature of information about these model systems presents a significant challenge to finding and aggregating information about mouse models and to identifying the most relevant models for a particular research question. The Mouse Models of Human Cancer database (http://tumor.informatics.jax.org) is an expertly curated FAIR-compliant knowledgebase that applies community annotation standards and nomenclature standards for genes, alleles, and strains to support accurate and comprehensive search results for mouse models of human cancer. The data in MMHCdb are curated from peer-reviewed scientific publications and direct data submissions from individual investigators and large-scale programs. A primary focus of the resource is to reflect the impact that genetic background has on the cancer-related characteristics of mouse models. The database includes information on spontaneous and genetically engineered cancer models as well as Patient Derived Xenografts and diversity panels. The database supports faceted searching for models by multiple criteria including, tumor type, genetic background of the mouse model, somatic mutations, and publication. Model descriptions include summaries of observed frequencies of tumors observed in the model and histopathology images. Information in MMHCdb is linked to cancer model data from other bioinformatics resources including the Patient Derived Cancer Models database (PDCM), PathBase, the Gene Expression Omnibus (GEO), and ArrayExpress. MMHCdb is supported by NCI R01 CA089713 Citation Format: Dale A. Begley, Debra M. Krupke, Emily Jocoy, Steven B. Neuhauser, Carol J. Bult. The mouse models of human cancer database (MMHCdb) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2839.

Abstract 4259: A novel human model of lung squamous cell carcinoma to deconvolve the cell-intrinsic effects of key dysregulated pathways

Abstract Tractable, patient relevant models are needed to investigate the essential components of normal cell transformation. We developed a unique in vitro model of early lung squamous cell carcinoma (LUSC) genetic alterations and driver pathway dysregulation using genetically modified primary human bronchial basal cells (hBCs) from three independent donors. The co-operation of ubiquitous and early LUSC alterations (TP53 and CDKN2A loss) and commonly deregulated pathways including squamous differentiation (SOX2), PI3K signaling (PTEN) and the oxidative stress response (KEAP1) was investigated using a combinatorial approach, culminating in eight unique hBC mutants harboring cumulative pathway alterations in a TP53-/-/CDKN2A-/- background. Phenotypes indicative of mutant hBC transformation were investigated using proliferation assays, organotypic cultures and anchorage independent growth assays. Organotypic cultures revealed LUSC pathway alterations to drive dramatic changes in airway histology, ranging from a normal epithelium to severe dysplasia. Furthermore, these analyses confirmed the role of SOX2-overexpression in initiating transformation by inducing squamous cell commitment and anchorage independent growth. We also identified a co-operation of SOX2-overexpression with the oxidative stress response and PI3K pathways to drive more advanced lesions in-vivo and in-vitro. Importantly, we were able to map changes in the expression of immunomodulatory factors with the pathway dysregulation. We demonstrated that this strategy constitute an alternative and patient-relevant system to model LUSC and identify genotype-phenotype correlations of clinical relevance. Citation Format: Julia Ogden, Robert Sellers, Anthony Oojageer, Sudhakar Sahoo, Anshuman Chaturvedi, Carlos Lopez-Garcia. A novel human model of lung squamous cell carcinoma to deconvolve the cell-intrinsic effects of key dysregulated pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4259.

Abstract 6897: Defining critical features for successful preclinical use of glioblastoma patient-derived xenografts (PDX) models for therapeutic assessment and clinical trial development

Abstract Mayo Clinic has developed 111 GBM PDXs from newly diagnosed (72 PDX) and recurrent (39 PDX) patients. This large collection of PDXs provides a broad representation of genetic heterogeneity seen in patients. GBM PDXs that perform well in both in vitro and in vivo settings can be extensively utilized in the preclinical setting for downstream analysis. Based on 20 years of experience, we have defined the attributes that best enable preclinical use of our GBM PDX models. Among them are short-term in vitro culturing performance, large scale genomic evaluation data, benchmarked responses to standards of care, and median time to moribund for untreated orthotopic tumors. PDXs with prolonged survival timelines delay study readouts and, in some situations, confound efficacy results due to age-related animal decline. Setting median survival thresholds as part of the upfront experimental design allows for more timely study readouts that are unaffected by these confounding variables. Our preferred cutoff for the Mayo GBM PDX collection is 70 days median survival (n=64). Using this 64 PDX line panel, regimens expected to double survival would produce results within 4-5 months. This timeframe is well within the range of robust health for athymic nude mice. This 64-line collection features both newly diagnosed (nGBM; n=42) and recurrent (rGBM; n=22) tumors. MGMT promoter methylation status was determined by qMS-PCR (25 methylated, 37 unmethylated, 2 indeterminate). Available WES (n=62) and RNAseq (n=42) data, along with our tissue microarray (n=42), are critical resources for biomarker-based evaluations aimed at defining patient populations that are most likely to benefit from a given therapeutic. From these data, the molecular subtypes available include 26 classical, 14 proneural, and 3 mesenchymal. Twenty-seven of these 64 PDX models have also been successfully cultured from fresh as well as cryopreserved cell stocks to further expand their experimental value. Correlative clinical data including diagnosis and treatments are also available (n=61). Current standards of care for GBM include radiation, temozolomide, and bevacizumab. Defining sensitivity to these therapies is an important first step for evaluating the integration of novel therapeutics with these standards. Since the creation of our GBM PDX collection, efficacy of these therapies has been evaluated as follows: RT 26 PDXs, TMZ 20 PDXs, and Avastin 23 PDXs. The Mayo Clinic GBM PDX collection provides an important platform for preclinical evaluation of novel therapeutics. Through careful selection of PDX lines with favorable growth characteristics and known genetic features, researchers can select the most relevant models for their study. This approach will enable more meaningful preclinical work in the hopes of providing more tangible clinical benefits for GBM patients. Citation Format: Danielle M. Burgenske, Ann C. Mladek, Katrina K. Bakken, Zeng Hu, Brett L. Carlson, Paul A. Decker, Jeanette E. Eckel-Passow, Jann N. Sarkaria. Defining critical features for successful preclinical use of glioblastoma patient-derived xenografts (PDX) models for therapeutic assessment and clinical trial development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6897.

Nanoparticle mediated photodynamic therapy as a method to ablate oral cavity squamous cell carcinoma in preclinical models.

Photodynamic therapy (PDT) is a tissue ablation technique able to selectively target tumour cells by activating the cytotoxicity of photosensitiser dyes with light. PDT is nonsurgical and tissue sparing, two advantages for treatments in anatomically complex disease sites such as the oral cavity. We have previously developed PORPHYSOME (PS) nanoparticles assembled from chlorin photosensitiser-containing building blocks (~94,000 photosensitisers per particle) and capable of potent PDT. In this study, we demonstrate the selective uptake and curative tumour ablation of PS-enabled PDT in three preclinical models of oral cavity squamous cell carcinoma: biologically relevant subcutaneous Cal-33 (cell-line) and MOC22 (syngeneic) mouse models, and an anatomically relevant orthotopic VX-2 rabbit model. Tumours selectively uptake PS (10 mg/kg, IV) with 6~40-fold greater concentration versus muscle 24 hours post-injection. Single PS-PDT treatment (100 J/cm2, 100 mW/cm2) of Cal-33 tumours yielded significant apoptosis in 65.7% of tumour cells. Survival studies following PS-PDT treatments demonstrated 90% (36/40) overall response rate across all three tumour models. Complete tumour response was achieved in 65% of Cal-33 and 91% of MOC22 tumour mouse models 14 days after PS-PDT, and partial responses obtained in 25% and 9% of Cal-33 and MOC22 tumours, respectively. In buccal VX-2 rabbit tumours, combined surface and interstitial PS-PDT (200 J total) yielded complete responses in only 60% of rabbits 6 weeks after a single treatment, whereas three repeated weekly treatments with PS-PDT (200 J/week) achieved complete ablation in 100% of tumours. PS-PDT treatments were well tolerated by animals with no treatment-associated toxicities and excellent cosmetic outcomes.

Singlet Fission in a New Series of Systematically Designed Through-space Coupled Tetracene Oligomers.

Singlet fission (SF) holds great promise for current photovoltaic technologies, where tetracenes, with their relatively high triplet energies, play a major role for application in silicon-based solar cells. However, the SF efficiencies in tetracene dimers are low due to the unfavorable energetics of their singlet and triplet energy levels. In the solid state, tetracene exhibits high yields of triplet formation through SF, raising great interest about the underlying mechanisms. To address this discrepancy, we designed and prepared a novel molecular system based on a hexaphenylbenzene core decorated with 2 to 6 tetracene chromophores. The spatial arrangement of tetracene units, induced by steric hindrance in the central part, dictates through-space coupling, making it a relevant model for solid-state chromophore organization. We then revealed a remarkable increase in SF quantum yield with the number of tetracenes, reaching quantitative (196 %) triplet pair formation in hexamer. We observed a short-lived correlated triplet pair and limited magnetic effects, indicating ineffective triplet dissociation in these through-space coupled systems. These findings emphasize the crucial role of the number of chromophores involved and the interchromophore arrangement for the SF efficiency. The insights gained from this study will aid designing more efficient and technology-compatible SF systems for applications in photovoltaics.

Neutrophil ALDH2 is a new therapeutic target for the effective treatment of sepsis-induced ARDS.

Acetaldehyde dehydrogenase 2 (ALDH2) mutations are commonly found in a subgroup of the Asian population. However, the role of ALDH2 in septic acute respiratory distress syndrome (ARDS) remains unknown. Here, we showed that human subjects carrying the ALDH2rs671 mutation were highly susceptible to developing septic ARDS. Intriguingly, ALDH2rs671-ARDS patients showed higher levels of blood cell-free DNA(cfDNA) and myeloperoxidase (MPO)-DNA than ALDH2WT-ARDS patients. To investigate the mechanisms underlying ALDH2 deficiency in the development of septic ARDS, we utilized Aldh2 gene knockout mice and Aldh2rs671 gene knock-in mice. In clinically relevant mouse sepsis models, Aldh2-/- mice and Aldh2rs671 mice exhibited pulmonary and circulating NETosis, a specific process that releases neutrophil extracellular traps (NETs) from neutrophils. Furthermore, we discovered that NETosis strongly promoted endothelial destruction, accelerated vascular leakage, and exacerbated septic ARDS. At the molecular level, ALDH2 increased K48-linked polyubiquitination and degradation of peptidylarginine deiminase 4 (PAD4) to inhibit NETosis, which was achieved by promoting PAD4 binding to the E3 ubiquitin ligase CHIP. Pharmacological administration of the ALDH2-specific activator Alda-1 substantially alleviated septic ARDS by inhibiting NETosis. Together, our data reveal a novel ALDH2-based protective mechanism against septic ARDS, and the activation of ALDH2 may be an effective treatment strategy for sepsis.