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1. Mechanisms of Drug Resistance in Ovarian Cancer and Associated Gene Targets

   https://doi.org/10.3390/cancers14246246  

   Alatise, Kharimat Lora; Gardner, Samantha; Alexander-Bryant, Angela (December 2022, Cancers)

   In the United States, over 100,000 women are diagnosed with a gynecologic malignancy every year, with ovarian cancer being the most lethal. One of the hallmark characteristics of ovarian cancer is the development of resistance to chemotherapeutics. While the exact mechanisms of chemoresistance are poorly understood, it is known that changes at the cellular and molecular level make chemoresistance challenging to treat. Improved therapeutic options are needed to target these changes at the molecular level. Using a precision medicine approach, such as gene therapy, genes can be specifically exploited to resensitize tumors to therapeutics. This review highlights traditional and novel gene targets that can be used to develop new and improved targeted therapies, from drug efflux proteins to ovarian cancer stem cells. The review also addresses the clinical relevance and landscape of the discussed gene targets. 

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2. Identifying gene expression patterns associated with drug-specific survival in cancer patients

   https://doi.org/10.1038/s41598-021-84211-y  

   Neary, Bridget; Zhou, Jie; Qiu, Peng (December 2021, Scientific Reports)

   null (Ed.)

   Abstract The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions. 

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3. Programming tumor evolution with selection gene drives to proactively combat drug resistance

   https://doi.org/10.1038/s41587-024-02271-7  

   Leighow, Scott M; Reynolds, Joshua A; Sokirniy, Ivan; Yao, Shun; Yang, Zeyu; Inam, Haider; Wodarz, Dominik; Archetti, Marco; Pritchard, Justin R (July 2024, Nature Biotechnology)
4. Characterizing the genetic architecture of drug response using gene-context interaction methods

   https://doi.org/10.1016/j.xgen.2024.100722  

   Sadowski, Michal; Thompson, Mike; Mefford, Joel; Haldar, Tanushree; Oni-Orisan, Akinyemi; Border, Richard; Pazokitoroudi, Ali; Cai, Na; Ayroles, Julien F; Sankararaman, Sriram; et al (December 2024, Cell Genomics)
5. A drug‐selectable acoustic reporter gene system for human cell ultrasound imaging

   https://doi.org/10.1002/btm2.10584  

   Howells, Alessandro_R; Welch, Phoebe_J; Kim, John; Forest, Craig_R; Shi, Chengzhi; Lian, Xiaojun_Lance (August 2023, Bioengineering & Translational Medicine)

   Abstract A promising new field of genetically encoded ultrasound contrast agents in the form of gas vesicles has recently emerged, which could extend the specificity of medical ultrasound imaging. However, given the delicate genetic nature of how these genes are integrated and expressed, current methods of producing gas vesicle‐expressing mammalian cell lines requires significant cell processing time to establish a clonal/polyclonal line that robustly expresses the gas vesicles sufficiently enough for ultrasound contrast. Here, we describe an inducible and drug‐selectable acoustic reporter gene system that can enable gas vesicle expression in mammalian cell lines, which we demonstrate using HEK293T cells. Our drug‐selectable construct design increases the stability and proportion of cells that successfully integrate all plasmids into their genome, thus reducing the amount of cell processing time required. Additionally, we demonstrate that our drug‐selectable strategy forgoes the need for single‐cell cloning and fluorescence‐activated cell sorting, and that a drug‐selected mixed population is sufficient to generate robust ultrasound contrast. Successful gas vesicle expression was optically and ultrasonically verified, with cells expressing gas vesicles exhibiting an 80% greater signal‐to‐noise ratio compared to negative controls and a 500% greater signal‐to‐noise ratio compared to wild‐type HEK293T cells. This technology presents a new reporter gene paradigm by which ultrasound can be harnessed to visualize specific cell types for applications including cellular reporting and cell therapies. 

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