Tool For Discovery Research Articles

Proximity labeling reveals new functional relationships between meiotic recombination proteins in S. cerevisiae.

Several protein ensembles facilitate crossover recombination and the associated assembly of synaptonemal complex (SC) during meiosis. In yeast, meiosis-specific factors including the DNA helicase Mer3, the "ZZS" complex consisting of Zip4, Zip2, and Spo16, the RING-domain protein Zip3, and the MutSγ heterodimer collaborate with crossover-promoting activity of the SC component, Zip1, to generate crossover-designated recombination intermediates. These ensembles also promote SC formation - the organized assembly of Zip1 with other structural proteins between aligned chromosome axes. We used proximity labeling to investigate spatial relationships between meiotic recombination and SC proteins in S. cerevisiae. We find that recombination initiation and SC factors are dispensable for proximity labeling of Zip3 by ZZS components, but proteins associated with early steps in recombination are required for Zip3 proximity labeling by MutSγ, suggesting that MutSγ joins Zip3 only after a recombination intermediate has been generated. We also find that zip1 separation-of-function mutants that are crossover deficient but still assemble SC fail to generate protein ensembles where Zip3 can engage ZZS and/or MutSγ. The SC structural protein Ecm11 is proximity labeled by ZZS proteins in a Zip4-dependent and Zip1-independent manner, but labeling of Ecm11 by Zip3 and MutSγ requires, at least in part, Zip1. Finally, mass spectrometry analysis of biotinylated proteins in eleven proximity labeling strains uncovered shared proximity targets of SC and crossover-associated proteins, some of which have not previously been implicated in meiotic recombination or SC formation, highlighting the potential of proximity labeling as a discovery tool.

Promising tools for future drug discovery and development in antiarrhythmic therapy.

Arrhythmia refers to irregularities in the rate and rhythm of the heart, with symptoms spanning from mild palpitations to life-threatening arrhythmias and sudden cardiac death (SCD). The complex molecular nature of arrhythmias complicates the selection of appropriate treatment. Current therapies involve the use of antiarrhythmic drugs (class I-IV) with limited efficacy and dangerous side effects and implantable pacemakers and cardioverter-defibrillators with hardware-related complications and inappropriate shocks. The number of novel antiarrhythmic drug in the development pipeline has decreased substantially during the last decade and underscores uncertainties regarding future developments in this field. Consequently, arrhythmia treatment poses significant challenges, prompting the need for alternative approaches. Remarkably, innovative drug discovery and development technologies show promise in helping advance antiarrhythmic therapies. Here, we review unique characteristics and the transformative potential of emerging technologies that offer unprecedented opportunities for transitioning from traditional antiarrhythmics to next-generation therapies. We assess stem cell technology, emphasizing the utility of innovative cell profiling using multi-omics, high-throughput screening, and advanced computational modeling in developing treatments tailored precisely to individual genetic and physiological profiles. We offer insights into gene therapy, peptide and peptibody approaches for drug delivery. We finally discuss potential strengths and weaknesses of such techniques in reducing adverse effects and enhancing overall treatment outcomes, leading to more effective, specific, and safer therapies. Altogether, this comprehensive overview introduces innovative avenues for personalized rhythm therapy, with particular emphasis on drug discovery, aiming to advance the arrhythmia treatment landscape and the prevention of SCD. Significance Statement Arrhythmias and sudden cardiac death account for 15-20% of deaths worldwide. However, current antiarrhythmic therapies are ineffective and with dangerous side effects. Here, we review the field of arrhythmia treatment underscoring the slow progress in advancing the cardiac rhythm therapy pipeline and the uncertainties regarding evolution of this field. We provide information on how emerging technological and experimental tools can help accelerate progress and address the limitations of antiarrhythmic drug discovery.

Computational exploration of novel ketoprofen derivatives: Molecular dynamics simulations and MM-PBSA calculations for COX-2 inhibition as promising anti-inflammatory drugs

Computer-aided drug design is widely employed to identify novel compounds for therapeutic applications. Ketoprofen (KTP), a commonly used and marketed nonsteroidal anti-inflammatory drug (NSAID), is effective in treating pain, fever, inflammation, and some cancers. In this research, we explored the behavior of six analogues designed by structurally modifying the KTP molecule. Specifically, KTP-A and KTP-B contain a –CN group at the ortho position, KTP-C and KTP-D have a –CN group at the meta position, and KTP-E and KTP-F feature a –CF3 group at the meta position. To assess these analogues, we conducted molecular dynamics simulations (MD) to study their inhibitory effects on human cyclooxygenase 2 (COX-2), providing detailed insights into the structure and dynamics of the protein both with and without ligands. MD simulation, enhanced by technological advances, has proven to be a powerful tool for new drug discovery. We further quantified the binding affinity of these drug molecules toward COX-2 using molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The dynamic properties were evaluated through analyses of root mean square deviations (RMSD), root mean square fluctuations (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), covariance matrix, principal component analysis (PCA), and Gibbs free energy landscapes (FEL). Ultimately, this study confirms that the six KTP derivatives are promising candidates for the treatment of inflammation, with KTP-B standing out as particularly effective.

From Proteomics to Diagnosis: Biomarker Discovery in Tuberculosis Research.

Tuberculosis (TB) is a leading cause of death from a single infectious disease worldwide. Early and accurate diagnosis is advantageous for timely detection and prompt treatment, thereby reducing the risk of disease transmission, which is essential for effective TB control. Biomarkers provide a valuable resource for TB diagnosis. Proteomic technologies have emerged as a powerful tool in biomarker discovery. In this perspective, we explore how proteomic technologies contribute to the discovery of TB diagnostic biomarkers. We also address the challenges and discuss prospective methods to augment the performance of biomarkers in diagnosing TB.

GGAS2SN: Gated Graph and SmilesToSeq Network for Solubility Prediction.

Aqueous solubility is a critical physicochemical property of drug discovery. Solubility is a key issue in pharmaceutical development because it can limit a drug's absorption capacity. Accurate solubility prediction is crucial for pharmacological, environmental, and drug development studies. This research introduces a novel method for solubility prediction by combining gated graph neural networks (GGNNs) and graph attention neural networks (GATs) with Smiles2Seq encoding. Our methodology involves converting chemical compounds into graph structures with nodes representing atoms and edges indicating chemical bonds. These graphs are then processed by using a specialized graph neural network (GNN) architecture. Incorporating attention mechanisms into GNN allows for capturing subtle structural dependencies, fostering improved solubility predictions. Furthermore, we utilized the Smiles2Seq encoding technique to bridge the semantic gap between molecular structures and their textual representations. Smiles2Seq seamlessly converts chemical notations into numeric sequences, facilitating the efficient transfer of information into our model. We demonstrate the efficacy of our approach through comprehensive experiments on benchmark solubility data sets, showcasing superior predictive performance compared to traditional methods. Our model outperforms existing solubility prediction models and provides interpretable insights into the molecular features driving solubility behavior. This research signifies an important advancement in solubility prediction, offering potent tools for drug discovery, formulation development, and environmental assessments. The fusion of GGNN and Smiles2Seq encoding establishes a robust framework for accurately forecasting solubility across various chemical compounds, fostering innovation in various domains reliant on solubility data.

Development of a Versatile High-through-put Oligonucleotide LC-MS Method to Accelerate Drug Discovery.

Liquid chromatography-mass spectrometry (LC-MS) is an effective tool for high-throughput quantification of oligonucleotides that is crucial for understanding their biological roles and developing diagnostic tests. This paper presents a high-throughput LC-MS/MS method that may be versatilely applied for a wide range of oligonucleotides, making it a valuable tool for rapid screening and discovery. The method is demonstrated using an in-house synthesized MALAT-1 Antisense oligonucleotide (ASO) as a test case. Biological samples were purified using a reversed liquid-liquid extraction process automated by a liquid handling workstation and analyzed with ion-pairing LC-MS/MS. The assay was evaluated for sensitivity (LLOQ = 2nM), specificity, precision, accuracy, recovery, matrix effect, and stability in rat cerebrospinal fluid (CSF) and plasma. Besides some existing considerations such as column selection, ion-pairing reagent, and sample purification, our work focused on the following four subtopics: 1) selecting the appropriate Multiple Reaction Monitoring (MRM) transition to maximize sensitivity for trace-level ASO in biological samples; 2) utilizing a generic risk-free internal standard (tenofovir) to avoid crosstalk interference from the oligo internal standard commonly utilized in the LC-MS assay; 3) automating the sample preparation process to increase precision and throughput; and 4) comparing liquid-liquid extraction (LLE) and solid-phase extraction (SPE) as sample purification methods in oligo method development. The study quantified the concentration of MALAT-1 ASO in rat CSF and plasma after intrathecal injection and used the difference between the two matrices to evaluate the injection technique. The results provide a solid foundation for further internal oligonucleotide discovery and development.

Multicellular ovarian cancer spheroids: novel 3D model to mimic tumour complexity

In vitro, spheroid models have become well established in cancer research because they can better mimic certain characteristics of in vivo tumours. However, interaction with the tumour microenvironment, such as cancer-associated fibroblasts, plays a key role in tumour progression. We initially focused on the interaction of tumour cells with fibroblasts. To model this interaction, we developed a spheroid model of ovarian cancer and fibroblasts. To this end, ovarian cancer cell lines and ex vivo primary cells were simultaneously and sequentially seeded with fibroblasts in a scaffold-free system at different ratios and subsequently characterized with respect to changes in morphology, proliferation, and viability. We demonstrated that co-cultures are able to form by far more compact spheroids, especially in cells that form aggregates in mono-culture. In addition, the co-cultures were able to increase proliferation and sensitivity to cisplatin. Simultaneous seeding led fibroblasts invade the core in both cell lines and primary cells. These results show differences in formation, firmness, and size between co-culture and mono-culture. Our model is designed to better represent and characterize the mutual influencing factors of fibroblasts and tumour cells. Fibroblast-supplemented multicellular spheroids are a valuable tool for tumour microenvironment interaction and new drug discovery.

Innovative On-Resin and in Solution Peptidomimetics Synthesis via Metal-Free Photocatalytic Approach.

Nowadays, peptidomimetics are widely studied, being useful tools in drug discovery and medicinal chemistry. The coupling between a carboxylic acid with an amine to form a peptide bond is the most common reaction to obtain peptides/peptidomimetics. In this work, we have investigated an innovative metal-free photoredox-catalyzed carbamoylation to form peptidomimetics thanks to the reaction between dihydropyridines functionalized with amino acids (or peptide sequences) and differently functionalized imines. As the organic photocatalyst, we used 4CzIPN, a donor-acceptor cyanoarene vastly used in photoredox catalysis. By easily modulating the amino acid (or peptide sequence), which is directly attached to the dihydropyridine, we proposed this key-reaction as new valuable method to obtain peptidomimetics, in situ building the not-natural portion of the sequence. Moreover, we successfully employed this methodology in solid phase peptide synthesis, both inserting the new photoredox-generated amino acid at the end or in the middle of the sequence. Peptides with different lengths and secondary structures were prepared, proving the success of this approach, even in sterically hindered environment. Herein, to the best of our knowledge, we describe the first photocatalytic protocol which allows the building of the peptide backbone, with the possibility of simultaneously inserting a non-coded amino acid in the sequence.

VirID: Beyond Virus Discovery-An Integrated Platform for Comprehensive RNA Virus Characterization.

RNA viruses exhibit vast phylogenetic diversity and can significantly impact public health and agriculture. However, current bioinformatics tools for viral discovery from metagenomic data frequently generate false positive virus results, overestimate viral diversity, and misclassify virus sequences. Additionally, current tools often fail to determine virus-host associations, which hampers investigation of the potential threat posed by a newly detected virus. To address these issues we developed VirID, a software tool specifically designed for the discovery and characterization of RNA viruses from metagenomic data. The basis of VirID is a comprehensive RNA-dependent RNA polymerase database to enhance a workflow that includes RNA virus discovery, phylogenetic analysis, and phylogeny-based virus characterization. Benchmark tests on a simulated data set demonstrated that VirID had high accuracy in profiling viruses and estimating viral richness. In evaluations with real-world samples, VirID was able to identify RNA viruses of all types, but also provided accurate estimations of viral genetic diversity and virus classification, as well as comprehensive insights into virus associations with humans, animals, and plants. VirID therefore offers a robust tool for virus discovery and serves as a valuable resource in basic virological studies, pathogen surveillance, and early warning systems for infectious disease outbreaks.

A web portal for exploring kinase-substrate interactions

Interactions between protein kinases and their substrates are critical for the modulation of complex signaling pathways. Currently, there is a large amount of information available about kinases and their substrates in disparate public databases. However, these data are difficult to interpret in the context of cellular systems, which can be facilitated by examining interactions among multiple proteins at once, such as the network of interactions that constitute a signaling pathway. We present KiNet, a user-friendly web portal that integrates and shares information about kinase-substrate interactions from multiple databases of post-translational modifications. KiNet enables the visual exploration of these interactions in systems contexts, such as pathways, domain families, and custom protein set inputs, in an interactive fashion. We expect KiNet to be useful as a knowledge discovery tool for kinase-substrate interactions, and the aggregated KiNet dataset to be useful for protein kinase studies and systems-level analyses. The portal is available at https://kinet.kinametrix.com/.

Topological coindices and QSPR analysis for some potential drugs used in lung cancer treatment via CoM and CoNM-polynomials

Topological indices are used to convert a chemical structure into a real number, usually to study the physicochemical and biological properties of molecules. The groundwork is prepared for the interpretation of the obtained data by processing with Quantitative Structure Property/Activity Relationship (QSPR/QSAR). In this study, the drugs lorlatinib, gefitinib, sotorasib, pralsetinib, crizotinib, adagrasib, alectinib, brigatinib, dacomitinib and entrectinib, which are potential to be used in the treatment of lung cancer, are discussed. Topological coindices are calculated with the help of CoM and CoNM polynomials obtained with the graph structures of these drugs. The relationship between topological coindices and physicochemical properties such as evaporation enthalpy, flash point, molar refraction, polarisation, surface tension, molar volume are investigated by QSPR analysis. At this stage, linear, logarithmic and quadratic regression methods have been used. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the used some drugs in the treatment of lung cancer. In addition, using comparative analysis, the actual values and the values calculated with the help of topological indices have been examined in terms of predictive ability. The findings of this search demonstrate topological indices’ potential as tools for cancer drug discovery and design.

Transition-Metal-Mediated Radiohalogenation using Aryl Boron Reagents

AbstractBiomolecules labeled with radioisotopes of iodine and fluorine play a major role in science and medicine, with a range of applications including molecular imaging of disease, as radiopharmaceuticals for therapy and as tools for drug discovery. Due to the relative stability of Csp2–halogen bonds, radioiodine and radiofluorine labels are commonly incorporated into tracers via arene substitution. The wide availability of aryl boron reagents has led to their use for the development of radioiodination and radiofluorination methods, particularly using transition-metal complexes for accelerated reactions. Here, we review the range of transition-metal-mediated methods that have been developed for radiohalogenation of aryl boron compounds. Applications of these transformations for the preparation of molecular imaging agents and the use of new technologies to advance automated synthesis or high-throughput discovery are also described.1 Introduction2 Radioiodination Using Aryl Boron Reagents2.1 Applications of Copper-Mediated Radioiodination Reactions3 Radiofluorination Using Aryl Boron Reagents3.1 Radiofluorination Using Palladium, Nickel and Silver Complexes3.2 Radiofluorination Using Copper Complexes3.3 Applications of Copper-Mediated Radiofluorination Reactions3.4 New Technology for Copper-Mediated Radiofluorination Reactions4 Conclusions

Modeling doxorubicin-induced-cardiotoxicity through breast cancer patient specific iPSC-derived heart organoid

Heart organoid (HO) technology has successfully overcome the limitations of two-dimensional (2D) disease modeling and drug testing, thereby emerging as a valuable tool in drug discovery for assessing toxicity and efficacy. However, its ability to distinguish drug responses among individuals remain unclear, which is crucial for developing predictive models. We addressed this gap by comparing human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) with human induced pluripotent stem cell-derived heart organoids (hiPSC-HOs) in the context of doxorubicin-induced cardiotoxicity (DIC). For this study, we utilized hiPSCs generated from breast cancer patients who had previously been treated with doxorubicin. By comparing groups with and without DIC, we examined various parameters, including cell viability, mRNA expression, protein expression and electrophysiological variations. The results of our analysis revealed significant differences between these groups, providing insights into hiPSC-HOs as a potential platform for testing differences in drug responses among patients.

Findings from the individualized management of a patient with Acyl-CoA Oxidase-1 (ACOX1) deficiency: A bedside-to-bench-to-bedside strategy

Acyl-CoA Oxidase-1 (ACOX1) deficiency (MIM 264470) is an autosomal recessive disease characterized by impairments in the desaturation of acyl-CoAs to 2-trans-enoyl-CoAs, which is the first step in the catalysis of the β-oxidative breakdown of very long chain fatty acids (VLCFA) occuring in peroxisomes. The deleterious accumulation of VLCFA in several organs, including the brain, is a key biochemical feature of this disease which has devastating neurological consequences. ACOX1 deficiency is ultra-rare; as such, few studies have been conducted to determine the leading causes of symptoms or uncover new therapeutics. When confronted with one such case, we decided to bring drug discovery tools to the patient's bedside in an attempt to identify a cure. A skin biopsy was performed on a young patient with ACOX1 deficiency, following which screening technologies and mass spectrometry analysis techniques were applied to design a cellular assay that enabled the direct measurement of the effect of small molecules on the patient's primary fibroblasts. This approach is particularly well adapted to inherited metabolic disorders such as ACOX1 deficiency. Through the evaluation of a proprietary library of repurposable drugs, we found that the anthelmintic drug niclosamide led to a significant reduction in VLCFA in vitro. This drug was subsequently administered to the patient for more than six years. This study outlines the screening and drug selection processes. Additionally, we present our comprehensive clinical and biochemical findings that aided in understanding the patient's natural history and analysis of the progression of the patient's symptoms throughout the treatment period. Although the patient's overall lifespan was extended compared to the average age at death in severe early onset cases of ACOX1 deficiency, we did not observe any definitive evidence of clinical or biochemical improvement during niclosamide treatment. Nonetheless, our study shows a good safety profile of long-term niclosamide administration in a child with a rare neurodegenerative disease, and illustrates the potential of individualized therapeutic strategies in the management of inherited metabolic disorders, which could benefit both patients and the broader scientific and medical communities.

WIMOAD: Weighted Integration of Multi-Omics data for Alzheimer's Disease (AD) Diagnosis.

As the most common subtype of dementia, Alzheimer's disease (AD) is characterized by a progressive decline in cognitive functions, especially in memory, thinking, and reasoning ability. Early diagnosis and interventions enable the implementation of measures to reduce or slow further regression of the disease, preventing individuals from severe brain function decline. The current framework of AD diagnosis depends on A/T/(N) biomarkers detection from cerebrospinal fluid or brain imaging data, which is invasive and expensive during the data acquisition process. Moreover, the pathophysiological changes of AD accumulate in amino acids, metabolism, neuroinflammation, etc., resulting in heterogeneity in newly registered patients. Recently, next generation sequencing (NGS) technologies have found to be a non-invasive, efficient and less-costly alternative on AD screening. However, most of existing studies rely on single omics only. To address these concerns, we introduce WIMOAD, a weighted integration of multi-omics data for AD diagnosis. WIMOAD synergistically leverages specialized classifiers for patients' paired gene expression and methylation data for multi-stage classification. The resulting scores were then stacked with MLP-based meta-models for performance improvement. The prediction results of two distinct meta-models were integrated with optimized weights for the final decision-making of the model, providing higher performance than using single omics only. Remarkably, WIMOAD achieves significantly higher performance than using single omics alone in the classification tasks. The model's overall performance also outperformed most existing approaches, highlighting its ability to effectively discern intricate patterns in multi-omics data and their correlations with clinical diagnosis results. In addition, WIMOAD also stands out as a biologically interpretable model by leveraging the SHapley Additive exPlanations (SHAP) to elucidate the contributions of each gene from each omics to the model output. We believe WIMOAD is a very promising tool for accurate AD diagnosis and effective biomarker discovery across different progression stages, which eventually will have consequential impacts on early treatment intervention and personalized therapy design on AD.

A deep learning framework combining molecular image and protein structural representations identifies candidate drugs for pain

Artificial intelligence (AI) and deep learning technologies hold promise for identifying effective drugs for human diseases, including pain. Here, we present an interpretable deep-learning-based ligand image- and receptor's three-dimensional (3D)-structure-aware framework to predict compound-protein interactions (LISA-CPI). LISA-CPI integrates an unsupervised deep-learning-based molecular image representation (ImageMol) of ligands and an advanced AlphaFold2-based algorithm (Evoformer). We demonstrated that LISA-CPI achieved ∼20% improvement in the average mean absolute error (MAE) compared to state-of-the-art models on experimental CPIs connecting 104,969 ligands and 33 G-protein-coupled receptors (GPCRs). Using LISA-CPI, we prioritized potential repurposable drugs (e.g., methylergometrine) and identified candidate gut-microbiota-derived metabolites (e.g., citicoline) for potential treatment of pain via specifically targeting human GPCRs. In summary, we presented that the integration of molecular image and protein 3D structural representations using a deep learning framework offers a powerful computational drug discovery tool for treating pain and other complex diseases if broadly applied.

Drug Discovery and Screening Tool Development for Tauopathies by Focusing on Pathogenic Tau Repeat 3 Oligomers.

Comprehending early amyloidogenesis is essential for the development of effective therapeutic strategies. In tauopathies like Alzheimer's disease (AD), the abnormal accumulation of tau protein is initiated by pathological tau seeds. Mounting evidence implies that the microtubule binding domain, consisting of three to four repeats, plays a pivotal role in this process, yet the exact region driving the formation of pathogenic species needs to be further scrutinized. Here, we chemically synthesized individual tau repeats to identify those exhibiting pathogenic prion-like characteristics. Notably, repeat 3 (R3) displayed a remarkable propensity to polymerize, form toxic filaments, and induce cognitive impairment, even in the absence of an aggregation-promoting inducer, highlighting its physiological relevance. Additionally, oligomeric R3 was identified as a particularly pathological form, prompting the establishment of a screening platform. Through screening, tolcapone was found to possess therapeutic efficacy against pathological tau aggregates in PS19 transgenic mice. This screening platform provides a valuable avenue for identifying compounds that selectively interact with peptides implicated in the progression of tauopathies.

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding of complex diseases. To this end, researchers have developed various network-based prediction techniques targeting specific molecular associations. However, limitations imposed by reductionism and network representation learning have led existing studies to narrowly focus on high prediction efficiency within single association type, thereby glossing over the discovery of unknown types of associations. Additionally, effectively utilizing network structure to fit the interaction properties of regulatory networks and combining specific case biomarker validations remains an unresolved issue in cancer biomarker prediction methods. To overcome these limitations, we propose a multi-view learning framework, CeRVE, based on directed graph neural networks (DGNN) for predicting unknown type cancer biomarkers. CeRVE effectively extracts and integrates subgraph information through multi-view feature learning. Subsequently, CeRVE utilizes DGNN to simulate the entire regulatory network, propagating node attribute features and extracting various interaction relationships between molecules. Furthermore, CeRVE constructed a comparative analysis matrix of three cancers and adjacent normal tissues through The Cancer Genome Atlas and identified multiple types of potential cancer biomarkers through differential expression analysis of mRNA, microRNA, and long noncoding RNA. Computational testing of multiple types of biomarkers for 72 cancers demonstrates that CeRVE exhibits superior performance in cancer biomarker prediction, providing a powerful tool and insightful approach for AI-assisted disease biomarker discovery.

The Notable Japanese Collections Dashboard Project: Unexpected Treasures and Opportunities for Collaboration

Abstract The Notable Japanese Collections in North America (NJC) is a digital collection discovery initiative and tool that identifies and promotes distinctive Japanese Studies collections in North America. Now entering its third year, this online database has amassed 250 print and digital collections sourced from college and research libraries and other cultural heritage institutions across the continent. Although far from complete, it already provides the most extensive list of print and digitized collections in the United States and Canada. This essay delves into the inception, collaborative journey, and long-term objectives of the NJC and culminates by spotlighting some of the fascinating holdings of its various repositories, spanning premodern to modern and contemporary periods.

Abstract C172: Diversifying translational research models to address outcomes disparities in lung cancer

Abstract Introduction: Racial disparities in lung cancer remains a persistent and challenging problem, with African Americans experiencing the highest mortality of any race or ethnic group. Emerging evidence suggests that multiple biological factors affect the development and progression of lung cancer. Three-dimensional organoid culture models have become important instruments to use for biomarker analysis and drug sensitivity screening. Despite this strong rationale and potential, there is a lack of representative preclinical models from African American patients with lung cancer. Furthermore, there are no studies evaluating the ability to create organoids successfully using samples from different races. As such, our goal was to establish a diverse biobank of organoid models and determine the rate of successful development based on race and other factors. Methods: Patients with lung cancer from a large urban medical center were recruited to participate in the study from 2021 to 2024. Specimens were collected from surgical pulmonary resection or endobronchial biopsy. Each tissue specimen was divided into 3 pieces and allocated for genomic, transcriptomic and organoid development. Once collected, specimens allocated for organoid development were processed within 8 hours. Cells were isolated from tissues by mechanical and enzymatic dissociation and plated in Matrigel drops overlayed with optimized organoid media. Patient demographics, tumor characteristics and method of specimen collection were recorded. Results: A total of 29 patients consented to the study and had evaluable specimens collected. Mean age was 67 years. Fifty-five percent of patients (16/29) were female. White patients were 55 percent of cohort (16/29), while Black patients made up 45% (13/29). Approximately 62 (18/29) percent of patients were diagnosed with adenocarcinoma, followed by squamous cell carcinoma in 28 percent (8/29) of patients. Tumor stage was 55%, 28%, 3% and 14% for stages I, II, III and IV respectively. Overall, there was a successful organoid development rate of 69 percent (20/29). There was a strong trend toward higher success rates in Black patients compared to White patients (85% vs. 56%, p = 0.10). There were no significant differences in successful organoid development based on gender (p = 0.43), histology (p = 0.18) or tumor stage (p = 0.84). Conclusions: Establishment of a racially diverse set of organoid models will be an invaluable tool for biomarker discovery and for screening novel therapeutic agents. Black patients appear to have similar to improved propensity for successful organoid development. Future efforts should be focused on creating diverse biobanks to strengthen precision oncology efforts for Black patients, collectively helping to reduce racial outcomes disparities in patients with lung cancer. Citation Format: Amanda Pilling, Hollis Hutchings, Avi Cohen, Shirish Gadgeel, Ikenna Okereke. Diversifying translational research models to address outcomes disparities in lung cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C172.