Genetic Approach Research Articles

Optimizing large-scale hydrogen storage: A novel hybrid genetic algorithm approach for efficient pipeline network design

As hydrogen production technologies continue to advance, efficient and high-capacity hydrogen storage solutions become increasingly crucial. To address the complex optimization challenges in the design of large-scale hydrogen storage pipeline networks, the study introduces a mixed integer nonlinear optimization model inspired by hydrogen pipeline design optimization theory. The model not only focuses on optimizing network design parameters but also aims to minimize investment costs. To achieve this, the study proposes an innovative hybrid genetic algorithm that combines the Modified Feasible Directions Method (MFDM) and Genetic Algorithm Theory (GAT), specifically targeting the optimization of pipeline diameter's discrete distribution challenges. Comparative analyses with SUMT and GA algorithms demonstrate the superiority of the approach. In continuous space, the algorithm achieves a lower convergence cost of 232.4437 × 104 Yuan, while in discrete space, the cost further decreases to 212.1636 × 104 Yuan. Additionally, the study delves into the sensitivity of the HGA-MFDM algorithm to ambient temperature and wellhead temperature in hydrogen storage facilities. The analysis reveals that wellhead temperature has a more significant impact on pipeline network investment, whereas ambient temperature exhibits a more pronounced effect on iteration curves. The findings provide valuable insights for the precise adjustment and optimization of hydrogen storage pipeline networks in practical applications.

Genetic aspects of ataxias in a cohort of Turkish patients.

Ataxia is one of the clinical findings of the movement disorder disease group. Although there are many underlying etiological reasons, genetic etiology has an increasing significance thanks to the recently developing technology. The aim of this study is to present the variants detected in WES analysis excluding non-genetic causes, in patients with ataxia. Thirty-six patients who were referred to us with findings of ataxia and diagnosed through WES or other molecular genetic analysis methods were included in our study. At the same time, information such as the onset time of the complaints, consanguinity status between parents, and the presence of relatives with similar symptoms were evaluated. If available, the patient's biochemical and radiological test results were presented. Thirty-six patients were diagnosed through WES or CES. The rate of detected autosomal recessive inheritance disease was 80.5%, while that of autosomal dominant inheritance disease was 19.5%. Abnormal cerebellum was detected on brain MRI images in 26 patients, while polyneuropathy was detected on EMG in eleven of them. While the majority of the patients were compatible with similar cases reported in the literature, five patients had different/additional features (variants in MCM3AP, AGTPBP1, GDAP2, and SH3TC2 genes). The diagnosis of ataxia patients with unknown etiology is made possible thanks to these clues. Consideration of a genetic approach is recommended in patients with ataxia of unknown etiology.

A multi-objective genetic algorithm approach applied to compact meander branch line couplers design for 5G-enabled IoT applications

A multi-objective genetic algorithm approach applied to compact meander branch line couplers design for 5G-enabled IoT applications

Circulating immune cell phenotyping is potentially relevant for diabetic retinopathy risk assessment

BackgroundInflammation is believed to play a central role in the development of diabetes mellitus and is a common feature of type 2 diabetes mellitus (T2DM). However, the association with diabetic retinopathy (DR) remains a topic of debate. MethodsThis study employed two-sample bidirectional Mendelian randomization (MR) analyses to establish causal associations between immune cell characteristics and DR. Using publicly available GWAS genetic data, we investigated the causal relationship between 731 immune cell characteristics and the risk of DR. A total of four types of immune features, including relative cell (RC), absolute cell (AC), median fluorescence intensities (MFI), and morphological parameters (MP), were included. Sensitivity analysis was conducted to assess the robustness, heterogeneity, and potential horizontal pleiotropy of the results. ResultsThirty-five immune cell phenotypes were correlated with the risk of developing DR among four immune traits (MFI, RC, AC, and MP), and DR resulted in altered expression of twenty-six immune cells. ConclusionWe have demonstrated a strong correlation between immune cell traits and DR using a genetic approach. This finding offers valuable insights for early DR prevention and future clinical research and treatment.

Load Forecasting For Regional Integrated Energy System based on Two-Phase Decomposition and Mixture Prediction Model

Current load forecasting methods struggle with the randomness and complexity of multiple loads in regional integrated energy systems, resulting in less accurate predictions. To address this problem, this paper proposes a novel two-phase decomposition hybrid forecasting model that combines complementary ensemble empirical mode decomposition, sample entropy, variational mode decomposition, and genetic algorithm-bidirectional long short term memory. The proposed approach begins by decomposing the load sequence into multiple intrinsic mode function components at different frequencies using complementary ensemble empirical mode decomposition. Afterward, the sample entropy values are calculated for each intrinsic mode function. The intrinsic mode function with the highest sample entropy value is subjected to a two-stage decomposition using the variational mode decomposition method. Through this technique, a series of stationary components is acquired. Subsequently, the bidirectional long short term memory model is optimized using the genetic algorithm, and the genetic algorithm-bidirectional long short term memory approach is employed to predict all the decomposed components. Finally, the prediction results are combined and reconstructed to obtain the final prediction value. Experimental results demonstrate the effective handling of non-stationary load sequences by the forecasting model, showcasing the highest level of accuracy in regional integrated energy system multiple loads forecasting.

Tracing the origin of alveolar stem cells in lung repair and regeneration.

Alveolar type 2 (AT2) cells are stem cells of the alveolar epithelia. Previous genetic lineage tracing studies reported multiple cellular origins for AT2 cells after injury. However, conventional lineage tracing based on Cre-loxP has the limitation of non-specific labeling. Here, we introduced a dual recombinase-mediated intersectional genetic lineage tracing approach, enabling precise investigation of AT2 cellular origins during lung homeostasis, injury, and repair. We found AT1 cells, being terminally differentiated, did not contribute to AT2 cells after lung injury and repair. Distinctive yet simultaneous labeling of club cells, bronchioalveolar stem cells (BASCs), and existing AT2 cells revealed the exact contribution of each to AT2 cells post-injury. Mechanistically, Notch signaling inhibition promotes BASCs but impairs club cells' ability to generate AT2 cells during lung repair. This intersectional genetic lineage tracing strategy with enhanced precision allowed us to elucidate the physiological role of various epithelial cell types in alveolar regeneration following injury.

Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification

Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effective for image classification, but they typically require large-scale training data and have major limitations such as requiring expertise to design network architectures and having poor interpretability. Evolutionary deep learning is a recent hot topic that combines evolutionary computation with deep learning. However, most evolutionary deep learning methods focus on evolving architectures of neural networks, which still suffer from limitations such as poor interpretability. To address this, this paper proposes a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. It can learn different types of image features from colour or gray-scale images, and construct effective and diverse ensembles for image classification. A flexible multi-layer representation enables the new approach to automatically construct shallow or deep models/trees for different tasks and perform effective transformations on the input data via multiple internal nodes. The new approach is applied to solve five image classification tasks with different training set sizes. The results show that it achieves better performance in most cases than deep learning methods for data-efficient image classification. A deep analysis shows that the new approach has good convergence and evolves models with high interpretability, different lengths/sizes/shapes, and good transferability.

Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach

Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamethoxazole (SMX) and total organic carbon (TOC) under varying levels of H2O2, Fe2+ concentration, pH, and temperature removal using both statistical and artificial intelligence techniques including Multiple Regression Analysis (MRA), Support Vector Regression (SVR) and Artificial Neural Network (ANN). In statistical analysis, ANN model showcased an elevated predictive accuracy compared to its counterparts due to having lowest RMSE values of 0.986 and 1.173 for both SMX and TOC, respectively. Sensitivity showcased H2O2/Fe2+ ratio, time and pH as pivotal for SMX degradation, while in simultaneous SMX and TOC reduction, fine tuning the time, pH, and temperature was essential. Leveraging a Hybrid Genetic Algorithm-Desirability Optimization approach, the trained ANN model revealed an optimal desirability of 0.941 out of 1000 solutions which yielded a 91.18% SMX degradation and 87.90% TOC removal under following specific conditions: treatment time of 48.5 min, Fe2+: 7.05 mg L−1, H2O2: 128.82 mg L−1, pH: 5.1, initial SMX: 97.6 mg L−1, and a temperature: 29.8 °C. LC/MS analysis reveals multiple intermediates with higher m/z (242, 270 and 288) and lower m/z (98, 108, 156 and 173) values identified, however no aliphatic hydrocarbon was isolated, because of the low mineralization performance of Fenton process. Furthermore, some inorganic fragments like NH4+ and NO3− were also determined in solution. This comprehensive research enriches AI modeling for intricate Fenton-based contaminant degradation, advancing sustainable antibiotic removal strategies.

MULTI-OBJECTIVE GENETIC ALGORITHM FOR THE ASSEMBLY LINE WORKER ASSIGNMENT AND BALANCING PROBLEM: A CASE STUDY IN THE AUTOMOTIVE SUPPLY INDUSTRY

The primary challenge in assembly line design is the need for more appropriately allocating tasks and workers to workstations. This study addresses the problem of line balancing and worker assignments, considering the performance disparities among workers during the line balancing process. In the relevant literature, this problem is known as the Assembly Line Worker Assignment and Balancing (ALWAB) problem. This research examines a multi-objective ALWAB Type-2 problem, simultaneously evaluating cycle time and squared load assignment objectives. The study is conducted based on a real-life scenario in a sub-industry automotive industry that manufactures cable equipment. To solve this problem, a multi-objective genetic algorithm approach is proposed. Recognising that the selection of parameter values will influence the algorithm’s performance, parameter calibration has been performed. A full factorial experimental design and the irace method have been utilised for this purpose. The results are compared with those using parameter values utilised for similar problems in the literature. Furthermore, a sensitivity analysis has been carried out to examine the impact of various relative weight values of the objectives on the result. The results indicate that the experimental design generally yields superior results compared to other methods.

Insight into Insulin-like Growth Factor-1 Receptor (IGF-1R) gene mutations: Implications for growth disorders and treatment: A review of the literature and case report

Background: Insulin-like Growth Factor -1 Receptor (IGF-1R) and its ligand, IGF-1, are crucial for human tissue growth and development. Mutations in IGF-1R and, to a lesser extent, IGF-1 genes lead to diverse growth disorders, characterized by intrauterine growth retardation and postnatal growth challenges. Case report: We present a case of a 6-year-old girl with speech and mild cognitive delays, exhibiting normal motor skills, no neurological issues, and standard vital signs. Her growth parameters indicated significant retardation, with a height standard deviation score (Ht-SDS) of -2 and an IGF-1 level of +1.8 SDS, normal thyroid function and delayed bone age. Genetic analysis identified a 3335 mbp interstitial loss in 15q26.1, encompassing the IGF-1R gene, critical for growth and development. Literature review: Analysis of 34 papers provided insight into the genetic complexities of IGF-1R and IGF-1 mutations. Studies ranged from familial cases to cohort studies, uncovering mutations including missense mutations, deletions, and copy number variations (CNVs), each associated with unique growth disorder phenotypes. Advances in diagnostic techniques like multiplex ligation-dependent probe amplification (MLPA) and whole exome sequencing (WES) have been pivotal in identifying these mutations, facilitating more targeted management approaches. Conclusions: This case and review of literature underscore the importance of an integrated genetic, endocrine, and developmental approach in managing growth hormone deficiency (GHD) and IGF-1 resistance. The variability in growth disorders and response to therapy highlights the need for personalized treatment plans, informed by genetic insights, to optimize outcomes for patients with these complex conditions.

Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach.

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis.

Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism.

Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict metabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an essential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review the current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. First, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the premise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking in multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1. Our meta-analysis resulted in parameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times by only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS.

Expression of Multicopy Tandem Recombinant Ginseng Hexapeptide in Bacillus subtilis and the Evaluation of Antiaging Activity.

Ginseng oligopeptides are naturally occurring small-molecule peptides extracted from ginseng that exhibit positive effects on health and longevity. However, the current industrial production of ginseng oligopeptides primarily relies on plant extraction and chemical synthesis. In this study, we proposed a novel genetic engineering approach to produce active ginseng peptides through multicopy tandem insertion (5 and 15 times). The recombinant ginseng peptides were successfully produced from engineered Bacillus subtilis with an increasing yield from 356.55 to 2900 mg/L as the repeats multiple. Additionally, an oxidative stress-induced aging model caused by H2O2 was established to evaluate whether the recombinant ginseng peptides, without enzymatic hydrolysis into individual peptides, also have positive effects on antiaging. The results demonstrated that all two kinds of recombinant ginseng peptides could also delay cellular aging through various mechanisms, such as inhibiting cell cycle arrest, suppressing the expression of pro-inflammatory factors, and enhancing cellular antioxidant capacity.

CGS-Mask: Making Time Series Predictions Intuitive for All

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease.

Abstract 6595: HDAC8 targeting impairs Foxp3+Treg cell function and promotes anti-tumor immunity

Abstract HDAC8 is an evolutionarily distinct, X-linked, zinc-dependent class I histone/protein deacetylase implicated in developmental disorders, parasitic infections, myopathy, and cancers such as neuroblastoma, gastric cancer, hepatocellular carcinoma, oral squamous cell carcinoma, and childhood acute lymphoblastic leukemia. Here, we highlight a hitherto unknown role of HDAC8 in immune cells by presenting data on conditional targeting of HDAC8 in T cells and the effects of selective HDAC8 inhibitors (HDAC8i) on T cell-dependent immune responses. First, as a genetic approach, we produced C57BL/6 mice with floxed exon 3 of the HDAC8 gene suitable for targeted deletion of HDAC8 and, after crossing with syngeneic CD4cre mice, demonstrated conditional deletion of HDAC8 and enhanced acetylation of SMC3, a classic HDAC8 substrate, in their T-cells. Next, in a pharmacologic approach, we tested isoform-selective inhibitors of HDAC8 (OJ-1, PCI-34051) and assessed effects on Foxp3+ T-regulatory (Treg) cell suppressive function. Finally, we performed flow cytometry, RNAseq, CHIP-seq and mass spectrometry to investigate the underlying mechanisms by which HDAC8 knockout affected Treg function. Our data demonstrate that knocking down or inhibiting HDAC8: (i) impaired murine Treg suppressive function in vitro and in vivo, including inhibiting the ability of Tregs to promote mouse cardiac allograft survival (p<0.01); (ii) decreased Treg function in homeostatic proliferation assays (p<0.01), e.g., involving adoptive transfer of WT or HDAC8-/- Tregs plus WT conventional T cells into immunodeficient hosts; (iii) decreased intratumoral Tregs, increased CD4 and CD8 T cells, increased production of effectors such as IFN-g and granzyme-B, and limited growth of syngeneic mouse non-small cell lung cancer cell lines (p<0.01) (whereas no effect of Hdac8i on tumor growth was seen in immunodeficient hosts, indicating the key action was via immune cell targeting). Mechanistically, HDAC8 co-precipitated with Foxp3 and NFAT5 and promoted the deacetylation of Foxp3 and NFAT5, and knockout of HDAC8 enhanced the acetylation level of NFAT5 and promoted cytokine expression. Hence, our combined genetic and pharmacologic studies establish the importance of HDAC8 in Foxp3+ Treg cells and show that therapy with selective HDAC8 inhibitors can be a novel therapeutic approach in immuno-oncology. Citation Format: Fanhua Kong, Liqing Wang, Aaron Beeler, Wayne W. Hancock. HDAC8 targeting impairs Foxp3+Treg cell function and promotes anti-tumor immunity [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 6595.

No relationship between male pubertal timing and depression - new insights from epidemiology and Mendelian randomization.

In males, the relationship between pubertal timing and depression is understudied and less consistent than in females, likely for reasons of unmeasured confounding. To clarify this relationship, a combined epidemiological and genetic approach was chosen to exploit the methodological advantages of both approaches. Data from 2026 males from a nationwide, representative study were used to investigate the non-/linear relationship between pubertal timing defined by the age at voice break and depression, considering a multitude of potential confounders and their interactions with pubertal timing. This analysis was complemented by Mendelian randomization (MR), which is robust to inferential problems inherent to epidemiological studies. We used 71 single nucleotide polymorphisms related to pubertal timing in males as instrumental variable to clarify its causal relationship with depression based on data from 807 553 individuals (246 363 cases and 561 190 controls) by univariable and multivariable MR, including BMI as pleiotropic phenotype. Univariable MR indicated a causal effect of pubertal timing on depression risk (inverse-variance weighted: OR 0.93, 95%-CI [0.87-0.99)], p = 0.03). However, this was not confirmed by multivariable MR (inverse-variance weighted: OR 0.95, 95%-CI [0.88-1.02)], p = 0.13), consistent with the epidemiological approach (OR 1.01, 95%-CI [0.81-1.26], p = 0.93). Instead, the multivariable MR study indicated a causal relationship of BMI with depression by two of three methods. Pubertal timing is not related to MDD risk in males.

Abstract 6988: TP53RK regulates DNA replication by CDC7-mediated MCM helicase phosphorylation in colorectal cancer

Abstract Background: We identify TP53 regulating kinase (TP53RK), which showed the highest cell growth inhibition efficiency in six colon cancer cell lines. TP53RK is overexpressed in various cancer types, including multiple myeloma and skin cancer, but the mechanism for its tumorigenesis in colon cancer is unknown. Therefore, this study aims to reveal the tumorigenesis mechanism of TP53RK, which is overexpressed in Colorectal Cancer (CRC), through global and phospho-proteomics. Method: A total 5 CRC cell lines (HT29, SW480, HCT116, H508 and CaCO2), colon normal fibroblast CRL1459, HCT116 p53 null cell and patient-derived normal organoid were used. Immunoblotting, MTT assay, Colony formation assay, Annexin-V assay and cell cycle analysis were performed for evaluation of TP53RK loss-of-function. With kinase-substrate enrichment analysis with phospho-proteome analysis, MCM2 was found to be a substrate for the kinase regulated by TP53RK. Results: High-throughput genetic screening approach have been widely applied to the study the gene function and molecular mechanism associated with tumorigenesis. By using genome-wide CRISPR/Cas9 library screening, we identify TP53RK, the first committed negative-selected gene, as a tyrosine kinase in the colorectal cancer(CRC) specific biomarker. Through proteome analysis, we found that depletion of TP53RK can hypo-phosphorylate the DNA helicase complex, MCM2 (minichromosome maintenance protein2). According to previous studies, the phosphorylation of MCM2 is regulated by CDC7 (cell division cycle7), and the abnormal states of CDC7 and MCM2 cause DNA replication disorder. Our results show that TP53RK expression is upregulated in CRC and its depletion results in decreased CDC7 expression and hypo-phosphorylation of the MCM complex. Decreased CDC7 expression due to its instability after TP53RK depletion can lead to DNA replication errors. DNA replication errors can lead cancer cells to apoptosis, suggesting that they may be a strategy for cancer treatment. In summary, we provide TP53RK as a novel prognostic and therapeutic target in CRC. Conclusion: To recapitulate briefly, our findings suggest that the interaction of TP53RK with CDC7 can regulate CDC7 protein stability and stabilized CDC7 can phosphorylate MCM2 to initiate DNA replication. In other words, because overexpression of TP53RK acts as an oncogene that promotes tumor progression in CRC, depletion of TP53RK can degrade CDC7 and induce colon cancer cells to death or regression. Citation Format: Younghee Choi, Ji-won Park, Jun-kyu Kang, Eun-ju Kim, Sang-Hyun Song, Eugene C. Yi, Tae-You Kim. TP53RK regulates DNA replication by CDC7-mediated MCM helicase phosphorylation in colorectal cancer [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 6988.

Abstract 5708: Silence exportin 7 sensitizes esophageal squamous cell cancer to CDK4/6 inhibition through block the cell cycle coresectory

Abstract Introduction: There is a lack of effective target therapies for esophageal squamous cell cancer. Our previous studies have demonstrated that CDK4/6 is a potency target for ESCC and combination with ERBB inhibitors is sufficient to achieve durable response. The aim of the current study is to prospectively map the landscape of sensitivity and resistance to CDK4/6 inhibition and develop novel combination therapies in ESCC. Experimental Procedures: We performed a genome-wide functional genetic screening and mini-pool designed CRISPR screen approaches to identify the modulators whose depletion sensitize CDK4/6 inhibition or confer resistance to the CDK4/6 inhibitor Palbociclib and elucidate new therapeutic strategies. Results: Our screen identified RB as the most enriched sgRNA, while guides targeting CCNE1 and CDK2 as the most depletion sgRNAs after CDK4/6 inhibition. These results validates our experimental approach as RB loss and CCNE1/CDK2 reactivation is the main mechanisms of CDK4/6 resistance. After gathering potential candidates from both screens, we found that genetically ablation of Exportin 7, a nuclear export factor, strongly sensitize CDK4/6 inhibition both in vitro and in vivo. RNAseq revealed a further repression of transcriptional activity of E2F and G2M by addition of XPO7depletion under CDK4/6 inhibitor treatment. To increase our insight in the molecular mechanism that could explain the synergy between XPO7 and CDK4/6, we further performed proteomics of nuclear and cytoplasm in ESCC lines with or without CDK4/6 inhibition. We found that silence XPO7 result in nuclear accumulation of mitosis-regulated genes such as CENPM, KIF4A and WEE1. Combined CDK4/6 inhibition with XPO7 depletion results in further downregulate of CENPM and KIF4A expression in nucleus, abrogating compensatory activation of cell cycle regulation and promote the cytostatic effect in ESCC. We further validated that CENPM and KIF4A depletion also sensitize CDK4/6 inhibition, and consequentially use WEE1 inhibitor after CDK4/6 inhibitor also effectively block the growth of ESCC lines. Conclusion: Overall, our comprehensive functional genetic screening approach revealed modulation of sensitive and resistance to the inhibition of CDK4/6 in ESCC. XPO7 was identified as a driver to change cell cycle landscape and merits as a therapeutic target combined with CDK4/6 inhibition in ESCC, which is beneficial to provide a novel treatment strategy. Citation Format: Zhong Wu, Lin Xu, Chuang Jiang, Adam Bass, Jin Zhou. Silence exportin 7 sensitizes esophageal squamous cell cancer to CDK4/6 inhibition through block the cell cycle coresectory [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 5708.

Abstract 1910: Advancing therapeutic interventions for NUT carcinoma: Orchestrating the precision of CRISPR/Cas9-mediated NUTM1 suppression and synergistic ADC strategies

Abstract Purpose: NUT carcinoma (NC) is aggressive carcinoma defined by translocations involving the NUTM1 and frequent squamous differentiation. NUTM1 gene encodes a testicular nuclear protein (NUT), exclusively expressed in the testis. The fusion of NUTM1 leads to abnormal cell growth, resulting in the development of NC. This study aims to discover advanced approaches for inducing the death of cancer cells, specifically targeting those expressing NUT proteins. Methods: Spatial Transcriptome analysis and RNA sequencing were applied to formalin-fixed paraffin-embedded NUTM1 fusion-positive cancer tissues. Virus-like particles were used to introduce CRISPR/Cas9 into cells expressing the NUTM1 fusion oncoprotein. The inhibitory effect was confirmed through multiple in vitro assays. The effectiveness of Anti-Trophoblastic cell-surface antigen (TROP-2) Antibody-Drug Conjugate (ADC) was demonstrated in vivo and in vitro in cells with increased TROP2 expression subsequent to NUT suppression. Results: NCs revealed a relatively uniform pattern of gene expression, forming six unique groups. Notably, the NUT protein predominantly located in the inner regions of the tumor tissues. We employed CRISPR/Cas9 technology to specifically target cells harboring the NUTM1 fusion gene. The precise genetic editing was remarkably effective, achieving up to 90% success in the targeted area. This led to a notable decrease in both RNA and protein levels of NUT expression, effectively disrupting associated downstream pathways. The cell growth rate was reduced in several NC cell lines, and differentiation, accompanied by morphological changes, was observed in most edited cells. RNA sequencing analysis of these edited cells uncovered a substantial increase in expression of cytosolic molecules, with a marked increase in TACSTD2 expression. Interestingly, when we combined this genetic editing approach with a targeted therapy - the TROP2-ADC loaded with interferon-beta - the results were even more striking. Cells treated with this combination showed almost double the rate of growth inhibition compared to those receiving only a single form of treatment. Conclusion: This study provides valuable insights into identifying novel therapeutic targets and elucidating potential mechanisms by analyzing NUT cancer tissues using various assays. Our results support that CRISPR/Cas9-mediated targeting of NUTM1 could be a useful approach for NUT oncoprotein ablation, especially when used in combination with other therapies such as ADCs. Further studies are needed to investigate the efficacy of NUT inhibition in preclinical and clinical settings. the elimination of tumor cells facilitated by CRISPR/Cas9, along with additional tumor markers targeted by ADCs, holds potential for developing treatment strategies against incurable cancers driven by genetic alterations. Citation Format: Juyoung Choi, Hwa Lin Yi, Hee Geon Park, Misook Lee, Daesik Kim, Sung Youl Hong, Young Kee Shin, Yoon-la Choi. Advancing therapeutic interventions for NUT carcinoma: Orchestrating the precision of CRISPR/Cas9-mediated NUTM1 suppression and synergistic ADC strategies [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 1910.

Abstract 5107: Targeting CTPS1 and nucleotide biosynthesis pathways as a novel therapeutic approach in MYC-amplified medulloblastoma

Abstract Although leukemia is the leading pediatric oncology diagnosis, brain tumors are the leading cause of pediatric cancer-related death. Medulloblastoma is the most common pediatric brain tumor, representing approximately 20% of all diagnoses. Of all four defined subgroups, MYC-amplified Group 3 medulloblastoma (G3 MB) tumors are the deadliest, with a < 50% 5-year overall survival rate. High risk patients receive large doses of chemo- and radio- therapy (CT/RT) with most survivors experiencing debilitating neurological and cognitive sequelae making the identification of novel targets of the utmost importance. Utilizing whole-genome CRISPR screening from the Pediatric Cancer Dependency Map, we identified the enzyme CTPS1 as a strong G3 MB-specific vulnerability. Recent studies corroborate our findings and suggest a dependence on the de novo pyrimidine biosynthesis pathway in MB. Targeted knockdown of CTPS1 restricts sphere formation and reduces proliferation of G3 MB tumor lines. We confirmed this genetic approach using a specific CTPS1 inhibitor, which also reduced proliferation but did not induce cell death, suggesting a cytostatic mechanism. Strikingly, we demonstrate that G3 MB is only dependent upon de novo pyrimidine synthesis and not de novo purine synthesis, despite regulation of both pathways by oncogenic MYC signaling. Previous work has shown that triggering an imbalance in pyrimidine and purine levels leads to replication stress and activation of the ATR/CHK1 pathway. Both genetic and pharmacologic inhibition of CTPS1 leads to CHK phosphorylation, allowing for cell survival despite lack of dNTPs. Previously, the CHK1 inhibitor, prexasertib, demonstrated robust brain accumulation and targeting of G3 MB in pre-clinical models. Dual inhibition of CHK1 and CTPS1 synergizes to significantly reduce cell viability in vitro and tumor growth in vivo. We further demonstrated robust synergy of CHK1 inhibition with either glutamine antagonists or other inhibitors of the de novo pyrimidine synthesis pathway, prolonging in vivo survival in an orthotopic xenograft G3 MB model. The results of this investigation confirm CTPS1 as a novel target in G3 MB, pinpoint a dependency on de novo pyrimidine rather than purine biosynthesis, and identifies a novel rational combination for treatment in MYC-amplified MB patients. Citation Format: Matthew Hathaway, Hawa Jagana, Michael Meechan, John Hemenway, Kate E. Gadek, Aya Miyaki, Isabella Terrones, Barbara Slusher, Siobhan Pattwell, Nicholas Vitanza, Myron K. Evans. Targeting CTPS1 and nucleotide biosynthesis pathways as a novel therapeutic approach in MYC-amplified medulloblastoma [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 5107.