Potential Vulnerabilities Research Articles

Disaster recovery strategies for cyber-physical systems considering the Interdependence

Improving the resilience of Cyber-Physical Systems (CPS) against extreme events is vital. Current research, overly focused on power system restoration, often misses the complex interplay between the physical and cyber aspects of these systems, leading to potential vulnerabilities. This paper first proposes a framework for CPS considering interdependence. The framework accounts for cyber-physical collaborative recovery and the connectivity between cyber nodes and control center in the cyber system. The model is built considering factors such as repair crew, repair time, and network reconstruction, aimed at rapidly restoring the structural and functional resilience of CPS in the shortest possible time. Next, it integrates complex network theory to propose different repair strategies based on structural and functional indicators. Finally, the repair efficiency of the proposed strategies is comprehensively evaluated using structural and functional metrics. Simulation results show that the repair efficiency of CPS is superior to traditional power grids, and moderate reduction in the repair time of cyber nodes and a decrease in network size both contribute to enhancing repair efficiency.

Risk management strategies for smart hotel supply chain

<p>In the dynamic and interconnected world of hospitality, smart hotel supply chains play a pivotal role in maintaining operational efficiency and service excellence. However, these supply chains are susceptible to many risks, including transportation disruptions, supplier insolvencies, and cybersecurity threats, which can severely impact business continuity and degrade service quality. This conceptual paper proposes a holistic framework for identifying, assessing, and mitigating supply chain risks within the context of smart hotels. By integrating advanced technologies and strategic management practices, this study aims to reinforce smart hotel supply chains against potential vulnerabilities, ensuring sustainable business operations and superior guest satisfaction. The paper explores various risk management measures and leverages supply chain management, hospitality, and cybersecurity insights to develop a robust risk management strategy tailored for smart hotels.<strong></strong></p>

Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster’s hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system’s ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures.

Evolution of system connectivity to support food production in the Indus Basin in Pakistan

Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.

Hardware-in-Loop (HIL) Testbed Design of Thermal Power Plant for Threat Modeling and Attack Vector Analysis

Industrial control systems (ICSs) are extensively utilized worldwide to control and regulate various processes in energy utilities. It consists of various field devices, control and monitoring devices and communication devices. This paper focuses on the testing and analysis of various attack vectors that could potentially occur in a hardware-in-loop (HIL) Industrial Control System (ICS) testbed designed for a 500 MW thermal power plant. In this testbed, four typical process scenarios have been identified that can be manipulated through cyber-attacks, leading to severe issues such as plant shutdown or even explosions. The four significant plant scenarios recognized include minimal coal mill levels and increased temperatures in the classifier, heightened primary airflow to the coal mill, the tripping of an ID fan, and adjustment of the Super-heater temperature to its lowest setting. Also, we utilize the STRIDE threat modeling methodology to accurately represents the elements of Cyber-Physical Systems (CPS), their inter-dependencies, and the potential attack entry points and system vulnerabilities.

Enhancing Wireless Sensor Network Security Through Mutual Information Analysis for Intrusion Detection and Resilience

Intrusion detection is a critical aspect of network security, involving the identification and response to unauthorized access or malicious activities within a system. It plays a crucial role in safeguarding networks and ensuring data integrity and confidentiality. The Mutual Information (MI) analysis is a vital component in the realm of intrusion detection and security within Wireless Sensor Networks (WSNs). By assessing the information shared between pairs of sensor nodes, MI analysis reveals underlying patterns and relationships in network data. This process involves calculating the mutual information for each node pair based on statistical properties of observed values. Strong correlations or dependencies between nodes are identified, aiding in the detection of critical nodes or clusters vulnerable to compromise. Additionally, MI analysis informs feature extraction by guiding the selection of informative features that capture network structure. It also serves as a proactive tool for identifying anomalies or deviations from expected patterns, which may signal intrusion attempts or malicious activities. Through monitoring changes in mutual information over time, MI analysis facilitates prompt responses to emerging threats, enhancing the resilience and security of WSNs. The work exhibits high accuracy, recall rates, and detection rates across various attack scenarios, underscoring its efficacy in identifying and mitigating security threats. The algorithm's efficiency, effectiveness, and reliability make it a promising solution for enhancing WSN security. Through sophisticated methodologies and adaptive defensive strategies, the proposed algorithm strengthens the robustness and dependability of WSNs, minimizing the risk of potential security vulnerabilities and ensuring comprehensive threat detection in real-world deployment scenarios.

Exploring the intersection of network security and database communication: a PostgreSQL Socket Connection case study

In this study, the network security of PostgreSQL database using Socket connection is deeply analyzed. By exploring Socket connections established by PostgreSQL over TCP, we find potential security threats and vulnerabilities during data transmission, which may expose database systems to network attacks such as unauthorized access and data leakage. In order to assess these security risks, this study simulated a variety of network attack scenarios, especially the implantation and detection of Webshell, to reveal the vulnerability of PostgreSQL to such network threats. Especially in defending against complex and changeable cyber threats such as Webshell attacks, this research also uses machine learning and artificial intelligence techniques to improve the automation level of security threat detection and response. These technologies can help identify complex attack patterns and improve resilience to emerging threats, thereby enhancing the overall security of PostgreSQL databases.

Medical Data in Wireless Body Area Networks: Device Authentication Techniques and Threat Mitigation Strategies Based on a Token-Based Communication Approach

Wireless Body Area Networks (WBANs), low power, and short-range wireless communication in a near-body area provide advantages, particularly in the medical and healthcare sector: (i) they enable continuous monitoring of patients and (ii) the recording and correlation of physical and biological information. Along with the utilization and integration of these (sensitive) private and personal data, there are substantial requirements concerning security and privacy, as well as protection during processing and transmission. Contrary to the star topology frequently used in various standards, the overall concept of a novel low-data rate token-based WBAN framework is proposed. This work further comprises the evaluation of strategies for handling medical data with WBANs and emphasizes the importance and necessity of encryption and security strategies in the context of sensitive information. Furthermore, this work considers the recent advancements in Artificial Intelligence (AI), which are opening up opportunities for enhancing cyber resilience, but on the other hand, also new attack vectors. Moreover, the implications of targeted regulatory measures, such as the European AI Act, are considered. In contrast to, for instance, the proposed star network topologies of the IEEE 802.15.6 WBAN standard or the Technical Committee (TC) SmartBAN of the European Telecommunication Standards Institute (ETSI), the concept of a ring topology is proposed which concatenates information in the form of a ‘data train’ and thus results in faster and more efficient communication. Beyond that, the conductivity of human skin is included in the approach presented to incorporate a supplementary channel. This direct contact requirement not only fortifies the security of the system but also facilitates a reliable means of secure communication, pivotal in maintaining the integrity of sensitive health data. The work identifies different threat models associated with the WBAN system and evaluates potential data vulnerabilities and risks to maximize security. It highlights the crucial balance between security and efficiency in WBANs, using the token-based approach as a case study. Further, it sets a foundation for future healthcare technology advancements, aiming to ensure the secure and efficient integration of patient data.

Detour-RS: Reroute Attack Vulnerability Assessment with Awareness of the Layout and Resource

Recent decades have witnessed a remarkable pace of innovation and performance improvements in integrated circuits (ICs), which have become indispensable in an array of critical applications ranging from military infrastructure to personal healthcare. Meanwhile, recent developments have brought physical security to the forefront of concern, particularly considering the valuable assets handled and stored within ICs. Among the various invasive attack vectors, micro-probing attacks have risen as a particularly menacing threat. These attacks leverage advanced focused ion beam (FIB) systems to enable post-silicon secret eavesdropping and circuit modifications with minimal traceability. As an evolved variant of micro-probing attacks, reroute attacks possess the ability to actively disable built-in shielding measures, granting access to the security-sensitive signals concealed beneath. To address and counter these emerging challenges, we introduce a layout-level framework known as Detour-RS. This framework is designed to automatically assess potential vulnerabilities, offering a systematic approach to identifying and mitigating exploitable weaknesses. Specifically, we employed a combination of linear and nonlinear programming-based approaches to identify the layout-aware attack costs in reroute attempts given specific target assets. The experimental results indicate that shielded designs outperform non-shielded structures against reroute attacks. Furthermore, among the two-layer shield configurations, the orthogonal layout exhibits better performance compared to the parallel arrangement. Furthermore, we explore both independent and dependent scenarios, where the latter accounts for potential interference among circuit edit locations. Notably, our results demonstrate a substantial near 50% increase in attack cost when employing the more realistic dependent estimation approach. In addition, we also propose time and gas consumption metrics to evaluate the resource consumption of the attackers, which provides a perspective for evaluating reroute attack efforts. We have collected the results for different categories of target assets and also the average resource consumption for each via, required during FIB reroute attack.

Abstract LB332: Spatial transcriptomic and proteomic analysis of the treatment resistant slow cycling micro environment in GBM

Abstract Within the Glioblastoma (GBM) microenvironment, there are micro-niches originating from independent lineages of cancer stem cells with distinct metabolic requirements that are characterized by Fast cycling cells (FCCs) and Slow cycling cells (SCC). The FCCs employ aerobic glycolysis for energy, while the treatment-resistant SCCs primarily rely on lipid metabolism. Our study allows for the molecular and spatial decoding of the heterogeneity within the GBM microenvironment, with a specific focus on decoding the dichotomy of the immune infiltrates and studying the interactions between the SCCs and the immune compartment in human GBM patients. Our research reveals a new paradigm of interaction within the tumor microenvironment (TME) of glioblastoma (GBM) patients, in which SCCs are instrumental in altering the tumor milieu to favor immune suppression. These SCCs actively recruit and harness sub-population of immune cells of myeloid origin that promote tumor growth and simultaneously provide metabolic aid to these treatment-resistant cells. Our research maps out the intricate metabolic dialogues within GBM and evaluates the potential therapeutic effect of disrupting this interaction to open up new treatment avenues. The findings based on our spatial transcriptomic and proteomic study on brain biopsies from human patients provide a deeper understanding of the critical links among the tumor's local environment, cellular metabolism, anti-tumor immunity, and potential treatment vulnerabilities. In summary, our investigation offers novel insights into how metabolic exchanges contribute to the evolution of GBM leading to the development of drug and immune resistant GBM. Our data is suggestive that therapies aimed at these metabolic interactions, especially those between SCCs and metabolically supporting immunosuppressive myeloid originating immune cells, hold potential as effective therapeutic strategy for GBM. Citation Format: Avirup Chakraborty, Diana Feier, Aryeh Silver, Changlin Yang, Miruna Anica, Avinash Pittu, Olusegun Sobanjo, Ethan Hodge, Mia Engelbart, Christina Von Roemeling, Maryam Rahman, Matthew Sarkisian, Jianping Huang, Jeffrey Harrison, Duane Mitchell, Loic Deleyrolle. Spatial transcriptomic and proteomic analysis of the treatment resistant slow cycling micro environment in GBM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB332.

Wearable Device Bluetooth/BLE Physical Layer Dataset

Wearable devices, such as headsets and activity trackers, rely heavily on the Bluetooth and/or the Bluetooth Low Energy wireless communication standard to exchange data with smartphones or other peripherals. Since these devices collect personal health and activity data, ensuring the privacy and security of the transmitted data is crucial. Therefore, we present a dataset that captures complete Bluetooth communications—including advertising, connection, data exchange, and disconnection—in an RF isolated environment using software-defined radio. We were able to successfully decode the captured Bluetooth packets using existing tools. This dataset provides researchers with the ability to fully analyze Bluetooth traffic and gain insight into communication patterns and potential security vulnerabilities.

The role of vulnerability assessments in USA agricultural animal operations

Major disaster events such as hurricanes, floods, fires, earthquakes and tornadoes pose significant challenges to agricultural production every year. Commercial livestock and poultry operations are highly dependent on favourable environment, weather conditions and infrastructure to thrive. Adverse conditions during extreme weather events and other emergencies can result in significant loss of animals and commodities due to disruptions in utilities and critical services, facility damage, contamination of feed and water supplies, environmental extremes and biosecurity lapses. The effects on production can be long lasting and cumulative. Being able to identify, assess and mitigate potential vulnerabilities that lower risk helps organisations to prepare for and recover from these events. This paper provides an overview of the vulnerability assessment process in the United States that focuses on general and better practices that should be considered when applying these principles to commercial livestock and poultry operations. This approach has broad advantages for all countries where livestock and agriculture are exposed to these risks.

تعزيز الامن السيبراني من خلال اختبار الاختراق الفعال وفحص الثغرات

The number of computer network attacks today is increasing with the sophisticated attack tools and complicated methods hence, building secure systems is required. The demand for regular penetration testing and vulnerability scanning has become an urgent issue. This paper focuses on increase the security of the system resources being tested; Determine the weakness in the popular operating systems; Focus on methodologies and approaches to analyze the system for security that leads to protect the system against external threats. An experimental setup of a virtual penetration testing environment lab is created on a system using virtualization software. By using of the most powerful tools and techniques used today, a successful penetration testing and vulnerability scanning through three phases of processes are implemented. Eventually, the results of this process will help identify potential vulnerabilities in the operating systems and ways to patch them up. Keywords: Cybersecurity, penetration testing, vulnerability scanning tools.

Adaptive Hybrid Learning for Websites Vulnerability prediction

This study presents a comprehensive exploration of machine learning (ML) techniques for predicting vulnerabilities in websites, which is a critical aspect of modern cybersecurity. With the advancement of digital threats and the complexity of cyber-attacks, conventional security strategies have become increasingly inadequate. By employing machine learning algorithms such as Random Forest and Gradient Boosting, this study formulates models adept at identifying potential vulnerabilities within the website code. This approach responds to the escalating demand for enhanced security measures, in the face of increasingly sophisticated digital threats. By integrating anomaly detection findings through the Isolation Forest algorithm, this study enriches the training dataset, enabling models to adapt to both known and emerging vulnerability patterns
 The Gradient Boosting model slightly outperformed the Random Forest model in terms of overall accuracy, achieving a precision of 97% for the non-vulnerability class, and the vulnerability class had a precision of 90%, leading to an overall accuracy of 96.25%., which is attributed to its ability to iteratively learn from previous errors, thereby enhancing its adaptability to new vulnerabilities. This study underscores the significant potential of ML to enhance cybersecurity measures against website vulnerabilities.

A Reliable Framework for Detection of Smart Contract Vulnerabilities for Enhancing Operability in Inter-Organizational Systems

Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement’s terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain.

A comprehensive survey of digital twins: Applications, technologies and security challenges

Alongside advancements in Artificial Intelligence (AI), significant progress has been made in big data processing, edge/cloud computing, and ubiquitous computing in the past two decades. These advancements catalyzed the development and adoption of Digital Twins (DT) across various domains, serving as virtual replicas of Physical Objects (POs). DTs provide advanced visualization and simulation capabilities, enabling effective estimation, optimization, and forecasting of PO's behaviors. However, the widespread adoption of DTs has introduced various security threats, vulnerabilities, and attacks. Despite ongoing research in DT applications and security, there is a lack of systematic review of the DT security literature across domains and architectural layers. This study fills this gap by systematically reviewing DT research, focusing on three interrelated aspects: DT applications, architectural layers, and security. We explore DT's architectural layers, functional requirements, application, and creation software to identify potential threats, attacks, and vulnerabilities specific to DT layers and application domains. We then systematize our findings under a unified security framework and pinpoint countermeasures against identified security challenges. Furthermore, our study explores DT's role in mitigating existing cyber threats, and we conclude our work by identifying open challenges and potential research directions.

Static Load Analysis of Inverted Umbrella Structure for Rain Water Harvesting

The study presents a comprehensive structural integrity assessment of inverted umbrella rainwater harvesting systems through an in-depth analysis conducted using the STAAD-PRO software. The inverted umbrella design, increasingly employed for rainwater harvesting in various settings, necessitates a rigorous examination of its structural performance to ensure reliability and safety. The study employs STAAD-PRO environment to evaluate the load-bearing capacities, stress distributions, and deformations exhibited by the inverted umbrella structures. This study contributes to the broader field of structural engineering by establishing a methodology for utilizing STAAD-PRO in the evaluation of unconventional structures such as inverted umbrella rainwater harvesting systems. Different kinds of live and dead loads acting on the structure was estimated and an emphasis is placed on understanding the dynamic interactions between the system components and the forces imposed by environmental factors, including live and dead loads and seismic activities. Wind load. The results of the STAAD-PRO analysis offer valuable insights into the structural behavior of the inverted umbrella rainwater harvesting systems under diverse loading conditions. By assessing critical points such as nodes, connections, and supporting elements, the study aims to identify potential vulnerabilities and proposes design modifications to enhance overall structural robustness. Total dead load acting on the structure was found as 2.88kN. The maximum and minimum force experienced by the structure in X, Y and Z direction was found as (2.33kN and 1.37N), (65.12N and 1.37N) and (261.15N and 6.02N) respectively. The structure was also found stable against bending, shear and tensile strength with utility ratio of less than 1. Ultimately, this investigation strives to foster the adoption of reliable and resilient inverted umbrella rainwater harvesting systems, advancing the integration of innovative engineering solutions into sustainable water resource practices.

Encryption of Card Details Using AES with a 128-bit Key a Secure Approach to Data Protection

Data security is a critical concern in today's digital age, particularly regarding the protection of sensitive information such as cardholder data. This research paper explores the implementation of Advanced Encryption Standard (AES) encryption with a 128-bit key as a means of safeguarding card details. The project demonstrates the encryption and decryption process, ensuring data confidentiality and integrity. Utilizing the pycryptodome library in Python, the AES encryption technique is applied to encrypt card details, offering a secure approach to data protection. The methodology section outlines the AES encryption process, emphasizing key generation, encryption, and decryption. A detailed explanation of the Python implementation highlights the importance of key length and randomness in the encryption process. The results section presents the outcomes of encrypting and decrypting card details using AES encryption, analyzing the efficiency and security of the encryption process. Additionally, potential vulnerabilities and challenges encountered during implementation are discussed, along with their resolutions. In the discussion section, the research findings are interpreted in the context of data security and encryption standards. A comparison between AES encryption and other encryption algorithms is provided, evaluating performance and security aspects. Consideration is given to future research directions in the field of data encryption and security. Overall, this research paper underscores the significance of AES encryption with a 128-bit key for protecting cardholder information. By implementing AES encryption, organizations can enhance data security measures, ensuring the confidentiality and integrity of sensitive data in digital transactions. This abstract provides a concise overview of the research paper, summarizing the objectives, methodology, results, and implications of implementing AES encryption for card data protection. It highlights the importance of data security and encryption techniques in safeguarding sensitive information in digital transactions. Tokenizing cards enhances security, ensures regulatory compliance, boosts customer trust, and streamlines payment processes while reducing liability for organizations. Future developments may involve tokenizing cards even without customer consent. Keywords: Tokenization, Payment systems, Encryption, Fraud prevention

High-Fidelity Gradient Inversion in Distributed Learning

Distributed learning frameworks aim to train global models by sharing gradients among clients while preserving the data privacy of each individual client. However, extensive research has demonstrated that these learning frameworks do not absolutely ensure the privacy, as training data can be reconstructed from shared gradients. Nevertheless, the existing privacy-breaking attack methods have certain limitations. Some are applicable only to small models, while others can only recover images in small batch size and low resolutions, or with low fidelity. Furthermore, when there are some data with the same label in a training batch, existing attack methods usually perform poorly. In this work, we successfully address the limitations of existing attacks by two steps. Firstly, we model the coefficient of variation (CV) of features and design an evolutionary algorithm based on the minimum CV to accurately reconstruct the labels of all training data. After that, we propose a stepwise gradient inversion attack, which dynamically adapts the objective function, thereby effectively and rationally promoting the convergence of attack results towards an optimal solution. With these two steps, our method is able to recover high resolution images (224*224 pixel, from ImageNet and Web) with high fidelity in distributed learning scenarios involving complex models and larger batch size. Experiment results demonstrate the superiority of our approach, reveal the potential vulnerabilities of the distributed learning paradigm, and emphasize the necessity of developing more secure mechanisms. Source code is available at https://github.com/MiLab-HITSZ/2023YeHFGradInv.

Abstract 1587: Multimodal spatial transcriptomics uncover distinct tumor microenvironment states and cell-cell communication networks in molecular pancreatic cancer subtypes

Abstract The tumor microenvironment (TME) presents a complex ecosystem comprising a diversity of immune and stromal cell populations that influence tumor progression, drug delivery and therapy outcome. High levels of inter- and intra-tumor heterogeneity in TME state are driven by molecular tumor phenotypes. Pancreatic ductal adenocarcinoma (PDAC), one of the most lethal cancer types, displays a high genetic heterogeneity along with an immunosuppressive TME. However, it remains largely elusive how distinct TME states are mechanistically influenced, and which molecular processes dictate the emergence of different modes of immunosuppression within the TME of molecular PDAC subtypes.Here, we performed a systematic analysis of functional associations between the major molecular PDAC subtypes, the classical and mesenchymal subtype, and their TME states. We describe the TME composition and complex cell-cell communication networks within PDAC subtypes using a multimodal integration of spatial transcriptomics (ST) with complementary approaches, such as scRNA-seq, MS-based Secretomics and multiplexed histocytometry. We generated ST data sets (Visium) of a tumor cohort derived from a Kras-driven PDAC mouse model. To analyze TME composition as well as spatial niches and cell type communities, we computationally enhanced the spot-resolution of ST datasets, followed by cell type deconvolution and cell-cell communication analysis to identify subtype-specific TME communities and communication networks.To this end, we generated a large resource of PDAC mouse models which represent molecular subtypes of the disease and mimic the heterogeneity of TME states found in human PDAC cohorts. This analysis revealed that a set of subtype-specific secreted factors shape the immunosuppressive PDAC TME via direct and indirect communication networks with immunosuppressive myeloid and T cells in molecular PDAC subtypes. Multimodal ST analysis delineated spatial subtype-specific TME communities as well as spatial communication patterns. We functionally investigate the tumor cell intrinsic regulation of the identified subtype-specific secreted factors, providing potential therapeutic vulnerabilities for more effective combinatorial subtype-specific therapies including immunotherapeutic approaches. Citation Format: Stefanie Baerthel, Chiara Falcomatà, Vanessa Goelling, Daniele Lucarelli, Rushin Gindra, Constantin Schmitt, Jonathan Swietlik, Felix Meissner, Marc Schmidt-Supprian, Dieter Saur. Multimodal spatial transcriptomics uncover distinct tumor microenvironment states and cell-cell communication networks in molecular pancreatic cancer subtypes [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 1587.