Mortality rates among patients successfully treated for hepatitis C in the era of interferon-free antivirals: population based cohort study.

OBJECTIVES: To quantify mortality rates for patients successfully treated for hepatitis C in the era of interferon-free, direct acting antivirals and compare these rates with those of the general population. DESIGN: Population based cohort study. SETTING: British Columbia, Scotland, and England (England cohort consists of patients with cirrhosis only). PARTICIPANTS: 21 790 people who were successfully treated for hepatitis C in the era of interferon-free antivirals (2014-19). Participants were divided into three liver disease severity groups: people without cirrhosis (pre-cirrhosis), those with compensated cirrhosis, and those with end stage liver disease. Follow-up started 12 weeks after antiviral treatment completion and ended on date of death or 31 December 2019. MAIN OUTCOME MEASURES: Crude and age-sex standardised mortality rates, and standardised mortality ratio comparing the number of deaths with that of the general population, adjusting for age, sex, and year. Poisson regression was used to identify factors associated with all cause mortality rates. RESULTS: 1572 (7%) participants died during follow-up. The leading causes of death were drug related mortality (n=383, 24%), liver failure (n=286, 18%), and liver cancer (n=250, 16%). Crude all cause mortality rates (deaths per 1000 person years) were 31.4 (95% confidence interval 29.3 to 33.7), 22.7 (20.7 to 25.0), and 39.6 (35.4 to 44.3) for cohorts from British Columbia, Scotland, and England, respectively. All cause mortality was considerably higher than the rate for the general population across all disease severity groups and settings; for example, all cause mortality was three times higher among people without cirrhosis in British Columbia (standardised mortality ratio 2.96, 95% confidence interval 2.71 to 3.23; P<0.001) and more than 10 times higher for patients with end stage liver disease in British Columbia (13.61, 11.94 to 15.49; P<0.001). In regression analyses, older age, recent substance misuse, alcohol misuse, and comorbidities were associated with higher mortality rates. CONCLUSION: Mortality rates among people successfully treated for hepatitis C in the era of interferon-free, direct acting antivirals are high compared with the general population. Drug and liver related causes of death were the main drivers of excess mortality. These findings highlight the need for continued support and follow-up after successful treatment for hepatitis C to maximise the impact of direct acting antivirals.

Association of COVID-19 Infection With Incident Diabetes.

Importance: SARS-CoV-2 infection may lead to acute and chronic sequelae. Emerging evidence suggests a higher risk of diabetes after infection, but population-based evidence is still sparse. Objective: To evaluate the association between COVID-19 infection, including severity of infection, and risk of diabetes. Design, Setting, and Participants: This population-based cohort study was conducted in British Columbia, Canada, from January 1, 2020, to December 31, 2021, using the British Columbia COVID-19 Cohort, a surveillance platform that integrates COVID-19 data with population-based registries and administrative data sets. Individuals tested for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction (RT-PCR) were included. Those who tested positive for SARS-CoV-2 (ie, those who were exposed) were matched on sex, age, and collection date of RT-PCR test at a 1:4 ratio to those who tested negative (ie, those who were unexposed). Analysis was conducted January 14, 2022, to January 19, 2023. Exposure: SARS-CoV-2 infection. Main Outcomes and Measures: The primary outcome was incident diabetes (insulin dependent or not insulin dependent) identified more than 30 days after the specimen collection date for the SARS-CoV-2 test with a validated algorithm based on medical visits, hospitalization records, chronic disease registry, and prescription drugs for diabetes management. Multivariable Cox proportional hazard modeling was performed to evaluate the association between SARS-CoV-2 infection and diabetes risk. Stratified analyses were performed to assess the interaction of SARS-CoV-2 infection with diabetes risk by sex, age, and vaccination status. Results: Among 629 935 individuals (median [IQR] age, 32 [25.0-42.0] years; 322 565 females [51.2%]) tested for SARS-CoV-2 in the analytic sample, 125 987 individuals were exposed and 503 948 individuals were unexposed. During the median (IQR) follow-up of 257 (102-356) days, events of incident diabetes were observed among 608 individuals who were exposed (0.5%) and 1864 individuals who were not exposed (0.4%). The incident diabetes rate per 100 000 person-years was significantly higher in the exposed vs nonexposed group (672.2 incidents; 95% CI, 618.7-725.6 incidents vs 508.7 incidents; 95% CI, 485.6-531.8 incidents; P < .001). The risk of incident diabetes was also higher in the exposed group (hazard ratio [HR], 1.17; 95% CI, 1.06-1.28) and among males (adjusted HR, 1.22; 95% CI, 1.06-1.40). The risk of diabetes was higher among people with severe disease vs those without COVID-19, including individuals admitted to the intensive care unit (HR, 3.29; 95% CI, 1.98-5.48) or hospital (HR, 2.42; 95% CI, 1.87-3.15). The fraction of incident diabetes cases attributable to SARS-CoV-2 infection was 3.41% (95% CI, 1.20%-5.61%) overall and 4.75% (95% CI, 1.30%-8.20%) among males. Conclusions and Relevance: In this cohort study, SARS-CoV-2 infection was associated with a higher risk of diabetes and may have contributed to a 3% to 5% excess burden of diabetes at a population level.

Risk factors for COVID-19 hospitalization after COVID-19 vaccination: a population-based cohort study in Canada.

OBJECTIVES: With the uptake of COVID-19 vaccines, there is a need for population-based studies to assess risk factors for COVID-19-related hospitalization after vaccination and how they differ from unvaccinated individuals. METHODS: We used data from the British Columbia COVID-19 Cohort, a population-based cohort that includes all individuals (aged ≥18 years) who tested positive for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction from January 1, 2021 (after the start of vaccination program) to December 31, 2021. We used multivariable logistic regression models to assess COVID-19-related hospitalization risk by vaccination status and age group among confirmed COVID-19 cases. RESULTS: Of the 162,509 COVID-19 cases included in the analysis, 8,546 (5.3%) required hospitalization. Among vaccinated individuals, an increased odds of hospitalization with increasing age was observed for older age groups, namely those aged 50-59 years (odds ratio [OR] = 2.95, 95% confidence interval [CI]: 2.01-4.33), 60-69 years (OR = 4.82, 95% CI: 3.29, 7.07), 70-79 years (OR = 11.92, 95% CI: 8.02, 17.71), and ≥80 years (OR = 24.25, 95% CI: 16.02, 36.71). However, among unvaccinated individuals, there was a graded increase in odds of hospitalization with increasing age, starting at age group 30-39 years (OR = 2.14, 95% CI: 1.90, 2.41) to ≥80 years (OR = 41.95, 95% CI: 35.43, 49.67). Also, comparing all the age groups to the youngest, the observed magnitude of association was much higher among unvaccinated individuals than vaccinated ones. CONCLUSION: Alongside a number of comorbidities, our findings showed a strong association between age and COVID-19-related hospitalization, regardless of vaccination status. However, age-related hospitalization risk was reduced two-fold by vaccination, highlighting the need for vaccination in reducing the risk of severe disease and subsequent COVID-19-related hospitalization across all population groups.

An Elastic Net Regression Model for Identifying Long COVID Patients Using Health Administrative Data: A Population-Based Study.

Background: Long coronavirus disease (COVID) patients experience persistent symptoms after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Healthcare utilization data could provide critical information on the disease burden of long COVID for service planning; however, not all patients are diagnosed or assigned long COVID diagnostic codes. We developed an algorithm to identify individuals with long COVID using population-level health administrative data from British Columbia (BC), Canada. Methods: An elastic net penalized logistic regression model was developed to identify long COVID patients based on demographic characteristics, pre-existing conditions, COVID-19-related data, and all symptoms/conditions recorded >28-183 days after the COVID-19 symptom onset/reported (index) date of known long COVID patients (n = 2430) and a control group (n = 24 300), selected from all adult COVID-19 cases in BC with an index date on/before October 31, 2021 (n = 168 111). Known long COVID cases were diagnosed in a clinic and/or had the International Classification of Diseases, Tenth Revision, Canada (ICD-10-CA) code for "post COVID-19 condition" in their records. Results: The algorithm retained known symptoms/conditions associated with long COVID, demonstrating high sensitivity (86%), specificity (86%), and area under the receiver operator curve (93%). It identified 25 220 (18%) long COVID patients among the remaining 141 381 adult COVID-19 cases, >10 times the number of known cases. Known and predicted long COVID patients had comparable demographic and health-related characteristics. Conclusions: Our algorithm identified long COVID patients with a high level of accuracy. This large cohort of long COVID patients will serve as a platform for robust assessments on the clinical course of long COVID, and provide much needed concrete information for decision-making.

Comparative Risk of Myocarditis/Pericarditis Following Second Doses of BNT162b2 and mRNA-1273 Coronavirus Vaccines.

BACKGROUND: Postmarketing evaluations have linked myocarditis to COVID-19 mRNA vaccines. However, few population-based analyses have directly compared the safety of the 2 mRNA COVID-19 vaccines. OBJECTIVES: This study aimed to compare the risk of myocarditis, pericarditis, and myopericarditis between BNT162b2 and mRNA-1273. METHODS: We used data from the British Columbia COVID-19 Cohort (BCC19C), a population-based cohort study. The exposure was the second dose of an mRNA vaccine. The outcome was diagnosis of myocarditis, pericarditis, or myopericarditis during a hospitalization or an emergency department visit within 21 days of the second vaccination dose. We performed multivariable logistic regression to assess the association between vaccine product and the outcomes of interest. RESULTS: The rates of myocarditis and pericarditis per million second doses were higher for mRNA-1273 (n = 31, rate 35.6; 95% CI: 24.1-50.5; and n = 20, rate 22.9; 95% CI: 14.0-35.4, respectively) than BNT162b2 (n = 28, rate 12.6; 95% CI: 8.4-18.2 and n = 21, rate 9.4; 95% CI: 5.8-14.4, respectively). mRNA-1273 vs BNT162b2 had significantly higher odds of myocarditis (adjusted OR [aOR]: 2.78; 95% CI: 1.67-4.62), pericarditis (aOR: 2.42; 95% CI: 1.31-4.46) and myopericarditis (aOR: 2.63; 95% CI: 1.76-3.93). The association between mRNA-1273 and myocarditis was stronger for men (aOR: 3.21; 95% CI: 1.77-5.83) and younger age group (18-39 years; aOR: 5.09; 95% CI: 2.68-9.66). CONCLUSIONS: Myocarditis/pericarditis following mRNA COVID-19 vaccines is rare, but we observed a 2- to 3-fold higher odds among individuals who received mRNA-1273 vs BNT162b2. The rate of myocarditis following mRNA-1273 receipt is highest among younger men (age 18-39 years) and does not seem to be present at older ages. Our findings may have policy implications regarding the choice of vaccine offered.

Cohort profile: the British Columbia COVID-19 Population Mixing Patterns Survey (BC-Mix).

PURPOSE: Several non-pharmaceutical interventions, such as physical distancing, handwashing, self-isolation, and school and business closures, were implemented in British Columbia (BC) following the first laboratory-confirmed case of COVID-19 on 26 January 2020, to minimise in-person contacts that could spread infections. The BC COVID-19 Population Mixing Patterns Survey (BC-Mix) was established as a surveillance system to measure behaviour and contact patterns in BC over time to inform the timing of the easing/re-imposition of control measures. In this paper, we describe the BC-Mix survey design and the demographic characteristics of respondents. PARTICIPANTS: The ongoing repeated online survey was launched in September 2020. Participants are mainly recruited through social media platforms (including Instagram, Facebook, YouTube, WhatsApp). A follow-up survey is sent to participants 2-4 weeks after completing the baseline survey. Survey responses are weighted to BC's population by age, sex, geography and ethnicity to obtain generalisable estimates. Additional indices such as the Material and Social Deprivation Index, residential instability, economic dependency, and others are generated using census and location data. FINDINGS TO DATE: As of 26 July 2021, over 61 000 baseline survey responses were received of which 41 375 were eligible for analysis. Of the eligible participants, about 60% consented to follow-up and about 27% provided their personal health numbers for linkage with healthcare databases. Approximately 83.5% of respondents were female, 58.7% were 55 years or older, 87.5% identified as white and 45.9% had at least a university degree. After weighting, approximately 50% were female, 39% were 55 years or older, 65% identified as white and 50% had at least a university degree. FUTURE PLANS: Multiple papers describing contact patterns, physical distancing measures, regular handwashing and facemask wearing, modelling looking at impact of physical distancing measures and vaccine acceptance, hesitancy and uptake are either in progress or have been published.

Impact of COVID-19 pandemic on HCV care cascade in Rwanda: Ecological study from July 2019 to June 2021.

Content available: Author Interview and Audio Recording.

Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study.

BACKGROUND: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. METHODS: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. RESULTS: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. INTERPRETATION: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2.

Impact of COVID-19-related public health measures on HCV testing in British Columbia, Canada: An interrupted time series analysis.

BACKGROUND & AIMS: Public health measures introduced to limit transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), also disrupted various healthcare services in many regions worldwide, including British Columbia (BC), Canada. We assessed the impact of these measures, first introduced in BC in March 2020, on hepatitis C (HCV) testing and first-time HCV-positive diagnoses within the province. METHODS: De-identified HCV testing data for BC residents were obtained from the provincial Public Health Laboratory. Weekly changes in anti-HCV, HCV RNA and genotype testing episodes and first-time HCV-positive (anti-HCV/RNA/genotype) diagnoses from January 2018 to December 2020 were assessed and associations were determined using segmented regression models examining rates before vs after calendar week 12 of 2020, when measures were introduced. RESULTS: Average weekly HCV testing and first-time HCV-positive diagnosis rates fell immediately following the imposition of public health measures by 62.3 per 100 000 population and 2.9 episodes per 1 000 000 population, respectively (P < .0001 for both), and recovered in subsequent weeks to near pre-March 2020 levels. Average weekly anti-HCV positivity rates decreased steadily pre-restrictions and this trend remained unchanged afterwards. CONCLUSIONS: Reductions in HCV testing and first-time HCV-positive diagnosis rates, key drivers of progression along the HCV care cascade, occurred following the introduction of COVID-19-related public health measures. Further assessment will be required to better understand the full impact of these service disruptions on the HCV care cascade and to inform strategies for the re-engagement of people who may have been lost to care because of these measures.

HCV reinfection rates after cure or spontaneous clearance among HIV-infected and uninfected men who have sex with men.

BACKGROUND & AIMS: Hepatitis C virus (HCV) reinfection among high-risk groups threatens HCV elimination goals. We assessed HCV reinfection rates among men who have sex with men (MSM) in British Columbia (BC), Canada. METHODS: We used data from the BC Hepatitis Testers Cohort, which includes nearly 1.7 million individuals tested for HCV or HIV in BC. MSM who had either achieved sustained virologic response (SVR) after successful HCV treatment, or spontaneous clearance (SC) and had ≥1 subsequent HCV RNA measurement, were followed from the date of SVR or SC until the earliest of reinfection, death, or last HCV RNA measurement. Predictors of reinfection were identified by Cox proportional modelling. The earliest study start date was 6 November 1997 and latest end date was 13 April 2018. RESULTS: Of 1349 HCV-positive MSM who met the inclusion criteria, 493 had SC while 856 achieved SVR. 349 (25.65%) had HIV coinfection. We identified 98 reinfections during 5203 person-years (PYs) yielding a reinfection rate of 1.88/100PYs. The reinfection rate among SC (2.74/100PYs) was more than twice that of those with SVR (1.03/100 PYs). Problematic alcohol use (aHR 1.73, 95% CI 1.003-2.92), injection drug use (aHR 2.60, 95% CI 1.57-4.29) and HIV coinfection (aHR 2.04, 95% CI 1.29-3.23) were associated with increased risk of HCV reinfection. Mental health counselling history (aHR 0.24, 95% CI 0.13-0.46) was associated with reduced HCV reinfection risk. CONCLUSIONS: There is the need to engage MSM in harm reduction and prevention services following treatment to reduce reinfection risk.

Mammographic density parameters and breast cancer tumor characteristics among postmenopausal women.

PURPOSE: Mammographic density is an important breast cancer risk factor, although it is not clear whether the association differs across breast cancer tumor subtypes. We examined the association between indicators of mammographic density and breast cancer risk by tumor subtype among postmenopausal women by investigating heterogeneity across tumor characteristics. METHODS: Mammographic density measures were determined for 477 breast cancer cases and 588 controls, all postmenopausal, in Vancouver, British Columbia, using digitized screening mammograms and Cumulus software. Mammographic dense (DA), non-dense (NDA), and percent dense (PDA) areas were treated as continuous covariates and categorized into quartiles according to the distribution in controls. For cases only, tests for heterogeneity between tumor subtypes were assessed by multinomial logistic regression. Associations between mammographic density and breast cancer risk were modeled for each subtype separately through unconditional logistic regression. RESULTS: Heterogeneity was apparent for the association of PDA with tumor size (p-heterogeneity=0.04). Risk did not differ across the other assessed tumor characteristics (p-heterogeneity values >0.05). CONCLUSION: These findings do not provide strong evidence that mammographic density parameters differentially affect specific breast cancer tumor characteristics.

Local Relapse After Breast-Conserving Therapy Versus Mastectomy for Extensive Pure Ductal Carcinoma In Situ ≥4 cm.

PURPOSE: The optimal treatment for patients with extensive pure ductal carcinoma in situ (DCIS) ≥4 cm is controversial. This study evaluates local relapse according to type of local therapy: mastectomy, breast-conserving surgery (BCS) alone, and BCS + radiation therapy (RT). METHODS AND MATERIALS: Subjects were female patients who received diagnoses of pure DCIS ≥4 cm between 1989 and 2010 and were referred to British Columbia Cancer. Clinicopathologic and treatment characteristics were compared between treatment cohorts. Local relapse (LR) was estimated using competing risk analysis. Multivariable analysis was performed using Cox regression analysis. RESULTS: Patients had the following treatments: 490 mastectomy, 38 BCS alone, and 192 BCS + RT. The 10-year cumulative incidence of LR was 16% after BCS (95% confidence interval [CI], 6-29%), 8% after BCS + RT (95% CI, 4-12%), and 2% after mastectomy (95% CI, 1-4%). On multivariable analysis, estrogen receptor-negative disease (hazard ratio [HR], 3.32; 95% CI, 1.08-10.18; P = .04) and positive margins (HR, 3.55; 95% CI, 1.56-8.05; P = .002) were associated with increased LR. BCS alone (HR, 7.87; 95% CI, 2.82-21.92; P < .0001), BCS + RT + no boost (HR, 3.80; 95% CI, 1.56-9.28; P = .003), and BCS + RT + boost (HR, 5.76; 95% CI, 2.59-12.83; P < .0001) were all associated with a higher risk of relapse relative to mastectomy. CONCLUSIONS: Mastectomy remains a standard local treatment option for extensive DCIS, but BCS + RT may also be reasonably considered in selected patients with a careful discussion of the benefits, side effects, and patient preferences.

Mammographic non-dense area and breast cancer risk in postmenopausal women: a causal inference approach in a case-control study.

PURPOSE: The association between high mammographic density (MD) and elevated breast cancer risk is well established. However, the role of absolute non-dense area remains unclear. We estimated the effect of the mammographic non-dense area and other density parameters on the risk of breast cancer. METHODS: This study utilizes data from a population-based case-control study conducted in Greater Vancouver, British Columbia, with 477 female postmenopausal breast cancer cases and 588 female postmenopausal controls. MD measures were determined from digitized screening mammograms using computer-assisted software (Cumulus). Marginal odds ratios were estimated by inverse-probability weighting using a causal diagram for confounder selection. Akaike information criteria and receiver operating characteristic curves were used to assess the goodness of fit and predictive power of unconditional logistic models containing MD parameters. RESULTS: The risk of breast cancer is 60% lower for the highest quartile compared to the lowest quartile of mammographic non-dense area (marginal OR 0.40, 95% CI 0.26-0.61, p-trend < 0.001). The cancer risk almost doubles for the highest quartile compared to the lowest quartile of dense area (marginal OR 1.81, 95% CI 1.19-2.43, p-trend < 0.001). For the highest quartile of percent density, breast cancer risk was more than three times higher than for the lowest quartile (marginal OR 3.15, 95% CI 1.90-4.40, p-trend < 0.001). No difference was seen in predictive accuracy between models using percent density alone, dense area alone, or non-dense area plus dense area. CONCLUSIONS: In this study, non-dense area is an independent risk factor after adjustment for dense area and other covariates, inversely related with the risk of breast cancer. However, non-dense area does not improve prediction over that offered by percent density or dense area alone.

Lupus-related single nucleotide polymorphisms and risk of diffuse large B-cell lymphoma.

OBJECTIVE: Determinants of the increased risk of diffuse large B-cell lymphoma (DLBCL) in SLE are unclear. Using data from a recent lymphoma genome-wide association study (GWAS), we assessed whether certain lupus-related single nucleotide polymorphisms (SNPs) were also associated with DLBCL. METHODS: GWAS data on European Caucasians from the International Lymphoma Epidemiology Consortium (InterLymph) provided a total of 3857 DLBCL cases and 7666 general-population controls. Data were pooled in a random-effects meta-analysis. RESULTS: Among the 28 SLE-related SNPs investigated, the two most convincingly associated with risk of DLBCL included the CD40 SLE risk allele rs4810485 on chromosome 20q13 (OR per risk allele=1.09, 95% CI 1.02 to 1.16, p=0.0134), and the HLA SLE risk allele rs1270942 on chromosome 6p21.33 (OR per risk allele=1.17, 95% CI 1.01 to 1.36, p=0.0362). Of additional possible interest were rs2205960 and rs12537284. The rs2205960 SNP, related to a cytokine of the tumour necrosis factor superfamily TNFSF4, was associated with an OR per risk allele of 1.07, 95% CI 1.00 to 1.16, p=0.0549. The OR for the rs12537284 (chromosome 7q32, IRF5 gene) risk allele was 1.08, 95% CI 0.99 to 1.18, p=0.0765. CONCLUSIONS: These data suggest several plausible genetic links between DLBCL and SLE.

Inherited Chromosomally Integrated Human Herpesvirus 6 and Breast Cancer.

Background: Inherited chromosomally integrated human herpesvirus 6 (iciHHV-6) is a condition observed in approximately 1% of the population. Whether such a genetic alteration predisposes to cancer development in currently unknown. Two studies were conducted to determine whether iciHHV-6 is associated with cancer development.Methods: First, a screen of 19,597 people from the province of Quebec (Canada) was conducted. A replication test, using data from a population-based case-control study of 1,090 women with incident breast cancer and 1,053 controls from British Columbia and Ontario (Canada) was conducted. DNA samples were analyzed by qPCR and droplet digital PCR to identify iciHHV-6+ carriers.Results: In the initial study, a potential association between iciHHV-6 positivity and breast cancer was identified [OR = 2.66; 95% confidence interval (CI), 0.95-7.44]. In the replication dataset, no association was found between iciHHV-6 positivity in women and breast cancer (OR = 0.87; 95% CI, 0.35-2.15).Conclusions: We found no statistically significant associations between inherited chromosomally integrated HHV-6 and breast cancer in women.Impact: These results do not provide evidence to suggest that iciHHV-6 is a risk factor for breast cancer. Cancer Epidemiol Biomarkers Prev; 26(3); 425-7. ©2016 AACR.

Quantitation of fixative-induced morphologic and antigenic variation in mouse and human breast cancers.

Quantitative Image Analysis (QIA) of digitized whole slide images for morphometric parameters and immunohistochemistry of breast cancer antigens was used to evaluate the technical reproducibility, biological variability, and intratumoral heterogeneity in three transplantable mouse mammary tumor models of human breast cancer. The relative preservation of structure and immunogenicity of the three mouse models and three human breast cancers was also compared when fixed with representatives of four distinct classes of fixatives. The three mouse mammary tumor cell models were an ER+/PR+ model (SSM2), a Her2+ model (NDL), and a triple negative model (MET1). The four breast cancer antigens were ER, PR, Her2, and Ki67. The fixatives included examples of (1) strong cross-linkers, (2) weak cross-linkers, (3) coagulants, and (4) combination fixatives. Each parameter was quantitatively analyzed using modified Aperio Technologies ImageScope algorithms. Careful pre-analytical adjustments to the algorithms were required to provide accurate results. The QIA permitted rigorous statistical analysis of results and grading by rank order. The analyses suggested excellent technical reproducibility and confirmed biological heterogeneity within each tumor. The strong cross-linker fixatives, such as formalin, consistently ranked higher than weak cross-linker, coagulant and combination fixatives in both the morphometric and immunohistochemical parameters.