The 22q11.2 deletion syndrome (22q11DS), the most common survivable human genetic deletion disorder, is caused by a hemizygous deletion of 30-40 contiguous genes on chromosome 22, many of which have not been well characterized. Clinical features seen in patients with this deletion, including intellectual disability, are not completely penetrant and vary in severity between patients, suggesting the involvement of variants elsewhere in the genome in the manifestation of the phenotype. Given that it is a relatively rare disorder (1/2000-6000 in humans), limited research has shed light into the contribution of these second-site variants to the developmental pathogenesis that underlies 22q11DS. As CNVs throughout the genome might constitute such a genetic risk factor for variability in the 22q11DS phenotypes such as intellectual disability, we sought to determine if the overall burden of rare CNVs in the genetic background influenced the phenotypic variability. We analyzed CNV and clinical data from 66 individuals with 22q11DS, and found that 77% (51/66) of individuals with the 22q11DS also carry additional rare CNVs (<0.1% frequency). We observed several trends between CNV burden and phenotype, including that the burden of large rare CNVs (>200 Kb in size) was significantly higher in 22q11DS individuals with intellectual disability than with normal IQ. Our analysis shows that rare CNVs may contribute to intellectual disability 22q11DS, and further analysis on larger 22q11DS cohorts should be performed to confirm this correlation.

BACKGROUND:Neurodevelopmental disorders (NDDs) such as autism spectrum disorder, intellectual disability, developmental disability, and epilepsy are characterized by abnormal brain development that may affect cognition, learning, behavior, and motor skills. High co-occurrence (comorbidity) of NDDs indicates a shared, underlying biological mechanism. The genetic heterogeneity and overlap observed in NDDs make it difficult to identify the genetic causes of specific clinical symptoms, such as seizures. METHODS:We present a computational method, MAGI-S, to discover modules or groups of highly connected genes that together potentially perform a similar biological function. MAGI-S integrates protein-protein interaction and co-expression networks to form modules centered around the selection of a single "seed" gene, yielding modules consisting of genes that are highly co-expressed with the seed gene. We aim to dissect the epilepsy phenotype from a general NDD phenotype by providing MAGI-S with high confidence NDD seed genes with varying degrees of association with epilepsy, and we assess the enrichment of de novo mutation, NDD-associated genes, and relevant biological function of constructed modules. RESULTS:The newly identified modules account for the increased rate of de novo non-synonymous mutations in autism, intellectual disability, developmental disability, and epilepsy, and enrichment of copy number variations (CNVs) in developmental disability. We also observed that modules seeded with genes strongly associated with epilepsy tend to have a higher association with epilepsy phenotypes than modules seeded at other neurodevelopmental disorder genes. Modules seeded with genes strongly associated with epilepsy (e.g., SCN1A, GABRA1, and KCNB1) are significantly associated with synaptic transmission, long-term potentiation, and calcium signaling pathways. On the other hand, modules found with seed genes that are not associated or weakly associated with epilepsy are mostly involved with RNA regulation and chromatin remodeling. CONCLUSIONS:In summary, our method identifies modules enriched with de novo non-synonymous mutations and can capture specific networks that underlie the epilepsy phenotype and display distinct enrichment in relevant biological processes. MAGI-S is available at https://github.com/jchow32/magi-s .

Ideological groups (both non-violent and violent) make extensive use of the Internet for recruiting and other purposes, yet little is known about the effectiveness of the influence of websites of differing ideologies on attitudes and behavior. Furthermore, although credibility and interactivity have been extensively studied in online settings, they have received scant attention with regards to ideological groups. Using a within-subjects design, this study explored how individuals' attitudes, emotional reactions, behaviors and behavioral intentions are affected by two separate websites, with one promoting a liberal ideology and one promoting a conservative ideology. Results indicated that individuals preferred the liberal ideology, that violent websites led to higher negative affect and lowered perceptions of credibility than the non-violent websites, and that violence decreased the likelihood of taking action. Additionally, high interactivity increased the salience of the credibility manipulations with regards to their impact on the likelihood of taking action.

Abstract Introduction Understanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models. Methods Data were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age (<55, 55–64, and ≥65 years). Results MD was positively associated with risk of invasive tumors across all ages, with a two-fold increased risk for high (>51%) versus average density (11-25%). Women ages <55 years with high MD had stronger increased risk of ductal carcinoma in situ (DCIS) compared to women ages 55–64 and ≥65 years (P age-interaction = 0.02). Among all ages, MD had a stronger association with large (>2.1 cm) versus small tumors and positive versus negative lymph node status (P’s < 0.01). For women ages <55 years, there was a stronger association of MD with ER-negative breast cancer than ER-positive tumors compared to women ages 55–64 and ≥65 years (P age-interaction = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group. Conclusion MD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women.

Background

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead.

Objective

To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures.

Design

Case-control.

Setting

San Francisco Mammography Registry and Mayo Clinic.

Participants

1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants.

Measurements

Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity.

Results

Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively.

Limitation

Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method.

Conclusion

Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density.

Primary funding source

National Cancer Institute.

Background

Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk.

Methods

We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.

Results

Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84-4.47, and OR 2.56, 95% CI 1.87-3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75-3.09, and OR 1.50, 95% CI 0.82-2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623-0.654, vs. C-statistic 0.614, 95% CI 0.598-0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.

Conclusions

Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

PURPOSE: We examined the associations of mammographic breast density with breast cancer risk by tumor aggressiveness and by menopausal status and current postmenopausal hormone therapy. METHODS: This study included 2596 invasive breast cancer cases and 4059 controls selected from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses Health Study, Nurses Health Study II, and San Francisco Mammography Registry. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density with breast cancer risk by tumor aggressiveness (defined as presence of at least two of the following tumor characteristics: size ≥2 cm, grade 2/3, ER-negative status, or positive nodes), stratified by menopausal status and current hormone therapy. RESULTS: Overall, the positive association of PD and borderline inverse association of NDA with breast cancer risk was stronger in aggressive vs. non-aggressive tumors (≥51 vs. 11-25% OR 2.50, 95% CI 1.94-3.22 vs. OR 2.03, 95% CI 1.70-2.43, p-heterogeneity = 0.03; NDA 4th vs. 2nd quartile OR 0.54, 95% CI 0.41-0.70 vs. OR 0.71, 95% CI 0.59-0.85, p-heterogeneity = 0.07). However, there were no differences in the association of DA with breast cancer by aggressive status. In the stratified analysis, there was also evidence of a stronger association of PD and NDA with aggressive tumors among postmenopausal women and, in particular, current estrogen+progesterone users (≥51 vs. 11-25% OR 3.24, 95% CI 1.75-6.00 vs. OR 1.93, 95% CI 1.25-2.98, p-heterogeneity = 0.01; NDA 4th vs. 2nd quartile OR 0.43, 95% CI 0.21-0.85 vs. OR 0.56, 95% CI 0.35-0.89, p-heterogeneity = 0.01), even though the interaction was not significant. CONCLUSION: Our findings suggest that associations of mammographic density with breast cancer risk differ by tumor aggressiveness. While there was no strong evidence that these associations differed by menopausal status or hormone therapy, they did appear more prominent among current estrogen+progesterone users.

High alcohol intake and breast density increase breast cancer (BC) risk, but their interrelationship is unknown. We examined whether volumetric density modifies and/or mediates the alcohol-BC association. BC cases (n = 2233) diagnosed from 2006 to 2013 in the San Francisco Bay area had screening mammograms 6 or more months before diagnosis; controls (n = 4562) were matched on age, mammogram date, race or ethnicity, facility, and mammography machine. Logistic regression was used to estimate alcohol-BC associations adjusted for age, body mass index, and menopause; interaction terms assessed modification. Percent mediation was quantified as the ratio of log (odds ratios [ORs]) from models with and without density measures. Alcohol consumption was associated with increased BC risk (2-sided Ptrend = .004), as were volumetric percent density (OR = 1.45 per SD, 95% confidence interval [CI] = 1.36 to 1.56) and dense volume (OR = 1.30, 95% CI = 1.24 to 1.37). Breast density did not modify the alcohol-BC association (2-sided P > .10 for all). Dense volume mediated 25.0% (95% CI = 5.5% to 44.4%) of the alcohol-BC association (2-sided P = .01), suggesting alcohol may partially increase BC risk by increasing fibroglandular tissue.

OBJECTIVE. Our previous work showed that variation measures, which represent breast architecture derived from mammograms, were significantly associated with breast cancer. For replication purposes, we examined the association of three variation measures (variation [V], which is measured in the image domain, and P₁ and p₁ [a normalized version of P₁], which are derived from restricted regions in the Fourier domain) with breast cancer risk in an independent population. We also compared these measures to volumetric density measures (volumetric percent density [VPD] and dense volume [DV]) from a commercial product. MATERIALS AND METHODS. We examined 514 patients with breast cancer and 1377 control patients from a screening practice who were matched for age, date of examination, mammography unit, facility, and state of residence. Spearman rank-order correlation was used to evaluate the monotonic association between measures. Breast cancer associations were estimated using conditional logistic regression, after adjustment for age and body mass index. Odds ratios were calculated per SD increment in mammographic measure. RESULTS. These variation measures were strongly correlated with VPD (correlation, 0.68-0.80) but not with DV (correlation, 0.31-0.48). Similar to previous findings, all variation measures were significantly associated with breast cancer (odds ratio per SD: 1.30 [95% CI, 1.16-1.46] for V, 1.55 [95% CI, 1.35-1.77] for P₁, and 1.51 [95% CI, 1.33-1.72] for p₁). Associations of volumetric density measures with breast cancer were similar (odds ratio per SD: 1.54 [95% CI, 1.33-1.78] for VPD and 1.34 [95% CI, 1.20-1.50] for DV). When DV was included with each variation measure in the same model, all measures retained significance. CONCLUSION. Variation measures were significantly associated with breast cancer risk (comparable to the volumetric density measures) but were independent of the DV.

Heuristics can interfere with information processing and hinder decision-making when more systematic processes that might lead to better decisions are ignored. Based on the heuristic-systematic model (HSM) of information processing, a serious training game (called MACBETH) was designed to address and mitigate cognitive biases that interfere with the analysis of evidence and the generation of hypotheses. Two biases are the focus of this paper - fundamental attribution error and confirmation bias. The efficacy of the serious game on knowledge and mitigation of biases was examined using an experiment in which participants (N = 703) either played the MACBETH game or watched an instructional video about the biases. Results demonstrate the game to be more effective than the video at mitigating cognitive biases when explicit training methods are combined with repetitive play. Moreover, explicit instruction within the game provided greater familiarity and knowledge of the biases relative to implicit instruction. Suggestions for game development for purposes of enhancing cognitive processing and bias mitigation based on the MACBETH game design are discussed. © 2014 Elsevier Ltd. All rights reserved.