Mutually Exclusive Research Articles

First-Come-First-Served as a Separate Principle

A lock is a mechanism to guarantee mutual exclusion with eventual progress, i.e., some degree of fairness. First-come-first-served (FCFS) progress is perfectly fair. FCFS progress can be offered by a locking algorithm or added by wrapping a non-FCFS lock with a separate FCFS algorithm. A new separate FCFS algorithm is presented that creates FCFS progress by wrapping a lock’s entry protocol (acquire). The algorithm addresses an important safety issue in locking, called barging, where arriving threads proceed before waiting threads or waiting threads are serviced with some bias. Barging increases latency for waiting threads and is non-intuitive to concurrent programmers, even though it is inherent to non-deterministic concurrent execution. A correctness proof is presented for the new FCFS algorithm and verified with the proof assistant PVS. Experimental tests are performed to ensure the presented wrapper provides FCFS progress when used to transform non-FCFS software and hardware locks. The performance of the non-FCFS and transformed FCFS counterparts is compared, and contrasted with locks using inherent FCFS progress. The results show the FCFS transforms are performant for most algorithms, providing an additional tool for application developers to achieve correctness without a significant global performance reduction.

Reallocating just 10 min to moderate-to-vigorous physical activity from other components of 24-hour movement behaviors improves cardiovascular health in adults

BackgroundAs components of a 24-hour day, sedentary behavior (SB), physical activity (PA), and sleep are all independently linked to cardiovascular health (CVH). However, insufficient understanding of components’ mutual exclusion limits the exploration of the associations between all movement behaviors and health outcomes. The aim of this study was to employ compositional data analysis (CoDA) approach to investigate the associations between 24-hour movement behaviors and overall CVH.MethodsData from 581 participants, including 230 women, were collected from the 2005–2006 wave of the US National Health and Nutrition Examination Survey (NHANES). This dataset included information on the duration of SB and PA, derived from ActiGraph accelerometers, as well as self-reported sleep duration. The assessment of CVH was conducted in accordance with the criteria outlined in Life’s Simple 7, encompassing the evaluation of both health behaviors and health factors. Compositional linear regression was utilized to examine the cross-sectional associations of 24-hour movement behaviors and each component with CVH score. Furthermore, the study predicted the potential differences in CVH score that would occur by reallocating 10 to 60 min among different movement behaviors.ResultsA significant association was observed between 24-hour movement behaviors and overall CVH (p < 0.001) after adjusting for potential confounders. Substituting moderate-to-vigorous physical activity (MVPA) for other components was strongly associated with favorable differences in CVH score (p < 0.05), whether in one-for-one reallocations or one-for-remaining reallocations. Allocating time away from MVPA consistently resulted in larger negative differences in CVH score (p < 0.05). For instance, replacing 10 min of light physical activity (LPA) with MVPA was related to an increase of 0.21 in CVH score (95% confidence interval (95% CI) 0.11 to 0.31). Conversely, when the same duration of MVPA was replaced with LPA, CVH score decreased by 0.67 (95% CI -0.99 to -0.35). No such significance was discovered for all duration reallocations involving only LPA, SB, and sleep (p > 0.05).ConclusionsMVPA seems to be as a pivotal determinant for enhancing CVH among general adult population, relative to other movement behaviors. Consequently, optimization of MVPA duration is an essential element in promoting overall health and well-being.

The interaction of upstream encroachment and blockchain technology application in a shipping supply chain

In shipping practice, ocean shipping (OS) companies launch their own cargo-canvassing channel to diversify revenue streams and further develop blockchain technology to improve their shipping service level. This study employs a game theory model to investigate the value of the OS company (i.e., upstream) encroaching and the application of blockchain technology by engaging in strategic interactions. Through our investigation, we find that it is disadvantageous for the OS company to encroach on the shipping market (apply blockchain technology) when the cost of encroachment (blockchain) is relatively high. Interestingly, by analyzing strategic interactions, we find that the application of blockchain technology influences the encroachment pattern, and the encroachment incentivizes the OS company to apply blockchain technology when the market demand for the encroaching channel is moderate. More interestingly, we innovatively propose the synergy, dysergy and mutual exclusivity effect to demonstrate our analysis. Furthermore, we discuss the impacts of the value of OS company’s encroachment and application of blockchain technology on the freight forwarder, and propose a “Blockchain Technology Compensation” (BTC) coordination mechanism to cope with the “win-lose” situation caused by the application of blockchain technology by the OS company.

The interplay of facilitation and competition drives the emergence of multistability in dryland plant communities.

Within communities, species are wrapped in a set of feedbacks with each other and with their environment. When such feedbacks are strong enough they can generate alternative stable states. So far, research on alternative stable states has mostly focused on systems with a small number of species and a limited diversity of interaction types. Here, we analyze a spatial model of plant community dynamics in stressed ecosystems such as drylands, where each species is characterized by a strategy, and the different species interact through facilitation and competition for space and resources, such as water. We identify three different types of multistability emerging from the interplay of competition and facilitation. Under low-stress levels, plant communities organize in small groups of coexisting species, maintained by space, competition and facilitation ("cliques"). Under higher stress levels, positive feedback from facilitation lead to the dominance of a single facilitating species ("mutual exclusion states"). At the highest stress levels, the single facilitating species left in the system coexists with the desert state. By linking community ecology and alternative stable states theory using a spatial plant community model for stressed ecosystems, our study contributes to highlight the importance of positive feedback loops for the stability of ecological communities.

Correctness Verification of Mutual Exclusion Algorithms by Model Checking

Mutual exclusion algorithms are at the heart of concurrent/parallel and distributed systems. It is well known that such algorithms are very difficult to analyze, and in the literature, different conjectures about starvation freedom and the number of by-passes (also called the overtaking factor) exist. The overtaking factor affects the (hopefully) bounded waiting time that a process competing for entering the critical section has to suffer before accessing the shared resource. This paper proposes a novel modeling approach based on Timed Automata and the Uppaal toolset, which proves effective for studying all the properties of a mutual exclusion algorithm for N≥2 processes, by exhaustive model checking. Although the approach, as already confirmed by similar experiments reported in the literature, is not scalable due to state explosion problems and can be practically applied until N≤5, it is of great value for revealing the true properties of analyzed algorithms. For dimensions N&gt;5, the Statistical Model Checker of Uppaal can be used, which, although based on simulations, can confirm properties by estimations and probabilities. This paper describes the proposed modeling and verification method and applies it to several mutual exclusion algorithms, thus retrieving known properties but also showing new results about properties often studied by informal reasoning.

Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks

Membership inference (MI) attacks threaten user privacy through determining if a given data example has been used to train a target model. Existing MI defenses protect the membership privacy through preemptive exclusion of members techniques and knowledge distillation. Unfortunately, using either of these two defenses alone, the defense effect can still offers an unsatisfactory trade-off between membership privacy and utility.Given that the defense method that directly combines these two defenses is still very limited (e.g., the test accuracy of the target model is decreased by about 40% (in our experiments)), in this work, we propose a dual defense (DD) method that includes the preemptive exclusion of high-risk member samples module and the knowledge distillation module, which thwarts the access of the resulting models to the private training data twice to mitigate MI attacks. Our defense method can be divided into two steps: the preemptive exclusion of high-risk member samples (Step 1) and the knowledge distillation to obtain the protected student model (Step 2). We propose three types of exclusions: existing MI attacks-based exclusions, sample distances of members and nonmembers-based exclusions, and mutual information value-based exclusions, to preemptively exclude the high-risk member samples. During the knowledge distillation phase, we add ground-truth labeled data to the reference dataset to decrease the protected student model's dependency on soft labels, aiming to maintain or improve its test accuracy. Extensive evaluation shows that DD significantly outperforms state-of-the-art defenses and offers a better privacy-utility trade-off. For example, DD achieves ∼100% test accuracy improvement over the distillation for membership privacy (DMP) defense for ResNet50 trained on CIFAR100. DD simultaneously achieves the reductions in the attack effectiveness (e.g., the TPR@0.01%FPR of enhanced MI attacks decreased by 2.10% on the ImageNet dataset, the membership advantage (MA) of risk score-based attacks decreased by 56.30%) and improvements of the target models' test accuracies (e.g., by 42.80% on CIFAR100).

Performant almost-latch-free data structures using epoch protection in more depth

AbstractMulti-core scalability presents a major implementation challenge for data system designers today. Traditional methods such as latching no longer scale in today’s highly parallel architectures. While the designer can make use of techniques such as latch-free programming to painstakingly design specialized, highly-performant solutions, such solutions are often intricate to build and difficult to reason about. Of particular interest to data system designers is a class of data structures we call almost-latch-free; such data structures can be made scalable in the common case, but have rare complications (e.g., dynamic resizing) that prevent full latch-free implementations. In this work, we present a new programming framework called Epoch-Protected Version Scheme (EPVS) to make it easy to build such data structures. EPVS makes use of epoch protection to preserve performance in the common case of latch-free operations, while allowing users to specify critical sections that execute under mutual exclusion for the rare, non-latch-free operations. We showcase the use of EPVS-based concurrency primitives in a few practical systems to demonstrate its competitive performance and intuitive guarantees. EPVS is available in open source as part of Microsoft’s FASTER project (Epoch Protected Version Scheme (source code) 2022; Microsoft FASTER 2022).

Abstract B027: A pathway-informed framework to infer synthetic lethal relationships in pediatric cancer

Abstract Synthetic lethality, the combination of two mutated genes which results in cell death, has been greatly investigated due to its therapeutic potential in cancer. Despite the common practice of experimentally detecting synthetic lethality, here we extend a computational framework that employs co-occurrence of gene mutations to infer synthetic lethality by incorporating pathways. Pathways aggregate the mutated genes into a functional unit and give power to uncover relationships containing genes of low mutation frequency e.g. in pediatric cancer, which otherwise would not be observed by testing individual gene pairs. Our proposed framework is an alternative avenue to a more focused synthetic lethality search by exploiting mutation data &amp; pathway knowledge. We infer potentially synthetic lethal relationships based on mutated gene co-occurrence. A less often than expected co-occurrence, or so-called mutual exclusivity (ME), indicates a potentially synthetic lethal relationship whereas one that occurs more often than expected (CO) indicates a potentially advantageous relationship. In this framework we use a selected set of biological pathways from Reactome to aggregate gene mutations per pathway for each individual tumor (SNVs and indels with high/moderate impact) within two public pediatric cancer datasets, TARGET and DKFZ. This results in mutated-pathway profiles per tumor which are tested for co-occurrence using rediscover. The test outputs significant co-occurring and mutually exclusive pathways from which we extract the underlying mutated gene pairs. The co-occurring and mutually exclusive gene pairs are then annotated for pathway epistasis, protein complexes and their presence in BioGRID and SynLethDB. Finally, the validity of the proposed gene pairs is examined in literature. The test detected 439 (ME: 369, CO:70) significant pathway pairs in TARGET and 49 (ME: 31, CO:18) in DKFZ across several cancer types. Out of these, 3185 gene pairs were extracted in TARGET (ME: 2671, CO: 514) and 331 (ME: 16, CO: 315) in DKFZ. These relationships are significantly more than the ones found when testing solely for gene pairs. Pathways aid in partially decreasing findings due to subtype or pathway epistasis. For example, FLT3-KRAS found in TARGET-B-ALL by the gene test is not detected by the pathway test. The test was, also, able to uncover mutually exclusive (ME) gene pairs in smaller datasets. The initially found ME genes TP53-DROSHA in Wilms tumors and KIT-NRAS in AML tumors in TARGET by the gene test, were detected in the corresponding cancer types’ DKFZ datasets. In addition to TP53-DROSHA, TP53-DGCR8 were found ME in Wilms tumors of DKFZ, indicating the microprocessor complex mutations ME to TP53 mutations. To sum up, we present a pathway-informed synthetic lethality inference framework for pediatric cancer to explore synthetic lethal relationships and other complex functional relationships between mutated genes by exploiting already existing data and database information as an alternative avenue for follow-up experimentation. Citation Format: Anastasia Spinou, Richard Gremmen, Puck Veen, Jarno Drost, Patrick Kemmeren. A pathway-informed framework to infer synthetic lethal relationships in pediatric cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B027.

Abstract B008: Utilizing pathway incompatibility for synthetic lethality: A therapeutic strategy for B-cell lymphoma

Abstract Previous pan-cancer analyses revealed co-occurring mutations exploitable for combination therapies and mutually exclusive mutations assumed to reflect functional redundancy (Mina M et al. 2017; Sanchez-Vega F et al. 2018). However, we recently highlighted an alternative scenario when mutual exclusivity reflects the incapability of two pathways to function together, a phenomenon we term “pathway incompatibility” (Chan LN et al. 2020). We found that STAT5-and ERK-activating genetic lesions were not only mutually exclusive in B-ALL cases, but also reciprocally inhibited each other at the signal transduction level, inducing cell death instead of promoting leukemogenesis. Notably, we demonstrated, for the first time, that pharmacological activation of STAT5 in ERK-driven B-ALL cells and ERK in STAT5-driven B-ALL cells represents a tractable synthetic lethality (Chan LN et al. 2020). We now take the important next step of identifying potential candidates for synthetic lethal genetic interactions resulting from pathway incompatibility in B-cell malignancies more broadly. Diffuse large B-cell lymphoma (DLBCL) is a rapidly growing blood cancer affecting older adults and constituting about 25% of all lymphoma cases in the US. Around 40% of patients develop relapsed or refractory DLBCL (Pfreundschuh M et al. 2006; Ngu H et al. 2022). Thus, there is an unmet need to develop new therapeutic strategies to improve clinical outcomes. Studying 1684 DLBCL cases from published datasets, we found that about 30% of DLBCL cases carry loss-of-function (LOF) CBP or gain-of-function (GOF) MYD88 genetic lesions. CBP encodes a histone acetyltransferase and, together with p300, functions as a transcriptional coactivator and tumor suppressor. Conversely, GOF MYD88 mutations drive lymphomagenesis by regulating kinases and transcription factors. Our analysis revealed that LOF CBP and GOF MYD88 genetic lesions co-occurred less frequently than expected by chance (odds ratio: 0.57, P=0.0), indicating their mutual exclusivity. Experiments with human DLBCL cell lines expressing wild-type MYD88 and CBP showed that CBP/p300 inhibitors did not impact cell viability. However, cells expressing a GOF MYD88 mutant or stimulated with a toll-like receptor (TLR) agonist to activate MYD88 became sensitized to CBP/p300 inhibitors, resulting in cell death. This suggests that LOF CBP and GOF MYD88 are incompatible for cell survival, with their co-occurrence resulting in synthetic lethality. We then tested a synthetic lethality-based therapeutic concept based on CBP-MYD88 incompatibility in DLBCL. We found that MYD88-mutated DLBCL cell lines were more sensitive to pharmacological inhibition of CBP/p300, while CBP-deficient DLBCL cell lines showed increased sensitivity to TLR agonist treatment. In summary, we identified mutual exclusivity between LOF CBP and GOF MYD88 lesions in DLBCL, where their co-occurrence induces cell death. Moreover, we demonstrated the therapeutic potential of synthetic lethality resulting from co-introduction of LOF CBP and GOF MYD88 in DLBCL. Citation Format: Mia Knupke, Lai Chan. Utilizing pathway incompatibility for synthetic lethality: A therapeutic strategy for B-cell lymphoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B008.

Abstract A022: Synthetic lethality of ERBB2 and CCND1 in breast cancer at scale

Abstract Introduction: Synthetically lethal mutations offer unique molecular targets for oncologic therapy. At scale, synthetic lethality (SL) is observed when alterations to one gene alone do not correspond with worse overall survival (OS), but simultaneous expression with another gene does correspond with worse OS. Mutually exclusive gene expression is not a requirement for SL. Existing breast cancer (BC) literature suggests synthetic lethality between CKS1B and PLK1, as well as BRCA1/BRCA2 and PARP1. Unaltered RB1 and altered CCND1 are also known to exhibit SL in the HER2-deficient environment of triple-negative BC. CCND1 and ERBB2 are typically amplified in invasive ductal carcinoma, with ERBB2 amplifications correlating to larger tumor size. This relationship is not observed in invasive lobular carcinoma, although expression of E2F1, a downstream transcription factor of CCND1, is inversely correlated with tumor grade. Methods: Our in silico approach investigated SL between ERBB2 and CCND1 in breast invasive carcinoma without distinction to lobular or ductal origin (TCGA, PanCancer Atlas 2018). We identified 17 genes of interest based on molecular alterations present in &amp;gt;10% of the sample (n=1084) and common mentions in BC literature. SL was determined if alterations to one gene alone did not significantly worsen OS, while alterations to two genes corresponded with worse OS. Results: Twenty-three SL interactions were observed. CCND1 and ERBB2 were selected because neither gene significantly worsened OS alone (p=0.915 &amp; p=0.0951 respectively). However, patients with alterations to both genes had significantly worse OS compared to those with alterations to one gene alone. CCND1 and ERBB2 alterations were present in 14.9% and 13.7% of the sample, respectively. HR=2.568 (p=0.0138) for SL patients compared to those with CCND1 alterations alone; HR=2.244 (p=0.0497) for SL patients compared to those with ERBB2 alterations alone. Mutual exclusivity was not significantly observed (p=0.084). Multivariate analysis was performed using Cox regression models; neither age (older or younger than 55 years) nor race (White, Black, Asian) confounded our results due to p&amp;gt;0.05. Conclusion: Our findings support potential therapeutic use of CCND1 targets in HER2+ BC, specifically for patients with SL between CCND1 and ERBB2. These include PLK1 inhibitors, commonly used in estrogen receptor-positive patients with CDK4/6 inhibitor resistance, along with HER2 inhibitors. A combination of MAP2K and PIK3CA inhibitors should also be investigated in HER2+ BC patients with CCND1 amplification, since HER2+ colorectal cancer patients have been demonstrated to fare better with this treatment. Citation Format: Rishi Nair, Evan P. White, Roy Khalife, Anthony M. Magliocco. Synthetic lethality of ERBB2 and CCND1 in breast cancer at scale [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A022.

Abstract PR013: Detecting pairwise and higher-order antagonistic epistatic effects among somatic cancer genotypes to discover synthetic lethality

Abstract The mutual exclusivity of somatic variants in cancer suggests that current somatic variants can exert antagonistic epistatic effects on the selection of new mutations, contributing to a complex landscape of evolutionary trajectories. However, quantifying pairwise and higher-order epistatic effects—which are essential to estimation of the trajectory of likely cancer genotypes—has been a challenge. We have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation, dependent on somatic cancer genotype. We coupled the continuous-time Markov chain model with an approach that deconvolutes the underlying mutation rates and selection intensities across trajectories of oncogenesis. By elucidating the routes of variant evolution and the strengths of selective forces at play, our approach lays the groundwork for quantifying antagonistic epistatic effects of specific somatic genotypes. We applied our approach to report the variant mutation rates, selection intensities, and consequent substitution rates for lung adenocarcinoma, comparing smoker and nonsmoker tumor cohorts. We describe the distinct influences of smoking on underlying mutation rates and on physiology affecting the selective regime of lung tissue. Inference of the most likely routes of site-specific variant evolution, as well as estimation of the selection strength operating on each step along the route represents a key component for prioritization, development, and implementation of personalized cancer therapies that target synthetic lethality. Citation Format: Jorge A. Alfaro-Murillo, Krishna Dasari, Jeffrey P. Townsend. Detecting pairwise and higher-order antagonistic epistatic effects among somatic cancer genotypes to discover synthetic lethality [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR013.

Strong and tough 3D-(SiC–Si3N4)/Al co-continuous composite with enhanced interfaces

Materials with high specific strength and toughness are urgently needed in the fields of electronic packaging, armor, and aerospace. SiC/Al composites show good potential for these applications but the problem of mutual exclusion of strength and toughness still needs to be solved. Herein, we propose a new strategy to simultaneously enhance its strength and toughness from the perspective of structural optimization and interface enhancement. Inspired by the nacre structure, a 3D-(SiC–Si3N4)/Al co-continuous composite was prepared by freeze-casting combined with the pressure infiltration method. During the process, elongated Si3N4 grains were introduced into the ceramic framework to design the internal lamella wall structure and increase the contact area with the aluminum substrate. Moreover, a favorable interfacial bonding was designed between Si3N4 and Al to improve interface wettability. The reliable microstructure of porous 3D-(SiC–Si3N4) preform was tailored through the adjustment of initial solid loading and the α-Si3N4 component. The effects of initial solid loading and the α-Si3N4 component on the microstructure, interfacial wettability, and mechanical properties of the final composites were investigated. The results showed that the SiC–Si3N4/Al composites possess high flexural strength (769 MPa) and fracture toughness (15.1 MPa m1/2), which is superior to data reported in general literature. Numerous elongated β-Si3N4 grains are produced on the ceramic skeleton, forming an interlocking structure with the aluminum matrix, which increases the contact area with the aluminum substrate. A chemical reaction layer was formed at the interface of the composite, greatly improving the interfacial bonding strength. This work provides a promising design guideline for developing and optimizing high-strength and tough composite.

Abstract B012: Predicting targetable paralog synthetic lethalities and functional redundancies in cancer genomes

Abstract Paralogs are prevalent in the human genome and are considered a rich source of synthetic lethality due to functional redundancy. For a cancer cell carrying a gene with Loss-Of-Function (LOF) mutation, inactivation of its paralog using gene editing can induce a selective decrease in viability, leaving normal cells that do not harbor the mutation unharmed. Previous studies have exploited this vulnerability of cancer genomes. However, the cancer-specificity of such synthetic lethality limits its application across different cancer types. Furthermore, the role of functional redundancy which can explain the cancer specificity has remained largely unexplored. In this study, we computationally predicted synthetic lethal paralogs along with mechanistically important functional redundancies between them in a cancer-specific manner. We applied our prediction method to publicly available data for an aggressive subtype of breast cancer called triple-negative breast cancer (TNBC), which lacks the expression key biomarkers and hence has the worst prognosis among other breast cancer subtypes. TNBC is characterized by the highest mutational load and the largest fraction of genome altered among the breast cancer subtypes providing a rich mutational landscape to identify LOF genes. Using the CRISPR inactivation screen data for cancer cell lines obtained from the Cancer Dependency Map (DepMap) project, we predicted sets of synthetic lethal paralogs that show a significantly greater viability decrease if a gene carries LOF and its paralog is inactivated, compared to the viability decrease due to the inactivation of only one of the paralogs. Consistent with previous findings of context-dependent synthetic lethality, we found that a relatively small fraction of TNBC-specific synthetic lethal paralogs overlapped with those found across other cancer types. To uncover the mechanistically important functional redundancies between paralogs, we analyzed the genomics and transcriptomics data from multiple sources: TNBC panel of primary tumors and patient-derived xenografts, Pan-Cancer Analysis of Whole Genomes (PCAWG), and the Cancer Cell Line Encyclopedia (CCLE). The functional redundancies varied across cancer types based on (1) mutual exclusivity of LOFs, (2) backup compensation of deleterious mutations, (3) backup upregulation, and (4) dosage balance. Overall, our findings show a strong context-dependency of synthetic lethal paralogs and estimates of functional redundancy, emphasizing the importance of such cancer-specific predictions in identifying targetable paralog synthetic lethalities. Collectively, the computational method and sets of targetable paralogs are a unique resource for developing precision oncology therapeutic strategies against TNBC and other cancers more broadly. Citation Format: Rohan Dandage, Elena Kuzmin. Predicting targetable paralog synthetic lethalities and functional redundancies in cancer genomes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B012.

Abstract A021: Hunting for synthetic lethal partners in DNA damage response-altered endometrial cancer

Abstract DNA damage response (DDR) genes are frequently altered in cancer, which may lead to protein loss-of-function (LoF) and result in particular DDR defects that can be therapeutically targeted via the concept of synthetic lethality (SL). There is a high prevalence of DDR alterations in endometrial cancer, a gynecologic malignancy with increased incidence and mortality in recent decades, and an unmet clinical need for a wider range of treatment options to reduce adverse effects and combat therapy resistance. In this study, a large-scale, agnostic bioinformatic screening approach was employed to discover novel potential SL targets specific to DDR LoF biomarkers in endometrial cancer for subsequent experimental validation in vitro. SL analysis was performed with the in-house Target / Biomarker / Lineage (TBL) bioinformatic pipeline (Division of Cancer Therapeutics, The Institute of Cancer Research) using datasets obtained from the Cancer Dependency Map (DepMap). The TBL pipeline statistically analyzed cancer cell line target gene dependency probabilities, inferred from Project Achilles and Project Score genome-wide CRISPR-Cas9 dropout screens, and integrated this with mutation and copy number variation genomic datasets. Potential interactions were filtered for target-biomarker mutual exclusivity using patient data from The Cancer Genome Atlas (TCGA). The ability of the TBL pipeline to identify known SL interactions was demonstrated through in silico identification of ARID1B/ARID1A in ovarian cancer and SMARCA2/SMARCA4 in lung cancer as significant hits. In addition, the identification of WRN/ARID1A as a significant hit in uterine cancer, given that ARID1A loss has been significantly associated with microsatellite instability (MSI) in endometrial cancer, is consistent with previous findings of WRN helicase as a SL partner with MSI in endometrial cancer. Analysis and downstream refinement of endometrial cancer significant hits led to the identification of seven final candidate target-biomarker gene pairs. These putative SL partners comprise novel potential endometrial cancer targets, with various functions ranging from nucleotide biosynthesis to epigenetic remodeling, and include novel potential and established LoF biomarkers relevant to endometrial cancer biology, namely DNA mismatch repair. In vitro validation of the final candidates by genetic or pharmacologic approaches to assess target gene dependency in endometrial cancer cell lines with altered or wildtype biomarker status will be discussed. These experiments will provide key experimental data to validate our bioinformatic approach to SL target-biomarker discovery, and may identify novel SL partners involving DDR LoF alterations in endometrial cancer that could lead to the development of new molecular targeted therapies and improved treatment options for this patient subpopulation. Citation Format: Julie Zhou, Konstantinos Mitsopoulos, Olivia Rossanese. Hunting for synthetic lethal partners in DNA damage response-altered endometrial cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A021.

Physical networks as network-of-networks

Physical networks are made of nodes and links that are physical objects embedded in a geometric space. Understanding how the mutual volume exclusion between these elements affects the structure and function of physical networks calls for a suitable generalization of network theory. Here, we introduce a network-of-networks framework where we describe the shape of each extended physical node as a network embedded in space and these networks are bound together by physical links. Relying on this representation, we introduce a minimal model of network growth and we show for a general class of physical networks that volume exclusion induces heterogeneity in both node volume and degree, with the two becoming correlated. These emergent properties strongly affect the dynamics on physical networks: by calculating their Laplacian spectrum as a function of the coupling strength between the nodes we show that degree-volume correlations suppress the role of hubs as early spreaders in diffusive dynamics. We apply the network-of-networks framework to describe several real systems and find properties analog to the minimal model networks. The prevalence of these properties points towards general growth mechanisms that do not depend on the specifics of the systems.

Artificial intelligence in healthcare: an Italian perspective on ethical and medico-legal implications.

Artificial intelligence (AI) is a multidisciplinary field intersecting computer science, cognitive science, and other disciplines, able to address the creation of systems that perform tasks generally requiring human intelligence. It consists of algorithms and computational methods that allow machines to learn from data, make decisions, and perform complex tasks, aiming to develop an intelligent system that can work independently or collaboratively with humans. Since AI technologies may help physicians in life-threatening disease prevention and diagnosis and make treatment smart and more targeted, they are spreading in health services. Indeed, humans and machines have unique strengths and weaknesses and can complement each other in providing and optimizing healthcare. However, the healthcare implementation of these technologies is related to emerging ethical and deontological issues regarding the fearsome reduction of doctors' decision-making autonomy and acting discretion, generally strongly conditioned by cognitive elements concerning the specific clinical case. Moreover, this new operational dimension also modifies the usual allocation system of responsibilities in case of adverse events due to healthcare malpractice, thus probably imposing a redefinition of the established medico-legal assessment criteria of medical professional liability. This article outlines the new challenges arising from AI healthcare integration and the possible ways to overcome them, with a focus on Italian legal framework. In this evolving and transitional context emerges the need to balance the human dimension with the artificial one, without mutual exclusion, for a new concept of medicine "with" machines and not "of" machines.

In vitro activity of cefiderocol in Pseudomonas aeruginosa isolates from people with cystic fibrosis recovered during three multicentre studies in Spain.

Despite the introduction of cystic fibrosis transmembrane conductance regulator (CFTR) modulators, Pseudomonas aeruginosa is still a major pathogen in people with cystic fibrosis (pwCF). We determine the activity of cefiderocol and comparators in a collection of 154 P. aeruginosa isolates recovered from pwCF during three multicentre studies performed in 17 Spanish hospitals in 2013, 2017 and 2021. ISO broth microdilution was performed and MICs were interpreted with CLSI and EUCAST criteria. Mutation frequency and WGS were also performed. Overall, 21.4% were MDR, 20.8% XDR and 1.3% pandrug-resistant (PDR). Up to 17% of the isolates showed a hypermutator phenotype. Cefiderocol demonstrated excellent activity; only 13 isolates (8.4%) were cefiderocol resistant by EUCAST (none using CLSI). A high proportion of the isolates resistant to ceftolozane/tazobactam (71.4%), meropenem/vaborbactam (70.0%), imipenem/relebactam (68.0%) and ceftazidime/avibactam (55.6%) were susceptible to cefiderocol. Nine out of 13 cefiderocol-resistant isolates were hypermutators (P < 0.001). Eighty-three STs were detected, with ST98 being the most frequent. Only one isolate belonging to the ST175 high-risk clone carried blaVIM-2. Exclusive mutations affecting genes involved in membrane permeability, AmpC overexpression (L320P-AmpC) and efflux pump up-regulation were found in cefiderocol-resistant isolates (MIC = 4-8 mg/L). Cefiderocol resistance could also be associated with mutations in genes related to iron uptake (tonB-dependent receptors and pyochelin/pyoverdine biosynthesis). Our results position cefiderocol as a therapeutic option in pwCF infected with P. aeruginosa resistant to most recent β-lactam/β-lactamase inhibitor combinations.

CCNE1 amplification as marker of poor prognosis and novel therapeutic target in advanced breast cancer.

1040 Background: Cyclin E1 (CCNE1) overexpression or gene amplification is linked to dismal outcomes in various tumors, including breast cancer (BC). Recent studies suggest that CCNE1 amplification is a potential target for new synthetic lethality-based treatments, including CDK2-selective and PKMYT1 inhibition. This study explores the clinical and genomic characteristics of CCNE1-amplified BCs. Methods: We analyzed genomic and clinical data from consecutive BCs that underwent clinical tumor-normal targeted panel sequencing (MSK-IMPACT) between April 2014 and December 2021. Allele-specific copy number, including CCNE1 amplification, and fraction genome altered (FGA) were calculated by FACETS. Mutual exclusivity and co-occurrence analyses were performed using CoMEt. Benjamini–Hochberg method for multiple testing correction (q) was applied. Real-world progression-free survival (rwPFS) was assessed by the Kaplan Meier method and Cox models, focusing on patients with available pre-treatment samples. Results: Out of 3,753 BCs, 125 (3.3%) had CCNE1 amplification. A higher occurrence of CCNE1 amplification was noted in post-treatment (n=2,368) compared to treatment-naïve tumors (n=1,385; 4% vs 2.4%, p=0.007). CCNE1 amplification was less common in hormone receptor (HR)+/HER2- BCs (2%) compared to HER2+ (7.6%) and triple-negative (7.2%) tumors (p&lt;0.001). BCs with CCNE1 amplification showed higher median FGA, indicating increased genomic instability. In primary BC, TP53 alterations were more frequent in CCNE1-amplified tumors (q&lt;0.001), while CDH1 alterations were mutually exclusive (q&lt;0.001) suggesting that lobular BCs rarely harbor CCNE1 amplification. Similar trends were seen in post-treatment samples. CCNE1 amplification detected at baseline was linked to shorter median rwPFS in HR+/HER2- metastatic BCs treated with CDK4/6 inhibitors plus endocrine therapy (8.8 vs 15.2 months in CCNE1-amplified [n=9] vs CCNE1-non amplified [n=402]; hazard ratio [HR] 2.82, 95% CI 1.38-5.75), and in HER2+ metastatic BCs treated with first-line THP regimen (7.3 vs 20.8 months in CCNE1-amplified [n=5] vs CCNE1-non amplified [n=106]; HR 3.1, 95% CI 1.24-7.87). In a CCNE1-amplified triple-negative cell model (HCC1569), treatment with the PKMYT1 inhibitor RP-6306 significantly reduced tumor growth. Conclusions: CCNE1 amplification in BCs is associated with greater genomic instability and characterizes a group of metastatic BCs with poor response to standard of care therapies. Novel therapies targeting CCNE1-amplified tumors are under evaluation in preclinical and clinical studies.

Characterization of driver oncogenic mutations of in-transit melanoma metastases.

9586 Background: In-transit melanoma (ITM) is a distinctive form of melanoma metastasis, marked by aggressive locoregional progression and characterized by the entrapment of tumor cells within the lymphatic channels before reaching regional lymph nodes (LN). The mechanisms leading to lymphatic trapping rather than progression to distant metastasis are not well-understood. Previous research indicates a clonal origin for ITM, suggesting specific early-stage genetic alterations may drive this clinical phenotype. In this study, we sought to identify driver oncogenic mutations specific to ITM that may serve as potential targets. Methods: We analyzed data from the MSK-IMPACT database, which includes targeted sequencing of 341-468 genes for patients with cutaneous melanoma. Patients were stratified into four groups based on the type of sample sequenced: in-transit, primary tumor, regional LN, and distant metastases. We examined the prevalence of driver mutations and genetic variants across these groups, adjusting for multiple comparisons. Results: A total of 528 patients were included, of which 74 were in the ITM, 103 in the primary, 133 in the regional LN, and 218 in the metastatic group. The mean age of the cohort was 59 years (SD = 16) and 67% were male. Among the in-transit samples, 68% were treatment-naïve at the time of sequencing, 36% received anti-PD1 monotherapy, and 12% received anti-PD-1 combination therapy. None of the patients received targeted therapy. NRAS Q61 driver mutations were significantly more common in ITM than all other groups (all p&lt;0.05). NF-1 driver mutations were significantly less common in ITM than distant metastasis (p=0.007) (Table). Comparative analysis across all genes showed a lower incidence of NF-1 mutations in ITM relative to metastatic samples (12% vs 38%, p=0.007). Gene interaction analysis showed mutual exclusivity between NRAS and PAK5 mutations in ITM (p=0.02), but not in other groups (p&gt;0.05). Conclusions: This study reveals distinct driver oncogenic mutations specific to ITM, characterized by a high incidence of NRAS mutations, retained NF-1 function and the novel finding of mutual exclusivity between NRAS and PAK5. Closer examination of the interplay between NRAS, NF-1 and PAK5 interactions in ITM may provide deeper insights into the molecular mechanisms of ITM and offer potential therapeutic targets for ITM. [Table: see text]

Reconceptualising transport-related social exclusion in rural China

In this paper, we propose a conceptual framework to elucidate transport-related social exclusion (TRSE) in rural China. Drawing from participant observations across 146 villages in Guangdong (2643 rural residents), we illuminate the multifaceted nature of TRSE in Chinese villages. In term of interregional transport challenges, rural residents are socially excluded due to inadequate transport infrastructure and services. In contrast, within the microcosm of intravillage transport, residents adeptly harness available transport resources to craft exclusionary strategies.This emergent form of TRSE, distinct from previous paradigms, sees villagers deploying transport as an instrument to deliberately exclude the Other based on the depth and nuances of their interpersonal relationships. This exclusion manifests through unique characteristics: the nebulous contours of marginalised groups, mutual exclusion between rural residents, ethical ambivalence, and its profound resonance with traditional cultural norms. Given the intricate layers that seem to eclipse the phenomena observed in urban contexts, we underscore the imperative for a more nuanced exploration of TRSE in rural landscapes.