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Based on data from the Global Cancer Statistics 2022, lung cancer stands as the most lethal cancer worldwide, with age-adjusted incidence and mortality rates of 23.6 and 16.9 per 100,000 people, respectively. Despite significant strides in precision oncology driven by large-scale international research consortia, there remains a critical need to deepen our understanding of the genomic landscape across diverse racial and ethnic groups. To address this challenge, we performed comprehensive in silico analyses and data mining to identify pathogenic variants in genes that drive lung cancer. We subsequently calculated the allele frequencies and assessed the deleteriousness of these oncogenic variants among populations such as African, Amish, Ashkenazi Jewish, East and South Asian, Finnish and non-Finnish European, Latino, and Middle Eastern. Our analysis examined 117,707 variants within 86 lung cancer-associated genes across 75,109 human genomes, uncovering 8042 variants that are known or predicted to be pathogenic. We prioritized variants based on their allele frequencies and deleterious scores, and identified those with potential significance for response to anti-cancer therapies through in silico drug simulations, current clinical pharmacogenomic guidelines, and ongoing late-stage clinical trials targeting lung cancer-driving proteins. In conclusion, it is crucial to unite global efforts to create public health policies that emphasize prevention strategies and ensure access to clinical trials, pharmacogenomic testing, and cancer research for these groups in developed nations.
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Epigenetic modifications, characterized by changes in gene expression without altering the DNA sequence, play a crucial role in the development and progression of cancer by significantly influencing gene activity and cellular function. This insight has led to the development of a novel class of therapeutic agents, known as epigenetic drugs. These drugs, including histone deacetylase inhibitors, histone acetyltransferase inhibitors, histone methyltransferase inhibitors, and DNA methyltransferase inhibitors, aim to modulate gene expression to curb cancer growth by uniquely altering the epigenetic landscape of cancer cells. Ongoing research and clinical trials are rigorously evaluating the efficacy of these drugs, particularly their ability to improve therapeutic outcomes when used in combination with other treatments. Such combination therapies may more effectively target cancer and potentially overcome the challenge of drug resistance, a significant hurdle in cancer therapy. Additionally, the importance of nutrition, inflammation control, and circadian rhythm regulation in modulating drug responses has been increasingly recognized, highlighting their role as critical modifiers of the epigenetic landscape and thereby influencing the effectiveness of pharmacological interventions and patient outcomes. Epigenetic drugs represent a paradigm shift in cancer treatment, offering targeted therapies that promise a more precise approach to treating a wide spectrum of tumors, potentially with fewer side effects compared to traditional chemotherapy. This progress marks a step towards more personalized and precise interventions, leveraging the unique epigenetic profiles of individual tumors to optimize treatment strategies.
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Introduction: Gastric cancer is one of the most prevalent types of cancer worldwide. The World Health Organization (WHO), the International Agency for Research on Cancer (IARC), and the Global Cancer Statistics (GLOBOCAN) reported an age standardized global incidence rate of 9.2 per 100,000 individuals for gastric cancer in 2022, with a mortality rate of 6.1. Despite considerable progress in precision oncology through the efforts of international consortia, understanding the genomic features and their influence on the effectiveness of anti-cancer treatments across diverse ethnic groups remains essential. Methods: Our study aimed to address this need by conducting integrated in silico analyses to identify actionable genomic alterations in gastric cancer driver genes, assess their impact using deleteriousness scores, and determine allele frequencies across nine global populations: European Finnish, European non-Finnish, Latino, East Asian, South Asian, African, Middle Eastern, Ashkenazi Jewish, and Amish. Furthermore, our goal was to prioritize targeted therapeutic strategies based on pharmacogenomics clinical guidelines, in silico drug prescriptions, and clinical trial data. Results: Our comprehensive analysis examined 275,634 variants within 60 gastric cancer driver genes from 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, identifying 13,542 annotated and predicted oncogenic variants. We prioritized the most prevalent and deleterious oncogenic variants for subsequent pharmacogenomics testing. Additionally, we discovered actionable genomic alterations in the ARID1A, ATM, BCOR, ERBB2, ERBB3, CDKN2A, KIT, PIK3CA, PTEN, NTRK3, TP53, and CDKN2A genes that could enhance the efficacy of anti-cancer therapies, as suggested by in silico drug prescription analyses, reviews of current pharmacogenomics clinical guidelines, and evaluations of phase III and IV clinical trials targeting gastric cancer driver proteins. Discussion: These findings underline the urgency of consolidating efforts to devise effective prevention measures, invest in genomic profiling for underrepresented populations, and ensure the inclusion of ethnic minorities in future clinical trials and cancer research in developed countries.
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OBJECTIVE: To identify factors associated with non-compliance to antidepressant's (AD) treatment in Santiago, Chile. METHODS: A cross-sectional study was carried out in a household randomized and representative sample of 1000 individuals aged 15 years and older. Treatment adherence was studied in AD consumers using logistic regression to estimate factors associated with non-compliance in doses or time of treatment. RESULTS: Antidepressant non-compliance was 52.8% and their main associated factor was income. Gender and educational level were also associated with AD non-compliance. CONCLUSIONS: Antidepressant non-compliance was associated with income and sociodemographic characteristics in Chilean patients. Therefore, health strategies to improve AD compliance should consider inequities in access to medications and characteristics of the AD consumers.