Computer Analysis Of Data Research Articles

Antibodies Targeting Human or Mouse VSIG4 Repolarize Tumor-Associated Macrophages Providing the Potential of Potent and Specific Clinical Anti-Tumor Response Induced across Multiple Cancer Types.

V-set immunoglobulin domain-containing 4 (VSIG4) is a B7 family protein with known roles as a C3 fragment complement receptor involved in pathogen clearance and a negative regulator of T cell activation by an undetermined mechanism. VSIG4 expression is specific for tumor-associated and select tissue-resident macrophages. Increased expression of VSIG4 has been associated with worse survival in multiple cancer indications. Based upon computational analysis of transcript data across thousands of tumor and normal tissue samples, we hypothesized that VSIG4 has an important role in promoting M2-like immune suppressive macrophages and that targeting VSIG4 could relieve VSIG4-mediated macrophage suppression by repolarizing tumor-associated macrophages (TAMs) to an inflammatory phenotype. We have also observed a cancer-specific pattern of VSIG4 isoform distribution, implying a change in the functional regulation in cancer. Through a series of in vitro, in vivo, and ex vivo assays we demonstrate that anti-VSIG4 antibodies repolarize M2 macrophages and induce an immune response culminating in T cell activation. Anti-VSIG4 antibodies induce pro-inflammatory cytokines in M-CSF plus IL-10-driven human monocyte-derived M2c macrophages. Across patient-derived tumor samples from multiple tumor types, anti-VSIG4 treatment resulted in the upregulation of cytokines associated with TAM repolarization and T cell activation and chemokines involved in immune cell recruitment. VSIG4 blockade is also efficacious in a syngeneic mouse model as monotherapy as it enhances efficacy in combination with anti-PD-1, and the effect is dependent on the systemic availability of CD8+ T cells. Thus, VSIG4 represents a promising new target capable of triggering an anti-cancer response via multiple key immune mechanisms.

Design, in silico Evaluation, and Determination of Antitumor Activity of Potential Inhibitors Against Protein Kinases: Application to BCR-ABL Tyrosine Kinase.

Despite significant progress made over the past two decades in the treatment of chronic myeloid leukemia (CML), there is still an unmet need for effective and safe agents to treat patients with resistance and intolerance to the drugs used in clinic. In this work, we designed 2-arylaminopyrimidine amides of isoxazole-3-carboxylic acid, assessed insilico their inhibitory potential against Bcr-Abl tyrosine kinase, and determined their antitumor activity in K562 (CML), HL-60 (acute promyelocytic leukemia), and HeLa (cervical cancer) cells. Based on the analysis of computational and experimental data, three compounds with the antitumor activity against K562 and HL-60 cells were identified. The lead compound efficiently suppressed the growth of these cells, as evidenced by the low IC50 values of 2.8±0.8μM (K562) and 3.5±0.2μM (HL-60). The obtained compounds represent promising basic structures for the design of novel, effective, and safe anticancer drugs able to inhibit the catalytic activity of Bcr-Abl kinase by blocking the ATP-binding site of the enzyme.

Understanding climate-related visual storytelling on TikTok

This cross-cultural study investigates the prevalence and impact of climate-related campaigns on TikTok, with a specific focus on climate-related visual storytelling in Indonesia, Japan, Pakistan, the Philippines, Thailand, the United Kingdom, and the United States. Computational methods are employed in the study to analyse a dataset of 7,564 videos, providing insights into prominent visual characteristics and regional variations. The findings underline the significance of cultural and political contexts in shaping climate storytelling on TikTok. Furthermore, this research explores the potential of computational visual data analysis in studying climate communication, demonstrating the integration of computer vision and topic modelling to examine visual styles and communicative functions in TikTok’s climate storytelling. The study enhances our understanding of climate communication on digital platforms and emphasizes the value of leveraging computational methods to gain meaningful cross-cultural insights into visual storytelling in the context of climate change.

Integration of spatially resolved transcriptomics into pathological research: Opportunities and challenges

The development of spatially resolved transcriptomics technologies has revolutionised research in recent years. By enabling the analysis of the state and position of cell types within a tissue section, these technologies have the potential to transform our understanding of pathological processes and translate this knowledge into improved treatments for patients. This review provides an overview of available technologies and discusses the potential challenges of integrating them into pathological research, with a particular focus on the computational analysis of such data.

Quantum Temporal Winds: Turbulence in Financial Markets

This paper leverages turbulence theory from physics to examine the similarities and differences between financial market volatility and turbulent phenomena on a statistical physics level. By drawing analogies between the dynamics of financial markets and fluid turbulence, an innovative analytical framework has been developed to enhance our understanding of the complexity inherent in financial markets. The research methodology involves a comparative analysis of several national stock market indices and simulated turbulent velocity time series, with a particular focus on key statistical properties such as probability distributions, correlation structures, and power spectral densities. Furthermore, a financial market capital flow model has been established, and corresponding solutions have been proposed. Through computational simulations and data analysis, it was discovered that financial market volatility shares some statistical characteristics with turbulence, yet there are significant differences in the shape of probability distributions and the timescales of correlations. This indicates that although financial markets exhibit patterns similar to turbulence, as a multivariate-driven complex system, their behavioral patterns do not completely correspond to natural turbulence phenomena, highlighting the limitations of directly applying turbulence theory to financial market analysis. Additionally, the study explores the use of Bézier curves to simulate market volatility and, based on these analyses, formulates trading strategies that demonstrate practical applications in risk management. This research provides fresh perspectives for the fields of financial market theory and econophysics, offering new insights into the complexity of financial markets and the prevention and management of financial risks.

Abstract PO3-24-06: Integrative genomic and CRISPR screen analysis identifies synthetic viability interactions in BRCA1/2 cancers

Abstract Background Pathogenic BRCA1 and BRCA2 (BRCA1/2) germline mutations contribute to hereditary breast cancer and are linked with increased risk of other cancers. Paradoxically, bi-allelic inactivation of BRCA1/2 (bBRCA1/2) is embryonically lethal. Although TP53 loss can at least in part mitigated the deleterious impact of Brca1 deficiency in mice, this is insufficient in human cells to compensate for decreased proliferative capacity. What other compensatory mechanisms facilitate oncogenesis remains unclear. Methods We performed an integrative analysis of cancer genomes from bBRCA1/2 tumors and CRISPR/Cas9 screens from cellular models to identify novel gene candidates that facilitate synthetic viability. First, we analyzed cancer genomes from ovarian and breast cancer derived from TCGA and identified genomic loci, which were enriched for recurrent copy number alterations (CNAs) in bBRCA1/2 tumors. We subsequently identified genes from these CNA loci that had corresponding changes in gene expression. Then, we used high-throughput genome-wide CRISPR/Cas9 knockout screens in BRCA1/2−/− cell lines (BRCA1−/- in RPE1 cells, BRCA2−/− in DLD1 cells) to identify which of these genes increased cell proliferation in vitro. Lastly, we experimentally validated these synthetic viable interactions using an orthogonal MCF12a system to evaluate changes in colony formation with a clonogenic cell survival assay. Results In human cancer genomes, we identified that bBRCA1/2 tumors harbored recurrent large-scale genetic deletions significantly more frequent than histologically matched control tumors. We identified 148 cytobands (23 distinct genomic loci) frequently deleted in bBRCA1/2 ovarian cancers (N=92), and 90 cytobands (15 distinct genomic loci) in bBRCA1/2 ER+ breast cancers (N=30). Subsequent computational analysis of gene expression data identified transcriptionally decreased genes in the frequently deleted loci. Cross-referencing with CRISPR/Cas9 screens in BRCA1/2−/− cells, we ultimately identified 277 and 218 genes that were altered in human tumors and putatively enhanced viability in BRCA1/2-null models but not in isogenic wild-type cell lines in BRCA1 and BRCA2 models, respectively. We subsequently selected 17 genes that either increased viability in BRCA1, BRCA2, or both contexts for experimental validation. Of these, about 40% (7 of 17) were experimentally validated (RIC8A, GNA12, ATMIN, IPO7, ATXN2, KDM1A, and NUP98) and had evidence of synthetic viability in this cellular context. Interestingly, RIC8A and GNA12 interact with each other, are both involved in G protein-coupled signaling pathway, and are synthetically viable with both BRCA1−/− and BRCA2−/− cells. In a re-analysis of a recently published genomic dataset on metastatic breast cancer, we found that homologous recombination deficient tumors developed additional loss of function mutations in RIC8A in the metastatic setting. Conclusions This study provides insights into the oncogenesis of BRCA1/2 malignancies and describes a high-throughput framework to identify synthetic viability interactions and causal driver genes affected by large-scale CNAs in human cancers. Citation Format: Yingjie Zhu, Xin Pei, Ardijana Novaj, Jeremy Setton, Fatemeh Derakhshan, Pier Selenica, Niamh McDermott, Sonali Sinha, Atif Khan, Simon Powell, Jorge Reis-Filho, Nadeem Riaz. Integrative genomic and CRISPR screen analysis identifies synthetic viability interactions in BRCA1/2 cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-24-06.

Modification Method of Expanded Polystyrene Granular Concrete Based on Computer Data Analysis

Expanded Polystyrene Granular concrete (EPS) is renowned for its exceptional lightweight and thermal insulation properties, yet it is hindered by its low strength. Despite the existence of numerous modification methods, there remains a dearth of quantification and summarization, particularly in the era of Artificial Intelligence (AI). Consequently, this study undertook the quantification of several conventional and novel modifications, uncovering intriguing challenges and discerning future trends in modification techniques. These methods encompass nanomaterials, phase change materials, ceramic concrete, and composite modifications, all aimed at bolstering mechanical properties and introducing novel functionalities to address diverse environmental, energy, and engineering requirements. Challenges encompass intricate processes, high costs, material compatibility issues, nascent processes, and application hurdles. Leveraging the advancements in simulation and AI technology, numerical simulations can meticulously replicate the microstructure of EPS granules and the physical attributes of concrete in numerical models, facilitating accurate prediction of material properties. The application of artificial intelligence techniques also includes utilizing big data analysis to optimize the proportions of various materials in EPS granular concrete, providing a valuable framework for the study of high-performance EPS granular concrete and significantly contributing to the enhancement of concrete performance.

Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain.

When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC-NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform. Database URL: https://www.opensciencechain.org.

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

Curare and GenExVis: a versatile toolkit for analyzing and visualizing RNA-Seq data

Even though high-throughput transcriptome sequencing is routinely performed in many laboratories, computational analysis of such data remains a cumbersome process often executed manually, hence error-prone and lacking reproducibility. For corresponding data processing, we introduce Curare, an easy-to-use yet versatile workflow builder for analyzing high-throughput RNA-Seq data focusing on differential gene expression experiments. Data analysis with Curare is customizable and subdivided into preprocessing, quality control, mapping, and downstream analysis stages, providing multiple options for each step while ensuring the reproducibility of the workflow. For a fast and straightforward exploration and visualization of differential gene expression results, we provide the gene expression visualizer software GenExVis. GenExVis can create various charts and tables from simple gene expression tables and DESeq2 results without the requirement to upload data or install software packages. In combination, Curare and GenExVis provide a comprehensive software environment that supports the entire data analysis process, from the initial handling of raw RNA-Seq data to the final DGE analyses and result visualizations, thereby significantly easing data processing and subsequent interpretation.

Evaluation of Open-Source Large Language Models for Metal-Organic Frameworks Research.

Along with the development of machine learning, deep learning, and large language models (LLMs) such as GPT-4 (GPT: Generative Pre-Trained Transformer), artificial intelligence (AI) tools have been playing an increasingly important role in chemical and material research to facilitate the material screening and design. Despite the exciting progress of GPT-4 based AI research assistance, open-source LLMs have not gained much attention from the scientific community. This work primarily focused on metal-organic frameworks (MOFs) as a subdomain of chemistry and evaluated six top-rated open-source LLMs with a comprehensive set of tasks including MOFs knowledge, basic chemistry knowledge, in-depth chemistry knowledge, knowledge extraction, database reading, predicting material property, experiment design, computational scripts generation, guiding experiment, data analysis, and paper polishing, which covers the basic units of MOFs research. In general, these LLMs were capable of most of the tasks. Especially, Llama2-7B and ChatGLM2-6B were found to perform particularly well with moderate computational resources. Additionally, the performance of different parameter versions of the same model was compared, which revealed the superior performance of higher parameter versions.

The system of headlights operation recognition using the digital twin method

Virtual digital representation of a physical object or system, created with precision through computer simulations, data analysis, and various digital technologies can be used as training set for real life situations. The principal aim behind creating a virtual representation is to furnish a dynamic, data-fueled, and digital doppelgänger of the physical asset. This digital counterpart serves multifaceted purposes, including the optimization of performance, the continuous monitoring of its well-being, and the augmentation of informed decision-making processes. Main advantage of employing a digital twin is its capacity to facilitate experimentation and assessment of diverse scenarios and conditions, all without impinging upon the actual physical entity. This capability translates into substantial cost savings and superior outcomes, as it allows for the early identification and mitigation of issues before they escalate into significant problems in the tangible world. Within our research endeavors, we've meticulously constructed a digital twin utilizing the Unity3D software. This digital replica faithfully mimics vehicles, complete with functioning headlamp toggles. Our lighting system employs polygons and normal vectors, strategically harnessed to generate an array of dispersed and reflected light effects. To ensure realism, we've meticulously prepared the scene to emulate authentic road conditions. For validation and testing, we integrated our model with the YOLO (You Only Look Once) neural network. A specifically trained compact YOLO model demonstrated impressive capabilities by accurately discerning the status of real vehicle headlamps. On average, it achieved an impressive recognition probability of 80%, affirming the robustness of our digital twin.

Abstract 2334: Computational methods for optimizing marker selection, clonal lineage reconstruction, and longitudinal tracking of clonal dynamics via circulating tumor DNA (ctDNA)

Abstract Background: Technologies for blood-based profiling of tumor DNA (“liquid biopsy”) have offered great prospects for non-invasive early cancer diagnosis, treatment monitoring, and clinical guidance, but further advances in computational models and data analysis to optimize monitoring protocols and develop liquid biopsy as a robust quantitative assay of tumor clonal evolution. Methods: We propose new computational methods to improve our ability to characterize tumor clonal dynamics from circulating tumor DNA (ctDNA). Our methods address how to apply ctDNA assays most effectively to two distinct questions in profiling tumor clonal dynamics: 1) How to apply longitudinal ctDNA data to refine phylogenetic tree models of clonal evolution, and 2) how to most effectively characterize changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these questions computationally in terms of a probabilistic framework for optimally identifying maximum likelihood markers for the preceding tasks and applying measured marker concentrations to their solution. We apply this framework by first evaluating the distribution of plausible clonal lineage models using bootstrap samples over pre-treatment tissue-based genetic variation data, then refining these lineage models and the clonal frequencies we can infer from them over successive longitudinal samples of selected markers. Results: We tested our method on synthetic data under various model assumptions and showed the method to be effective at refining distribution tree models and clonal frequencies over longitudinal samples so as to minimize measures of tree distance relative to the ground truth. We further apply the methods to a real ovarian cancer case and show their ability to develop and refine a clonal lineage model and assess clonal frequencies. Conclusion: Our methods show the power of ctDNA assays in conjunction with computational optimization to lead to improved marker selection, clonal lineage reconstruction, and tracking of clonal dynamics, with potential for more precise and quantitative profiling of tumor progression enabling improved clinical decision-making. Citation Format: Xuecong Fu, Yueqian Deng, Zhicheng Luo, William Laframboise, David Bartlett, Russell Schwartz. Computational methods for optimizing marker selection, clonal lineage reconstruction, and longitudinal tracking of clonal dynamics via circulating tumor DNA (ctDNA) [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 2334.

Development of a mathematical model and algorithm for the sequence of fault detection of the ship's hull

The labor intensity of the hull fault detection process, which generally consists of work performed directly on the vessel and the preparation of the final report, can be reduced through the use of modern methods of computer data analysis. The sequence of measurements of defects on the vessel (residual thicknesses, deformations, cement and other sealings, cracks, holes) can be justified by solving a modified traveling salesman problem, reflecting the optimization of the path between points in space, with the goal function in the form of the spent finite time, which is the sum of the time for performing the measurements themselves, moving between the points of such measurements, various technical works, for example, installing a ladder for access to the area located above human growth and its movement to the right sector of work. The article develops a mathematical model of human motion in the space around the vessel, an algorithm of movement. The adaptation of the genetic algorithm for the problem under consideration is given, including the features of solution coding, crossing and mutation of solutions, and choosing the best option. The results of the test debugging simulation for the bow of the river pusher hull showed the operability of the developed algorithm and the possibility of its application for research and practical tasks.

Tissue Usage Preference and Intrinsically Disordered Region Remodeling of Alternative Splicing Derived Proteoforms in the Heart.

A computational analysis of mass spectrometry data was performed to uncover alternative splicing derived protein variants across chambers of the human heart. Evidence for 216 non-canonical isoforms was apparent in the atrium and the ventricle, including 52 isoforms not documented on SwissProt and recovered using an RNA sequencing derived database. Among non-canonical isoforms, 29 show signs of regulation based on statistically significant preferences in tissue usage, including a ventricular enriched protein isoform of tensin-1 (TNS1) and an atrium-enriched PDZ and LIM Domain 3 (PDLIM3) isoform 2 (PDLIM3-2/ALP-H). Examined variant regions that differ between alternative and canonical isoforms are highly enriched with intrinsically disordered regions. Moreover, over two-thirds of such regions are predicted to function in protein binding and RNA binding. The analysis here lends further credence to the notion that alternative splicing diversifies the proteome by rewiring intrinsically disordered regions, which are increasingly recognized to play important roles in the generation of biological function from protein sequences.

Application of machine learning in chemical engineering: outlook and perspectives

Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.

Music genre selection based on computer data analysis for user preference using fuzzy classification by deep learning model

The rapid advancement of communication and information technology has led to the expansion and blossoming of digital music. Recently, music feature extraction and classification have emerged as a research hotspot due to the difficulty of quickly and accurately retrieving the music that consumers are looking for from a large volume of music repositories. Traditional approaches to music classification rely heavily on a wide variety of synthetically produced aural features. In this research, we propose a novel approach to selecting the musical genre from user playlists by using a classification and feature selection machine learning model. To filter, normalise, and eliminate missing variables, we collect information on the playlist’s music genre and user history. The characteristics of this data are then selected using a convolutional belief transfer Gaussian model (CBTG) and a fuzzy recurrent adversarial encoder neural network (FRAENN). The experimental examination of a number of music genre selection datasets includes measures of training accuracy, mean average precision, F-1 score, root mean squared error (RMSE), and area under the curve (AUC). Results show that this model can both create a respectable classification result and extract valuable feature representation of songs using a wide variety of criteria.

Good models borrow, great models steal: intellectual property rights and generative AI

Abstract Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners of the data that are “scraped” (lawfully or unlawfully, with or without permission) should be compensated for that use. The second question revolves around the ownership of the output generated by AI, which is continually improving in quality and scale. These topics fall in the realm of intellectual property, a legal framework designed to incentivize and reward only human creativity and innovation. For some years, however, Britain has maintained a distinct category for “computer-generated” outputs; on the input issue, the EU and Singapore have recently introduced exceptions allowing for text and data mining or computational data analysis of existing works. This article explores the broader implications of these policy choices, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy, which might be rendered unsustainable. Lessons may be found in the music industry, which also went through a period of unrestrained piracy in the early digital era, epitomized by the rise and fall of the file-sharing service Napster. Similar litigation and legislation may help navigate the present uncertainty, along with an emerging market for “legitimate” models that respect the copyright of humans and are clear about the provenance of their own creations.

Syrians in Turkey and their naturalization as Turkish citizens: A computational text analysis of newspaper data

Following the civil war in Syria in 2011, migration from Syria in massive numbers has caused various political, demographic, societal, and cultural challenges in Turkey. The focus of this research is on how the issue of naturalization of Syrians as Turkish citizens has been presented in Turkish newspapers and to what extent the dominant perspective on the issue has shifted over time. For our computational textual analysis of Turkish newspaper materials published between 2012 and 2019, we compiled a corpus consisting of 15,276 news items from four newspapers featuring the co-occurrence of the words “Syria*” (Suriye*) and “citizen*” (vatandaş*) in the main body of their text. The newspapers include mainstream and pro-government (Milliyet with liberal tendencies, Sabah with center-right tendencies), and left-wing (Sol, Evrensel) perspectives. A two-step textual analysis was conducted on the corpus. First, dictionary-based topic modeling was used to chart which broader topics Turkish citizenship of Syrians is predominantly linked to the data, and how the predominance of topics shifted over time. Second, a collocation analysis of the word “refugee” (mülteci) yields a more fine-grained picture of the specific words and expressions that tend to appear often when Syrian refugees are discussed. In both steps, an aggregated general analysis was complemented with separate newspaper-specific analyses, thus allowing for the comparison of different newspapers. Findings show that Syrians in Turkey predominantly feature in the context of diplomatic and societal topics. While mainstream news sources approach the issue less critically, left-wing newspapers show more sensitivity to migrant rights in Turkey.

Abstract A036: The genetic landscape of head and neck cancer using brush biopsy

Abstract Oral potentially malignant disorders (OPMDs) are characterized by the presence of dysplasia and DNA copy number aberrations (CNAs) and subsequently progress to oral squamous cell carcinoma (OSCC). OSCC has a 5-year survival rate of only about 50% and varies greatly depending on site and stage. The combination of next-generation sequencing and advanced computational data analysis approaches has revolutionized our understanding of the genomic underpinnings of cancer development and progression. This can reveal individual targetable alterations, mutational load, complex mutation signatures, and tumor-specific antigens, which might inform the utilization of targeted therapies and early diagnosis. Oral brush biopsy represents an attractive non-invasive advantage in the characterization of premalignant and malignant oral lesions. Still, a sufficient number of abnormal cells is mandatory to diagnose. The current study tested the possibility of recreating the genomic landscape of oral cancer using brush biopsy. Materials and methods: As a pilot study to address this question, we analyzed matched tissue and brush biopsy of a patient with synchronous OPMD and OSCC. Brush and tissue biopsy was taken from normal epithelium, OPMD, and OSCC. We scrutinized the evolution and heterogeneity of these three brush/tissue biopsy pairs. The techniques used include whole exome sequencing (WES), mutational signature analysis to evaluate smoking and other aberrant signatures, copy number analysis (using ASCAT), and subclonal architecture reconstruction (using DPclust). Results: To analyze intra-tumoral heterogeneity (ITH), ASCAT and DPclust were used to call copy number aberrations (CNA) and cluster somatic SNVs based on cancer cell fraction (CCF), respectively. We observed a large number of shared SNVs between the OPMD and OSCC lesions suggesting a common evolutionary origin. After adjusting the ploidy and purity based on DPClust results, ASCAT profiles revealed differences in aberrant tumor cell fraction, gains and loss of heterozygosity, and copy number neutral events between the samples. It showed almost similar profiles between the respective lesions' brush and tissue samples, suggesting that brush biopsy can recapitulate the genomic landscape. The OPMD and OSCC lesions showed loss of heterozygosity in chromosome 9 and chromosome 17, which play a potential role in tumorigenesis with loss of function of P53. We inferred the subclonal architecture of each sample, illuminating the clonal relationships between samples and distinct lineages of precursor clones developing independently. Conclusion: Collectively, these results emphasize the possibility of using brush biopsy to reconstruct the genomic characterization of OPMD and OSCC show prognostic potential for diagnosis and intervention strategies, and accelerate progress in precision cancer care. Citation Format: Evit John, Tom Lesluyes, Xiao Zhao, Peter Van Loo. The genetic landscape of head and neck cancer using brush biopsy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A036.