How can the "strengths" of risk factors, in the sense of how well they discriminate cases from controls, be compared when they are measured on different scales such as continuous, binary, and ...integer? Given that risk estimates take into account other fitted and design-related factors-and that is how risk gradients are interpreted-so should the presentation of risk gradients. Therefore, for each risk factor X0, I propose using appropriate regression techniques to derive from appropriate population data the best fitting relationship between the mean of X0 and all the other covariates fitted in the model or adjusted for by design (X1, X2, … , Xn). The odds per adjusted standard deviation (OPERA) presents the risk association for X0 in terms of the change in risk per s = standard deviation of X0 adjusted for X1, X2, … , Xn, rather than the unadjusted standard deviation of X0 itself. If the increased risk is relative risk (RR)-fold over A adjusted standard deviations, then OPERA = expln(RR)/A = RR(s). This unifying approach is illustrated by considering breast cancer and published risk estimates. OPERA estimates are by definition independent and can be used to compare the predictive strengths of risk factors across diseases and populations.
The clinical management of BRCA1 and BRCA2 mutation carriers requires accurate, prospective cancer risk estimates.
To estimate age-specific risks of breast, ovarian, and contralateral breast cancer ...for mutation carriers and to evaluate risk modification by family cancer history and mutation location.
Prospective cohort study of 6036 BRCA1 and 3820 BRCA2 female carriers (5046 unaffected and 4810 with breast or ovarian cancer or both at baseline) recruited in 1997-2011 through the International BRCA1/2 Carrier Cohort Study, the Breast Cancer Family Registry and the Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer, with ascertainment through family clinics (94%) and population-based studies (6%). The majority were from large national studies in the United Kingdom (EMBRACE), the Netherlands (HEBON), and France (GENEPSO). Follow-up ended December 2013; median follow-up was 5 years.
BRCA1/2 mutations, family cancer history, and mutation location.
Annual incidences, standardized incidence ratios, and cumulative risks of breast, ovarian, and contralateral breast cancer.
Among 3886 women (median age, 38 years; interquartile range IQR, 30-46 years) eligible for the breast cancer analysis, 5066 women (median age, 38 years; IQR, 31-47 years) eligible for the ovarian cancer analysis, and 2213 women (median age, 47 years; IQR, 40-55 years) eligible for the contralateral breast cancer analysis, 426 were diagnosed with breast cancer, 109 with ovarian cancer, and 245 with contralateral breast cancer during follow-up. The cumulative breast cancer risk to age 80 years was 72% (95% CI, 65%-79%) for BRCA1 and 69% (95% CI, 61%-77%) for BRCA2 carriers. Breast cancer incidences increased rapidly in early adulthood until ages 30 to 40 years for BRCA1 and until ages 40 to 50 years for BRCA2 carriers, then remained at a similar, constant incidence (20-30 per 1000 person-years) until age 80 years. The cumulative ovarian cancer risk to age 80 years was 44% (95% CI, 36%-53%) for BRCA1 and 17% (95% CI, 11%-25%) for BRCA2 carriers. For contralateral breast cancer, the cumulative risk 20 years after breast cancer diagnosis was 40% (95% CI, 35%-45%) for BRCA1 and 26% (95% CI, 20%-33%) for BRCA2 carriers (hazard ratio HR for comparing BRCA2 vs BRCA1, 0.62; 95% CI, 0.47-0.82; P=.001 for difference). Breast cancer risk increased with increasing number of first- and second-degree relatives diagnosed as having breast cancer for both BRCA1 (HR for ≥2 vs 0 affected relatives, 1.99; 95% CI, 1.41-2.82; P<.001 for trend) and BRCA2 carriers (HR, 1.91; 95% CI, 1.08-3.37; P=.02 for trend). Breast cancer risk was higher if mutations were located outside vs within the regions bounded by positions c.2282-c.4071 in BRCA1 (HR, 1.46; 95% CI, 1.11-1.93; P=.007) and c.2831-c.6401 in BRCA2 (HR, 1.93; 95% CI, 1.36-2.74; P<.001).
These findings provide estimates of cancer risk based on BRCA1 and BRCA2 mutation carrier status using prospective data collection and demonstrate the potential importance of family history and mutation location in risk assessment.
Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of ...CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening.
We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry.
In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62–0.64) for men and 0.62 (95% confidence interval, 0.61–0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, for individuals with the highest vs the lowest 10% of risk.
We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, which is based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.
Display omitted
Abstract
Background
We developed a method to make Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) using observational data for related individuals and considering ...changes in a pair of regression coefficients. ICE FALCON has some similarities to Mendelian randomization (MR) but uses in effect all the familial determinants of the exposure, not just those captured by measured genetic variants, and does not require genetic data nor make strong assumptions. ICE FALCON can assess tracking of a measure over time, an issue often difficult to assess using MR due to lack of a valid instrumental variable.
Methods
We describe ICE FALCON and present two empirical applications with simulations.
Results
We found evidence consistent with body mass index (BMI) having a causal effect on DNA methylation at the ABCG1 locus, the same conclusion as from MR analyses but providing about 2.5 times more information per subject. We found evidence that tracking of BMI is consistent with longitudinal causation, as well as familial confounding. The simulations supported the validity of ICE FALCON.
Conclusions
There are conceptual similarities between ICE FALCON and MR, but empirically they are giving similar conclusions with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable, or when the assumptions underlying MR analysis are suspect. ICE FALCON could provide insights into causality for a wide range of public health questions.
Fibroglandular breast tissue appears dense on mammogram, whereas fat appears nondense. It is unclear whether absolute or percentage dense area more strongly predicts breast cancer risk and whether ...absolute nondense area is independently associated with risk.
We conducted a meta-analysis of 13 case-control studies providing results from logistic regressions for associations between one standard deviation (SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects models to calculate pooled odds ratios and 95% confidence intervals (CIs). All tests were two-sided with P less than .05 considered to be statistically significant.
Among premenopausal women (n = 1776 case patients; n = 2834 control subjects), summary odds ratios were 1.37 (95% CI = 1.29 to 1.47) for absolute dense area, 0.78 (95% CI = 0.71 to 0.86) for absolute nondense area, and 1.52 (95% CI = 1.39 to 1.66) for percentage dense area when pooling estimates adjusted for age, body mass index, and parity. Corresponding odds ratios among postmenopausal women (n = 6643 case patients; n = 11187 control subjects) were 1.38 (95% CI = 1.31 to 1.44), 0.79 (95% CI = 0.73 to 0.85), and 1.53 (95% CI = 1.44 to 1.64). After additional adjustment for absolute dense area, associations between absolute nondense area and breast cancer became attenuated or null in several studies and summary odds ratios became 0.82 (95% CI = 0.71 to 0.94; P heterogeneity = .02) for premenopausal and 0.85 (95% CI = 0.75 to 0.96; P heterogeneity < .01) for postmenopausal women.
The results suggest that percentage dense area is a stronger breast cancer risk factor than absolute dense area. Absolute nondense area was inversely associated with breast cancer risk, but it is unclear whether the association is independent of absolute dense area.
Although high-risk mutations in identified major susceptibility genes (DNA mismatch repair genes and
) account for some familial aggregation of colorectal cancer, their population prevalence and the ...causes of the remaining familial aggregation are not known.
We studied the families of 5,744 colorectal cancer cases (probands) recruited from population cancer registries in the United States, Canada, and Australia and screened probands for mutations in mismatch repair genes and
We conducted modified segregation analyses using the cancer history of first-degree relatives, conditional on the proband's age at diagnosis. We estimated the prevalence of mutations in the identified genes, the prevalence of HR for unidentified major gene mutations, and the variance of the residual polygenic component.
We estimated that 1 in 279 of the population carry mutations in mismatch repair genes (
= 1 in 1,946,
= 1 in 2,841,
= 1 in 758,
= 1 in 714), 1 in 45 carry mutations in
, and 1 in 504 carry mutations associated with an average 31-fold increased risk of colorectal cancer in unidentified major genes. The estimated polygenic variance was reduced by 30% to 50% after allowing for unidentified major genes and decreased from 3.3 for age <40 years to 0.5 for age ≥70 years (equivalent to sibling relative risks of 5.1 to 1.3, respectively).
Unidentified major genes might explain one third to one half of the missing heritability of colorectal cancer.
Our findings could aid gene discovery and development of better colorectal cancer risk prediction models.
.
The cost-effectiveness of population-based panel testing for high- and moderate-penetrance ovarian cancer (OC)/breast cancer (BC) gene mutations is unknown. We evaluate the cost-effectiveness of ...population-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 mutation testing compared with clinical criteria/family history (FH) testing in unselected general population women.
A decision-analytic model comparing lifetime costs and effects of criteria/FH-based BRCA1/BRCA2 testing is compared with BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing in those fulfilling clinical criteria/strong FH of cancer (≥10% BRCA1/BRCA2 probability) and all women age 30 years or older. Analyses are presented for UK and US populations. Identified carriers undergo risk-reducing salpingo-oophorectomy. BRCA1/BRCA2/PALB2 carriers can opt for magnetic resonance imaging/mammography, chemoprevention, or risk-reducing mastectomy. One-way and probabilistic sensitivity analysis (PSA) enabled model uncertainty evaluation. Outcomes include OC, BC, and additional heart disease deaths. Quality-adjusted life-years (QALYs), OC incidence, BC incidence, and incremental cost-effectiveness ratio (ICER) were calculated. The time horizon is lifetime and perspective is payer.
Compared with clinical criteria/FH-based BRCA1/BRCA2 testing, clinical criteria/FH-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing is cost-effective (ICER = £7629.65/QALY or $49 282.19/QALY; 0.04 days' life-expectancy gained). Population-based testing for BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 mutations is the most cost-effective strategy compared with current policy: ICER = £21 599.96/QALY or $54 769.78/QALY (9.34 or 7.57 days' life-expectancy gained). At £30 000/QALY and $100 000/QALY willingness-to-pay thresholds, population-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 panel testing is the preferred strategy in 83.7% and 92.7% of PSA simulations; criteria/FH-based panel testing is preferred in 16.2% and 5.8% of simulations, respectively. Population-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing can prevent 1.86%/1.91% of BC and 3.2%/4.88% of OC in UK/US women: 657/655 OC cases and 2420/2386 BC cases prevented per million.
Population-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing is more cost-effective than any clinical criteria/FH-based strategy. Clinical criteria/FH-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing is more cost-effective than BRCA1/BRCA2 testing alone.
PDF
