Identification Of Vulnerabilities Research Articles

WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer

As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of current seed energy distribution strategies, the gray wolf optimizer (GWO) is improved by integrating four strategies, leading to the development of the improved gray wolf optimizer (IGWO). Lastly, an adaptive search algorithm is proposed, and the WolfFuzz prototype tool is implemented. In vulnerability recurrence scenarios, WolfFuzz is 3.2× faster on average compared with the baseline and reproduces 76.4% of existing bugs faster. WolfFuzz also discovers nine different types of bugs in seven real-world programs.

Tumor microenvironment-induced FOXM1 regulates ovarian cancer stemness

In ovarian tumors, the omental microenvironment profoundly influences the behavior of cancer cells and sustains the acquisition of stem-like traits, with major impacts on tumor aggressiveness and relapse. Here, we leverage a patient-derived platform of organotypic cultures to study the crosstalk between the tumor microenvironment and ovarian cancer stem cells. We discovered that the pro-tumorigenic transcription factor FOXM1 is specifically induced by the microenvironment in ovarian cancer stem cells, through activation of FAK/YAP signaling. The microenvironment-induced FOXM1 sustains stemness, and its inactivation reduces cancer stem cells survival in the omental niche and enhances their response to the PARP inhibitor Olaparib. By unveiling the novel role of FOXM1 in ovarian cancer stemness, our findings highlight patient-derived organotypic co-cultures as a powerful tool to capture clinically relevant mechanisms of the microenvironment/cancer stem cells crosstalk, contributing to the identification of tumor vulnerabilities.

Post Fire Assessment of RCC Buildings Through Rapid Visual Screening

The effectiveness of rapid visual screening (RVS) in determining the fire risk of reinforced concrete (RCC) buildings is examined in this review. RVS provides a quick and practical approach to evaluate fire risks, addressing the shortcomings of traditional methods. Through a comprehensive examination of methodology, advantages, limitations, and future directions, this study aims to enhance understanding and application of RVS in fire assessment. The study employs a systematic approach to evaluate the effectiveness of RVS, analyzing its application in assessing fire vulnerability in RCC structures. Key aspects of RVS methodology, including visual inspection, identification of structural vulnerabilities, and categorization of damage, scrutinized. A case study of the Chopasani Housing Boards building in Jodhpur provides empirical evidence of RVS's utility in identifying fire-related structural vulnerabilities. The concluded, the analysis reveals that RVS effectively categorizes fire-induced damage in RCC buildings, facilitating prioritization of mitigation efforts. Damage classifications, ranging from mild to extreme, are discerned through RVS, underscoring its ability to swiftly assess structural vulnerabilities. Additionally, RVS analysis elucidates the residual strength of RCC components post-incident, providing valuable insights for retrofitting and maintenance efforts. Overall, this study highlights the pivotal role of RVS in bolstering structural fire safety measures, emphasizing its practicality, cost-effectiveness, and accessibility. However, limitations such as subjectivity and inadequate assessment of complex structures necessitate ongoing refinement and integration with advanced technologies. The findings underscore the importance of RVS in enhancing fire safety assessment practices and mitigating fire risks in urban buildings, particularly in the context of reinforced concrete structures.

ҚОНАҚ ҮЙ ҚЫЗМЕТІНІҢ БИЗНЕС - ПРОЦЕСТЕРІН МОДЕЛЬДЕУ

The article is devoted to the management and modeling of business processes of a hotel enterprise. One of the current trends in the development of the hotel business is a significant increase in competition. Therefore, in order to create a competitive advantage, hotel companies use various approaches and tools aimed at attracting customers, providing quality services and improving the image of the hotel. One of these approaches for hotel companies is the implementation and management of innovative and efficient business processes. A necessary step in the effective application of the process approach in the management of an enterprise is the modeling of business processes. During the research, diagrams of business processes of hotel services were developed using the standardized BPMN methodology. Using these BPMN diagrams, you can determine the end result of the entire process, and analyze the activities of the enterprise. The proposed model can be used in the development of hotel service standards, identification of weaknesses and vulnerabilities in the hotel service, training of hotel staff, as well as in the educational process in the training of highly qualified specialists.

Vulnerability of fresh agricultural products supply chain: Assessment, interrelationship analysis and control strategies

Most research in fresh agricultural products supply chain (FAPSC) vulnerability identification and evaluation is based on independent perspectives. Few studies have studied the interrelationships, transmission mechanisms, and control strategies from a network approach. Given this, this paper first identifies 7 vulnerability categories and 36 vulnerability factors based on empirical data collected from practitioners of FAPSC using grounded theory (GT). The interrelationships among the identified vulnerability factors are established immediately afterward based on complex network theory. Finally, key vulnerability factors and vulnerability impact transmission paths are identified based on the results by calculating the basic network characteristic indicators. The results show that market supply-demand imbalance and human resources issues are the key vulnerability factors. The five factors of market supply-demand imbalance, environmental pollution, socio-economic downturn, human resources issues, and arable land area as the intermediate transmission factors of the vulnerability network play a key role in vulnerability transmission. Based on this, strategies such as information sharing and measuring the environmental performance of fresh agricultural product suppliers are proposed. This study can help stakeholders control the vulnerabilities and ensure the stable operation of the FAPSC.

Identification of Ground Vulnerability Based on Kg, PGA, and GSS Assessment by Single Station Microtremor in Bendosari Hamlet, Kalasan Village, Yogyakarta

Bendosari Hamlet, Kalasan, Yogyakarta is a research area that is one of the areas affected by the earthquake that occurred on May 27, 2006. The earthquake with a magnitude of 6.4 caused quite severe damage to buildings around the research area. Based on the phenomenon of earthquakes that have occurred, there needs to be further study related to vulnerable areas in the event of another earthquake. Research data is primary data taken using a single statiun microtremor tool with an overall acquisition point of 50 measurement points. This study aims to find out the value of seismic vulnerability index (Kg), Peak Ground Acceleration (PGA), and Ground Shear Strain (GSS). Using the Horizontal to Vertical Spectral Ratio (HVSR) method, the dominant frequency result (f0) and amplification factor value (A0) from data that has been recorded through the microtremor tool. Based on research that has been done, the results are obtained in the f0rm of seismic vulnerability index values that are in the range of 0.49 - 30.18, the value of the acceleration of ground movement is in the range of 0.21 - 0.61 Gal, and the ground shear strain value is in the range of 4 x 10-6 to 38 x 10-5. Point K26 which is the original documentation of buildings affected by the earthquake, analyzed that the value of Vs of 858.112 m / s on the vs ground profile indicates that there is hard soil at a depth after 27.2 m. This shows that the thickness of subsurface sedimentary rocks at point K26 is quite thick. Keywords: Bendosari Hamlet; Mikrotremor; GSS; HVSR; PGA; Seismic Vulnerability

Poor Decision Making and Sociability Impairment Following Central Serotonin Reduction in Inducible TPH2-Knockdown Rats.

Serotonin is an essential neuromodulator for mental health and animals' socio-cognitive abilities. However, we previously found that a constitutive depletion of central serotonin did not impair rat cognitive abilities in stand-alone tests. Here, we investigated how a mild and acute decrease in brain serotonin would affect rats' cognitive abilities. Using a novel rat model of inducible serotonin depletion via the genetic knockdown of tryptophan hydroxylase 2 (TPH2), we achieved a 20% decrease in serotonin levels in the hypothalamus after three weeks of non-invasive oral doxycycline administration. Decision making, cognitive flexibility, and social recognition memory were tested in low-serotonin (Tph2-kd) and control rats. Our results showed that the Tph2-kd rats were more prone to choose disadvantageously in the long term (poor decision making) in the Rat Gambling Task and that only the low-serotonin poor decision makers were more sensitive to probabilistic discounting and had poorer social recognition memory than other low-serotonin and control individuals. Flexibility was unaffected by the acute brain serotonin reduction. Poor social recognition memory was the most central characteristic of the behavioral network of low-serotonin poor decision makers, suggesting a key role of social recognition in the expression of their profile. The acute decrease in brain serotonin appeared to specifically amplify the cognitive impairments of the subgroup of individuals also identified as poor decision makers in the population. This study highlights the great opportunity the Tph2-kd rat model offers to study inter-individual susceptibilities to develop cognitive impairment following mild variations of brain serotonin in otherwise healthy individuals. These transgenic and differential approaches together could be critical for the identification of translational markers and vulnerabilities in the development of mental disorders.

VCGERG

Vulnerability can lead to data loss, privacy leakage and financial loss. Accurate detection and identification of vulnerabilities is essential to prevent information leakage and APT attacks. This paper explores the possibility of digging the valuable information in vulnerability reports deeply. We propose a new model, VCGERG, which products a graph using key information from vulnerability reports and embeds the graph into the vector space using a keywords-LINE graph embedding algorithm based on the attention of neighboring nodes. VCGERG model uses the OVR random forest algorithm to classify vulnerabilities. Our model can get the complicated local and global information of the graph in large-scale dataset and achieve better results. In order to verify the effectiveness of our model, it is evaluated on many experiments. Compared with other models, our method has a higher accuracy rate of 0.975.

Particle Swarm Algorithm for Smart Contract Vulnerability Detection Based on Semantic Web

In recent years, smart contracts have risen rapidly in the blockchain field, but security issues have also become increasingly prominent. Due to the lack of unified evaluation standards, the security analysis of smart contracts mainly relies on complex and not easily scalable expert rules. To address these issues, we employ slicing techniques to reduce the interference of extraneous code on the detection process, apply normalisation techniques to eliminate the differences between different compiler versions and use particle swarm optimisation algorithms to determine the similarity between contracts, thus improving the accuracy and efficiency of detection. In addition, we combine a variety of features such as static analysis, dynamic analysis and symbolic execution to gain a more comprehensive understanding of contract characteristics and behaviours for more accurate vulnerability identification. Experimental results show that the scheme significantly improves the detection capability and provides a new solution for the security detection of smart contracts.

Identification of landslide vulnerability in the Sendangsari Area, Pajangan District, Bantul, Yogyakarta based on the Weighting Method of Regulation of the Minister of Public Works No. 22/PRT/M/2007

Dusun Krebet, in the Sendangsari Village, sub-district of Pajangan, Bantul regency, is one of the villages situated in a limestone hill area. Due to its location in the hilly terrain, both morphologically and in terms of its geological composition, the Krebet village has the potential for and is at risk of landslides. This research identifies areas that fall into the category of landslide-prone disaster zones using the Weighting Method specified in the Indonesian Minister of PUPR Regulation No. 22/PRT/M/2007. The determination of landslide-prone disaster zones is based on seven parameters, which are slope, geological conditions, land use, water flow density, soil thickness, rainfall conditions, and seismic activity map overlay is conducted with the assistance of weighting based on the Indonesian Ministry of PUPR regulations, which represents a subjective factor according to the researcher’s perspective in the study area. This process results in two risk zones: low landslide risk and medium landslide risk. The research based on the Ministry of PUPR Regulation No. 22/PRT/M/2007 indicates that the Pajangan District is predominantly characterized by a low level of vulnerability to landslide disasters, with only a small area falling into the moderate vulnerability category. This is because the regulation places greater emphasis on the potential damage to infrastructure and does not give as much attention to disaster mitigation aspects that need to be implemented.

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

Common Vulnerabilities and Exposures Assessment of Private Higher Educational Institutions Using Web Application Security

The advancements in technology has led to advancements in threats and vulnerabilities as well. Academic websites are not spared from this. This study aims to evaluate the common vulnerabilities and exposures of private higher educational institution’s website using Open Web Application Security Project. Seven private higher education institutions were evaluated using Open Web Application Security Project Zed Attack Proxy and Open Web Application Security Project top 10. The top vulnerabilities the higher educational institutions were exposed to were broken access control, insecure design and software and data integrity failures. There were two higher educational institutions with high level of risks. Also, a total of thirty-two risks or vulnerabilities were identified in the study. The identification of vulnerabilities or exposures in websites will help the private schools to become aware of possible cyberattacks to their institution’s website. This will help the institution to identify and prevent further loss or damage.

ВЫЯВЛЕНИЕ ПРОБЛЕМ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИ МЕТОДОМ СИСТЕМАТИЧЕСКОГО ОБЗОРА ЛИТЕРАТУРЫ

The field of information security covers a wide range of technical, organizational and social aspects, requiring information security specialists to take a unique and comprehensive approach to the development, support, operation, evaluation and improvement of information security management systems. An important element of successful information protection is the accurate identification and assessment of risks and vulnerabilities that may become a source of threats. In existing publications, as a rule, it is assumed that specialists have the necessary experience and knowledge to conduct risk analysis. However, in the dynamic world of information technology and threats, an information security specialist faces a huge amount of information and potential risks that are constantly evolving. Because of this, it is an impossible task to keep in mind all kinds of threats to information security. 
 The purpose is to identify actual problems of information security, potentially acting as sources of information security risk, based on the method of systematic review of the literature. Results: 73 actual problems of information security have been identified, which an information security specialist should take into account in his activities. Understanding these issues makes it possible to develop more effective protection strategies, as well as to make informed and informed decisions when developing and applying security measures.

Risk Reduction in Transportation Systems: The Role of Digital Twins According to a Bibliometric-Based Literature Review

Urban areas, with their dense populations and complex infrastructures, are increasingly susceptible to various risks, including environmental challenges and infrastructural strain. This paper delves into the transformative potential of digital twins—virtual replicas of physical entities—for mitigating these risks. It specifically explores the role of digital twins in reducing disaster risks, such as those posed by earthquakes and floods, through a comprehensive bibliometric-based literature review. Digital twins could contribute to risk reduction by combining data analytics, simulation, and predictive modeling by creating virtual replicas of physical entities and integrating real-time data streams to better address and manage risks in urban environments. In detail, they can help city planners and decision-makers analyze complex urban systems, simulate potential scenarios, and predict potential outcomes. This proactive approach allows both the identification of vulnerabilities and better implementation of targeted mitigation strategies to enhance urban resilience and sustainability. More informed decisions can be made relying on simulations, and it can also be possible to optimize resource allocation and better respond to emerging challenges. This work reviews the key publications in this domain, with the aim of finding relevant papers that can be useful to urban planners and policy-makers. The paper concludes by discussing the broader implications of these findings and identifying challenges in the widespread adoption of digital twin technology, including data privacy concerns and the need for interdisciplinary collaboration. It also outlines prospective avenues for future research in this emerging field.

ANDVI: Automated Network Device and Vulnerability Identification in SCADA/ICS by Passive Monitoring

ANDVI: Automated Network Device and Vulnerability Identification in SCADA/ICS by Passive Monitoring

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.

Incorporating Climate Adaptation into a Forest Management Plan: A Case Study on the Research and Teaching Forest of Michigan Technological University

Abstract We incorporated climate adaptation into a forest management plan for Michigan Technological University’s Ford Forest, a 2,000-ha property in Michigan’s Upper Peninsula used for education, research, and timber revenues. Our process was an opportunity to test the existing climate adaptation literature, pulling from multiple sources to meet the diverse needs of the institution at the time the plan was created. We present outcomes as well as lessons learned summarized in a series of broad takeaway messages. (1) Climate adaptation is a means to an end. The end must be defined and is made up of critical values and an adapted state. (2) Given the instability inherent in climate change, achieving adaptation means adjusting forest attributes in response to change but also preparing for unforeseen outcomes via adaptive management. (3) Decisions surrounding goals affect the entire process. Considering climate change–driven constraints when setting goals will improve outcomes. (4) Flawed decision-making is a risk associated with certain organizational contexts and affects the identification of goals, vulnerabilities, and adaptation options. (5) Climate adaptation actions cannot be evaluated in the short term for efficacy because climate change is ongoing. However, the appropriateness of a plan in responding to anticipated change can be evaluated. Study Implications: This work informs strategic planning for climate change adaptation in forests. We created a climate-informed forest management plan using adaptation frameworks for a university-owned forest in Upper Michigan at the confluence of the northern hardwoods and boreal forest ecotypes. We offer assessment of our plan outcomes and insights into how our decision-making context affected them. This case study expands on the ongoing scientific conversation, incorporating concepts from management science, on how best to adapt natural systems to climate change to protect human values derived from ecosystem services.

Abstract 3349: Identification of new potential vulnerabilities for differentiation-based therapeutic strategies against osteosarcoma

Abstract Osteosarcoma (OS), a mesenchymal bone tumor affecting mainly children and adolescents is characterized by a particularly aggressive behavior, with 20% of patients showing lung metastasis already at diagnosis. Current OS treatment strategies rely on multi-drug chemotherapy, and the prognosis for metastatic cases remains grim, underscoring the pressing need for novel therapeutic approaches. OS is considered a differentiation-related disorder, stemming from the inability of mesenchymal stem cells or osteoblastic progenitors to progress towards terminal differentiation. In our study we explored the impact of targeting ZEB1, a transcription factor known to play a pivotal role in maintaining mesenchymal and stemness characteristics in cancer, on OS cells. We employed CRISPR-Cas9 and shRNA interference techniques to inhibit ZEB1 expression in mouse immune competent OS models. In vitro, ZEB1 deficiency significantly diminished the stemness potential of OS cells, as assessed by spheroid formation assays, while promoting osteogenic differentiation. In vivo, ZEB1 deletion in OS cells led to a significant reduction in primary tumor growth. Tumors originating from ZEB1 KO cells exhibited a more differentiated morphology and higher extracellular matrix deposition compared to controls. Intriguingly, the absence of ZEB1 in tumor cells also influenced the immune infiltrate, with ZEB1-deficient tumors showing significantly fewer pro-tumoral CD206+ M2-like macrophages. This observation is in line with the significant down-modulation of CCL2 expression we detected in ZEB1 KO cells. When injected intravenously, ZEB1-deficient cells produced fewer and smaller lung metastases than control cells. Transcriptional changes induced by ZEB1 deletion were investigated by gene expression profile (GEP) and identified 849 down-regulated and 1093 up-regulated genes in ZEB1 knockout (KO) clones versus ZEB1-competent controls. Gene set enrichment analysis showed a down-modulation of pathways related to cellular proliferation and survival, such as MTORC1 signaling, MYC targets, GM2 checkpoint, E2F targets and oxidative phosphorylation in the absence of ZEB1. Single gene analysis identified among the most up-regulated genes in ZEB1-deficient cells Sfrp1, a negative regulator of the WNT/b-CAT pathway, which in turn regulates MYC, whose pathway is indeed repressed in ZEB1 KO cells. We hypothesize that a high expression of SFRP1 could vicariate the effects of ZEB1 inhibition on OS cell stemness, differentiation state, and in vivo aggressiveness, and therefore could act as a tumor suppressor, which could be potentially exploited for therapeutic strategies. Citation Format: Caterina Cascini, Daniele Lecis, Valeria Cancila, Katia Scotlandi, Claudio Tripodo, Mario P. Colombo, Claudia Chiodoni. Identification of new potential vulnerabilities for differentiation-based therapeutic strategies against osteosarcoma [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 3349.

The Eμ-Ret mouse is a novel model of hyperdiploid B-cell acute lymphoblastic leukemia

The presence of supernumerary chromosomes is the only abnormality shared by all patients diagnosed with high-hyperdiploid B cell acute lymphoblastic leukemia (HD-ALL). Despite being the most frequently diagnosed pediatric leukemia, the lack of clonal molecular lesions and complete absence of appropriate experimental models have impeded the elucidation of HD-ALL leukemogenesis. Here, we report that for 23 leukemia samples isolated from moribund Eμ-Ret mice, all were characterized by non-random chromosomal gains, involving combinations of trisomy 9, 12, 14, 15, and 17. With a median gain of three chromosomes, leukemia emerged after a prolonged latency from a preleukemic B cell precursor cell population displaying more diverse aneuploidy. Transition from preleukemia to overt disease in Eμ-Ret mice is associated with acquisition of heterogeneous genomic abnormalities affecting the expression of genes implicated in pediatric B-ALL. The development of abnormal centrosomes in parallel with aneuploidy renders both preleukemic and leukemic cells sensitive to inhibitors of centrosome clustering, enabling targeted in vivo depletion of leukemia-propagating cells. This study reveals the Eμ-Ret mouse to be a novel tool for investigating HD-ALL leukemogenesis, including supervision and selection of preleukemic aneuploid clones by the immune system and identification of vulnerabilities that could be targeted to prevent relapse.

Abstract 2500: Technical feasibility of CRISPR-based functional profiling for solid tumor precision medicine

Abstract Introduction: Next generation sequencing (NGS) has revolutionized precision oncology by enabling identification of cancer patients harboring actionable mutations who may benefit from corresponding targeted therapies. However, sequencing-based biomarkers are not detected for most patients and imperfectly predict response to therapy even when present. To address this unmet need, we have developed a CRISPR-based diagnostics platform for ‘personalized functional genomics’, allowing identification of patient-specific therapeutic vulnerabilities irrespective of mutation status. We have previously applied this approach to primary hematologic malignancies, and here report application to solid tumor-derived samples. Methods: Patient-derived solid tumor samples from several tumor types including breast, ovarian, colon and lung were obtained from 3 sources: fresh tissue (obtained from patients via core biopsy, surgical resection, or malignant fluid sampling), primary Patient-Derived Cell Lines (PDCLs, generated from surgical material and adapted for ex vivo culture), and commercially obtained viably frozen dissociated tumor cells (DTCs). Samples were dissociated to single cell suspensions and transduced with a lentivirus encoding Cas9 and an sgRNA library covering a custom targeted oncology gene panel. The panel consists of genes encoding targets of FDA-approved oncology drugs, as well as reference genes. After a short culture period, transduced cells were harvested and relative sgRNA distribution was measured by amplicon NGS. Changes in sgRNA abundance were collapsed into gene-level log2-fold changes, and significantly depleted genes scored as functional dependencies. For select PDCLs, cells were treated with cognate small molecule inhibitors over the course of 4 days. Cell viability was read out using CTG assay and a calculated IC50 lower than 500nM inferred as inhibitor sensitive. Results: In a double-blind study using primary PDCLs, functional dependencies identified by CRISPR perfectly discriminated alpelisib sensitive (2/2) from resistant samples (6/6). Using fresh primary samples from varying cancer types and specimen sources, we achieve a technical success rate >70%. Among gene dependencies identified, targets of FDA-approved oncology drugs and those under late-stage clinical investigation were both observed including BRAF, CDK4/6, VEGFR1/2 and PARP1/2. Conclusion: We demonstrate technical proof of concept of the FX platform for CRISPR-based personalized functional genomics. This approach is feasible when applied to primary patient samples from a variety of tumor types and tissue sources, and results are validated by comparison with orthogonal methods. CRISPR-based functional profiling represents a powerful adjunct to traditional genomic profiling, for the first time allowing drug target dependency mapping in a patient-specific and mutation-agnostic manner. Citation Format: Karl Hodel, Andreas Varkaris, Nicholas Chevalier, Maci Meyers, Allison Kehlmann, Stephane Corbel, Anastasia Stavridi, Peter Kilfeather, Leonardo Hagmann, Kathleen Tran, Yali Li, Christian Schmedt, Ioannis Sanidas, Srinath Sampath, Srihari Sampath, Dejan Juric. Technical feasibility of CRISPR-based functional profiling for solid tumor precision medicine [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 2500.