Biobehavioral factors predict an exosome biomarker of ovarian carcinoma disease progression
Menée à partir d'échantillons plasmatiques prélevés, avant traitement chirurgical ou néo-adjuvant, sur 100 patientes atteintes d'un carcinome ovarien, cette étude identifie une signature moléculaire, basée sur l'ARN exosomal, capable de prédire la progression de la maladie et sensible à des facteurs de risque psycho-sociaux (isolement social, symptômes dépressifs)
Background : Biobehavioral factors such as social isolation and depression have been associated with disease progression in ovarian and other cancers. Here, the authors developed a noninvasive, exosomal RNA profile for predicting ovarian cancer disease progression and subsequently tested whether it increased in association with biobehavioral risk factors.
Methods : Exosomes were isolated from plasma samples from 100 women taken before primary surgical resection or neoadjuvant (NACT) treatment of ovarian carcinoma and 6 and 12 months later. Biobehavioral measures were sampled at all time points. Plasma from 76 patients was allocated to discovery analyses in which morning presurgical/NACT exosomal RNA profiles were analyzed by elastic net machine learning to identify a biomarker predicting rapid (≤6 months) versus more extended disease-free intervals following initial treatment. Samples from a second subgroup of 24 patients were analyzed by mixed-effects linear models to determine whether the progression-predictive biomarker varied longitudinally as a function of biobehavioral risk factors (social isolation and depressive symptoms).
Results : An RNA-based molecular signature was identified that discriminated between individuals who had disease progression in ≤6 months versus >6 months, independent of clinical variables (age, disease stage, and grade). In a second group of patients analyzed longitudinally, social isolation and depressive symptoms were associated with upregulated expression of the disease progression propensity biomarker, adjusting for covariates.
Conclusion : These data identified a novel exosome-derived biomarker indicating propensity of ovarian cancer progression that is sensitive to biobehavioral variables. This derived biomarker may be potentially useful for risk assessment, intervention targeting, and treatment monitoring.
Cancer , résumé, 2022