Data Processing Workflow Research Articles

Estimating LoD-s Based on the Ionization Efficiency Values for the Reporting and Harmonization of Amenable Chemical Space in Nontargeted Screening LC/ESI/HRMS.

Nontargeted LC/ESI/HRMS aims to detect and identify organic compounds present in the environment without prior knowledge; however, in practice no LC/ESI/HRMS method is capable of detecting all chemicals, and the scope depends on the instrumental conditions. Different experimental conditions, instruments, and methods used for sample preparation and nontargeted LC/ESI/HRMS as well as different workflows for data processing may lead to challenges in communicating the results and sharing data between laboratories as well as reduced reproducibility. One of the reasons is that only a fraction of method performance characteristics can be determined for a nontargeted analysis method due to the lack of prior information and analytical standards of the chemicals present in the sample. The limit of detection (LoD) is one of the most important performance characteristics in target analysis and directly describes the detectability of a chemical. Recently, the identification and quantification in nontargeted LC/ESI/HRMS (e.g., via predicting ionization efficiency, risk scores, and retention times) have significantly improved due to employing machine learning. In this work, we hypothesize that the predicted ionization efficiency could be used to estimate LoD and thereby enable evaluating the suitability of the LC/ESI/HRMS nontargeted method for the detection of suspected chemicals even if analytical standards are lacking. For this, 221 representative compounds were selected from the NORMAN SusDat list (S0), and LoD values were determined by using 4 complementary approaches. The LoD values were correlated to ionization efficiency values predicted with previously trained random forest regression. A robust regression was then used to estimate LoD values of unknown features detected in the nontargeted screening of wastewater samples. These estimated LoD values were used for prioritization of the unknown features. Furthermore, we present LoD values for the NORMAN SusDat list with a reversed-phase C18 LC method.

The Global Context Camera (CTX) Mosaic of Mars: A Product of Information‐Preserving Image Data Processing

AbstractThe Mars Reconnaissance Orbiter and its Context Camera (CTX) have acquired more than 100,000 separate panchromatic images that capture nearly the entire surface of Mars at ∼5–6 m/pixel. This paper describes a data processing workflow used to generate the first contiguous global mosaic of CTX data, which represents a large improvement in spatial resolution over existing 100 m/pixel contiguous global mosaics. We describe the overarching strategy for the mosaic's construction, which was to maximize the scientific utility of a continuous mosaic that is 5.7 trillion pixels in size. The pipeline used for data processing prioritized traceability and reproducibility of the final mosaic, such that the provenance of all pixels is reported, equipping scientists with information to differentiate mosaic artifacts from surface landforms and to incorporate critical image metadata into their analyses. The CTX data set synthesized into a global CTX mosaic facilitates ready analysis and provides a new capability in transitioning global studies of Mars from high‐resolution investigations of individual images to systematic studies of the entire Martian surface at outcrop‐resolving quality without regard to image boundaries.

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy.

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

A Reliable System for Quantitative G-Protein Activation Imaging in Cancer Cells.

Fluorescence resonance energy transfer (FRET) biosensors have proven to be an indispensable tool in cell biology and, more specifically, in the study of G-protein signalling. The best method of measuring the activation status or FRET state of a biosensor is often fluorescence lifetime imaging microscopy (FLIM), as it does away with many disadvantages inherent to fluorescence intensity-based methods and is easily quantitated. Despite the significant potential, there is a lack of reliable FLIM-FRET biosensors, and the data processing and analysis workflows reported previously face reproducibility challenges. Here, we established a system in live primary mouse pancreatic ductal adenocarcinoma cells, where we can detect the activation of an mNeonGreen-Gαi3-mCherry-Gγ2 biosensor through the lysophosphatidic acid receptor (LPAR) with 2-photon time-correlated single-photon counting (TCSPC) FLIM. This combination gave a superior signal to the commonly used mTurquoise2-mVenus G-protein biosensor. This system has potential as a platform for drug screening, or to answer basic cell biology questions in the field of G-protein signalling.

ANALYSIS OF THE USE OF BLOCKCHAIN AND SMART CONTRACT TECHNOLOGIES FOR MANAGEMENT AUTOMATION AND ENSURING BUSINESS RESILIENCE

Increasing competition, the need to enhance efficiency and transparency of business processes, as well as the requirements to ensure the security and reliability of operations, demand new tools and approaches for automating management and ensuring the sustainability of the enterprise. Currently, it is blockchain and smart contract technologies that are opening up vast opportunities for this. Therefore, the research is aimed at a comprehensive analysis of the features and directions of using blockchain and smart contract technologies for automating management and ensuring the sustainability of the enterprise. Within the research, it has been proven that the use of blockchain and smart contract technologies creates a flexible and secure process for developing conditions and strategies that allow an enterprise to function effectively and remain capable of ensuring its own progressive development even in conditions of high stochasticity, economic turbulence, or crises. It is concluded that among the main directions where the use of blockchain technologies for ensuring enterprise resilience is worthwhile, the following should be highlighted: supply chain management, balanced management, security management, and basic risk management, as well as managing relationships with customers and partners. In unity, these outlined directions establish the principles of automating enterprise management, enhance its ability to function effectively, and direct its progressive development. It is concluded that among the main directions where the use of smart contracts for ensuring enterprise resilience is beneficial, the following should be highlighted: access and security management, remote tracking of resource utilization, automation of production and commercial processes, improvement of customer service, and management of data processing workflows. In unity, these outlined directions establish the principles of enterprise management automation, guiding and expanding the enterprise's capabilities for progressive development. In the context of ensuring enterprise resilience, the goal of applying smart contracts and blockchain technologies can vary. However, the use of these outlined technological solutions should guarantee the enterprise's ability to withstand external and internal influences.

A High-Precision Earthquake Catalog for Nevada

Abstract The state of Nevada is home to one of the most seismically active regions in the world, with crustal deformation associated with the Walker Lane transitioning into Basin and Range tectonics as one traverses from west to east across the state. Despite hosting numerous prominent earthquake sequences over the past century and beyond, at present, there exists no unified research-quality earthquake catalog for the state and its surrounding region. Here, we present a newly compiled, high-precision catalog of more than 180,000 earthquakes occurring around Nevada from 2008 to 2023. The data processing workflow to create this catalog includes an absolute location step that accounts for topography and 3D variations in subsurface wavespeed, and a relative relocation step that refines event positions using differential times measured from waveform cross-correlation. We also provide an update to the local magnitude scale that better accounts for the observed distance attenuation of waveform amplitudes as well as local site effects. We describe some fundamental insights that can be derived from the new catalog, including regional variations in event depth distributions and sequence clustering statistics, and publish the catalog to the wider community to facilitate future research efforts.

Navigating the complexity of detrital rutile provenance: methodological insights from the Neotethys Orogen in Anatolia

Abstract. Sedimentary provenance is a powerful tool for reconstructing convergent margin evolution. However, single mineral approaches, like detrital zircon, have struggled to track sediment input from mafic and metamorphic sources. Detrital rutile complements detrital zircon datasets by offering a path forward in sedimentary provenance reconstructions where metamorphic terranes are potential source regions. However, U–Pb geochronology in rutile can be difficult due to low uranium concentrations and incorporation of common Pb, and multiple workflows are currently in use. Here, we investigate U–Pb and trace element data reduction, processing, and common Pb correction workflows using new detrital rutile U–Pb geochronology and trace element geochemistry results from the Late Cretaceous to Eocene Central Sakarya and Sarıcakaya basins in Anatolia. A significant number of analyses were rejected (54 %) due to signal intensity limitations, namely low U, low Pb, anomalous signal, and inclusions. We identify this as a universal limitation of large-n detrital rutile studies and recommend the systematic reporting of the amount of discarded analysis and the processes for rejection in all studies using detrital rutile U–Pb geochronology. Additionally, we show that (1) the 208Pb and 207Pb common Pb reduction schemes produce similar age distributions and can be used interchangeably, while (2) the Stacey–Kramers distance is a suitable metric for quantifying U–Pb discordance, but a discordance filter is not recommended. (3) Instead, filtering U–Pb data by a power law function based on the corrected date uncertainty is appropriate. (4) The exclusion of low uranium concentration rutile biases date distributions and favors pelitic-derived, higher Zr-in-rutile temperature, and higher U–Pb concordance grains. (5) Paired U–Pb and trace elements can be used to evaluate potential bias in U–Pb data rejection, which reveals that data rejection does not bias the provenance interpretations. Finally, (6) The signature of sediment recycling can be identified through U–Pb dates and Zr-in-rutile temperatures. To better navigate the complexity of detrital rutile datasets and to facilitate the standardization of data reporting approaches, we provide open-access code as Jupyter notebooks for data processing and analysis steps, including common Pb corrections, uncertainty filters, discordance calculations, and trace element analysis.

Implementation of a LC-MS based multi-attribute method (MAM) and intact multi-attribute method (iMAM) workflow for the characterisation of a GLP-Fc fusion protein

Over the past few years, the implementation of mass spectrometry (MS) in QC laboratories has become a more common occurrence. The multi-attribute method (MAM), and emerging intact multi-attribute method (iMAM), are powerful analytical tools utilising liquid chromatography−mass spectrometry (LC-MS) methods that enable the monitoring of critical quality attributes (CQAs) in biotherapeutic proteins in compliant settings. Both MAM and iMAM are intended to replace or supplement several conventional assays with a single LC-MS method utilising MS data in combination with robust, semi-automated data processing workflows. MAM and iMAM workflows can also be implemented into current Good Manufacturing Practices environments due to the availability of CFR 11 compliant chromatography data system software. In this study, MAM and iMAM are employed for the analysis of 4 batches of a glucagon-like peptide-Fc fusion protein. MAM approach involved a first the discovery phase for the identification of CQAs and second, the target monitoring phase of the selected CQAs in other samples. New peak detection was performed on the data set to determine the appearance, absence or change of any peak. For native iMAM workflow both size exclusion and strong cation exchange chromatography were optimized for the identification and monitoring of CQAs at the intact level.

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

LC-MS/MS software data processing and review workflow improvements for clinical research

LC-MS/MS software data processing and review workflow improvements for clinical research

Orbital Prosthesis Rehabilitation in Biomedical Engineering by Means of Computer Vision-Photogrammetry and 3D Prototyping

Eye loss may be caused as a result of eye trauma, accidents, or malignant tumors, which leads the patient to undergo surgery to remove the damaged parts. This research examines the potential of computer vision represented by Structure from Motion (SfM) photogrammetry in fabricating the orbital prosthesis as a noninvasive and low-cost technique. A low-cost camera was used to collect the data towards extracting the dense 3D data of the patient facial features following Structure from Motion-Multi View Stereo (SfM-MVS) algorithms. To restore the defective orbital, a Reverse Engineering (RE) based approach has been applied using the similarity RE algorithms based on the opposite healthy eye to rehabilitate the defected orbital precisely. Following quality assurance and best-fitting statistical analysis, the digital model of the restored eye was converted into a physical model using 3D prototyping. This is later used to fabricate the mold for casting medical-grade silicone to obtain the final orbital prosthesis. The results show the power of SfM photogrammetry by offering a high-accuracy model of 0.048 mm and 0.186 mm relative errors acquired in the horizontal and vertical directions, respectively. These results boost the RE implementation in medicine to reconstruct the patient's damaged eye by mirroring the image of the healthy eye using RE algorithms. Therefore, the margin matching results claim perfect data capture settings and successful data processing workflow as designed in the first place. Consequently, one can claim this approach effectively rehabilitates maxillofacial deformities as an alternative to invasive restoration approaches. The presented approach provided a low-cost and safe workflow that avoids the patient the risks of exposure to harmful rays or magnetic fields available in other sensors.

Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS2 library: Sishen formula as a case

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) and in-house high-definition MS2 library, to enhance the identification of prototypes and metabolites of the compound formulae in vivo, taking Sishen formula (SSF) as a template. Seven different MS2 acquisition methods were compared, which demonstrated the potency of a hybrid scan approach (namely high-definition data-independent/data-dependent acquisition (HDDIDDA)) in the identification precision, MS1 coverage, and MS2 spectra quality. The HDDIDDA data for 55 reference compounds, four component drugs, and SSF, together with the rat bio-samples (e.g., plasma, urine, feces, liver, and kidney), were acquired. Based on the UNIFI™ platform (Waters), the efficient data processing workflows were established by combining mass defect filtering (MDF)-induced classification, diagnostic product ions (DPIs), and neutral loss filtering (NLF)-dominated structural confirmation. The high-definition MS2 spectral libraries, dubbed in vitro-SSF and in vivo-SSF, were elaborated, enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples. Consequently, 118 prototypes and 206 metabolites of SSF were identified, with the identification rate reaching 80.51% and 79.61%, respectively. The metabolic pathways mainly involved the oxidation, reduction, hydrolysis, sulfation, methylation, demethylation, acetylation, glucuronidation, and the combined reactions. Conclusively, the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

A non-targeted analysis workflow for the identification of organic contaminants in a sludge water based on fragmentation matching score and metadata

ABSTRACT Non-targeted analysis (NTA) methods have been increasingly used to discover organic contaminants in environmental samples. The identification efficiency of conventional NTA workflow is often very low due to many factors such as low fragmentation score between experimental MS2 data and reference MS2 data/in-silico MS2 data. Additionally, the prioritisation of possible candidates is also challenging due to the large number of candidates obtained from MS acquisitions. In this study, a multiplatform data processing workflow applicable to non-targeted LC-MS data is proposed to improve the identification accuracy of organic contaminants in water. This workflow consists of (1) scanning quadrupole data-independent acquisition (SONAR) to acquire relatively clean MS1 and MS2 data, (2) data deconvolution using Progenesis QI Software to filter/select the appropriate ions (MS1 data), (3) the selected MS1 data matching against U.S. EPA Chemistry Dashboard to get a list of the environmentally related candidates with metadata (e.g., data sources, toxicity, and exposure), (4) experimental MS2 data matching against in-silico fragmentation data of the obtained candidate by MetFrag to obtain a fragmentation score, (5) compound identification through ranking the candidates by MetFrag based on fragmentation score and metadata. The identification efficiency of the metadata-based NTA workflow was evaluated using a 33-component mixture of chemicals in a complex sludge water. Out of 33 spiked compounds, 26 compounds had been identified using the defined NTA workflow. Most of the 26 identified compounds were recognized as 1st hit of the candidates obtained from the library searching. Overall, the incorporation of fragmentation score and metadata to rank the candidates has significantly improved the accuracy of the organic contaminant identification in the sludge water, compared to the conventional NTA workflow based solely on the fragmentation score.

Blank sample denoising algorithm (BSDA): An effective spectral noise reduction in water sample LIBS detection

Laser-induced breakdown spectroscopy (LIBS), as an elemental composition analysis technique, has many unique advantages and great potential for applications in water detection. However, the quality of LIBS spectral signals, such as signal-to-noise ratio and stability, is often poor due to the matrix effects of water, limiting its practical performance. To effectively remove the inherent weak radiation in experimental spectral data that can be easily mistaken for noise, this paper proposes a denoising algorithm for processing spectral data using a self-built blank sample spectral database of deionized water samples, and designs a complete data processing workflow. It includes steps such as blank sample data screening, internal standard correction, blank sample correction, and spectral smoothing. Against the backdrop of marine applications, experimental spectral data for target elements Na, Mg, Ca, K, Sr, and Li were processed with this algorithm. The results show that after algorithm processing, the spectral quality was significantly improved, with the signal-to-noise ratio and detection limits of various elements improved by at least one order of magnitude. The signal-for Li increased by up to 36 times, and the detection limit for K decreased by up to 25.2 times. Additionally, tiny spectral peaks that could not be observable in the original spectral data could be effectively extracted after processing. From a technical implementation perspective, the database establishment and data process are simple and practical, with universal applicability. Therefore, this method has good potential and wide foregrounds in many other water sample LIBS detection technologies.

Empirical Green’s function analysis of some induced earthquake pairs from the Groningen gas field

We have applied the empirical Green’s function (EGF) method to 53 pairs of earthquakes, with magnitudes ranging from M = 0.4 to M = 3.4, induced by gas production from the Groningen field in the Netherlands. For a subset of the events processed, we find that the relative source time functions obtained by the EGF deconvolution show clear indications of a horizontal component of rupture propagation. The earthquake monitoring network used has dense azimuthal coverage for nearly all events such that wavelet duration times can be picked as a function of source-station azimuth and inverted using the usual Doppler broadening model to estimate rupture propagation strike, distance, and velocity. Average slip velocities have also been estimated and found to be in agreement with typical published values. We have used synthetic data, from both a simple convolutional model of the seismogram and more sophisticated finite difference rupture simulations, to validate our data processing workflow and develop kinematic models which can explain the observed characteristics of the field data. Using a measure based on the L1-norm to discriminate results of differing quality, we find that the highest quality results show very good alignment of the rupture propagation with directions of the detailed fault map, obtained from the full-field 3D seismic data. The dip direction rupture extents were estimated from the horizontal rupture propagation distances and catalogue magnitudes showing that, for all but the largest magnitude event (the M = 3.4 event of 8th January 2018), the dip-direction extent is sufficiently small to be contained wholly within the reservoir.

An untargeted analytical workflow based on Kendrick mass defect filtering reveals dysregulations in acylcarnitines in prostate cancer tissue

BackgroundMetabolomics is nowadays considered one the most powerful analytical for the discovery of metabolic dysregulations associated with the insurgence of cancer, given the reprogramming of the cell metabolism to meet the bioenergetic and biosynthetic demands of the malignant cell. Notwithstanding, several challenges still exist regarding quality control, method standardization, data processing, and compound identification. Therefore, there is a need for effective and straightforward approaches for the untargeted analysis of structurally related classes of compounds, such as acylcarnitines, that have been widely investigated in prostate cancer research for their role in energy metabolism and transport and β-oxidation of fatty acids. ResultsIn the present study, an innovative analytical platform was developed for the straightforward albeit comprehensive characterization of acylcarnitines based on high-resolution mass spectrometry, Kendrick mass defect filtering, and confirmation by prediction of their retention time in reversed-phase chromatography. In particular, a customized data processing workflow was set up on Compound Discoverer software to enable the Kendrick mass defect filtering, which allowed filtering out more than 90 % of the initial features resulting from the processing of 25 tumoral and adjacent non-malignant prostate tissues collected from patients undergoing radical prostatectomy. Later, a partial least square–discriminant analysis model validated by repeated double cross-validation was built on the dataset of 74 annotated acylcarnitines, with classification rates higher than 93 % for both groups, and univariate statistical analysis helped elucidate the individual role of the annotated metabolites. SignificanceHydroxylation of short- and medium-chain minor acylcarnitines appeared to be a significant variable in describing tissue differences, suggesting the hypothesis that the neoplastic growth is linked to oxidation phenomena on selected metabolites and reinforcing the need for effective methods for the annotation of minor metabolites.

Advancements in Satellite Remote Sensing for Predicting Large-Scale Wildfire Risks: An Image Processing Algorithmic Framework

With the escalation of global warming and human activities, large-scale wildfires have become increasingly frequent, posing significant threats to both ecological environments and human societal safety. Satellite remote sensing technology plays a pivotal role in wildfire monitoring and risk assessment, providing extensive geographical coverage and continuous monitoring capabilities. Traditional methods for predicating wildfire risk, however, face limitations in processing large-scale remote sensing data, especially in cloud detection and temporal information analysis. In response to this challenge, a novel set of image processing algorithms has been developed to enhance the efficiency and accuracy of wildfire risk prediction. Initially, a cloud detection and removal algorithm based on deep learning is introduced. This algorithm effectively identifies and eliminates cloud interference in remote sensing images, thereby significantly improving the quality and usability of image data. Subsequently, a temporal information capturing technique is proposed, capable of processing vast amounts of remote sensing data and extracting time series features. This technique provides robust data support for wildfire risk prediction. The application of these technologies not only improves the efficiency of the data processing workflow but also enhances the timeliness and accuracy of the prediction model, holding significant practical importance for guiding actual wildfire prevention and response measures.

Food authentication goes green: Determination of the geographical origin of grain maize (Zea mays L.) using direct analysis in real time mass spectrometry (DART-MS)

Time savings, environmental friendliness and cost reduction are important criteria to determine the authenticity of feed and food in routine analysis. One analytical technique that is especially promising in terms of these criteria in metabolomics studies is Direct Analysis in Real Time Mass Spectrometry (DART-MS). In the present study, DART-MS is applied to 200 grain maize samples from seven geographical origins to develop a data processing workflow and an approach for origin analysis of grain maize. For the first time, such a large sample set was investigated using a DART-MS-based non-targeted lipidomics approach. Various open-source programs were used for pre-processing and random forest (RF) combined with repeated cross-validation was used for classification, resulting in an accuracy of 84.4%. Subsequently, the most important markers identified by RF, belonging to the classes of mono-, di- and triacylglycerols, were identified using DART-MS2. The chosen method significantly minimizes the time and solvent consumption compared to classic liquid chromatography electrospray ionization mass spectrometric (LC-ESI-MS) strategies and thus offers a more cost-effective and environmentally friendly alternative. Furthermore, the results show the high potential for determining the geographical origin of grain maize using DART-MS.

Advancing Permafrost Monitoring With Autonomous Electrical Resistivity Tomography (A‐ERT): Low‐Cost Instrumentation and Open‐Source Data Processing Tool

AbstractPermafrost is a widespread phenomenon in the cold regions of the globe and is under‐represented in global monitoring networks. This study presents a novel low‐cost, low‐power, and robust Autonomous Electrical Resistivity Tomography (A‐ERT) monitoring system and open‐source processing tools for permafrost monitoring. The processing workflow incorporates diagnostic and filtering tools and utilizes open‐source software, ResIPy, for data inversion. The workflow facilitates quick and efficient extraction of key information from large data sets. Field experiments conducted in Antarctica demonstrated the system's capability to operate in harsh and remote environments and provided high‐temporal‐resolution imaging of ground freezing and thawing dynamics. This data set and processing workflow allow for a detailed investigation of how meteorological conditions impact subsurface processes. The A‐ERT setup can complement existing monitoring networks on permafrost and is suitable for continuous monitoring in polar and mountainous regions, contributing to cryosphere research and gaining deeper insights into permafrost and active layer dynamics.

Abstract 6475: Leveraging real-world pancreatic cancer datasets to drive drug discovery and patient health

Abstract Pancreatic cancer is the third leading cause of cancer-related death in the United States. Current therapeutic options offer a dismal overall survival with the 5-year survival at just ~12%. Analysis of the clinical and molecular underpinnings of pancreatic cancer is critical to developing both early detection methodologies as well as novel therapeutic options. The aggressiveness and deadly nature of this disease warranted the development of a data repository and analysis system dedicated to pancreatic cancer data. The Pancreatic Cancer Action Network’s (PanCAN) SPARK platform is a cloud-based data and analytics platform, powered by Velsera, that integrates real-world patient health data from PanCAN research initiatives and accelerates research by making pancreatic cancer data easier to access and use. Encompassing clinical, imaging, and genomics data from over 600 patients with pancreatic cancer within PanCAN’s Know Your Tumor ® (KYT) precision medicine service, the SPARK platform connects with petabytes of publicly available cancer data via the Cancer Genomics Cloud (CGC), also powered by Velsera. The CGC is part of NCI’s Cancer Research Data Commons (CRDC), a cloud-based data science infrastructure that connects data with analytics tools to allow researchers to share, integrate, analyze, visualize, and drive scientific discovery. Here, we demonstrate the application of these datasets by providing a case study demonstrating how to combine and enrich data to accelerate pancreatic cancer research. Currently, the genomic and proteomic data available on CRDC amounts to 402 and 304 cases of pancreatic tumor samples, respectively. We will use the capabilities of the SPARK and CGC platforms, which provide ready-to-use tools for multi-omics analysis that require no coding knowledge. Using the KYT and CRDC open-access pancreatic cancer data, we aim to demonstrate how to perform integrated analysis of data from diverse scientific domains, and share with collaborators all in one space, streamlining and increasing the potential for new scientific discoveries. Further expansion of the PanCAN and CGC datasets will undoubtedly provide a more comprehensive understanding of pancreatic cancer tumor biology. SPARK and CGC’s cloud based computation infrastructure, along with numerous available cancer datasets and easy-to-use multi-omics data processing workflows and data analytic tools will be instrumental in this process. Citation Format: Kawther Abdilleh, Zelia Worman, Rowan Beck, Cera Fisher, Divya Sain, Jack DiGiovanna, Lynn Matrisian, Sudheer Doss, Brandi Davis-Dusenbery. Leveraging real-world pancreatic cancer datasets to drive drug discovery and patient health [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 6475.