A Mediation Analysis of Obesity and Adiponectin Association with Postmenopausal Breast Cancer Risk: A Nested Cohort Study in the International Breast Cancer Intervention Study II (IBIS-II) Prevention Trial.

Obesity is a risk factor for postmenopausal breast cancer (BC), and evidence suggests a role for adiponectin in the relationship between obesity and BC. We investigated whether adiponectin or other biomarkers mediate the effect of body mass index (BMI) on postmenopausal BC risk in a cohort study nested in the IBIS-II Prevention Trial. We measured adiponectin, leptin, IGF-I, IGFBP-1, high-sensitivity C-reactive protein, glycemia, insulin, HOMA-IR index, and SHBG in baseline and 12-month serum samples from 123 cases and 302 matched controls in the placebo arm of the IBIS-II Prevention trial. We conducted the main mediation analysis considering baseline BMI as an exposure and the 12-month adiponectin increase as a mediator after adjustment for the Tyrer-Cuzick score and the lipid-lowering medications/supplements use. In the multivariable Cox model, both the 12-month adiponectin increase (HR, 0.60; 95%CI, 0.36-1.00) and BMI were associated with BC risk (HR, 1.05; 95%CI, 1.00-1.09), with a 40% reduction in women with a 12-month increase in adiponectin. A significantly higher cumulative hazard of BC events was observed in obese women (BMI > 30) with decreased adiponectin (p = 0.0087). No mediating effect of the adiponectin increase on the total effect of BMI on BC risk was observed (natural indirect effect: HR, 1.00; 95%CI, 0.98-1.02). Raising adiponectin levels might be an attractive target for postmenopausal BC prevention.

A prognostic and predictive computational pathology immune signature for ductal carcinoma in situ: retrospective results from a cohort within the UK/ANZ DCIS trial.

BACKGROUND: The density of tumour-infiltrating lymphocytes (TILs) could be prognostic in ductal carcinoma in situ (DCIS). However, manual TIL quantification is time-consuming and suffers from interobserver and intraobserver variability. In this study, we developed a TIL-based computational pathology biomarker and evaluated its association with the risk of recurrence and benefit of adjuvant treatment in a clinical trial cohort. METHODS: In this retrospective cohort study, a computational pathology pipeline was developed to generate a TIL-based biomarker (CPath TIL categories). Subsequently, the signature underwent a masked independent validation on H&E-stained whole-section images of 755 patients with DCIS from the UK/ANZ DCIS randomised controlled trial. Specifically, continuous biomarker CPath TIL score was calculated as the average TIL density in the DCIS microenvironment and dichotomised into binary biomarker CPath TIL categories (CPath TIL-high vs CPath TIL-low) using the median value as a cutoff. The primary outcome was ipsilateral breast event (IBE; either recurrence of DCIS [DCIS-IBE] or invasive progression [I-IBE]). The Cox proportional hazards model was used to estimate the hazard ratio (HR). FINDINGS: CPath TIL-score was evaluable in 718 (95%) of 755 patients (151 IBEs). Patients with CPath TIL-high DCIS had a greater risk of IBE than those with CPath TIL-low DCIS (HR 2·10 [95% CI 1·39-3·18]; p=0·0004). The risk of I-IBE was greater in patients with CPath TIL-high DCIS than those with CPath TIL-low DCIS (3·09 [1·56-6·14]; p=0·0013), and the risk of DCIS-IBE was non-significantly higher in those with CPath TIL-high DCIS (1·61 [0·95-2·72]; p=0·077). A significant interaction (pinteraction=0·025) between CPath TIL categories and radiotherapy was observed with a greater magnitude of radiotherapy benefit in preventing IBE in CPath TIL-high DCIS (0·32 [0·19-0·54]) than CPath TIL-low DCIS (0·40 [0·20-0·81]). INTERPRETATION: High TIL density is associated with higher recurrence risk-particularly of invasive recurrence-and greater radiotherapy benefit in patients with DCIS. Our TIL-based computational pathology signature has a prognostic and predictive role in DCIS. FUNDING: National Cancer Institute under award number U01CA269181, Cancer Research UK (C569/A12061; C569/A16891), and the Breast Cancer Research Foundation, New York (NY, USA).

Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank.

BACKGROUND: Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorization needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI). METHODS: We analyzed data from 126,490 post-menopausal women of "White British" ancestry, aged 40 to 69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer-Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected. RESULTS: The Harrell's C statistic of the 10-year risk from the Tyrer-Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models [0.080 (95% confidence interval (CI), 0.053-0.104) and 0.051 (95% CI, 0.030-0.073), respectively], with negligible impact on controls. CONCLUSIONS: The addition of a PRS for breast cancer to the well-established Tyrer-Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification. IMPACT: These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.

Long-term prediction by DNA methylation of high-grade cervical intraepithelial neoplasia: Results of the ARTISTIC cohort.

Methylation markers have shown potential for triaging high-risk HPV-positive (hrHPV+) women to identify those at increased risk of invasive cervical cancer (ICC). Our aim was to assess the performance of the S5 DNA methylation classifier for predicting incident high-grade cervical intraepithelial neoplasia (CIN) and ICC among hrHPV+ women in the ARTISTIC screening trial cohort. The S5 classifier, comprising target regions of tumour suppressor gene EPB41L3 and L1 and L2 regions of HPV16, HPV18, HPV31, and HPV33, was assayed by pyrosequencing in archived hrHPV+ liquid-based samples from 343 women with high-grade disease (139 CIN2, 186 CIN3, and 18 ICC) compared to 800 hrHPV+ controls. S5 DNA methylation correlated directly with increasing severity of disease and inversely with lead time to diagnosis. S5 could discriminate between hrHPV+ women who developed CIN3 or ICC and hrHPV+ controls (p <.0001) using samples taken on average 5 years before diagnosis. This relationship was independent of cytology at baseline. The S5 test showed much higher sensitivity than HPV16/18 genotyping for identifying prevalent CIN3 (93% vs. 61%, p = .01) but lower specificity (50% vs. 66%, p <.0001). The S5 classifier identified most women at high risk of developing precancer and missed very few prevalent advanced lesions thus appearing to be an objective test for triage of hrHPV+ women. The combination of methylation of host and HPV genes enables S5 to combine the predictive power of methylation with HPV genotyping to identify hrHPV-positive women who are at highest risk of developing CIN3 and ICC in the future.

Body composition changes during breast cancer preventive treatment with anastrozole: Findings from the IBIS-II trial.

Background: Uptake to anastrozole for breast cancer prevention is low, partly due to women's concerns about side effects including gains in weight and specifically gains in body fat. Previous evidence does not link anastrozole with gains in weight, but there is a lack of data on any effects on body composition i.e. changes in fat and fat free mass. Here we assess association of anastrozole with body composition changes in a prospective sub-study from the second international breast intervention trial (IBIS-II). Methods: Participants had DXA scans at baseline and for five years of anastrozole/placebo and beyond (between March 2004 and September 2017. Primary outcomes were changes in body weight, body fat and fat free mass at 9-18 months. A linear model was used to estimate the size of a differential effect in these outcomes by randomised treatment allocation adjusted for baseline value and time since last scan, age, 10-year breast cancer risk, smoking and HRT status. Results: 203 postmenopausal women were recruited (n = 95 anastrozole, n = 108 placebo), mean age 58 years (SD = 5.4), BMI 28.0 kg/m2 (SD = 5.5). There was no evidence of a strong association between anastrozole or placebo and endpoints at 9-18 months; effect size (95 %CI) for anastrozole minus placebo for body weight (per/kg) -0.11 (-1.29-1.08); body fat 0.11 (-0.75-0.96) and fat free mass -0.30 (-0.79-0.19). Conclusions: There is unlikely to be a clinically significant change to body composition with anastrozole for breast cancer prevention.

Comparing the performance of 2 human papillomavirus assays for a new use indication: a real-world evidence-based evaluation in the United States.

BACKGROUND: The US Food and Drug Administration supports innovations to facilitate new indications for high-risk human papillomavirus testing. This report describes the retrospective testing of stored specimens and analysis of existing data to efficiently and cost-effectively support a new indication for the Onclarity human papillomavirus assay (Becton, Dickinson and Company, BD Life Sciences - Integrated Diagnostic Solutions, Sparks, MD). The performance of this index test was compared with that of a predicate test, the cobas human papillomavirus assay (Roche Diagnostics, Indianapolis, IN). Both human papillomavirus assays are based on real-time polymerase chain reaction platforms that detect the presence of 14 high-risk human papillomavirus genotypes. The predicate assay reports human papillomavirus types 16 and 18 as individual results and the other 12 human papillomavirus genotypes as 1 pooled result. The index assay reports 9 independent results (human papillomavirus types 16, 18, 31, 33/58, 35/39/68, 45, 51, 52, and 56/59/66). Both the index and predicate assays are approved by the Food and Drug Administration for cervical cancer screening, but at the time that this study was initiated, the index human papillomavirus assay was not approved for use with cervical specimens collected in PreservCyt (Hologic, Inc, San Diego, CA) liquid-based cytology media. OBJECTIVE: The performance of the index human papillomavirus assay was compared with that of the predicate human papillomavirus assay for the detection of cervical intraepithelial neoplasia grades 2 or greater and 3 or greater (≥CIN2 or ≥CIN3) using PreservCyt liquid-based cytology specimens collected from women aged 21 to 65 years. In addition, the ability of the index test's extended genotyping to stratify ≥CIN2 and ≥CIN3 risks, using these specimens, was evaluated. STUDY DESIGN: The New Mexico HPV Pap Registry was used to select an age- and cytology-stratified random sample of 19,879 women undergoing opportunistic cervical screening and follow-up in routine clinical practice across New Mexico. A subset (n = 4820) of PreservCyt specimens was selected from 19,879 women for paired testing by the index and predicate human papillomavirus assays within age and cytology strata and included women with or without cervical biopsy follow-up. Point estimate differences and ratios were calculated for cervical disease detection and positivity rates, respectively, with 95% confidence intervals to determine statistical significance. The cumulative risk of ≥CIN2 or ≥CIN3, with up to 5-year follow-up, was estimated for the index assay using Kaplan-Meier methods. RESULTS: The 5-year cumulative ≥CIN3 detection rates were 5.6% for the index assay and 4.6% for the predicate assay (difference, 1.0%; 95% confidence interval, 0.5%-1.5%). The ≥CIN3 positivity rates within <1 year were 95.3% for the index assay and 94.5% for the predicate assay (ratio, 1.01; 95% confidence interval, 0.98-1.06). The ≥CIN3 cumulative positivity rates for the index and predicate assays were also similar at 5 years. Among cases of ≥CIN3, the positive agreement rates between the index and predicate assays for human papillomavirus types 16 and 18 were 100.0% (95% confidence interval, 95.0%-100.0%) and 90.9% (95% confidence interval, 62.3%-98.4%), respectively. Human papillomavirus type 16 carried the highest ≥CIN2 or ≥CIN3 risk, followed by human papillomavirus types 18/31/33/58/52/45 and human papillomavirus types 35/56/59/51/56/59/66. CONCLUSION: The index and predicate human papillomavirus assays demonstrated equivalent performance, and extended human papillomavirus genotyping, using the index assay, provided effective ≥CIN2 and ≥CIN3 risk stratification, supporting a new indication for use of the index assay with PreservCyt.

Effect of baseline oestradiol serum concentration on the efficacy of anastrozole for preventing breast cancer in postmenopausal women at high risk: a case-control study of the IBIS-II prevention trial.

BACKGROUND: An increased risk of breast cancer is associated with high serum concentrations of oestradiol and testosterone in postmenopausal women, but little is known about how these hormones affect response to endocrine therapy for breast cancer prevention or treatment. We aimed to assess the effects of serum oestradiol and testosterone concentrations on the efficacy of the aromatase inhibitor anastrozole for the prevention of breast cancer in postmenopausal women at high risk. METHODS: In this case-control study we used data from the IBIS-II prevention trial, a randomised, controlled, double-blind trial in postmenopausal women aged 40-70 years at high risk of breast cancer, conducted in 153 breast cancer treatment centres across 18 countries. In the trial, women were randomly assigned (1:1) to receive anastrozole (1 mg/day, orally) or placebo daily for 5 years. In this pre-planned case-control study, the primary analysis was the effect of the baseline oestradiol to sex hormone binding globulin (SHBG) ratio (oestradiol-SHBG ratio) on the development of all breast cancers, including ductal carcinoma in situ (the primary endpoint in the trial). Cases were participants in whom breast cancer was reported after trial entry and until the cutoff on Oct 22, 2019, and who had valid blood samples and no use of hormone replacement therapy within 3 months of trial entry or during the trial. For each case, two controls without breast cancer were selected at random, matched on treatment group, age (within 2 years), and follow-up time (at least that of the matching case). For each treatment group, we applied a multinominal logistic regression likelihood-ratio trend test to assess what change in the proportion of cases was associated with a one-quartile change in hormone ratio. Controls were used only to determine quartile cutoffs. Profile likelihood 95% CIs were used to indicate the precision of estimates. A secondary analysis also investigated the effect of the baseline testosterone-SHBG ratio on breast cancer development. We also assessed relative benefit of anastrozole versus placebo (calculated as 1 - the ratio of breast cancer cases in the anastrozole group to cases in the placebo group). The trial was registered with ISRCTN (number ISRCTN31488319) and completed recruitment on Jan 31, 2012, but long-term follow-up is ongoing. FINDINGS: 3864 women were recruited into the trial between Feb 2, 2003, and Jan 31, 2012, and randomly assigned to receive anastrozole (n=1920) or placebo (n=1944). Median follow-up time was 131 months (IQR 106-156), during which 85 (4·4%) cases of breast cancer in the anastrozole group and 165 (8·5%) in the placebo group were identified. No data on gender, race, or ethnicity were collected. After exclusions, the case-control study included 212 participants from the anastrozole group (72 cases, 140 controls) and 416 from the placebo group (142 cases, 274 controls). A trend of increasing breast cancer risk with increasing oestradiol-SHBG ratio was found in the placebo group (trend per quartile 1·25 [95% CI 1·08 to 1·45], p=0·0033), but not in the anastrozole group (1·06 [0·86 to 1·30], p=0·60). A weaker effect was seen for the testosterone-SHBG ratio in the placebo group (trend 1·21 [1·05 to 1·41], p=0·011), but again not in the anastrozole group (trend 1·18 [0·96 to 1·46], p=0·11). A relative benefit of anastrozole was seen in quartile 2 (0·55 [95% CI 0·13 to 0·78]), quartile 3 (0·54 [0·22 to 0·74], and quartile 4 (0·56 [0·23 to 0·76]) of oestradiol-SHBG ratio, but not in quartile 1 (0·18 [-0·60 to 0·59]). INTERPRETATION: These results suggest that serum hormones should be measured more routinely and integrated into risk management decisions. Measuring serum hormone concentrations is inexpensive and might help clinicians differentiate which women will benefit most from an aromatase inhibitor. FUNDING: Cancer Research UK, National Health and Medical Research Council (Australia), Breast Cancer Research Foundation, and DaCosta Fund.

Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study.

BACKGROUND: Women with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman's entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability. METHODS: In total, 132,439 women, aged 40-73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR-χ2) and (3) concordance indices. RESULTS: In total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR-χ2 = 379.6 (degrees of freedom (df) = 2) vs. 307.7 (df = 3)), which increased to 35% (ΔLR-χ2 = 251.2 vs. 186.7) for follow-up after three mammograms (n = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7-8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4-5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003-0.012). CONCLUSIONS: Estimating mammographic density using a woman's history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening.

An optimization framework to guide the choice of thresholds for risk-based cancer screening.

It is uncommon for risk groups defined by statistical or artificial intelligence (AI) models to be chosen by jointly considering model performance and potential interventions available. We develop a framework to rapidly guide choice of risk groups in this manner, and apply it to guide breast cancer screening intervals using an AI model. Linear programming is used to define risk groups that minimize expected advanced cancer incidence subject to resource constraints. In the application risk stratification performance is estimated from a case-control study (2044 cases, 1:1 matching), and other parameters are taken from screening trials and the screening programme in England. Under the model, re-screening in 1 year for the highest 4% AI model risk, in 3 years for the middle 64%, and in 4 years for 32% of the population at lowest risk, was expected to reduce the number of advanced cancers diagnosed by approximately 18 advanced cancers per 1000 diagnosed with triennial screening, for the same average number of screens in the population as triennial screening for all. Sensitivity analyses found the choice of thresholds was robust to model parameters, but the estimated reduction in advanced cancers was not precise and requires further evaluation. Our framework helps define thresholds with the greatest chance of success for reducing the population health burden of cancer when used in risk-adapted screening, which should be further evaluated such as in health-economic modelling based on computer simulation models, and real-world evaluations.

Evaluation of an AI Model to Assess Future Breast Cancer Risk.

Background Accurate breast cancer risk assessment after a negative screening result could enable better strategies for early detection. Purpose To evaluate a deep learning algorithm for risk assessment based on digital mammograms. Materials and Methods A retrospective observational matched case-control study was designed using the OPTIMAM Mammography Image Database from the National Health Service Breast Screening Programme in the United Kingdom from February 2010 to September 2019. Patients with breast cancer (cases) were diagnosed following a mammographic screening or between two triannual screening rounds. Controls were matched based on mammography device, screening site, and age. The artificial intelligence (AI) model only used mammograms at screening before diagnosis. The primary objective was to assess model performance, with a secondary objective to assess heterogeneity and calibration slope. The area under the receiver operating characteristic curve (AUC) was estimated for 3-year risk. Heterogeneity according to cancer subtype was assessed using a likelihood ratio interaction test. Statistical significance was set at P < .05. Results Analysis included patients with screen-detected (median age, 60 years [IQR, 55-65 years]; 2044 female, including 1528 with invasive cancer and 503 with ductal carcinoma in situ [DCIS]) or interval (median age, 59 years [IQR, 53-65 years]; 696 female, including 636 with invasive cancer and 54 with DCIS) breast cancer and 1:1 matched controls, each with a complete set of mammograms at the screening preceding diagnosis. The AI model had an overall AUC of 0.68 (95% CI: 0.66, 0.70), with no evidence of a significant difference between interval and screen-detected (AUC, 0.69 vs 0.67; P = .085) cancer. The calibration slope was 1.13 (95% CI: 1.01, 1.26). There was similar performance for the detection of invasive cancer versus DCIS (AUC, 0.68 vs 0.66; P = .057). The model had higher performance for advanced cancer risk (AUC, 0.72 ≥stage II vs 0.66

Quantifying the effects of risk-stratified breast cancer screening when delivered in real time as routine practice versus usual screening: the BC-Predict non-randomised controlled study (NCT04359420).

BACKGROUND: Risk stratification as a routine part of the NHS Breast Screening Programme (NHSBSP) could provide a better balance of benefits and harms. We developed BC-Predict, to offer women when invited to the NHSBSP, which collects standard risk factor information; mammographic density; and in a sub-sample, a Polygenic Risk Score (PRS). METHODS: Risk prediction was estimated primarily from self-reported questionnaires and mammographic density using the Tyrer-Cuzick risk model. Women eligible for NHSBSP were recruited. BC-Predict produced risk feedback letters, inviting women at high risk (≥8% 10-year) or moderate risk (≥5-<8% 10-year) to have appointments to discuss prevention and additional screening. RESULTS: Overall uptake of BC-Predict in screening attendees was 16.9% with 2472 consenting to the study; 76.8% of those received risk feedback within the 8-week timeframe. Recruitment was 63.2% with an onsite recruiter and paper questionnaire compared to <10% with BC-Predict only (P < 0.0001). Risk appointment attendance was highest for those at high risk (40.6%); 77.5% of those opted for preventive medication. DISCUSSION: We have shown that a real-time offer of breast cancer risk information (including both mammographic density and PRS) is feasible and can be delivered in reasonable time, although uptake requires personal contact. Preventive medication uptake in women newly identified at high risk is high and could improve the cost-effectiveness of risk stratification. TRIAL REGISTRATION: Retrospectively registered with clinicaltrials.gov (NCT04359420).

Breast cancer polygenic risk scores derived in White European populations are not calibrated for women of Ashkenazi Jewish descent.

PURPOSE: Polygenic risk scores (PRSs) are a major component of accurate breast cancer (BC) risk prediction but require ethnicity-specific calibration. Ashkenazi Jewish (AJ) population is assumed to be of White European (WE) origin in some commercially available PRSs despite differing effect allele frequencies (EAFs). We conducted a case-control study of WE and AJ women from the Predicting Risk of Cancer at Screening Study. The Breast Cancer in Northern Israel Study provided a separate AJ population-based case-control validation series. METHODS: All women underwent Illumina OncoArray single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]) analysis. Two PRSs were assessed, SNV142 and SNV78. A total of 221 of 2243 WE women (discovery: cases = 111; controls = 110; validation: cases = 651; controls = 1772) and 221 AJ women (cases = 121; controls = 110) were included from the UK study; the Israeli series consisted of 2045 AJ women (cases = 1331; controls = 714). EAFs were obtained from the Genome Aggregation Database. RESULTS: In the UK study, the mean SNV142 PRS demonstrated good calibration and discrimination in WE population, with mean PRS of 1.33 (95% CI 1.18-1.48) in cases and 1.01 (95% CI 0.89-1.13) in controls. In AJ women from Manchester, the mean PRS of 1.54 (1.38-1.70) in cases and 1.20 (1.08-1.32) in controls demonstrated good discrimination but overestimation of BC relative risk. After adjusting for EAFs for the AJ population, mean risk was corrected (mean SNV142 PRS cases = 1.30 [95% CI 1.16-1.44] and controls = 1.02 [95% CI 0.92-1.12]). This was recapitulated in the larger Israeli data set with good discrimination (area under the curve = 0.632 [95% CI 0.607-0.657] for SNV142). CONCLUSION: AJ women should not be given BC relative risk predictions based on PRSs calibrated to EAFs from the WE population. PRSs need to be recalibrated using AJ-derived EAFs. A simple recalibration using the mean PRS adjustment ratio likely performs well.

Impact of HPV testing in opportunistic cervical screening: Support for primary HPV screening in the United States.

Human papillomavirus (HPV) testing for cervical screening increases diagnosis of precancer and reduces the incidence of cervical cancer more than cytology alone. However, real-world evidence from diverse practice settings is lacking for the United States (U.S.) to support clinician adoption of primary HPV screening. Using a population-based registry, which captures all cervical cytology (with or without HPV testing) and all cervical biopsies, we conducted a real-world evidence study of screening in women aged 30 to 64 years across the entire state of New Mexico. Negative cytology was used to distinguish cotests from reflex HPV tests. A total of 264 198 cervical screening tests (with exclusions based on clinical history) were recorded as the first screening test between 2014 and 2017. Diagnoses of cervical intraepithelial neoplasia grades 2 or 3 or greater (CIN2+, CIN3+) from 2014 to 2019 were the main outcomes. Of cytology-negative screens, 165 595 (67.1%) were cotests and 4.8% of these led to biopsy within 2 years vs 3.2% in the cytology-only group. Among cytology-negative, HPV tested women, 347 of 398 (87.2%) CIN2+ cases were diagnosed in HPV-positive women, as were 147 of 164 (89.6%) CIN3+ cases. Only 29/921 (3.2%) CIN3+ and 67/1964 (3.4%) CIN2+ cases were diagnosed in HPV-negative, cytology-positive women with biopsies. Under U.S. opportunistic screening, across a diversity of health care delivery practices, and in a population suffering multiple disparities, we show adding HPV testing to cytology substantially increased the yield of CIN2+ and CIN3+. CIN3+ was rarely diagnosed in HPV-negative women with abnormal cytology, supporting U.S. primary HPV-only screening.

Measuring cancer burden in prostatic needle core biopsies: simplified assessments outperform complex measurements in assessing outcome: evidence to assist pathologist efficiency and minimize datasets.

AIMS: The optimal method of measuring cancer extent in prostate cancer (PCa) biopsies is unknown. METHODS AND RESULTS: Nine hundred eighty-one men with clinically localised PCa managed conservatively were reviewed with follow up. The number of positive cores (NPC), the Maximum Cancer Length in a core (MCL), Total Cancer Length (TCL), and percentage of positive cores (%+cores) was calculated and univariate and multivariate analysis performed using prostate-specific antigen (PSA), T-stage, and Gleason score. The presence of stromal gaps (SG) was recorded. Univariate models were run where SG made a difference to the MCL. All variables showed significant association with PCa death in univariate models. In multivariate models, incorporating PSA, T-stage, and Gleason score, only %+cores was a significant predictor of outcome, with a 10% increase in %+cores resulting in a hazard ratio (HR) of 1.07 (likelihood-ratio test P > Χ2  = 0.01). There were 120 patients where SG made a difference to the MCL and a total of 20 events in this group. Including SG, on univariate analysis the median MCL was 10 mm and HR was 1.16 (P = 0.007), not including SG, the median MCL was 6 mm and HR was 1.23 (P = 6.3 × 10-4 ). Inclusion or exclusion of SG made no significant difference to TCL as a predictor of outcome. CONCLUSION: Cancer extent is a strong predictor of PCa death but only %+cores added to the multivariate model. Expressed as a fraction of NPC/total number of cores, this is the simplest method of assessment, which we favour over more complicated methods in nontargeted biopsies.

Obesity at age 20 and weight gain during adulthood increase risk of total and premature all-cause mortality: findings from women attending breast screening in Manchester.

BACKGROUND: Obesity in early adulthood is associated with lower breast cancer rates in later life. This could be interpreted as a positive reinforcement of excess weight amongst younger women however, the wider implications of higher weights are less well known. This study examined the association between both obesity in early adulthood and body mass index (BMI) change through adulthood, and all-cause mortality. METHODS: The Predicting Risk of Cancer At Screening (PROCAS) study recruited 57,902 women aged 46-73 years (median age 57.2, IQR 51.8-63.7 years) from the Greater Manchester National Health Service breast screening programme in North West England between 2009 and 2015. It was used to assess associations between BMI at 20 years and cohort entry with all-cause mortality ascertained via deaths recorded on the National Breast Screening System to June 2020. Hazard ratios were estimated using proportional hazards (Cox) regression adjusted for factors at entry to the cohort: age, deprivation, bilateral oophorectomy, hormone-replacement therapy, menopausal status, ethnicity, alcohol intake, physical activity, and BMI. RESULTS: The prevalence of overweight (25-30 kg/m2) and obesity (> 30 kg/m2) were 10.4% and 2.5% respectively at 20 years, increasing to 35.2% and 25.9% respectively at cohort entry. After a mean 8.7 years follow-up we observed that overweight (HR = 1.27, 95%CI = 1.10-1.47) and obesity (HR = 2.11, 95%CI = 1.67-2.66) at 20 years had a higher mortality rate compared with healthy weight. Women who were underweight/healthy weight at 20 years and gained weight to obesity at entry had a slightly increased mortality rate compared with women who were underweight/healthy weight at both time points (HR 1.16, 95%CI = 1.02-1.32). Women with overweight (HR = 1.36, 95%CI = 1.06-1.75) or obesity (HR = 1.90, 95%CI = 1.45-2.48) at both 20 years and entry had a higher mortality rate than women who were underweight/healthy weight at both points. CONCLUSIONS: Women who self-reported overweight and obesity at 20 years had a shorter life expectancy in this cohort of women attending breast cancer screening. Weight gain from 20 years was common in this group. Girls and women should be supported to maintain a healthy weight throughout the lifespan to help increase life expectancy. Trial registration number NCT04359420, retrospectively registered 24/04/2020.

A case-control study to evaluate the impact of the breast screening programme on breast cancer incidence in England.

BACKGROUND: There is uncertainty about overdiagnosis in mammography screening. METHODS: We aimed to estimate the effect of screening on breast cancer incidence and overdiagnosis in the NHS Breast Screening Programme in England. The study included 57,493 cases and 105,653 controls, with cases defined as women diagnosed at ages 47-89 with primary breast cancer, invasive or ductal carcinoma in situ, in 2010 or 2011. Where possible, two controls were selected per case, matched on date of birth and screening area. Conditional logistic regression was used to estimate the effect of screening on breast cancer risk, with adjustment for potential self-selection bias. Results were combined with national incidence data to estimate absolute rates of overdiagnosis. Overdiagnosis was calculated as the cumulative excess of cancers diagnosed in the age group 50-77 in a woman attending three-yearly screening between ages 50 and 70 compared with a woman attending no screens. RESULTS: The estimated number of cases overdiagnosed in women attending all screens in the programme was 679.3 per 100,000 without adjustment for self-selection bias and 261.2 per 100,000 with adjustment. These corresponded to an estimated 9.5% of screen-detected cancers overdiagnosed without adjustment and 3.7% with adjustment for self-selection. CONCLUSIONS: The NHS Breast Screening Programme in England confers at worst modest levels of overdiagnosis.