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1. Are there increases in CRISPR/Cas off-target effects in homologous recombination repair deficient Saccharomyces cerevisiae cells?

   Castillo, S; Santoro, I (January 2023, Southeastern biology)

   CRISPR/Cas technology is increasingly being used as a common methodology in many cancer biology studies due to the ease and convenience of the technique. Precise editing of genomic DNA has been achieved upon repair of CRISPR-induced DNA double-strand breaks (DSBs) by homologous recombination (HR). HR repairs DNA DSBs with high fidelity and therefore, deficiencies in HR result in genome instability. These deficiencies have been demonstrated in many cancers. RAD51-dependent HR is a very important pathway for repairing DSBs. Previous studies have shown that genome editing using CRISPR technology relies on the repair of site-specific DNA DSBs induced by the RNA-guided Cas9 endonuclease. Furthermore, previous studies have shown that the efficiency of CRISPR-mediated HR can be improved by the stimulation of HR promoting factors, such as the RAD51 recombinase. Despite the ease and efficient use the CRISPR/Cas technology for genome editing, one limitation is the potential occurrence of associated off-target effects. If CRISPR technology is planned to be used to target cancer cells with defective HR capabilities, will off-target mutations be likely to occur? In order to answer this question, a system was developed in Saccharomyces cerevisiae using green fluorescent protein (GFP) as a reporter to identify off-target CRISPR-induced DSBs. This study set out to test the number of off-target DSBs that could be introduced by CRISPR-induced genome editing in a RAD51-deficient HR model. We were curious whether loss of RAD51-dependent HR would increase the abundance of off-target CRISPR-induced DSBs in mutant yeast strains as compared to those with a functioning HR-dependent DNA repair pathway. Preliminary findings using this system will be presented. 

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2. Image Processing Pipeline to Compute Homologous Recombination Score

   https://doi.org/10.1145/3535694.3535704  

   Vundavilli, Haswanth; Tumiati, Manuela; Hautaniemi, Sampsa; Datta, Aniruddha; Kauppi, Liisa (April 2022, 12th International Conference on Biomedical Engineering and Technology (2022), Tokyo, Japan)

   DNA double-strand breaks (DSBs) occur frequently in eukaryotic cells, and the homologous recombination pathway (HR) is one of the major pathways required to repair these breaks. However, tumor cells that are able to repair DSBs are unlikely to die due to damage incurred by DNA damaging chemotherapies, such as platinum compounds. While platinum-based therapies have been effective in treating various cancers, they also carry harsh side effects, and thus ideally platinum should be used when the probability of treatment resistance is low. HR scores provide a measure for patients’ tumor’s HR capacity and have been shown to predict their chemotherapy response and long-term survival. Calculating this score manually from immunofluorescence microscopy images for each patient is error-prone and time-consuming. Herein, we propose an image processing pipeline that takes as input imaging data from three emission channels (representing nuclei, S-phase cells, and HR-mediated repair in a tumor slice) from an epifluorescence microscope and computes the HR score. Our open-source methodology forms a rationale to develop similar approaches in predicting chemotherapeutic responses and facilitating to make treatment decisions. 

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3. Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer

   https://doi.org/10.1177/11769351221131124  

   Neary, Bridget; Lin, Shuting; Qiu, Peng (January 2022, Cancer Informatics)

   Background: Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored. Method: In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis. Results: We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites. Conclusion: These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction. 

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4. Identification of Protein Markers Predictive of Drug-Specific Survival Outcome in Cancers

   https://doi.org/10.1007/978-3-030-91415-8_6  

   Lin, Shuting; Zhou, Jie; Xiao, Yiqiong; Neary, Bridget; Teng, Yong; Qiu, Peng (November 2021, In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science)

   Wei, Yanjie; Li, Min; Skums, Pavel; Cai, Zhipeng (Ed.)

   Novel discoveries of biomarkers predictive of drug-specific responses not only play a pivotal role in revealing the drug mechanisms in cancers, but are also critical to personalized medicine. In this study, we identified drug-specific biomarkers by integrating protein expression data, drug treatment data and survival outcome of 7076 patients from The Cancer Genome Atlas (TCGA). We first defined cancer-drug groups, where each cancer-drug group contains patients with the same cancer and treated with the same drug. For each protein, we stratified the patients in each cancer-drug group by high or low expression of the protein, and applied log-rank test to examine whether the stratified patients show significant survival difference. We examined 336 proteins in 98 cancer-drug groups and identified 65 protein-cancer-drug combinations involving 55 unique proteins, where the protein expression levels are predictive of drug-specific survival outcomes. Some of the identified proteins were supported by published literature. Using the gene expression data from TCGA, we found the mRNA expression of ∼11% of the drug-specific proteins also showed significant correlation with drug-specific survival, and most of these drug-specific proteins and their corresponding genes are strongly correlated. 

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5. Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers

   https://doi.org/10.1038/s41598-022-10662-6  

   Lin, Shuting; Zhou, Jie; Xiao, Yiqiong; Neary, Bridget; Teng, Yong; Qiu, Peng (December 2022, Scientific Reports)

   Abstract Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine. 

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