Modifiable pre-treatment factors are associated with quality of life in women with gynaecological cancers at diagnosis and one year later: Results from the HORIZONS UK national cohort study.

OBJECTIVE: Personalised care requires the identification of modifiable risk factors so that interventions can be implemented rapidly following a gynaecological cancer diagnosis. Our objective was to determine what pre-treatment factors are associated with quality of life (QOL) at baseline (pre-treatment) and 12 months. METHODS: 1222 women with a confirmed diagnosis of endometrial, ovarian, cervical or vulvar cancer from 82 UK NHS hospitals agreed to complete questionnaires at baseline, three and 12 months. Questionnaires included measures of QOL, health, lifestyle, support and self-management. The primary outcome measure was QOL as measured by Quality of Life in Adult Cancer Survivors (QLACS). Sites provided clinical data at baseline, six and 12 months. Linear regression models were constructed to examine the association between baseline characteristics and QOL outcomes. RESULTS: QOL declined between baseline and 3 months, followed by an improvement at 12 months. Baseline (pre-treatment) factors associated with worse QOL at both baseline and 12 months were depression, anxiety, living in a more deprived area and comorbidities which limit daily activities, whereas higher self-efficacy and age of 50+ years were associated with better QOL. CONCLUSIONS: Depression, anxiety and self-efficacy are modifiable risk factors that can impact on QOL. Screening for these, and assessment of whether comorbidities limit daily activities, should be incorporated in a holistic needs assessment and interventions to improve self-efficacy should be made available. Care can then be personalised from the outset to enable all women with a gynaecological cancer the opportunity to have the best QOL.

Consortium analysis of gene and gene-folate interactions in purine and pyrimidine metabolism pathways with ovarian carcinoma risk.

SCOPE: We reevaluated previously reported associations between variants in pathways of one-carbon (1-C) (folate) transfer genes and ovarian carcinoma (OC) risk, and in related pathways of purine and pyrimidine metabolism, and assessed interactions with folate intake. METHODS AND RESULTS: Odds ratios (OR) for 446 genetic variants were estimated among 13,410 OC cases and 22,635 controls, and among 2281 cases and 3444 controls with folate information. Following multiple testing correction, the most significant main effect associations were for dihydropyrimidine dehydrogenase (DPYD) variants rs11587873 (OR = 0.92; p = 6 × 10⁻5) and rs828054 (OR = 1.06; p = 1 × 10⁻4). Thirteen variants in the pyrimidine metabolism genes, DPYD, DPYS, PPAT, and TYMS, also interacted significantly with folate in a multivariant analysis (corrected p = 9.9 × 10⁻6) but collectively explained only 0.2% of OC risk. Although no other associations were significant after multiple testing correction, variants in SHMT1 in 1-C transfer, previously reported with OC, suggested lower risk at higher folate (p(interaction) = 0.03-0.006). CONCLUSION: Variation in pyrimidine metabolism genes, particularly DPYD, which was previously reported to be associated with OC, may influence risk; however, stratification by folate intake is unlikely to modify disease risk appreciably in these women. SHMT1 SNP-by-folate interactions are plausible but require further validation. Polymorphisms in selected genes in purine metabolism were not associated with OC.