A Flare Risk Index Informed by Select Immune Mediators in Systemic Lupus Erythematosus.

OBJECTIVE: Systemic lupus erythematosus (SLE) is marked by immune dysregulation linked to varied clinical disease activity. Using a unique longitudinal cohort of SLE patients, this study sought to identify optimal immune mediators informing an empirically refined flare risk index (FRI) reflecting altered immunity prior to clinical disease flare. METHODS: Thirty-seven SLE-associated plasma mediators were evaluated by microfluidic immunoassay in 46 samples obtained in SLE patients with an imminent clinical disease flare (preflare) and 53 samples obtained in SLE patients without a flare over a corresponding period (pre-nonflare). SLE patients were selected from a unique longitudinal cohort of 106 patients with classified SLE (meeting the American College of Rheumatology 1997 revised criteria for SLE or the Systemic Lupus International Collaborating Clinics 2012 revised criteria for SLE). Autoantibody specificities, hybrid SLE Disease Activity Index (hSLEDAI) scores, clinical features, and medication usage were also compared at preflare (mean ± SD 111 ± 47 days prior to flare) versus pre-nonflare (99 ± 21 days prior to nonflare) time points. Variable importance was determined by random forest analysis with logistic regression subsequently applied to determine the optimal number and type of analytes informing a refined FRI. RESULTS: Preflare versus pre-nonflare differences were not associated with demographics, autoantibody specificities, hSLEDAI scores, clinical features, nor medication usage. Forward selection and backward elimination of mediators ranked by variable importance resulted in 17 plasma mediator candidates differentiating preflare from pre-nonflare visits. A final combination of 11 mediators best informed a newly refined FRI, which achieved a maximum sensitivity of 97% and maximum specificity of 98% after applying decision curve analysis to define low, medium, and high FRI scores. CONCLUSION: We verified altered immune mediators associated with imminent disease flare, and a subset of these mediators improved the FRI to identify SLE patients at risk of imminent flare. This molecularly informed, proactive management approach could be critical in prospective clinical trials and the clinical management of lupus.

A steroid metabolizing gene variant in a polyfactorial model improves risk prediction in a high incidence breast cancer population.

BACKGROUND: We have combined functional gene polymorphisms with clinical factors to improve prediction and understanding of sporadic breast cancer risk, particularly within a high incidence Caucasian population. METHODS: A polyfactorial risk model (PFRM) was built from both clinical data and functional single nucleotide polymorphism (SNP) gene candidates using multivariate logistic regression analysis on data from 5022 US Caucasian females (1671 breast cancer cases, 3351 controls), validated in an independent set of 1193 women (400 cases, 793 controls), and reassessed in a unique high incidence breast cancer population (165 cases, 173 controls) from Marin County, CA. RESULTS: The optimized PFRM consisted of 22 SNPs (19 genes, 6 regulating steroid metabolism) and 5 clinical risk factors, and its 5-year and lifetime risk prediction performance proved significantly superior (~ 2-fold) over the Gail model (Breast Cancer Risk Assessment Tool, BCRAT), whether assessed by odds (OR) or positive likelihood (PLR) ratios over increasing model risk levels. Improved performance of the PFRM in high risk Marin women was due in part to genotype enrichment by a CYP11B2 (-344T/C) variant. CONCLUSIONS AND GENERAL SIGNIFICANCE: Since the optimized PFRM consistently outperformed BCRAT in all Caucasian study populations, it represents an improved personalized risk assessment tool. The finding of higher Marin County risk linked to a CYP11B2 aldosterone synthase SNP associated with essential hypertension offers a new genetic clue to sporadic breast cancer predisposition.

Vitamin D receptor polymorphisms and breast cancer risk in a high-incidence population: a pilot study.

BACKGROUND: Marin County, CA has very high incidence of breast cancer. Traditional risk factors, such as those included in the Gail model, do not effectively stratify breast cancer in this population. This retrospective case-control pilot study evaluates DNA from volunteers from a previous Marin County breast cancer epidemiology study. A polyfactorial risk model (OncoVue; InterGenetics Incorporated) that incorporates 22 polymorphisms in 19 genes and 5 clinical risk factors was used to stratify risk in Marin County women. STUDY DESIGN: DNA genotyping was performed on 164 Caucasian women diagnosed with primary breast cancer in Marin County from 1997 to 1999 and 174 age- and ethnicity-matched control subjects. Individual lifetime risks were determined using the polyfactorial risk model and genotype frequencies in women at elevated risk were compared with the overall genotypes. RESULTS: The vitamin D receptor VDR ApaI A2/A2 (rs7975232) homozygous polymorphism was present in high frequency in elevated-risk women. Sixty-four percent of elevated-risk women had the VDR Apa1 A2/A2 genotype compared with only 34% in the overall study, a statistically significant 1.9-fold difference (p = 0.0003). VDR Apa1 A2/a1 and a1/a1 genotypes were also present, but in lower frequencies. CONCLUSIONS: The high frequency of the VDR Apa1 A2/A2 homozygous polymorphism in women designated as elevated risk for breast cancer by the polyfactorial risk model might be related to the high incidence rates of breast cancer in Marin County, CA. Vitamin D supplementation could modify risk of breast cancer in this population.

Polymorphisms in genes involved in sex hormone metabolism, estrogen plus progestin hormone therapy use, and risk of postmenopausal breast cancer.

Hormone therapy, estrogen plus progestin (E+P) particularly, is associated with increased risk of breast cancer. Functionally relevant polymorphisms in genes involved in sex hormone metabolism may alter exposure to exogenous sex hormones and affect risk of postmenopausal breast cancer. We evaluated associations of common polymorphisms in genes involved in estrogen and/or progesterone metabolism, E+P use, and their interactions with breast cancer risk in a case-control study of postmenopausal women (324 cases; 651 controls) nested within the VITAL cohort. None of the polymorphisms studied was, by itself, statistically significantly associated with breast cancer risk. E+P use was significantly associated with increased breast cancer risk (> or =10 years versus never; odds ratio, 1.9; 95% confidence interval, 1.3-2.8; P(trend) = 0.0002). Statistically significant interactions between CYP1A1 Ile(462)Val (P(interaction) = 0.04), CYP1A1 MspI (P(interaction) = 0.003), CYP1B1 Val(432)Leu (P(interaction) = 0.007), CYP1B1 Asn(453)Ser (P(interaction) = 0.04) and PGR Val(660)Leu (P(interaction) = 0.01), and E+P use were observed. The increased risk of breast cancer associated with E+P use was greater among women with at least one rare allele of the CYP1A1 Ile(462)Val, CYP1A1 MspI, CYP1B1 Asn(453)Ser, and PGR Val(660)Leu polymorphisms than among women homozygous for the common allele of these polymorphisms. Risk of breast cancer increased little with increasing years of E+P use among women with at least one CYP1B1 Val(432) allele; a large increase in risk was seen among women homozygous for CYP1B1 Leu(432). Our results support the hypothesis that specific polymorphisms in genes involved in sex hormone metabolism may modify the effect of E+P use on breast cancer risk.

Age-specific association of steroid hormone pathway gene polymorphisms with breast cancer risk.

BACKGROUND: Breast cancer (BC) is a complex disease, and the incidence rates for BC increase with age. Both environmental factors and genetics have an impact on the risk of BC. Although the effects of environmental factors may vary with age, it has been assumed generally that the penetrance of single nucleotide polymorphisms (SNPs) is constant throughout life. In the current study, the results demonstrated that certain SNPs exhibit BC risk associations that vary considerably with age. METHODS: SNPs in 12 steroid hormone pathway genes were investigated for associations with BC risk in white women who were enrolled in an age-matched, case-control (1:2 for cases and controls, respectively) study that consisted of a discovery set (n = 5000 women) and an independent validation set (n = 1583 women). RESULTS: Significant age-related trends were identified and confirmed for SNPs in 4 genes associated with BC risk. The cytosine/cytosine (C/C) genotype of cytochrome P450 XIB2 (CYP11B2) was associated with decreased risk at younger ages (ages 30-44 years) but an increased risk at older ages (ages 55-69 years). The homozygous cytosine-guanine (CG/CG) genotype of uridine phosphorylase glycosyltransferase 1A7 (UGT1A7) was associated with increased risk at younger ages but decreased risk at older ages. Associations in cytochrome P450 19 (CYP19) and progesterone receptor (PGR) were confined to middle age (ages 45-54 years). CONCLUSIONS: The identification of age-specific genetic associations may have profound implications for future etiologic studies of BC and for the use of SNP genotyping to accurately predict the risk of BC in women.

Oligogenic combinations associated with breast cancer risk in women under 53 years of age.

Common, but weakly penetrant, functional polymorphisms probably account for most of the genetic risk for breast cancer in the general population. Current polygenic risk models assume that component genes act independently. To test for potential gene-gene interactions, single nucleotide polymorphisms in ten genes with known or predicted roles in breast carcinogenesis were examined in a case-control study of 631 Caucasian women diagnosed with breast cancer under the age of 53 years and 1,504 controls under the age of 53 years. Association of breast cancer risk with individual genes and with two- and three-gene combinations was analyzed. Sixty-nine oligogenotypes from 37 distinct two- and three-gene combinations met stringent criteria for significance. Significant odds ratios (ORs) covered a 12-fold range: 0.5-5.9. Of the observed ORs, 17% differed significantly from the ORs predicted by a model of independent gene action, suggesting epistasis, i.e., that these genes interact to affect breast cancer risk in a manner not predictable from single gene effects. Exploration of the biological basis for these oligogenic interactions might reveal etiologic or therapeutic insights into breast cancer and other cancers.