Genetic Approach Research Articles

Modulating versatile pathways using a cleavable PEG shell and EGFR-targeted nanoparticles to deliver CRISPR-Cas9 and docetaxel for triple-negative breast cancer inhibition.

Human antigen R (HuR), an RNA-binding protein, is implicated in regulating mRNA stability and translation in cancer, especially in triple-negative breast cancer (TNBC), a highly aggressive form. CRISPR/Cas9-mediated HuR knockout (HuR CRISPR) presents a promising genetic therapeutic approach, but it encounters transfection limitations. Docetaxel (DTX), an effective cytotoxic agent against metastatic breast cancer (BC), faces challenges related to vehicle-associated adverse events in DTX formulations. Therefore, we designed multifunctional nanoparticles with pH-sensitive PEG derivatives and targeting peptides to enable efficient HuR CRISPR and DTX delivery to human TNBC MDA-MB-231 cells and tumor-bearing mice. Our findings indicated that these nanoparticles displayed pH-responsive cytotoxicity, precise EGFR targeting, efficient tumor penetration, successful endosomal escape, and accurate nuclear and cytoplasmic localization. They also demonstrated the ability to spare normal cells and prevent hemolysis. Our study concurrently modulated multiple pathways, including EGFR, Wnt/β-catenin, MDR, and EMT, through the regulation of EGFR/PI3K/AKT, HuR/galectin-3/GSK-3β/β-catenin, and P-gp/MRPs/BCRP, as well as YAP1/TGF-β/ZEB1/Slug/MMPs. The combined treatment arrested the cell cycle at the G2 phase and inhibited EMT, effectively impeding tumor progression. Tissue distribution, biochemical assays, and histological staining revealed the enhanced safety profile of pH-responsive PEG- and peptide-modified nanoformulations in TNBC mice. The DTX-embedded and peptide-modified nanoparticles mitigated the side effects of DTX, enhanced cytotoxicity in TNBC MDA-MB-231 cells, and exhibited remarkable antitumor efficacy and safety in TNBC-bearing mice with HuR CRISPR deletion. Collectively, the combination therapy of DTX and CRISPR/Cas9 offers an effective platform for delivering antineoplastic agents and gene-editing systems to combat tumor resistance and progression in TNBC.

A Multi-Tree Genetic Programming-Based Ensemble Approach to Image Classification With Limited Training Data [Research Frontier

A Multi-Tree Genetic Programming-Based Ensemble Approach to Image Classification With Limited Training Data [Research Frontier

Thermal Performance Optimization of Nano-enhanced Phase Change Material-Based Heat Pipe Using Combined Artificial Neural Network and Genetic Algorithm Approach

Thermal Performance Optimization of Nano-enhanced Phase Change Material-Based Heat Pipe Using Combined Artificial Neural Network and Genetic Algorithm Approach

Reliability Genetic Algorithms Optimization RGAO Approach Based FORM and Monte Carlo Simulation: Application for Bridge Structures

Reliability Genetic Algorithms Optimization RGAO Approach Based FORM and Monte Carlo Simulation: Application for Bridge Structures

AI techniques applied to network intrusion detection system by using genetic approaches

The ability to identify and assess potential security risks to a communication network is a critical function of network intrusion detection systems. The data it provides regarding the frequency and type of assaults is a valuable addition to other network security measures, including firewalls. Typically, an NIDS will have a sensor that scans all incoming and outgoing packets on the monitored network, flags those that seem suspicious, and then sends the flagged packets and an alert message to a server system, which will then store them and analyse them in conjunction with other events. First, we standardised the protocol structure. Then, we used a genetic approach to generate mutation and cross-over values for device identification. Finally, we used a modified J48 decision tree algorithm to conduct our search. The developed algorithms/ones outperform the existing methods in terms of performance. As an initial step in detecting an intrusion, the 64-byte structured protocol standardisation technique is created. The wire shark tool is used to monitor the communication process network, taking into account all potential transactions. An array is created from the recorded packets. Included in the array are details about the frames, as well as the protocol type, hardware device type, source and destination IP addresses, MAC addresses, and data. This information is transformed by the 64-byte protocol structured standardisation. Because all of the necessary attributes are determined in the same place by this common protocol structure procedure, finding the MAC and IP addresses in packets should take as little time as possible. As a second step, the product and device details are supplied by the least and most important 16 bits of the MAC address. Using the cross-over and mutation functions, the genetic iv method compares the invader device to the Current Active Directory List (CADL).

Investigating the role of RNA-binding protein Ssd1 in aneuploidy tolerance through network analysis.

RNA-binding proteins (RBPs) play critical cellular roles by mediating various stages of RNA life cycles. Ssd1, an RBP with pleiotropic effects, has been implicated in aneuploidy tolerance in Saccharomyces cerevisiae but its mechanistic role remains unclear. Here we used a network-based approach to inform on Ssd1's role in aneuploidy tolerance, by identifying and experimentally perturbing a network of RBPs that share mRNA targets with Ssd1. We identified RBPs whose bound mRNA targets significantly overlap with Ssd1 targets. For 14 identified RBPs, we then used a genetic approach to generate all combinations of genotypes for euploid and aneuploid yeast with an extra copy of chromosome XII, with and without SSD1 and/or the RBP of interest. Deletion of 10 RBPs either exacerbated or alleviated the sensitivity of wild-type and/or ssd1∆ cells to chromosome XII duplication, in several cases indicating genetic interactions with SSD1 in the context of aneuploidy. We integrated these findings with results from a global over-expression screen that identified genes whose duplication complements ssd1∆ aneuploid sensitivity. The resulting network points to a sub-group of proteins with shared roles in translational repression and p-body formation, implicating these functions in aneuploidy tolerance. Our results reveal a role for new RBPs in aneuploidy tolerance and support a model in which Ssd1 mitigates translation-related stresses in aneuploid cells.

Lowering the PAPR of the optical OTFS-based 6G radio with a hybrid PTS-PSO genetic approach

Orthogonal Time Frequency Space (OTFS) is regarded as one of the best contenders for sixth-generation (6G) radio systems due to its ability to obtain excellent performance in high-speed scenarios. Peak-to-average power ratio (PAPR) significantly impacts and degrades the efficiency of the power amplifier used in OTFS-based 6G systems. The proposed article presents a genetic hybrid algorithm, Partial transmission sequence-particle swarm optimization (PTS-PSO), obtaining an optimal PAPR performance with low computation complexity. The PSO generates the best phase factor compared to the conventional phase generation method of PTS, making PTS-PSO more effective. The impacts such as PAPR, bit error rate (BER), power spectral density (PSD), and complexity of the proposed PTS-PSO are compared with the conventional selective mapping (SLM) and PTS methods for 64, 256, and 512 OTFS sub-carriers. The projected PTS-PSO attained a PAPR and PSD gain of 1–6 dB and − 540 while preserving the BER performance. The experimental results demonstrate that the projected PTS-PSO outperforms the conventional SLM and PTS algorithms with trivial intricacy.

IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers

Abstract Understanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called IDEEA (Information Diffusion model for integrating gene Expression and EEG data in identifying Alzheimer’s disease markers) involves systematically linking two different but complementary modalities: transcriptomics and EEG data. We preprocess these two data types by calculating the spectral and transcriptional sample distances, over 11 brain regions encompassing 6 distinct frequency bands. Subsequently, we employ a genetic algorithm approach to integrate the distinct features of the preprocessed data. Our experimental results show that
IDEEA converges rapidly to local optima gene subsets, in fewer than 250 iterations. Our algorithm identifies novel genes along with genes that have previously been linked to AD. It is also capable of detecting genes with transcription patterns specific to individual EEG bands as well as those with common patterns among bands. In particular, the alpha2 (10-13 Hz) frequency band yielded 8 AD associated genes out of the top 100 most frequently selected genes by our algorithm, with a p-value of
0.05. Our method not only identifies AD-related genes but also genes that interact with AD genes in terms of transcription regulation. We evaluated various aspects of our approach, including the genetic algorithm performance, band-pair association and gene interaction topology. Our approach reveals AD-relevant genes with transcription patterns inferred from EEG alone, across various frequency bands, avoiding the risky brain tissue collection process. This is a significant advancement toward the early identification of AD using non-invasive EEG recordings.

Predicting Tilapia Productivity in Geothermal Ponds: A Genetic Algorithm Approach for Sustainable Aquaculture Practices

This study presents a case focused on sustainable farming practices, specifically the cultivation of tilapia (Mozambican and aureus species) in ponds with geothermal water. This research aims to optimize the hydrochemical regime of experimental ponds to enhance the growth metrics and external characteristics of tilapia breeders. The dataset encompasses the hydrochemical parameters and the fish feeding base from experimental geothermal ponds where tilapia were cultivated. Genetic algorithms (GA) were employed for hyperparameter optimization (HPO) of deep neural networks (DNN) to enhance the prediction of fish productivity in each pond under varying conditions, achieving an R2 score of 0.94. This GA-driven HPO process is a robust method for optimizing aquaculture practices by accurately predicting how different pond conditions and feed bases influence the productivity of tilapia. By accurately determining these factors, the model promotes sustainable practices, improving breeding outcomes and maximizing productivity in tilapia aquaculture. This approach can also be applied to other aquaculture systems, enhancing efficiency and sustainability across various species.

Prediction of Broadband and Highly-Efficient Electromagnetic Wave-Absorbing SiC@SiO2 Nanowire Aerogel by Genetic Algorithm.

Searching for lightweight and high-temperature stable electromagnetic wave-absorbing materials with broad absorbing bandwidth and high efficiency is of significance for applications in daily life and industry. Optimizing the dielectric properties of SiC nanowire aerogel by both compositional and structural designs is an efficient way to obtain simultaneous efficient wave-dissipation ability and good impendence matching and thus the desired properties. However, due to the complex effects of dielectric parameters on the wave-absorbing properties, rational design of high-performance electromagnetic wave-absorbing materials remains challenging. Herein, we propose a genetic algorithm-based approach to predict broadband and highly efficient electromagnetic wave-absorbing materials in a SiC@SiO2 nanowire aerogel-based system. The obtained SiC@SiO2 nanowire aerogels exhibit a gradient multilayered structure with a low dielectric outer layer, a medium layer with alternatively distributed electromagnetic wave transparent and attenuation layers, and an inner high attenuation layer, giving it a broadband electromagnetic wave-absorbing performance covering almost all the 2-18 GHz bandwidth and simultaneous high efficiency. The results show that the genetic algorithm-based approach is efficient in predicting high-performance electromagnetic wave-absorbing ceramic aerogels.

Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

A Genetic algorithm approach for improving the probabilistic inventory model with the continuous review: A practical application in the department of pharmacy

This study investigates to shed light on artificial intelligence techniques, specifically the genetic algorithm, to improving traditional solutions, as well as finding the best policy for the problem of probabilistic inventory with continuous review by finding the optimal reorder point and the optimal economic quantity to avoid the risk of stock out (shortage), reduce total costs, and reach the optimal solution with little time and effort. The research was conducted in the stores of the department of pharmacy of the Ninawa health department for the period from January 1, 2021, to January 1, 2023, on a sample of three drugs (most demanded drugs). An analysis of the demand data for the study sample was conducted using the statistical program (SPSS Statistics Version 22) to determine the type of inventory. it was found that the type of inventory is probabilistic and the demand data follows a normal distribution. Based on this foundation, the mathematical model for the study problem was built. Given the complexity of the steps and iterations of the traditional solution, the solution steps were applied using the R programming language to reach the model's solution. Additionally, the solution steps were programmed for the genetic algorithm and applied using the R programming language. The results of the genetic algorithm showed an improvement in the traditional solution results by reducing total costs. Based on the results of the genetic algorithm, the economic order quantity, safety stock, reorder period, and safety period were found. Paper type: Research Paper

Epigenomic heterogeneity as a source of tumour evolution.

In the past decade, remarkable progress in cancer medicine has been achieved by the development of treatments that target DNA sequence variants. However, a purely genetic approach to treatment selection is hampered by the fact that diverse cell states can emerge from the same genotype. In multicellular organisms, cell-state heterogeneity is driven by epigenetic processes that regulate DNA-based functions such as transcription; disruption of these processes is a hallmark of cancer that enables the emergence of defective cell states. Advances in single-cell technologies have unlocked our ability to quantify the epigenomic heterogeneity of tumours and understand its mechanisms, thereby transforming our appreciation of how epigenomic changes drive cancer evolution. This Review explores the idea that epigenomic heterogeneity and plasticity act as a reservoir of cell states and therefore as a source of tumour evolution. Best practices to quantify epigenomic heterogeneity and explore its various causes and consequences are discussed, including epigenomic reprogramming, stochastic changes and lasting memory. The design of new therapeutic approaches to restrict epigenomic heterogeneity, with the long-term objective of limiting cancer development and progression, is also addressed.

Study of the relationships of polymorphisms of the folate cycle genes with the levels of homocysteine and folic acid as risk factors of cardiovascular disorders in the post-covid period

Enzymes of the folate cycle participate in the process of remethylation of homocysteine (HC) to methionine, where folates are coenzymes for methyl transfer. The aim of the work was to identify the interrelationships of gene polymorphisms of associations between folate cycle enzyme gene polymorphisms and cardiometabolic risk factors such as increased serum homocysteine levels and folic acid deficiency in patients in the post-covid period. In 51 patients who suffered from COVID-19, a general clinical and laboratory examination, and assessment of serum homocysteine and folic acid concentrations by immunoenzymatic method was carried out. Polymorphisms of the genes: methylenetetrahydrofolate reductase (677C>T and 1298A>C), methionine synthase-reductase (66A>G) and methionine synthase (2756A>G) were determined by real time polymerase chain reaction. For each of the named polymorphisms, the examined patients were divided into 3 groups according to the nucleotide alleles in the corresponding position: 1) homozygous dominant, 2) heterozygous and 3) homozygous recessive. For the methylenetetrahydro­folatereductase gene at position 677, serum levels of homocysteine and folate did not differ between the groups 1 and 2 with genotypes 677 C/C (n=26) and 677C/T (n=21), (p>0.05); in group 3, the recessive genotype 677 T/T was found in only 4 people (8%), that did not allow comparison of indices. The distribution of patients into 3 groups according to the genotype of the same gene at position 1298 revealed that the recessive 1298 C/C mutation in group 3 (n=9) associated with an increased homocysteine level (19.56±1.89 μmol/l), versus 10.68±0.76 (p=0.012) and 11.63±1.25 μmol/l (p=0.013) in groups 1 and 2, with no difference in folate levels between groups. Group 3 differed by a higher degree of obesity, a higher frequency of hypertension disease and chronic heart failure (in 85% of patients in group 3, against 41 and 50% in groups 1 and 2), a higher number of platelets, a longer duration of hospitalization due to COVID-19, a higher level of D-dimer. The study of groups of patients, divided depending on the genotype of methioninesynthase reductase at position 66, showed that carriers of the recessive homozygous 66 G/G mutation (group 3, n=15) had increased serum homocysteine level (16.56±1.64 μmol/l) in comparison with individuals of group 1 (n=17) 66 A/A (10.28±1.17 μmol/l; p=0.004) and group 2 (n=19) 66 A/G (11.32±1.17 μmol/l, p=0.013). In group 3, a longer duration of hospitalization due to COVID-19 was noted (17.15±1.65 vs. 11.88±0.97 days, p=0.008), higher frequency of hypertension (67% vs. 35%) and heart failure (67% against 29%) compared to group 1. The use of a molecular genetic approach made it possible to establish that the presence of recessive mutations of the folate cycle genes is associated with a potential predisposition to hyperhomocysteinemia, thrombophilia, severe forms of cardiometabolic complications and coronavirus disease.

PWAS Hub for exploring gene-based associations of common complex diseases.

PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects.

Genetic parameters for Mycoplasma ovipneumoniae nasal DNA copy number provide progress to promote domestic and bighorn sheep coexistence on public lands

Discord between domestic sheep producers and bighorn sheep conservationists primarily involves concerns over the transmission of Mycoplasma ovipneumoniae between domestic and wild sheep. To discern if selective breeding within the Rambouillet could serve to reduce transmission, a genetic approach utilizing an existing phenotype was evaluated for additive genetic variance. The phenotype employs the measure of nasally shed bacterial DNA, or M. ovipneumoniae nasal DNA copy number (MONCN). Following estimation of the additive genetic variation associated with MONCN, the heritability and repeatability for this trait was calculated. The level of MONCN was measured at least three times per year at the U.S. Sheep Experiment Station (USSES) from ewes ranging from 1 to 7 years of age. Repeated measures animal models were evaluated from 320 ewes with a total of 1223 samples (mean = 3.8 per ewe). The MONCN values ranged from 0 (non-detected) to 71,654 copies per 2 μl of extracted nasal swab DNA. Four genetic models were evaluated with the categorical model having the highest heritability (0.12 ± 0.09) and repeatability (0.60 ± 0.05). Fixed effects included season/year (n = 9) and ewe age (n = 7). Outcomes from this research resulted in heritability estimates from which breeding values can be estimated to select USSES ewes and rams for reduced MONCN. This research provides a practical approach that can be immediately implemented to manage domestic sheep to be more compatible with bighorn sheep on public lands, providing positive welfare benefits for both species.

Relation of CMV and brain atrophy to trajectories of immunosenescence in diverse populations.

Immunosenescence (ISC), the aging of the immune system, has largely been studied in populations of European descent. Here, circulating immune cell cytometric data from African-American, Hispanic, and non-Hispanic White participants were generated. Known and novel age effects were identified using either a meta-analysis approach or a parallel genetic approach. Most results are consistent across the three populations, but some cell populations display evidence of heterogeneity, such as a PD-L1 + CD56 + NK cell subset. The study estimated "Immunological Age" (IA) during physiologic aging. While we found no relation of IA to Multiple Sclerosis, IA is associated with entorhinal cortex atrophy, a presymptomatic feature of Alzheimer's disease, linking neurodegeneration and peripheral immunity. ISC trajectories were also inferred, highlighting age, CMV status, and genetic ancestry as key influences. Our assessment offers reference ISC trajectories for personalization of assessments of immune function over the life course in diverse populations.

A multi-objective dual dynamic genetic algorithm-based approach for thermoelectric optimization of integrated urban energy systems

In order to reduce the loss of energy in the process of conversion and utilization, and to improve the energy utilization effect and economic benefits of urban integrated energy system, a thermoelectricity optimization method of urban integrated energy system based on multi-objective double dynamic genetic algorithm is proposed. The method analyzes the operation characteristics of the urban integrated energy system through its operation model, constructs a multi-objective optimization model for the thermoelectricity of the urban integrated energy system, determines the optimization multi-objective function that minimizes the operation cost, minimizes the carbon emission, minimizes the technical dissatisfaction and the related constraints, and then solves the optimization model by using the dual dynamic genetic algorithm to output the results of the optimization of the thermoelectricity of the urban integrated energy system.The test results show that the algorithm has a crowding distance of 0.75 or above when solving, and the overall unit energy consumption cost reduction ratio is about 22.97 %. The state of charge results of the four energy storage power stations are all below 10 %, and the lowest convergence speed is only 7 seconds, effectively improving energy utilization efficiency.

7324 Regulation of Super Enhancers by Phosphorylated Progesterone Receptors Drives Breast Cancer Stem Cell Expansion

Abstract Disclosure: N. Gillis: None. C.A. Lange: None. Current treatments for estrogen receptor (ER) positive breast cancer largely target rapidly dividing tumor cells. However, aggressive breast cancers often contain long-lived and weakly proliferative cancer stem cells (CSCs) that readily escape treatments aimed at cycling cells. We previously reported that progesterone receptors (PR) and their target genes aid the growth of these CSCs and help them evade anti-estrogen therapy. As proliferation and stemness are directed by separate signaling pathways that lead to the expression of distinct gene programs, it's crucial to understand how PR facilitates this cellular transformation at the genomic level. Establishing a gene expression program that may limit proliferation but encourages stemness requires coordination between transcription factors and their coregulators to alter the nuclear microenvironment and chromatin landscape.To reveal these mechanisms, we profiled PR and ER genomic binding using CUT&RUN in the T47D luminal breast cancer cell line grown in ultra-low attachment (3D) mammosphere conditions. Our analysis of genomic binding profiles displayed a pattern of PR/ER co-occupancy at distant sites more than 10 kilobases away from the nearest transcriptional start site. We used Rank Ordering of Super Enhancer (ROSE) analysis on these PR/ER binding sites to predict which are likely super enhancer elements capable of coordinating the regulation of multi-gene subsets. We employed a genetic approach to directly test the function of novel enhancer sites linked to PR-target genes by disrupting the PR/ER binding with a dCas9-KRAB construct directed to the predicted enhancer elements. We propose that abnormal growth factor signaling in breast cancer cells permits PR/ER complex formation which promotes expression of genes that induce endocrine resistance and CSC expansion through regulation of super enhancer elements. Further studies to elucidate these mechanisms are ongoing. Understanding the intricacies of PR/ER crosstalk is the crucial next step in developing new therapies that target PR-driven processes in ER+ breast cancers. Presentation: 6/3/2024

8038 Targeting Breast Cancer Stem Cells With Oncolytic Mammalian Orthoreovirus (MRV)

Abstract Disclosure: N.I. Roman Ortiz: None. J. Davydova: None. P. Danthi: None. J.H. Ostrander: None. Breast cancer is the most frequently diagnosed malignant cancer in women and is the second leading cause of cancer deaths among women in the United States. Patients with ER+ breast cancer can experience recurrence for more than 10 years after initial diagnosis. Breast cancer stem-like cells (BCSC) are slowly proliferative and exist as a minority sub-population (0-5%) within the tumor. Existing treatments target rapidly dividing cells, and BCSC are unresponsive to these therapies. Due to their self-renewal capabilities, BCSCs can fuel recurrence, metastasis, and resistance to therapies. Notably, no FDA-approved therapeutics selectively target BCSCs. Mammalian orthoreovirus (MRV) is an oncolytic virus tested in clinical trials for many cancer types including metastatic breast cancer. MRV was deemed safe but lacked efficacy as standalone treatment. We tested various laboratory MRV strains (T1L, R2) with T3D, a similar strain to the one evaluated in clinical trials. Our studies suggest that T3D is less effective in killing ER+ breast cancer cells and BCSCs compared to T1L and R2. To understand how T1L and R2 are more effective at killing ER+ breast cancer we aim to test the stages of MRV infection and how MRV induces cell death in ER+ therapy sensitive and resistant cells. MRV can enter cells by receptor mediated endocytosis through attachment with Junctional Adhesion Molecule A (JAM-A) and sialic acids as co-receptors. We have found that T1L attaches to cells in a JAM-A-dependent manner in ER+ sensitive cancer cells, while ER+ therapy resistant cells have increased resistance to JAM-A mediated endocytosis. Previous work has shown that apoptosis is involved in MRV-induced cell death. We found that apoptosis inhibitors in MRV-treated cells increase cell viability, suggesting that MRV-induces apoptotic cell death in ER+ breast cancer cells. Our current studies involve using RNA sequencing as a tool to assess gene expression of ER+ therapy sensitive and resistant cells infected with MRV at early and late stages of infection to determine key genes and pathways associated with cell death.We also aim to generate novel MRV strains with enhanced BCSC killing. We used a forward genetic approach and serially passaged MRV strains in BCSC enriched tumorsphere cultures of MCF-7 paclitaxel resistant (TaxR) cells to generate a selective oncolytic virus that can infect and kill BCSCs. Preliminary data has shown that serially passaged (SP) MRV strains (T1L SP, R2 SP) are more effective in decreasing cell viability and inhibiting tumorsphere formation compared to the parental strains (T1L and R2). Current studies are focused on sequencing SP clones to identify mutations that enhance BCSC death. We expect these studies to enhance MRV virotherapeutics for combined use with clinically relevant inhibitors (i.e. CDK4/6 inhibitors), anti-estrogens, or immunotherapy to prevent and treat metastatic ER+ breast cancer. Presentation: 6/2/2024