Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

Purpose. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions.Methods. Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose and simulated low dose mammograms with known labels. The models are further tested on a dataset with 296 matching standard and real low dose images allowing performance on the low dose images to be ascertained.Results. Prediction quality on standard and simulated low dose images compared to labels is similar for all equivalent model training and image resolution versions. Increasing resolution results in improved performance of both feature extraction methods for standard and simulated low dose images, while the trained models show high performance across the resolutions. For the trained models the Spearman rank correlation coefficient between predictions of standard and low dose images at low resolution is 0.951 (0.937 to 0.960) and at the highest resolution 0.956 (0.942 to 0.965). If pairs of model predictions are averaged, similarity increases.Conclusions. Deep learning mammographic density predictions on low dose mammograms are highly correlated with standard dose equivalents for feature extraction and end-to-end approaches across multiple image resolutions. Deep learning models can reliably make high quality mammographic density predictions on low dose mammograms.

Germline testing of BRCA1, BRCA2, PALB2 and CHEK2 c.1100delC in 1514 triple negative familial and isolated breast cancers from a single centre, with extended testing of ATM, RAD51C and RAD51D in over 400.

BACKGROUND: The identification of germline pathogenic gene variants (PGVs) in triple negative breast cancer (TNBC) is important to inform further primary cancer risk reduction and TNBC treatment strategies. We therefore investigated the contribution of breast cancer associated PGVs to familial and isolated invasive TNBC. METHODS: Outcomes of germline BRCA1, BRCA2 and CHEK2_c.1100delC testing were recorded in 1514 women (743-isolated, 771-familial), and for PALB2 in 846 women (541-isolated, 305-familial), with TNBC and smaller numbers for additional genes. Breast cancer free controls were identified from Predicting Risk Of Cancer At Screening and BRIDGES (Breast cancer RIsk after Diagnostic GEne Sequencing) studies. RESULTS: BRCA1_PGVs were detected in 52 isolated (7.0%) and 195 (25.3%) familial cases (isolated-OR=58.9, 95% CI: 16.6 to 247.0), BRCA2_PGVs in 21 (2.8%) isolated and 67 (8.7%) familial cases (isolated-OR=5.0, 95% CI: 2.3 to 11.2), PALB2_PGVs in 9 (1.7%) isolated and 12 (3.9%) familial cases (isolated-OR=8.8, 95% CI: 2.5 to 30.4) and CHEK2_c.1100delC in 0 isolated and 3 (0.45%) familial cases (isolated-OR=0.0, 95% CI: 0.00 to 2.11). BRCA1_PGV detection rate was >10% for all familial TNBC age groups and significantly higher for younger diagnoses (familial: <50 years, n=165/538 (30.7%); ≥50 years, n=30/233 (12.9%); p<0.0001). Women with a G3_TNBC were more likely to have a BRCA1_PGV as compared with a BRCA2 or PALB2_PGV (p<0.0001). 0/743 isolated TNBC had the CHEK2_c.1100delC PGV and 0/305 any ATM_PGV, but 2/240 (0.83%) had a RAD51D_PGV. CONCLUSION: PGVs in BRCA1 are associated with G3_TNBCs. Familial TNBCs and isolated TNBCs <30 years have a >10% likelihood of a PGV in BRCA1. BRCA1_PGVs are associated with younger age of familial TNBC. There was no evidence for any increased risk of TNBC with CHEK2 or ATM PGVs.

Radiation treatment of benign tumors in NF2-related-schwannomatosis: A national study of 266 irradiated patients showing a significant increase in malignancy/malignant progression.

Background: Radiation treatment of benign tumors in tumor predisposition syndromes is controversial, but short-term studies from treatment centers suggest safety despite apparent radiation-associated malignancy being reported. We determined whether radiation treatment in NF2-related schwannomatosis patients is associated with increased rates of subsequent malignancy (M)/malignant progression (MP). Methods: All UK patients with NF2 were eligible if they had a clinical/molecular diagnosis. Cases were NF2 patients treated with radiation for benign tumors. Controls were matched for treatment location with surgical/medical treatments based on age and year of treatment. Prospective data collection began in 1990 with addition of retrospective cases in 1969. Kaplan-Meier analysis was performed for malignancy incidence and survival. Outcomes were central nervous system (CNS) M/MP (2cm annualized diameter growth) and survival from index tumor treatment. Results: In total, 1345 NF2 patients, 266 (133-Male) underwent radiation treatments between 1969 and 2021 with median first radiotherapy age of 32.9 (IQR = 22.4-46.0). Nine subsequent CNS malignancies/MPs were identified in cases with only 4 in 1079 untreated (P < .001). Lifetime and 20-year CNS M/MP was ~6% in all irradiated patients-(4.9% for vestibular schwannomas [VS] radiotherapy) versus <1% in the non-irradiated population (P < .001/.01). Controls were well matched for age at NF2 diagnosis and treatment (Males = 133%-50%) and had no M/MP in the CNS post-index tumor treatment (P = .0016). Thirty-year survival from index tumor treatment was 45.62% (95% CI = 34.0-56.5) for cases and 66.4% (57.3-74.0) for controls (P = .02), but was nonsignificantly worse for VS radiotherapy. Conclusion: NF2 patients should not be offered radiotherapy as first-line treatment of benign tumors and should be given a frank discussion of the potential 5% excess absolute risk of M/MP.

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).

TP53 c.455C>T p.(Pro152Leu) pathogenic variant is a lower risk allele with attenuated risks of breast cancer and sarcoma.

Germline (likely) pathogenic TP53 variants cause Li-Fraumeni syndrome (LFS), typically associated with sarcoma, brain, breast and adrenal tumours. Although classical LFS is highly penetrant, the p.R337H variant, common in Brazil, is typically associated with childhood adrenal tumours and an older onset age of other LFS tumours. Previously, we reported the finding of p.P152L in 6 children from 5 families with adrenal tumours. We have now assessed cancer risks over the subsequent 23 years, and in one further family with p.P152L. Cancer risks were compared with those in the 11 families known to our service with classical dominant negative mutations affecting neighbouring codons 245 and 248 (codon 245/248).Compared with codon 245/248 families, we found lower age-related risks for all non-adrenal tumours in codon 152 families (p<0.0001) with an absence of breast cancer as compared with 100% penetrance by age 36 years in codon 245/248 families (p<0.0001), and lower rates of sarcoma in non-irradiated individuals (p=0.0001). Although there were more adrenal tumours in codon 152 families (6/26 individuals, 1/27 for codon 245/248), this was not significant (p=0.05).Understanding codon-specific cancer risks in LFS is important for accurate personalised cancer risk assessment, and subsequent prevention and early detection strategies.

Detection of pathogenic variants in breast cancer susceptibility genes in bilateral breast cancer.

PURPOSE: To investigate the frequency of germline pathogenic variants (PVs) in women with bilateral breast cancer. METHODS: We undertook BRCA1/2 and CHEK2 c.1100delC molecular analysis in 764 samples and a multigene panel in 156. Detection rates were assessed by age at first primary, Manchester Score, and breast pathology. Oestrogen receptor (ER) status of the contralateral versus first breast cancer was compared on 1081 patients with breast cancer with BRCA1/BRCA2 PVs. RESULTS: 764 women with bilateral breast cancer have undergone testing of BRCA1/2 and CHEK2; 407 were also tested for PALB2 and 177 for ATM. Detection rates were BRCA1 11.6%, BRCA2 14.0%, CHEK2 2.4%, PALB2 1.0%, ATM 1.1% and, for a subset of mainly very early onset tumours, TP53 4.6% (9 of 195). The highest PV detection rates were for triple negative cancers for BRCA1 (26.4%), grade 3 ER+HER2 for BRCA2 (27.9%) and HER2+ for CHEK2 (8.9%). ER status of the first primary in BRCA1 and BRCA2 PV heterozygotes was strongly predictive of the ER status of the second contralateral tumour since ~90% of second tumours were ER- in BRCA1 heterozygotes, and 50% were ER- in BRCA2 heterozygotes if the first was ER-. CONCLUSION: We have shown a high rate of detection of BRCA1 and BRCA2 PVs in triple negative and grade 3 ER+HER2- first primary diagnoses, respectively. High rates of HER2+ were associated with CHEK2 PVs, and women ≤30 years were associated with TP53 PVs. First primary ER status in BRCA1/2 strongly predicts the second tumour will be the same ER status even if unusual for PVs in that gene.

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.

The effect of variable labels on deep learning models trained to predict breast density.

Purpose. High breast density is associated with reduced efficacy of mammographic screening and increased risk of developing breast cancer. Accurate and reliable automated density estimates can be used for direct risk prediction and passing density related information to further predictive models. Expert reader assessments of density show a strong relationship to cancer risk but also inter-reader variation. The effect of label variability on model performance is important when considering how to utilise automated methods for both research and clinical purposes.Methods. We utilise subsets of images with density labels from the same 13 readers and 12 reader pairs, and train a deep transfer learning model which is used to assess how label variability affects the mapping from representation to prediction. We then create two end-to-end models: one that is trained on averaged labels across the reader pairs and the second that is trained using individual reader scores, with a novel alteration to the objective function. The combination of these two end-to-end models allows us to investigate the effect of label variability on the model representation formed.Results. We show that the trained mappings from representations to labels are altered considerably by the variability of reader scores. Training on labels with distribution variation removed causes the Spearman rank correlation coefficients to rise from 0.751 ± 0.002 to either 0.815 ± 0.026 when averaging across readers or 0.844 ± 0.002 when averaging across images. However, when we train different models to investigate the representation effect we see little difference, with Spearman rank correlation coefficients of 0.846 ± 0.006 and 0.850 ± 0.006 showing no statistically significant difference in the quality of the model representation with regard to density prediction.Conclusions. We show that the mapping between representation and mammographic density prediction is significantly affected by label variability. However, the effect of the label variability on the model representation is limited.

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.

Differential involvement of germline pathogenic variants in breast cancer genes between DCIS and low-grade invasive cancers.

PURPOSE: To investigate frequency of germline pathogenic variants (PVs) in women with ductal carcinoma in situ (DCIS) and grade 1 invasive breast cancer (G1BC). METHODS: We undertook BRCA1/2 analysis in 311 women with DCIS and 392 with G1BC and extended panel testing (non-BRCA1/2) in 176/311 with DCIS and 156/392 with G1BC. We investigated PV detection by age at diagnosis, Manchester Score (MS), DCIS grade and receptor status. RESULTS: 30/311 (9.6%) with DCIS and 16/392 with G1BC (4.1%) had a BRCA1/2 PV (p=0.003), and 24/176-(13.6%) and 7/156-(4.5%), respectively, a non-BRCA1/2 PV (p=0.004). Increasing MS was associated with increased likelihood of BRCA1/2 PV in both DCIS and G1BC, although the 10% threshold was not predictive for G1GB. 13/32 (40.6%) DCIS and 0/17 with G1BC <40 years had a non-BRCA1/2 PV (p<0.001). 0/16 DCIS G1 had a PV. For G2 and G3 DCIS, PV rates were 10/98 (BRCA1/2) and 9/90 (non-BRCA1/2), and 8/47 (BRCA1/2) and 8/45 (non-BRCA1/2), respectively. 6/9 BRCA1 and 3/26 BRCA2-associated DCIS were oestrogen receptor negative-(p=0.003). G1BC population testing showed no increased PV rate (OR=1.16, 95% CI 0.28 to 4.80). CONCLUSION: DCIS is more likely to be associated with both BRCA1/2 and non-BRCA1/2 PVs than G1BC. Extended panel testing ought to be offered in young-onset DCIS where PV detection rates are highest.

A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants: application to BRCA1 and BRCA2.

A large number of variants identified through clinical genetic testing in disease susceptibility genes, are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion), can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analyses of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC), and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity - findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1, BRCA2 and other high-risk genes with known penetrance.

Magnitude and attributed reasons for adult weight gain amongst women at increased risk of breast cancer.

BACKGROUND: Excess weight (BMI ≥25.0 kg/m2) and weight gain during adult life increase the risk of postmenopausal breast cancer in women who are already at increased risk of the disease. Reasons for weight gain in this population can inform strategies for weight gain prevention. METHODS: Baseline data from six weight loss studies for women at increased risk of breast cancer (age 31-74 years) were collated. Self-reported patterns of adult weight gain and attributed reasons for weight gain before joining the weight loss study were reported for the whole population and secondary analyses reported the different reasons given by women with/without children, pre-/peri- or postmenopausal, and moderate/high risk of breast cancer. RESULTS: Five hundred and one women with a mean age of 47.6 (SD 8.4) years and median BMI of 29.9 (IQR 27.0-34.7) kg/m2 were included in the analyses. The median weight gain since young adulthood (18-20 years) was 20.5 (IQR 14.0-29.7) kg or 33.7 (23.4-50.2) % and median annual weight gain was 0.73 (IQR 0.51-1.08) kg. Four hundred and one women were included in analysis of weight gain reasons. The main five self-reported reasons for weight gain were children / childcare / pregnancy (stated by 55.9% of participants), followed by inactivity (41.9%), comfort or boredom eating (38.2%), portion size (32.4%), and stress (27.4%). Reasons appeared broadly similar between the different groups in the secondary analyses. CONCLUSIONS: We have highlighted common reasons for weight gain in women at increased risk of breast cancer. This will inform future interventions to support women to avoid weight gain in adulthood which would reduce the burden of breast cancer. TRIAL REGISTRATION: NIHR NRR N0226132725, ISRCTN52913838, ISRCTN77916487, ISRCTN91372184, ISRCTN10803394 and ISRCTN16431108.

Incorporating progesterone receptor expression into the PREDICT breast prognostic model.

BACKGROUND: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2). METHOD: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance. RESULTS: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10-6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted. CONCLUSION: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration.

Breast cancer risks associated with missense variants in breast cancer susceptibility genes.

BACKGROUND: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. METHODS: We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. RESULTS: The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. CONCLUSIONS: These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility.

Breast cancer risk stratification in women of screening age: Incremental effects of adding mammographic density, polygenic risk, and a gene panel.

PURPOSE: There is great promise in breast cancer risk stratification to target screening and prevention. It is unclear whether adding gene panels to other risk tools improves breast cancer risk stratification and adds discriminatory benefit on a population basis. METHODS: In total, 10,025 of 57,902 women aged 46 to 73 years in the Predicting Risk of Cancer at Screening study provided DNA samples. A case-control study was used to evaluate breast cancer risk assessment using polygenic risk scores (PRSs), cancer gene panel (n = 33), mammographic density (density residual [DR]), and risk factors collected using a self-completed 2-page questionnaire (Tyrer-Cuzick [TC] model version 8). In total, 525 cases and 1410 controls underwent gene panel testing and PRS calculation (18, 143, and/or 313 single-nucleotide polymorphisms [SNPs]). RESULTS: Actionable pathogenic variants (PGVs) in BRCA1/2 were found in 1.7% of cases and 0.55% of controls, and overall PGVs were found in 6.1% of cases and 1.3% of controls. A combined assessment of TC8-DR-SNP313 and gene panel provided the best risk stratification with 26.1% of controls and 9.7% of cases identified at <1.4% 10-year risk and 9.01% of controls and 23.3% of cases at ≥8% 10-year risk. Because actionable PGVs were uncommon, discrimination was identical with/without gene panel (with/without: area under the curve = 0.67, 95% CI = 0.64-0.70). Only 7 of 17 PGVs in cases resulted in actionable risk category change. Extended case (n = 644)-control (n = 1779) series with TC8-DR-SNP143 identified 18.9% of controls and only 6.4% of stage 2+ cases at <1.4% 10-year risk and 20.7% of controls and 47.9% of stage 2+ cases at ≥5% 10-year risk. CONCLUSION: Further studies and economic analysis will determine whether adding panels to PRS is a cost-effective strategy for risk stratification.

Mammographic breast density and breast cancer risk in the Saudi population: a case-control study using visual and automated methods.

OBJECTIVE: This study aims to establish risk of breast cancer based on breast density among Saudi women and to compare cancer prediction using different breast density methods. METHODS: 1140 pseudonymised screening mammograms from Saudi females were retrospectively collected. Breast density was assessed using Breast Imaging Reporting and Data System (BI-RADS) density categories and visual analogue scale (VAS) of 285 cases and 855 controls matched on age and body mass index. In a subset of 160 cases and 480 controls density was estimated by two automated methods, Volpara Density™ and predicted VAS (pVAS). Odds ratios (ORs) between the highest and second categories in BI-RADS and Volpara density grades, and highest vs lowest quartiles in VAS, pVAS and Volpara Density™, were estimated using conditional logistic regression. RESULTS: For BI-RADS, the OR was 6.69 (95% CI 2.79-16.06) in the highest vs second category and OR = 4.78 (95% CI 3.01-7.58) in the highest vs lowest quartile for VAS. In the subset, VAS was the strongest predictor OR = 7.54 (95% CI 3.86-14.74), followed by pVAS using raw images OR = 5.38 (95% CI 2.68-10.77) and Volpara Density ™ OR = 3.55, (95% CI 1.86-6.75) for highest vs lowest quartiles. The matched concordance index for VAS was 0.70 (95% CI 0.65-0.75) demonstrating better discrimination between cases and controls than all other methods. CONCLUSION: Increased mammographic density was strongly associated with risk of breast cancer among Saudi women. Radiologists' visual assessment of breast density is superior to automated methods. However, pVAS and Volpara Density ™ also significantly predicted breast cancer risk based on breast density. ADVANCES IN KNOWLEDGE: Our study established an association between breast density and breast cancer in a Saudi population and compared the performance of automated methods. This provides a stepping-stone towards personalised screening using automated breast density methods.

The importance of ethnicity: Are breast cancer polygenic risk scores ready for women who are not of White European origin?

Polygenic risk scores (PRS) for disease risk stratification show great promise for application in general populations, but most are based on data from individuals of White European origin. We assessed two well validated PRS (SNP18, SNP143) in the Predicting-Risk-of-Cancer-At-Screening (PROCAS) study in North-West England for breast cancer prediction based on ethnicity. Overall, 9475 women without breast cancer at study entry, including 645 who subsequently developed invasive breast cancer or ductal carcinoma in situ provided DNA. All were genotyped for SNP18 and a subset of 1868 controls were genotyped for SNP143. For White Europeans both PRS discriminated well between individuals with and without cancer. For n = 395 Black (n = 112), Asian (n = 119), mixed (n = 44) or Jewish (n = 120) women without cancer both PRS overestimated breast cancer risk, being most marked for women of Black and Jewish origin (P < .001). SNP143 resulted in a potential mean 40% breast cancer risk overestimation in the combined group of non-White/non-European origin. SNP-PRS that has been normalized based on White European ethnicity for breast cancer should not be used to predict risk in women of other ethnicities. There is an urgent need to develop PRS specific for other ethnicities, in order to widen access of this technology.

High likelihood of actionable pathogenic variant detection in breast cancer genes in women with very early onset breast cancer.

BACKGROUND: While the likelihood of identifying constitutional breast cancer-associated BRCA1, BRCA2 and TP53 pathogenic variants (PVs) increases with earlier diagnosis age, little is known about the correlation with age at diagnosis in other predisposition genes. Here, we assessed the contribution of known breast cancer-associated genes to very early onset disease. METHODS: Sequencing of BRCA1, BRCA2, TP53 and CHEK2 c.1100delC was undertaken in women with breast cancer diagnosed ≤30 years. Those testing negative were screened for PVs in a minimum of eight additional breast cancer-associated genes. Rates of PVs were compared with cases ≤30 years from the Prospective study of Outcomes in Sporadic vs Hereditary breast cancer (POSH) study. RESULTS: Testing 379 women with breast cancer aged ≤30 years identified 75 PVs (19.7%) in BRCA1, 35 (9.2%) in BRCA2, 22 (5.8%) in TP53 and 2 (0.5%) CHEK2 c.1100delC. Extended screening of 184 PV negative women only identified eight additional actionable PVs. BRCA1/2 PVs were more common in women aged 26-30 years than in younger women (p=0.0083) although the younger age group had rates more similar to those in the POSH cohort. Out of 26 women with ductal carcinoma in situ (DCIS) alone, most were high-grade and 11/26 (42.3%) had a PV (TP53=6, BRCA2=2, BRCA1=2, PALB2=1). This PV yield is similar to the 61 (48.8%) BRCA1/2 PVs identified in 125 women with triple-negative breast cancer. The POSH cohort specifically excluded pure DCIS which may explain lower TP53 PV rates in this group (1.7%). CONCLUSION: The rates of BRCA1, BRCA2 and TP53 PVs are high in very early onset breast cancer, with limited benefit from testing of additional breast cancer-associated genes.