Exploring Novel Therapeutic Strategies For Immune Checkpoint Inhibitor-induced Myocarditis Through Gene Expression Analysis

• By: HAMANO, Hirofumi (Okayama University, Japan)
• Co-author(s): Dr Hirofumi Hamano (Department of Pharmacy, Okayama, Japan / Department of Clinical Pharmacology and Pharmacy, Okayama, Japan)
  M.S. Reina Yamamoto (Department of Medicinal Pharmacology, Okayama, Japan)
  Dr. Yuta Tanaka (Department of Pharmacy, Okayama, Japan)
  Dr. Ikuto Kimura (Department of Pharmacy, Okayama, Japan)
  Dr. Hiroto Okuda (Department of Pharmacy, Okayama, Japan)
  Dr. Yasuhisa Tatebe (Department of Pharmacy, Okayama, Japan)
  Dr. Tatsuaki Takeda (Department of Health Data Science, Okayama, Japan)
  Dr. Jun Matsumoto (Department of Health Data Science, Okayama, Japan)
  Dr. Toshihiro Koyama (Department of Health Data Science, Okayama, Japan)
  Dr. Takashi Uehara (Department of Medicinal Pharmacology, Okayama, Japan)
  Dr. Yoshito Zamami (Department of Pharmacy, Okayama, Japan / Department of Clinical Pharmacology and Pharmacy, Okayama, Japan)
  
• Abstract:

  Background: Myocarditis induced by Immune checkpoint inhibitors (ICIs) presents a formidable challenge in contemporary oncology, with mortality rates exceeding 40%. Despite the severity of this adverse event, its rarity — with incidence rates ranging from 0.1 to 1.1% — significantly impedes the accumulation of comprehensive clinical insights. Consequently, the underlying mechanisms of ICI-induced myocarditis and effective treatment strategies remain largely undefined, underscoring the critical need for innovative research approaches.
  Our study leveraged the power of bioinformatics to bridge this knowledge gap. We aim to identify novel therapeutic targets and potential drug candidates that could mitigate or prevent the cardiotoxic effects of ICIs by harnessing publicly available gene expression datasets to pave the way for safer cancer immunotherapy protocols.
  Methods: Our research methodology employed a meticulous analysis of RNA-seq data derived from the myocardial cells of patients across three distinct groups: patients with ICI-induced myocarditis (n=9, E-MTAB-8867 dataset), patients with dilated cardiomyopathy (n=11), and individuals diagnosed with viral myocarditis (n=5, GSE120567 dataset). These datasets were sourced from the Esteemed Array Express and GEO databases curated by the European Bioinformatics Institute (EBI) and the National Center for Biotechnology Information (NCBI), respectively. Following an exhaustive preprocessing routine, we applied advanced statistical methods (the Walt test, with the threshold for significance set to p=0.05, and a false discovery rate (FDR) of 5%) to identify genes uniquely associated with ICI-induced myocarditis. Building on these findings, we explored the LINCS database [a comprehensive repository maintained by the National Institutes of Health (NIH) that documents gene expression modifications triggered by an array of chemical compounds] to identify drugs capable of modulating the identified gene expression patterns.
  Result: Our analysis revealed a profound alteration in the myocardial gene expression landscape attributable to ICI therapy, with 387 genes exhibiting heightened expression and 797 showing reduced activity compared to the dilated and viral myocarditis profiles. Intriguingly, this distinctive genetic signature led to the identification of four promising drug candidates, each with the potential to counteract gene expression aberrations linked to ICI-induced myocarditis.
  Discussion: This study represents a significant step forward in the quest for effective countermeasures against cardiotoxicity associated with immune checkpoint inhibition. We laid the groundwork for a novel paradigm in the management of ICI-induced myocarditis by integrating gene expression data with drug efficacy profiles. Our findings highlight the utility of bioinformatics in uncovering hidden patterns within complex datasets and underscore the potential of this approach in translating genomic insights into tangible therapeutic strategies. We plan to further refine our list of candidate drugs through rigorous validation processes, employing spontaneous adverse event reports and detailed clinical information databases to ensure the reliability and applicability of our proposed interventions in a clinical setting.