The biology of hope: Inflammatory and neuroendocrine profiles in ovarian cancer patients.

INTRODUCTION: Although the concept of hope is highly relevant for cancer patients, little is known about its association with cancer-relevant biomarkers. Here we examined how hope was related to diurnal cortisol and interleukin-6 (IL-6), a pro-inflammatory cytokine previously associated with tumor biology and survival in ovarian cancer. Secondly, we examined whether hope and hopelessness are distinctly associated with these biomarkers. METHOD: Participants were 292 high-grade ovarian cancer patients who completed surveys and provided saliva samples 4x/daily for 3 days pre-surgery to assess diurnal cortisol. Blood (pre-surgery) and ascites were assessed for IL-6. Hope and hopelessness were assessed using standardized survey items from established scales (Center for Epidemiological Studies Depression Scale; Profile of Mood States, Functional Assessment of Cancer Therapy). Two hopeless items were z-scored and combined into a composite for analysis. Regression models related these variables to nocturnal cortisol, cortisol slope, plasma and ascites IL-6, adjusting for cancer stage, BMI, age, and depression. RESULTS: Greater hope was significantly related to a steeper cortisol slope, ß = -0.193, p = 0.046, and lower night cortisol, ß = -0.227, p = 0.018, plasma IL-6, ß = -0.142, p = 0.033, and ascites IL-6, ß = -0.290, p = 0.002. Secondary analyses including both hope and hopelessness showed similar patterns, with distinct relationships of hope with significantly lower nocturnal cortisol ß = -0.233,p = 0.017 and ascites IL-6, ß = -0.282,p = 0.003, and between hopelessness and a flatter cortisol slope, ß = 0.211, p = 0.031. CONCLUSIONS: These data suggest a biological signature of hope associated with less inflammation and more normalized diurnal cortisol in ovarian cancer. These findings have potential clinical utility but need replication with more diverse samples and validated assessments of hope.

Well-being and stress vulnerability in ovarian cancer survivors during the COVID-19 pandemic.

OBJECTIVE: This study was designed to examine (1) whether ovarian cancer (OC) survivors would have greater well-being vs. elevated distress compared to community members during a universal health stressor (COVID-19) and (2) how resources and risk factors at diagnosis predicted vulnerability to a subsequent health-related stressor. METHODS: One hundred seventeen OC survivors were recruited from two academic medical centers and compared to a community-based sample on COVID-related distress and disruption. Latent class analysis identified differentially impacted groups of survivors. RESULTS: Survivors reported lower distress than community members. Predictors of higher distress included shorter-term survivorship, greater disruption, and poorer emotional well--being (EWB) at diagnosis. Survivors were divided into high- and low-COVID-19-impact subgroups; high-impact individuals endorsed higher perceived stress and lower EWB at diagnosis. CONCLUSION: Survivors reported lower COVID-related distress than community participants. While depression at diagnosis did not predict later distress, EWB was a strong predictor of response to a novel health-related stressor.

Biobehavioral factors predict an exosome biomarker of ovarian carcinoma disease progression.

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.

Characterizing the intersection of Co-occurring risk factors for illicit drug abuse and dependence in a U.S. nationally representative sample.

Few studies have attempted to characterize how co-occurring risk factors for substance use disorders intersect. A recent study examined this question regarding cigarette smoking and demonstrated that co-occurring risk factors generally act independently. The present study examines whether that same pattern of independent intersection of risk factors extends to illicit drug abuse/dependence using a U.S. nationally representative sample (National Survey on Drug Use and Health, 2011-2013). Logistic regression and classification and regression tree (CART) modeling were used to examine risk of past-year drug abuse/dependence associated with a well-established set of risk factors for substance use (age, gender, race/ethnicity, education, poverty, smoking status, alcohol abuse/dependence, mental illness). Each of these risk factors was associated with significant increases in the odds of drug abuse/dependence in univariate logistic regressions. Each remained significant in a multivariate model examining all eight risk factors simultaneously. CART modeling of these 8 risk factors identified subpopulation risk profiles wherein drug abuse/dependence prevalence varied from <1% to >80% corresponding to differing combinations of risk factors present. Alcohol abuse/dependence and cigarette smoking had the strongest associations with drug abuse/dependence risk. These results demonstrate that co-occurring risk factors for illicit drug/abuse dependence generally intersect in the same independent manner as risk factors for cigarette smoking, underscoring further fundamental commonalities across these different types of substance use disorders. These results also underscore the fundamental importance of differences in the presence of co-occurring risk factors when considering the often strikingly different prevalence rates of illicit drug abuse/dependence in U.S. population subgroups.