Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature / 医学前沿

Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

Repurposing econazole as a pharmacological autophagy inhibitor to treat pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is characterized by the highest mortality among carcinomas. The pathogenesis of PDAC requires elevated autophagy, inhibition of which using hydroxychloroquine has shown promise. However, current realization is impeded by its suboptimal use and unpredictable toxicity. Attempts to identify novel autophagy-modulating agents from already approved drugs offer a rapid and accessible approach. Here, using a patient-derived organoid model, we performed a comparative analysis of therapeutic responses among various antimalarial/fungal/parasitic/viral agents, through which econazole (ECON), an antifungal compound, emerged as the top candidate. Further testing in cell-line and xenograft models of PDAC validated this activity, which occurred as a direct consequence of dysfunctional autophagy. More specifically, ECON boosted autophagy initiation but blocked lysosome biogenesis. RNA sequencing analysis revealed that this autophagic induction was largely attributed to the altered expression of activation transcription factor 3 (ATF3). Increased nuclear import of ATF3 and its transcriptional repression of inhibitor of differentiation-1 (ID-1) led to inactivation of the AKT/mammalian target of rapamycin (mTOR) pathway, thus giving rise to autophagosome accumulation in PDAC cells. The magnitude of the increase in autophagosomes was sufficient to elicit ER stress-mediated apoptosis. Furthermore, ECON, as an autophagy inhibitor, exhibited synergistic effects with trametinib on PDAC. This study provides direct preclinical and experimental evidence for the therapeutic efficacy of ECON in PDAC treatment and reveals a mechanism whereby ECON inhibits PDAC growth.