Mid-level Molecular Phenotypes for the Identification of Genetic Signatures of Anthracycline-induced Cardiotoxicity Hazard

Cardinale Goldstein^*
*Correspondence: Cardinale Goldstein, Department of Statistics and Biotechnology, University of Salamanca, 37007 Salamanca, Spain, Email:

Keywords

Cardiotoxicity • Anthracyclines • Tumors

Introduction

Anthracyclines, such as doxorubicin and daunorubicin, are potent chemotherapeutic agents used to treat a variety of cancers, including leukemias, lymphomas and solid tumors. However, their clinical use is limited by the risk of cardiotoxicity, which can manifest as cardiomyopathy and heart failure, sometimes years after the completion of therapy. The variability in individual susceptibility to AIC suggests a genetic component to this adverse effect. Understanding the genetic underpinnings of AIC could facilitate the development of personalized treatment plans, minimizing cardiac risks while maintaining antitumor efficacy. Mid-level molecular phenotypes refer to biological changes that occur downstream of genetic variants but upstream of clinical phenotypes. These include gene expression profiles, protein abundance, and metabolite concentrations. By examining these intermediate phenotypes, researchers can gain insights into the functional consequences of genetic variations and identify biomarkers that predict disease risk.

Literature Review

Gene expression profiling involves measuring the activity of thousands of genes simultaneously to create a comprehensive picture of cellular function. In the context of AIC, several studies have utilized gene expression profiling to identify genes and pathways that are differentially expressed in response to anthracycline exposure. For example, a study by Blanco et al. (2012) identified differential expression of genes involved in oxidative stress and apoptosis in patients who developed cardiotoxicity after anthracycline treatment. These findings suggest that individuals with heightened oxidative stress responses may be more susceptible to AIC. Integrating gene expression data with genomic data, such as Single Nucleotide Polymorphisms (SNPs), can further refine the identification of genetic variants associated with altered gene expression and increased cardiotoxicity risk. Proteomics, the large-scale study of proteins, provides a direct assessment of the functional molecules within cells. Proteins are the primary effectors of cellular processes and changes in protein levels can offer insights into the mechanisms underlying AIC. A proteomic analysis revealed that patients who experienced AIC had elevated levels of cardiac troponins, which are markers of cardiac injury, and alterations in proteins involved in mitochondrial function and energy metabolism. These findings underscore the role of mitochondrial dysfunction in AIC and highlight potential protein biomarkers for early detection of cardiotoxicity [1,2].

Metabolomics involves the comprehensive analysis of metabolites, the small molecule substrates, intermediates, and products of metabolism. Changes in metabolite levels can reflect alterations in metabolic pathways and cellular homeostasis. Studies have shown that anthracycline treatment can disrupt energy metabolism and lead to the accumulation of toxic metabolic intermediates. For instance, a metabolomic study identified elevated levels of acylcarnitines in patients who developed AIC, suggesting impaired fatty acid oxidation. These metabolite profiles can serve as biomarkers for predicting AIC and understanding its pathophysiology. Integrating mid-level molecular phenotypes with genetic data involves combining genomic information, such as SNPs, with data from transcriptomics, proteomics, and metabolomics. This integrative approach can help identify genetic variants that influence molecular phenotypes and contribute to disease risk [3].

Discussion

Expression Quantitative Trait Loci (eQTL) analysis is a powerful method for linking genetic variants to gene expression levels. By identifying SNPs that are associated with changes in gene expression, researchers can pinpoint genetic loci that regulate gene activity and may contribute to AIC. For example, an eQTL identified several SNPs associated with the expression of genes involved in oxidative phosphorylation and mitochondrial function in patients treated with anthracyclines. These findings suggest that genetic variants influencing mitochondrial function may predispose individuals to AIC. Proteogenomics combines proteomic and genomic data to identify genetic variants that affect protein abundance and function. This approach can reveal how genetic differences impact protein levels and contribute to disease phenotypes. A proteogenomic study identified genetic variants associated with changes in the levels of proteins involved in cardiac contractility and energy metabolism in patients treated with anthracyclines. These findings provide insights into the molecular mechanisms of AIC and identify potential protein biomarkers for risk assessment [4].

Genome-wide association studies (GWAS) can be applied to metabolomic data to identify genetic variants associated with changes in metabolite levels. This approach can uncover genetic loci that regulate metabolic pathways and contribute to disease susceptibility. A metabolomic GWAS identified several genetic variants associated with altered levels of metabolites involved in energy metabolism and oxidative stress in patients treated with anthracyclines. These findings highlight the role of metabolic dysregulation in AIC and suggest potential genetic markers for predicting cardiotoxicity risk. The integration of mid-level molecular phenotypes with genetic data has led to several important findings in the study of AIC. These discoveries have implications for predicting cardiotoxicity risk, understanding the mechanisms underlying AIC, and developing preventive strategies. Identifying genetic variants and molecular biomarkers associated with AIC can improve risk prediction and enable personalized treatment plans. Patients with high-risk genetic profiles could be monitored more closely for signs of cardiotoxicity, and alternative treatment regimens could be considered to minimize cardiac risk [5].

Integrative studies have highlighted several key pathways involved in AIC, including oxidative stress, mitochondrial dysfunction, and impaired energy metabolism. Understanding these mechanisms can inform the development of cardioprotective strategies and guide the design of new anthracycline analogs with reduced cardiotoxicity. The identification of molecular biomarkers associated with AIC can inform the development of preventive strategies, such as the use of cardioprotective agents. For example, dexrazoxane is a cardioprotective agent that has been shown to reduce the risk of AIC by chelating iron and reducing oxidative stress. Understanding the molecular mechanisms of AIC can guide the use of such agents in patients with high-risk profiles [6].

Conclusion

Large-scale integrative studies combining genomic, transcriptomic, proteomic, and metabolomic data are needed to identify robust biomarkers of AIC. Such studies should include diverse patient populations to ensure the generalizability of findings and to uncover population-specific risk factors.Functional validation of identified genetic variants and molecular biomarkers is essential to establish their causal roles in AIC. Experimental studies using cell and animal models can help elucidate the mechanisms by which these variants and biomarkers contribute to cardiotoxicity.Translating research findings into clinical practice requires the development of reliable and accessible assays for identified biomarkers. Prospective clinical trials are needed to evaluate the utility of these biomarkers in predicting AIC risk and guiding treatment decisions.Further mechanistic studies are needed to understand how anthracyclines cause cardiotoxicity at the molecular level. This includes investigating the roles of non-coding RNAs, epigenetic modifications, and protein-protein interactions in AIC. Mid-level molecular phenotypes provide a valuable framework for identifying genetic signatures associated with anthracycline-induced cardiotoxicity. By integrating genomic, transcriptomic, proteomic, and metabolomic data, researchers can uncover biomarkers that predict cardiotoxicity risk, elucidate the mechanisms underlying AIC, and develop strategies for prevention and personalized treatment. Continued research in this field holds promise for improving the safety and efficacy of anthracycline-based chemotherapy.

Acknowledgement

None.

Conflict of Interest

There are no conflicts of interest by author.

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