Hanson, Heidi A.

AbstractBackgroundThe psychological and cardiovascular health impacts on family members of patients who have been diagnosed with cancer have not been well characterized. The purpose of this study is to determine whether a family member’s cancer diagnosis influences the risk of psychological illness and cardiovascular disease in first‐degree relatives and spouses of patients affected by cancer.MethodsThis retrospective cohort analysis evaluated the risk of psychological illness and cardiovascular disease in first‐degree relatives and spouses of patients diagnosed with a genitourinary cancer between 1990 and 2015 compared to relatives of those not diagnosed with a genitourinary cancer. The Utah Population Database was used and familial linkage was determined. Follow‐up included 1‐, 3‐, and 5‐year intervals. Patients residing outside of Utah and first‐degree relatives and spouses with psychological or cardiovascular disease diagnosed before a family member’s cancer diagnosis were excluded.ResultsA total of 49,284 patients with a genitourinary cancer were identified with 77,938 first‐degree relatives and spouses. A matched control group included 246,775 patients with 81,022 first‐degree relatives and spouses. Via Cox proportional hazards models, a 10% increased risk of developing a psychological illness (hazard ratio [HR], 1.10; 95% CI, 1.00–1.20) and a 28% increased risk of developing cardiovascular disease (HR, 1.28; 95% CI, 1.17–1.41) at 1 year after a family member’s cancer diagnosis were found.ConclusionsThis study provides population‐level evidence to support the hypothesis that cancer diagnoses will lead to adverse health outcomes for family members of patients with cancer. Increased clinical attention and support are needed to reduce the harm to families caused by cancer.

Abstract Introduction Patients living in rural areas have worse cancer‐specific outcomes. This study examines the effect of family‐based social capital on genitourinary cancer survival. We hypothesized that rural patients with urban relatives have improved survival relative to rural patients without urban family. Methods We examined rural and urban based Utah individuals diagnosed with genitourinary cancers between 1968 and 2018. Familial networks were determined using the Utah Population Database. Patients and relatives were classified as rural or urban based on 2010 rural–urban commuting area codes. Overall survival was analyzed using Cox proportional hazards models. Results We identified 24,746 patients with genitourinary cancer with a median follow‐up of 8.72 years. Rural cancer patients without an urban relative had the worst outcomes with cancer‐specific survival hazard ratios (HRs) at 5 and 10 years of 1.33 (95% CI 1.10–1.62) and 1.46 (95% CI 1.24–1.73), respectively relative to urban patients. Rural patients with urban first‐degree relatives had improved survival with 5‐ and 10‐year survival HRs of 1.21 (95% CI 1.06–1.40) and 1.16 (95% CI 1.03–1.31), respectively. Conclusions Our findings suggest rural patients who have been diagnosed with a genitourinary cancer have improved survival when having relatives in urban centers relative to rural patients without urban relatives. Further research is needed to better understand the mechanisms through which having an urban family member contributes to improved cancer outcomes for rural patients. Better characterization of this affect may help inform policies to reduce urban–rural cancer disparities.

Abstract Background: Transcriptome studies are gaining momentum in genomic epidemiology, and the need to incorporate these data in multivariable models alongside other risk factors brings demands for new approaches. Methods: Here we describe SPECTRA, an approach to derive quantitative variables that capture the intrinsic variation in gene expression of a tissue type. We applied the SPECTRA approach to bulk RNA sequencing from malignant cells (CD138+) in patients from the Multiple Myeloma Research Foundation CoMMpass study. Results: A set of 39 spectra variables were derived to represent multiple myeloma cells. We used these variables in predictive modeling to determine spectra-based risk scores for overall survival, progression-free survival, and time to treatment failure. Risk scores added predictive value beyond known clinical and expression risk factors and replicated in an external dataset. Spectrum variable S5, a significant predictor for all three outcomes, showed pre-ranked gene set enrichment for the unfolded protein response, a mechanism targeted by proteasome inhibitors which are a common first line agent in multiple myeloma treatment. We further used the 39 spectra variables in descriptive modeling, with significant associations found with tumor cytogenetics, race, gender, and age at diagnosis; factors known to influence multiple myeloma incidence or progression. Conclusions: Quantitative variables from the SPECTRA approach can predict clinical outcomes in multiple myeloma and provide a new avenue for insight into tumor differences by demographic groups. Impact: The SPECTRA approach provides a set of quantitative phenotypes that deeply profile a tissue and allows for more comprehensive modeling of gene expression with other risk factors.