Genome-Wide Association Study Identifies ROBO2 as a Novel Susceptibility Gene for Anthracycline-Related Cardiomyopathy in Childhood Cancer Survivors

Genetic and epigenetic bases of long-term adverse effects of childhood cancer therapy

Over the past decade, genome-scale molecular profiling of large childhood cancer survivorship cohorts has led to unprecedented advances in our understanding of the genetic and epigenetic bases of therapy-related adverse health outcomes in this vulnerable population. To facilitate the integration of knowledge generated from these studies into formulating next-generation precision care for survivors of childhood cancer, we summarize key findings of genetic and epigenetic association studies of long-term therapy-related adverse effects including subsequent neoplasms and cardiomyopathies among others. We also discuss therapy-related genotoxicities including clonal haematopoiesis and DNA methylation, which may underlie accelerated molecular ageing. Finally, we highlight enhanced risk prediction models for survivors of childhood cancer that incorporate both genetic factors and treatment exposures, aiming to achieve enhanced accuracy in predicting risks for this population. These new insights will hopefully inspire future studies that harness both expanding omics resources and evolving data science methodology to accelerate the translation of precision medicine for survivors of childhood cancer.

Anthracycline-induced cardiomyopathy: risk prediction, prevention and treatment

Anthracyclines are the cornerstone of treatment for many malignancies. However, anthracycline cardiotoxicity is a considerable concern given that it can compromise the clinical effectiveness of the treatment and patient survival despite early discontinuation of therapy or dose reduction. Patients with cancer receiving anthracycline treatment can have a reduction in their quality of life and likelihood of survival due to cardiotoxicity, irrespective of their oncological prognosis. Increasing knowledge about anthracycline cardiotoxicity has enabled the identification of patients who are candidates for anthracycline regimens and those who might develop anthracycline-induced cardiomyopathy. Anthracycline cardiotoxicity is a unique and evolving phenomenon that begins with myocardial cell damage, progresses to reduced left ventricular ejection fraction, and culminates in symptomatic heart failure if it is not promptly detected and treated. Early risk stratification can be guided by imaging or biomarkers. In this Review, we present a comprehensive and clinically useful approach to cardiomyopathy related to anthracycline therapy, encompassing its epidemiology, definition, mechanisms, novel classifications, risk factors and patient risk stratification, diagnostic approaches (including imaging and biomarkers), treatment guidelines algorithms, and the role of new cardioprotective drugs that are used for the treatment of heart failure.

New insights into mitochondrial quality control in anthracycline-induced cardiotoxicity: molecular mechanisms, therapeutic targets, and natural products

Anthracyclines (ANTs) are widely used in cancer therapy, particularly for lymphoma, sarcoma, breast cancer, and childhood leukemia, and have become the cornerstone of chemotherapy for various malignancies. However, it is associated with fatal and dose-dependent cardiovascular complications, especially cardiotoxicity. Mitochondrial quality control mechanisms, encompassing mitophagy, mitochondrial dynamics, and mitochondrial biogenesis, maintain mitochondrial homeostasis in the cardiovascular system. Recent studies have highlighted that mitochondrial quality control mechanisms play considerable roles in ANTs-induced cardiotoxicity (AIC). In addition, natural products targeting mitochondrial quality control mechanisms have emerged as potential therapeutic strategies to alleviate AIC. This review summarizes the types, incidence, prevention, treatment, and pathomechanism of AIC, delves into the molecular mechanisms of mitochondrial quality control in the pathogenesis of AIC, and explores natural products that target these mechanisms, so as to provide potential targets and therapeutic drugs for address the clinical challenges in AIC prevention and treatment, where no effective medicines are available.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Background

Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.

Methods

Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.

Results

A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.

Conclusions

This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

Anthracycline Toxicity: Light at the End of the Tunnel?

Anthracycline-induced cardiotoxicity (AIC) is a serious and common side effect of anthracycline therapy. Identification of genes and genetic variants associated with AIC risk has clinical potential as a cardiotoxicity predictive tool and to allow the development of personalized therapies. In this review, we provide an overview of the function of known AIC genes identified by association studies and categorize them based on their mechanistic implication in AIC. We also discuss the importance of functional validation of AIC-associated variants in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to advance the implementation of genetic predictive biomarkers. Finally, we review how patient-specific hiPSC-CMs can be used to identify novel patient-relevant functional targets and for the discovery of cardioprotectant drugs to prevent AIC. Implementation of functional validation and use of hiPSC-CMs for drug discovery will identify the next generation of highly effective and personalized cardioprotectants and accelerate the inclusion of approved AIC biomarkers into clinical practice.

Priorities in Cardio-Oncology Basic and Translational Science: GCOS 2023 Symposium Proceedings: JACC: CardioOncology State-of-the-Art Review

Despite improvements in cancer survival, cancer therapy-related cardiovascular toxicity has risen to become a prominent clinical challenge. This has led to the growth of the burgeoning field of cardio-oncology, which aims to advance the cardiovascular health of cancer patients and survivors, through actionable and translatable science. In these Global Cardio-Oncology Symposium 2023 scientific symposium proceedings, we present a focused review on the mechanisms that contribute to common cardiovascular toxicities discussed at this meeting, the ongoing international collaborative efforts to improve patient outcomes, and the bidirectional challenges of translating basic research to clinical care. We acknowledge that there are many additional therapies that are of significance but were not topics of discussion at this symposium. We hope that through this symposium-based review we can highlight the knowledge gaps and clinical priorities to inform the design of future studies that aim to prevent and mitigate cardiovascular disease in cancer patients and survivors.

Gene-Level Analysis of Anthracycline-Induced Cardiomyopathy in Cancer Survivors: A Report From COG-ALTE03N1, BMTSS, and CCSS

Background

Anthracyclines are highly effective in treating cancer, albeit with increased cardiomyopathy risk. Although risk is attributed to associations with single nucleotide polymorphisms (SNPs), multiple SNPs on a gene and their interactions remain unexamined.

Objectives

This study examined gene-level associations with cardiomyopathy among cancer survivors using whole-exome sequencing data.

Methods

For discovery, 278 childhood cancer survivors (129 cases; 149 matched control subjects) from the COG (Children's Oncology Group) study ALTE03N1 were included. Logic regression (machine learning) was used to identify gene-level SNP combinations for 7,212 genes and ordinal logistic regression to estimate gene-level associations with cardiomyopathy. Models were adjusted for primary cancer, age at cancer diagnosis, sex, race/ethnicity, cumulative anthracycline dose, chest radiation, cardiovascular risk factors, and 3 principal components. Statistical significance threshold of 6.93 × 10-6 accounted for multiple testing. Three independent cancer survivor populations (COG study, BMTSS [Blood or Marrow Transplant Survivor Study] and CCSS [Childhood Cancer Survivor Study]) were used to replicate gene-level associations and examine SNP-level associations from discovery genes using ordinal logistic, conditional logistic, and Cox regression models, respectively.

Results

Median age at cancer diagnosis for discovery cases and control subjects was 6 years and 8 years, respectively. Gene-level association for P2RX7 (OR: 0.10; 95% CI: 0.04-0.27; P = 2.19 × 10-6) was successfully replicated (HR: 0.65; 95% CI: 0.47-0.90; P = 0.009) in the CCSS cohort. Additional signals were identified on TNIK, LRRK2, MEFV, NOBOX, and FBN3. Individual SNPs across all discovery genes, except FBN3, were replicated.

Conclusions

In our study, SNP sets having 1 or no copies of P2RX7 variant alleles were associated with reduced risk of cardiomyopathy, presenting a potential therapeutic target to mitigate cardiac outcomes in cancer survivors.

Comprehensive investigating of mismatch repair genes (MMR) polymorphisms in participants with chronic hepatitis B virus infection

Background and aim: In this study, we focused on the relationship between single nucleotide polymorphisms in MMR genes and the occurrence and development of HBV infection. Materials and methods: A total of 3,128 participants were divided into five groups: negative control group (NeC), spontaneous clearance group (SC), chronic hepatitis B group (CHB), liver cirrhosis group (LC) and hepatocellular carcinoma group (HCC), CHB, liver cirrhosis and hepatocellular carcinoma constitute HLD. We conducted three case-control studies: NeC (840 cases) vs. HLD (1792 cases), SC (486 cases) vs. HLD (1792 cases) and CHB + LC (1,371 cases) vs. HCC (421 cases). 11 polymorphic loci in MLH1, MLH3, MSH5, PMS1 and PMS2 were involved in genotyping by Sequenom MassArray. The SNPStats performed Hardy-Weinberg equilibrium test. Linkage disequilibrium patterns were visualized using Haploview4.2. The GMDR (v0.9) was conducted to generalized multifactor dimension reduction analysis. The correlation, multiplicative interaction and additive interaction analyses were calculated by Logistic Regression through SPSS21.0. Matrix and programmed excel were also involved in the calculation of additive interaction. Results: In NeC vs. HLD group, MSH5-rs1150793(G) was a risk base to HBV susceptibility (nominal p = 0.002, OR = 1.346). We found multiplicative interaction between MLH1-rs1540354 (AA + AT) and PMS1-rs1233255 (AA) (nominal p = 0.024, OR = 1.240). There was additive interaction between PMS1-rs1233255 (AA) and PMS1-rs256554(CA + CC). In SC vs. HLD group, MLH1-rs1540354 (TT) was a risk genotype (nominal p < 0.05, OR>1). Through haplotype analysis, we found the linkage disequilibrium of three loci in MLH1. The results of GMDR showed the optimal five-locus model about the spontaneous clearance of HBV. In CHB + LC vs. HCC group, PMS2-rs12112229(A) was related to the cancerization of liver. Conclusion: We found rs1150793(G), rs1540354(T) and rs12112229(A) were significantly related to HBV susceptibility, spontaneous clearance of HBV and cancerization after infection, respectively.