Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms

Several recent genome-wide association studies have identified genetic variants associated with breast cancer. However, how much these genetic variants may help advance breast cancer risk prediction based on other clinical features, like mammographic findings, is unknown. We conducted a retrospective case-control study, collecting mammographic findings and high-frequency/low-penetrance genetic variants from an existing personalized medicine data repository. A Bayesian network was developed using Tree Augmented Naive Bayes (TAN) by training on the mammographic findings, with and without the 22 genetic variants collected. We analyzed the predictive performance using the area under the ROC curve, and found that the genetic variants significantly improved breast cancer risk prediction on mammograms. We also identified the interaction effect between the genetic variants and collected mammographic findings in an attempt to link genotype to mammographic phenotype to better understand disease patterns, mechanisms, and/or natural history.

The WISDOM Study: breaking the deadlock in the breast cancer screening debate.

There are few medical issues that have generated as much controversy as screening for breast cancer. In science, controversy often stimulates innovation; however, the intensely divisive debate over mammographic screening has had the opposite effect and has stifled progress. The same two questions-whether it is better to screen annually or bi-annually, and whether women are best served by beginning screening at 40 or some later age-have been debated for 20 years, based on data generated three to four decades ago. The controversy has continued largely because our current approach to screening assumes all women have the same risk for the same type of breast cancer. In fact, we now know that cancers vary tremendously in terms of timing of onset, rate of growth, and probability of metastasis. In an era of personalized medicine, we have the opportunity to investigate tailored screening based on a woman's specific risk for a specific tumor type, generating new data that can inform best practices rather than to continue the rancorous debate. It is time to move from debate to wisdom by asking new questions and generating new knowledge. The WISDOM Study (Women Informed to Screen Depending On Measures of risk) is a pragmatic, adaptive, randomized clinical trial comparing a comprehensive risk-based, or personalized approach to traditional annual breast cancer screening. The multicenter trial will enroll 100,000 women, powered for a primary endpoint of non-inferiority with respect to the number of late stage cancers detected. The trial will determine whether screening based on personalized risk is as safe, less morbid, preferred by women, will facilitate prevention for those most likely to benefit, and adapt as we learn who is at risk for what kind of cancer. Funded by the Patient Centered Outcomes Research Institute, WISDOM is the product of a multi-year stakeholder engagement process that has brought together consumers, advocates, primary care physicians, specialists, policy makers, technology companies and payers to help break the deadlock in this debate and advance towards a new, dynamic approach to breast cancer screening

Epidemiological studies on breast cancer risk factors and screening

This thesis aims to enhance cancer prevention by investigating the factors and outcomes associated with false-positive (FP) mammography recalls, as well as understanding the association between breast cancer risk factors of women and cancer risk among their relatives. Specifically, four studies were conducted using data from Swedish national registers, the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort, and the Linné-Bröst1 (Libro-1) cohort. In Study I, we characterized factors associated with FP mammography recalls, comparing women with a FP recall to those who were not recalled and to those who had a true-positive recall (screen-detected cancer). We found that several mammographic and non-mammographic factors, as well as high breast cancer risk scores, were associated with having a FP recall. However, these factors were either equally or more strongly associated with having a truepositive recall. In Study II, using a matched-cohort design, we examined the risk of subsequent breast cancer among women with a FP mammography recall. We observed a long-term increased breast cancer risk after a FP recall, compared with women who were not recalled. The elevated breast cancer risk differed by age and mammographic density at the matching mammography. In addition, the increased risk for breast cancer diagnosed on the ipsilateral side to the FP recall decreased over time and was highest within the first four years of follow-up. In Study III, we investigated whether specific breast cancer risk factors in women were associated with their sisters' breast cancer incidence. We found that for women with high breast cancer risk prediction scores, benign breast disease (BBD), and high mammographic density, there was an increased risk of breast cancer for their sisters. In Study IV, we investigated the associations of both carriership of protein-truncating variants (PTV) in eight genes and breast cancer polygenic risk scores (PRS) in women, with the risk of cancers in their first-degree relatives. We observed an elevated breast cancer risk among female relatives of women with PTVs, and among those with high breast cancer PRS. Additionally, we found a slightly elevated risk of cancers related to hereditary breast and ovarian cancer syndrome (HBOC)—other than breast cancer—among relatives of women with either high PRS or PTVs in the studied genes. In summary, this thesis provides valuable information for both screening processes and genetic counseling. Although none of the studied factors are viable for interventions aimed at reducing FP recalls—due to the risk of simultaneously missing tumors—our results may aid in tailoring individualized surveillance plans for women with a FP recall. Additionally, our results suggest that women’s breast density and breast cancer risk scores—information that will be available at screening—may be useful for estimating the breast cancer risk in their sisters. Furthermore, PTVs in non-BRCA genes might offer insights into cancer aggregation in families. Overall, this thesis advances evidence-based cancer prevention in the era of precision medicine

Risk assessment and prevention of breast cancer

One woman in eight develops breast cancer during her lifetime in the Western world. Measures are warranted to reduce mortality and to prevent breast cancer. Mammography screening reduces mortality by early detection. However, approximately one fourth of the women who develop breast cancer are diagnosed within two years after a negative screen. There is a need to identify the short-term risk of these women to better guide clinical followup. Another drawback of mammography screening is that it focuses on early detection only and not on breast cancer prevention. Today, it is known that women attending screening can be stratified into high and low risk of breast cancer. Women at high risk could be offered preventive measures such as low-dose tamoxifen to reduce breast cancer incidence. Women at low risk do not benefit from screening and could be offered less frequent screening. In study I, we developed and validated the mammographic density measurement tool STRATUS to enable mammogram resources at hospitals for large scale epidemiological studies on risk, masking, and therapy response in relation to breast cancer. STRATUS showed similar measurement results on different types of mammograms at different hospitals. Longitudinal studies on mammographic density could also be analysed more accurate with less nonbiological variability. In study II, we developed and validated a short-term risk model based on mammographic features (mammographic density, microcalcifications, masses) and differences in occurrences of mammographic features between left and right breasts. The model could optionally be expanded with lifestyle factors, family history of breast cancer, and genetic determinants. Based on the results, we showed that among women with a negative mammography screen, the short-term risk tool was suitable to identify women that developed breast cancer before or at next screening. We also showed that traditional long-term risk models were less suitable to identify the women who in a short time-period after risk assessment were diagnosed with breast cancer. In study III, we performed a phase II trial to identify the lowest dose of tamoxifen that could reduce mammographic density, an early marker for reduced breast cancer risk, to the same extent as standard 20 mg dose but cause less side-effects. We identified 2.5 mg tamoxifen to be non-inferior for reducing mammographic density. The women who used 2.5 mg tamoxifen also reported approximately 50% less severe vasomotor side-effects. In study IV, we investigated the use of low-dose tamoxifen for an additional clinical use case to increase screening sensitivity through its effect on reducing mammographic density. It was shown that 24% of the interval cancers have a potential to be detected at prior screen. In conclusion, tools were developed for assessing mammographic density and breast cancer risk. In addition, two low-dose tamoxifen concepts were developed for breast cancer prevention and improved screening sensitivity. Clinical prospective validation is further needed for the risk assessment tool and the low-dose tamoxifen concepts for the use in breast cancer prevention and for reducing breast cancer mortality

Heritability of mammographic breast density, density change, microcalcifications, and masses

Background: Mammographic features influence breast cancer risk and are used in risk prediction models. Understanding how genetics influence mammographic features is important since the mechanisms through which they are associated with breast cancer are not well known. Methods: Mammographic screening history and detailed questionnaire data for 56,820 women from the KARMA prospective cohort study were used. The heritability of mammographic features such as dense area (MD), microcalcifications, masses, and density change (MDC – cm2/year) were estimated using 1,940 sister pairs. We investigated the association between a genetic predisposition to breast cancer and mammographic features, among women with family history of breast cancer information (N=49,674) and a polygenic risk score (PRS, N=9,365). Results: Heritability was estimated at 58% (95% CI: 48%, 67%) for MD, 23% (2%, 45%) for microcalcifications, and 13% (1%, 25%) for masses. The estimated heritability for MDC was essentially null (2%, 95% CI: -8%, 12%). The association between a genetic predisposition to breast cancer (using PRS) and MD and microcalcifications was positive, while for masses this was borderline significant. In addition, for MDC, having a family history of breast cancer was associated with slightly greater MD reduction. Conclusions: We confirmed previous findings of heritability in MD, and also found heritability of the number of microcalcifications and masses at baseline. Since these features are associated with breast cancer risk, and can improve detecting women at short-term risk of breast cancer, further investigation of common loci associated with mammographic features is warranted to better understand the etiology of breast cancer.Swedish Research Council, 2018-02547Swedish Cancer Society, CAN 19 0266Stockholm County Council, LS 1211-1594Swedish Research Council, 70867902Accepte

Mammographic features are associated with cardiometabolic disease risk and mortality

Open Access via the OUP Open Access Agreement Acknowledgements: The authors thank all the participants in the KARMA study and personnel for their devoted work during data collection. They also would like to acknowledge Jose ́ Tapia for helping in data management. Funding: This work was supported by “Ma ̈rit and Hans Rausing’s Initiative Against Breast Cancer” and was financed by the Swedish Research Council (Grant 2018-02547 to K.C.), the Swedish Cancer Society (Grant 19 0266 and 19 0267 to K.C.), FORTE (Grant 2016-00081 to K.C.), and the Karolinska Institutet’s Research Foundation (Grant 2018-02146 to F.G.). F.G. was a Leopoldina Postdoctoral Fellow (Grant No. LPDS 2018-06) funded by the Academy of Sciences Leopoldina. H.Y. was supported by Start-up Fund for high-level talents of Fujian Medical University (Grant .No. XRCZX2020007) and Start-up Fund for Scientific Research, Fujian Medical University (Grant No. 2019QH1002). The funding agency had no role in the study design, data collection, analyses, and data interoperation, in writing the manuscript, or in the decision to submit the manuscript for publication.Peer reviewedPublisher PD

유방 촬영술 영상 자료의 딥러닝 적용을 통한 유방암 위험도 평가 : 유방 치밀도 자동 평가 방법 기반

학위논문(석사)--서울대학교 대학원 :보건대학원 보건학과,2019. 8. 성주헌.Introduction : Mammographic density adjusted for age and body mass index (BMI) is the most predictive marker of breast cancer after familial causes and genetic markers. The aim of this study was to develop deep learning (DL) algorithm to assess mammographic density. Methods : Total 2464 participants (834 cases and 1630 controls) were collected from Asan Medical Center and Samsung Medical Center, Korea. Cranio-caudal view mammographic images were obtained using full-field digital mammography system. Mammographic densities were measured using CUMULUS software. The resulting DL algorithm was tested on a held-out test set of 493 women. Agreement on DL and expert was assessed with correlation coefficient and weighted κ statistics. Risk associations of DL measures were evaluated with area under curve (AUC) and odds per adjusted standard deviation (OPERA). Results : The DL model showed very good agreement with expert for both percent density and dense area (r = 0.94 - 0.96 and κ = 0.89 - 0.91). Risk associations of DL measures were comparable to manual measures of expert. DL measures adjusted for age and BMI showed strong risk associations with breast cancer (OPERA = 1.51 - 1.63 and AUC = 0.61 - 0.64). Conclusions : DL model can be used to measure mammographic density which is a strong risk factor of breast cancer. This study showed the potential of DL algorithm as a mammogram-based risk prediction model in breast cancer screening test.유방 내 유방 실질 조직의 양을 반영하는 유방 밀도는 맘모그램에서 나타나는 밝은 부분으로 정의되며, 유방암의 강력한 위험인자로 널리 알려져 있다. 하지만 유방 밀도는 측정하는데 시간과 비용이 많이 든다는 단점으로 인해 유방암 검진 과정에서 제한적으로 사용돼 왔다. 본 연구의 목적은 유방암 검진에서 유방암 예측 모형에 포함해 활용할 수 있는 딥러닝 기반 유방 밀도 측정치를 개발하는 것이다. 본 연구는 아산 병원과 삼성 서울병원의 유방암 검진 자료로부터 수집된 총 2464 명의 참여자 (환자: 834 명, 대조군 : 1630 명) 를 대상으로 수행되었다. 환자의 경우 병변이 발생한 유방의 반대쪽 유방, 대조군의 경우 임의로 고른 유방을 대상으로 유방 밀도 측정에 5년 이상의 경력을 가진 전문가가 CUMULUS 프로그램을 활용하여 유방 밀도 (치밀 유방 부위, cm2 및 치밀도 백분율, %) 를 측정하였다. 이 전문가 측정치를 훈련 데이터로 하여 완전 합성곱 신경망 (Fully Convolutional Network) 기반 딥러닝 모델을 구축하였고, 이를 테스트 데이터에 대해 적용해 전문가 측정치와의 일치도 및 유방암 예측력을 평가하였다. 딥러닝 모델은 전문가와 높은 일치도 (r = 0.94 - 0.96, weighted κ = 0.89 – 0.91) 를 보였다. 또한 나이와 BMI를 보정한 딥러닝 기반 측정치의 유방암 예측력을 평가한 결과, 딥러닝 모델이 전문가와 비슷한 수준의 예측력을 갖는다는 것을 확인하였다 (전문가, AUC = 0.62 – 0.63, 딥러닝 모델, AUC = 0.61 – 0.64). 본 연구는 딥러닝이 현재의 노동 집약적인 유방 밀도 측정법을 보완할 수 있는 가능성을 보여주었다. 이는 비용-효율적인 방법으로 유방 밀도 측정치를 유방암 예측 모형에 포함시킬 수 있는 기회를 제공한다. 이러한 맘모그램 기반 유방암 위험도 예측 모형이 유방암 검진 과정에 적용된다면 보다 정밀한 유방암 위험도 평가를 통해 효과적으로 유방암 고위험군을 선별할 수 있으며, 고위험군에 대한 맞춤형 예방 전략이 적용된다면 장기적으로 유방암 조기 발견 및 사망률 감소에 기여할 수 있을 것으로 기대한다.1 Introduction 1 2 Materials and Methods 3 2.1 Data collection 3 2.2 Measurement of mammographic density 4 2.3 Development of DL model 6 2.3.1 Establishing ground truth 6 2.3.2 Image preprocessing 6 2.3.3 Establishing DL model 6 2.3.4 Estimation of mammographic density 11 2.4 Statistical methods 14 2.4.1 Agreement statistics 14 2.4.2 Evaluation of risk association 15 3 Results 16 3.1 Characteristics of study participants 16 3.2 Agreement of DL model 17 3.3 Breast cancer risk profiles 21 4 Discussion 24 Bibliography 26 초록 29Maste

Determinants and influence of mammographic features on breast cancer risk

Mammographic density and mammographic microcalcifications are the key imaging features in mammography examination. Mammographic density is known as a strong risk factor for breast cancer and is the radiographic appearance of epithelial and fibrous tissue which appears white on a mammogram. While, the dark part of a mammogram represents the fatty tissue. Mammographic microcalcifications appear as small deposits of calcium and they are one of the earliest sign of breast cancer. Malignant microcalcifications are seen in both in situ and invasive lesions. In this thesis we used the data from the prospective KARMA cohort to study the association between established breast cancer risk factors with mammographic density change over time (Study I), to examine the association between annual mammographic density change and risk of breast cancer (Study II), to investigate the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry (Study III), and finally to elucidate the association between microcalcification clusters, their asymmetry, and risk of overall and subtype specific breast cancer (Study IV). The lifestyle and reproductive factors were assessed using web-based questionnaires. Average mammographic density and total microcalcification clusters were measured using a Computer Aided Detection system (CAD) and the STRATUS method, respectively. In Study I, the average yearly dense area change was -1.0 cm . Body mass index (BMI) and physical activity were statistically associated with density change. Beside age, lean and physically active women had the largest decrease in mammographic density per year. In Study II, overall, 563 women were diagnosed with breast cancer and annual mammographic density change did not seem to influence the risk of breast cancer. Furthermore, density change does not seem to modify the association between baseline density and risk of breast cancer. In Study III, age, mammographic density, genetic factors related to breast cancer, having more children, longer duration of breast-feeding were significantly associated with increased risk of presence of microcalcification clusters. In Study IV, 676 women were diagnosed with breast cancer. Further, women with 33 microcalcification clusters had 2 times higher risk of breast cancer compared to women with no clusters. Microcalcification clusters were associated with both in situ and invasive breast cancer. Finally, during postmenopausal period, microcalcification clusters influence risk of breast cancer to the similar extend as baseline mammographic density. In conclusion, we have identified novel determinants of mammographic density changes and potential predictors of suspicious mammographic microcalcification clusters. Further, our results suggested that annual mammographic density change does not influence breast cancer risk, while presence of suspicious microcalcification clusters was strongly associated with breast cancer risk