ERK inhibitor(Kura Oncology)

Anthracycline-induced cardiotoxicity (AIC) is a unique type of cardiomyopathy that limits the clinical use of anthracyclines in cancer therapy. Although several cardiomyopathy-related pathways have been identified, including extracellular signal-regulated kinase (ERK) signaling, pathway-specific interventions for AIC remain unclear.

The aim of this study was to investigate the role of Erk signaling in AIC using zebrafish genetics.

A zebrafish model of AIC was used to screen genes in known cardiomyopathy pathways, including Erk signaling. Heterozygous and homozygous mutants were evaluated for their modifying effects on AIC. In parallel, pharmacologic studies with ERK inhibitors were conducted to assess dose-dependent therapeutic effects of Erk inhibition.

mek1^+/- and erk1^+/- mutants conferred protective effects in adult zebrafish with AIC. Consistent with this, Erk phosphorylation was aberrantly elevated in AIC hearts. Although heterozygous mutants mitigated AIC phenotypes, homozygous erk1^-/- mutants caused cardiac dysfunction and worsened AIC. Similarly, pharmacologic inhibition of Erk with temuterkib was therapeutic at low doses but induced dose-dependent cardiotoxicity. Mechanistically, the AIC model exhibited accelerated cardiac senescence, which can be attenuated by Erk inhibition.

Aberrant Erk activation contributes to AIC, and controlled Erk inhibition may offer therapeutic benefit, potentially via antiaging mechanisms. However, optimization is essential, as excessive inhibition can be cardiotoxic.

Acute myeloid leukemia (AML) is a heterogeneous malignancy with diverse genetic mutations and oncogenic pathways influencing treatment response. Despite therapeutic advances, relapse and resistance remain persistent issues. This study integrates genomic and transcriptomic profiling to identify biomarkers of high-risk AML, informing personalized medicine strategies. Nonnegative matrix factorization was applied to RNA sequencing data from the BeatAML cohort ( N = 462) for patient subtyping, with survival analysis using the Kaplan–Meier method. Immune profiling via xCell and Gene Set Variation Analysis assessed the tumor microenvironment, with findings validated in the Cancer Genome Atlas AML cohort ( N = 173). Using a random forest machine learning model, we developed a 20-gene signature identifying a high-risk subgroup comprising approximately 20% of patients with AML. The high-risk AML subtype was enriched for recurrent FLT3 , NPM1 , and DNMT3A mutations, activation of PI3K/AKT/mTOR, and complement pathways. Immune profiling revealed an immunosuppressive microenvironment with increased M2 macrophages and mesenchymal stem cells. The 20-gene signature predicted high-risk AML with high accuracy (area under the curve = 0.995, F1 = 0.89). AML cell lines representing high- and low-risk phenotypes identified using the 20-gene signature were tested for drug sensitivity, including the standard-of-care cytarabine, and two targeted therapies, the PI3K inhibitor LY294002 and the MAPK inhibitor selumetinib, selected based on enriched pathways in high-risk AML. High-risk AML cell lines exhibited reduced cytarabine sensitivity but greater responsiveness to PI3K and MAPK/ERK inhibitors, consistent with pathway enrichment results. These findings support molecular stratification and predictive signatures as tools to guide therapy in high-risk AML. Further clinical validation is warranted.